The substantial increase in cardiovascular diseases (CVD) in China over the last three decades warrants comprehensive preventive primary and secondary strategies. Prolonged prehospital delay (PHD) has been identified as a substantial barrier to timely therapeutic interventions for acute myocardial infarction (AMI). Despite worldwide efforts to decrease the patient’s decision-making time, minimal change has been achieved so far. Here, we aim to describe the conceptual framework and methods and outline key data of the MEDEA FAR-EAST Study, which aimed to elucidate in-depth barriers contributing to delay in Chinese AMI-patients.Data sources of this multicenter cross-sectional observational study are a standardized bedside interview, a self-administered tailored questionnaire tool and the patient chart. PHD was defined as the main outcome and triangulated at bedside. Standard operation procedures ensured uniform data collection by trained study personnel. The study was ethically approved by Tongji-Hospital and applied to all participating hospitals.Among 379 consecutively screened patients, 296 (78.1%) fulfilled eligibility criteria. A total of 241 (81.4%) AMI-patients were male and 55 (18.6%) female. Mean age was 62.9 years. Prehospital delay time was assessed for 294 (99.3%) patients. Overall median PHD was 151 min with no significant sex difference. Symptom mismatch was present in 200 (69.7%) patients and 106 (39.0%) patients did not attribute their symptoms to cardiac origin. A total of 33 (12.4%) patients suffered from depression, 31 (11.7%) from anxiety and 141 (53.2%) patients employed denial as their major coping style.This is the first study on prehospital delay with emphasis on psychological variables in Chinese AMI-patients. A comprehensive assessment tool to measure clinical and psychological factors was successfully implemented. Socio-demographic key data proved a good fit into preexisting Chinese literature. Potential barriers including cardiac denial and symptom-mismatch were assessed for the first time in Chinese AMI-patients. The pretested selection of instruments allows future in depth investigations into barriers to delay of Chinese AMI-patients and enables inter-cultural comparisons. In China, the burden of cardiovascular disease (CVD) is substantially increasing and is now the leading cause of death , accountTherapeutic interventions of AMI are highly time-dependent . DespiteSubstantial efforts have been undertaken to reduce patient-related prehospital delay in numerous populations and secondary based prevention campaigns , yet, thAt AMI-onset, several factors such as slow symptom-onset, intermittent symptoms, symptom vagueness  and expeLittle is known about the impact of depression, anxiety and other psychosocial conditions on the delay process. It is yet not unlikely that depressed patients might delay due to a lack of motivation and energy to seek help  whereas The MEDEA Study in Munich  was developed to overcome shortcomings in delay research focusing on symptom patterns only, and to broaden the horizons of this research. It provided a conceptual framework and subsequent data assessment to identify barriers to help-seeking behavior during an acute infarction situation. The multicenter cross-sectional MEDEA FAR-EAST Study was designed to replicate this study, with its major objective being the application of a holistic approach in addressing patient-related delay by examining somatic, clinical, health-psychology related concepts and influential affective factors on prehospital delay in Chinese AMI-patients. The preselection of instruments was guided by (a) theoretical framework, (b) validated instruments in the Chinese language and (c) and the applicability within the particular Chinese cultural background. The aim of this paper is to present the framework, methods and descriptive study data on prehospital delay times as well as factors related to patient characteristics and symptom-onset.The main inclusion criterion was hospitalization with an acute myocardial infarction (AMI), confirmed by typical symptoms at onset, and elevated cardiac biomarkers (troponin I or troponin T) as well as corresponding ECG-diagnosis. No restrictions were made regarding age and sex. Exclusion criteria were an out-of-hospital cardiac arrest as well as cognitive impairment and language barrier.2 in its urban areas [Shanghai is the largest city in China with a total population of 24.2 million people and an average population density of 3816 people per kman areas . Across an areas . Furtheran areas . Combinean areas . This hean areas . Insteadan areas . Medicalan areas . Ambulanan areas , possiblan areas .The patient sample was recruited from four cardiology departments in Shanghai, all providing acute coronary care units. After preselection of instruments , a Chinese version of the study protocol was presented to the Ethics Commission of the Tongji-University affiliated Tongji-Hospital (by W. Ma). The Commission approved the study on 16th of March 2016 (伦审-KYSB-2016-74). The ethics approval applied to all participating centers. Recruitment started in Tongji-Hospital in Mid-April together with Yangpu-Hospital and 455 People’s Hospital. By end of July, recruitment was expanded to Tenth-Hospital. Recruitment was ended in Mid-January 2017.n = 86). On average, between one to 2 h were spent by trained personnel conducting the interview and assisting patients with completing the questionnaire. Regular newsletters informed all study-affiliated members of the study’s progress and developments. The study personnel received training prior to their participation and were closely monitored during the data acquisition process in order to avoid reporting bias. Standard operating procedures (SOP) were implemented to ensure the training of new members and these methods were kept consistent for all participating hospitals.Patient inclusion and data collection were performed within two days after hospitalization . The onset-time was triangulated by trained personnel in the interview, using events from the patient’s daily routine to help them establish the chronology of symptom-onset. Patients were asked to give a broad estimate of the time of symptom-onset, which was then further specified by placing it in relation to times of regular activities, meals, sleeping habits and other routines. This technique has previously been developed and tested by Moser et al., who found that this technique enabled patients who did not initially remember onset-time to successfully recall it . The hosAt bedside, comprehensive data on sociodemographic and health-related behaviors were assessed . Health attitudes and frequency of doctor consultation prior to AMI helped to portray patient’s overall approach to health. Angina pectoris six months prior to AMI was assessed following the Rose Angina Questionnaire  which alThe acute onset of MI was a central part of the interview to outline details of symptom presentation. The duration, intensity and character  of chest pain and/or other cardiac symptoms were documented. Patient interpretation of the onset-symptoms was measured by the items patient expectation and symptom attribution.The Response of Symptoms Questionnaire  was usedThe self-administered questionnaire assessed variable psychometric variables including psychological characteristics, personality concepts as well as AMI-related knowledge. Except for the instrument measuring AMI-related knowledge, all psychometric instruments used were standardized instruments, summarized in detail in Table We used two scales to measure affective disorders. Depression was measured using the Major Depression Inventory (MDI), a 10-domain instrument able to generate an ICD-10 diagnosis. Following the suggestion of Bech et al. , a cut-oDenial regarding cardiac illness was measured in the 8-item Cardiac Denial of Impact Score (CDIS) . A scoreStress was measured by two items, a) the INTER-HEART Stress Scale (IHS) , a 3-iteHealth locus of control was measured using the Multidimensional Health Locus of Control Scale (MHLC). It consists of 12 items and was initially developed by Wallston et al.  and featThe social support was measured by the Chinese 10-item Social Support Rating Scale (SSRS) . It measWell-being was measured by the WHO-5 , a 5-iteType-D personality was measured using the Type-D Scale 14 (DS-14) which is divided into two subscales with 7 items each, measuring social inhibition and negative affectivity . Type-A Resilience was measured in a short 5-item version (RS-5) developed from the original version of the RS-14 . This toKnowledge of AMI-symptoms and appropriate actions to be taken when experiencing those symptoms was measured in the questionnaire in two subscales. In the interview, sources of AMI-knowledge were identified, including physician, media, heart association and friends/acquaintances as possible sources.As can be further seen in Table Detail of the infarction diagnosis, elevation of cardiac biomarkers and the number of days spent in CCU, complications throughout the stay as well as therapy of the MI were documented in the hospital chart. The patient chart additionally examined if the patient was previously transferred from a hospital not capable of treating the patient for AMI.The study coordinator entered the data in an Excel sheet. Checklists were created in order to monitor progress of data collection.It was estimated that 30% of participants would reach the hospital within < 120 min. With the assumption of a 5% error we estimated a sample size of 310 patients to ensure a power of 0.80 in proving a clinically relevant odds ratio of 2.0.Differences between continuous variables were assessed using Mann Whitney Test. All statistical analysis was run in IBM SPSS Statistics version 20 . The distribution of delay was available as a continuous variable in minutes and heavily left-skewed, thus reporting medians delay times. Dichotomized measures of symptom expectation  and symptom attribution (cardiac vs. non-cardiac) were used. The significance level α was set at .05. Differences in median PHD were assessed using the non-parametric Wilcoxon test. The analysis and description in this paper follow the STROBE guidelines for cross-sectional studies .n = 25, 30.1%) language barrier or/and cognitive impairment , as well as other reasons such as unconfirmed diagnosis  or missing data . One patient (1.2%) was in a too critical condition to be interviewed and three patients (3.6%) were already in hospital when they experienced AMI. For six patients (7.2%), the reason for exclusion remained undocumented. In the present analysis, two patients had missing values of onset time, so that prehospital delay could not be determined. A dropout-analysis showed that older age was a significant factor of dropout, with patients dropping out being a mean of 69.3 years old . Comparison of included and excluded patients showed no significant differences in gender (p = 0.46).A total of 379 patients were considered eligible from mid-April 2016 to mid-January 2017, of which 83 (21.9%) were excluded . The mean age, the proportion of male patients and prehospital delay did not vary significantly between the four recruitment hospitals . Among the study population, 29 (9.8%) had attended six years of elementary school and 114 (38.5%) an additional three years of junior-high school. 93 (31.4%) patients attained a senior-high school degree and 46 (15.5%) had a university degree.A total of 296 patients participated in the MEDEA FAR-EAST Study and completed the bedside interview. Among them, 241 (81.4%) were male and 55 (18.6%) female  had completed no education at all. 236 (80.5%) out of 293 patients were medically insured. A total of 24 (8.1%) patients had previously experienced AMI. A total of 178 (60.1%) patients experienced prodromal chest pain (PCP) prior to AMI, which could be classified as angina pectoris in 76 (25.7%) cases. Among our patient sample, 31 (11.7%) patients suffered from anxiety, 33 (12.4%) from depression, and 141 (53.2%) from cardiac denial during the 6 months prior to AMI. The response rate for all three questionnaires was at least 89.5%.Upon symptom-onset of AMI, 121 40.9%) of patients experienced a ‘classical’ onset with chest pain and radiating pain into left arm, right arm, neck/jaw, shoulders or epigastric region (see Table 0.9% of pp = 0.88)  for men and 143 min for woman . This difference was not significant (Despite an often imperative and severe pain pattern in the face of an acute myocardial infarction, and despite increasing knowledge in the population of the need to access therapeutic help as quick as possible, inadequate delay times of AMI patients remain an universal problem with a reported average median delay time of about 204 min among 23 studies worldwide . In contThe study has been performed in Shanghai - one of the world’s biggest cities. Nevertheless, our descriptive analysis demonstrates that the population under investigation in the MEDEA FAR-EAST Study has comparable characteristics to related investigations in China. This is particularly true concerning mean age , 14, 16,The MEDEA FAR-EAST Study is, to the best of our knowledge, the first clinical study in the context of cardiac emergency facilities in China to comprehensively assess clinical, psychological and health-behavior related features in a homogeneous population of acute cardiac patients. In order to gain a deeper understanding of patients’ decision processes, it is also one of the first studies worldwide to assess depression, anxiety and self-perceived stress, but also the patient’s level of resilience and attitude towards the medical system. It not only screens for typical but also atypical symptoms and complaints, and furthermore assesses factors like symptom expectation and knowledge.Our results showed that the majority of patients were male, 60 years of age or older, and had attended school for at least six years. A majority of patients were experiencing acute myocardial infarction for the first time. About 12% of them suffered from anxiety and/or depression while cardiac denial was more common, with approximately half of the patients exhibiting cardiac denial in the prodromal phase. To date, the prevalence of depression, anxiety and cardiac denial prior to AMI has not been investigated in China. In the German counterpart study, prevalence of anxiety and cardiac denial among STEMI-patients was 11% and 42%, which correlates well with our figures , 29. In In the acute situation, the vast majority of patients experienced chest pain which corresponds to figures previously reported in China . “It is The strength of this study is that efforts were made to reduce recall bias by conducting a bedside interview within a narrow time-frame upon hospitalization, while additionally using an approved technique for triangulation of onset-time. Yet recall bias might still exist, as data collection was retrospective. Due to the cross-sectional study design, causal attributions cannot be made. Furthermore, despite our best efforts, consecutive inclusion is likely but cannot be fully guaranteed. The study was conducted in an urban setting in China which may prevent conclusions for rural areas and countries other than China. Furthermore, prehospital delay puts patients at risk of sudden cardiac arrest, the major cause of out-of-hospital death in the prehospital phase . These pSubstantial progress has been made in minimizing transportation and door-to-needle time. However, patient delay remains a major unresolved concern of public health. In order to develop effective prevention campaigns for the future, the patient-related barriers to seek medical help must be better understood. Clarifying psychological and bio-behavioral factors as well as intercultural comparisons will provide key evidence for developing new approaches in prevention.Symptom perception and miscellaneous psychological and behavioral factors may contribute equally, in a complex interaction, to inner barriers of the patient’s decision-making process. The present investigation is the first study in a Chinese population to capture comprehensive data applying theory-guided standardized instruments, allowing focus on the particularly vulnerable decision process. The prevalence of potential contributors to prehospital delay, such as symptom interpretation, and psychological barriers such as depression and cardiac denial were measured. In particular, symptom mismatch and cardiac denial were present in more than half of the patients, and we aim to conduct further analysis in the future to examine their association with prehospital delay.Furthermore, barriers in treatment seeking behavior have been shown to vary by region and culture . It is o


P = .027). Besides, women younger than 60 years-old also had poor compliance . Other factors such as POP stage, history of hysterectomy, and types of pessaries, did not show significant influence on the long-term compliance in this study. Therefore, to evaluate the severity of SUI after reduction before providing pessary treatment is important to predict long-term compliance. Meanwhile, long-term pessary treatment seems to be more acceptable to elderly patients.Vaginal pessary treatment for pelvic organ prolapse (POP) is relatively safe and cost-effective. Since long-term use is an important key to keep the benefit of pessary treatment, we would like to investigate the factors which might affect the compliance of vaginal pessaries. In this retrospective study, 65 women were included, and we found poor compliance in women with severe stress urinary incontinence (SUI) after reduction (1-hour pad test >10 gm vs ≦10 gm, 57.1% vs. 84.3%,  However, there are still a large proportion of patients who are medically contraindicated for surgery or not willing to receive surgery for POP. In addition, the chance of repetitive surgery due to recurrence of POP or complications is high, and the reoperation rate using mesh kits was estimated to be up to 8.5%. Therefore, nonsurgical intervention such as vaginal pessary treatment should always be considered first since it is a safe and effective alternative to manage POP.,7Pelvic organ prolapse (POP) affects millions of women's quality of life and the prevalence is expected to increase due to the aging of the population. It was also reported after 1 year of pessary treatment, the progression of POP could be prevented. Thus, the treatment success relies on patients’ adherence to the vaginal pessaries. The 3 months short-term compliance of vaginal pessary treatment is ranging from 41% to 76%–11 and the reasons of failure varied, including young age, previous hysterectomy, SUI, and the severity of POP.–15 However, there is little data available regarding the factors that influence the long-term compliance. Since consistency is important, it is worth to investigate the reasons why patients quit vaginal pessaries.For women with symptomatic POP, Abdool et al reported similar improvement when they were treated with vaginal pessaries or surgery for 1 year.,16 Pessary treatment may ease urinary frequency, urgency, and symptoms of bladder outlet obstruction of the patients, and thus, these patients may have good compliance. But to those who have symptoms of SUI, their urinary leakage may become worse after reduction of POP with pessaries. As a result, they may give up pessary treatment and seek for surgical intervention. Therefore, we hypothesized that severe SUI after reduction may result in poor long-term compliance.According to the Women's Health Initiative studies, urinary symptoms are the most common and bothersome symptoms to women with POP.The aim of our study was to determine if severe SUI after reduction or any other factors could affect long-term compliance of vaginal pessaries for women with POP.2A retrospective chart review study of women with POP was conducted at Kaohsiung Veteran General Hospital between January 2015 and June 2018. Institutional Review Board approval for the study was obtained (IRB No 18-CT6-17/180430-1). All women with stage II or more symptomatic POP who started to receive vaginal pessary treatment from January 2015 to December 2016 were enrolled. For those patients who had neurological diseases such as Parkinson disease or Alzheimer disease, or not able to take care of the pessary by themselves, were not eligible to pessary use. According to the chart review, there were no patients who had allergy to silicon. Women with the following conditions were excluded for further analysis(1)failure of initial pessary fitting,(2)incomplete initial data collection, such as initial pelvic organ prolapse quantification (POP-Q) record and evaluation of SUI,(3)with a history of previous surgery for POP.The basic patient characteristics including age, parity, body mass index, and the past history of medical diseases were collected at the time of pessary fitting. All enrolled women were required to do 1-hour pad test after reduction of POP by vaginal pessaries, and the method was according to the International Continence Society instructions. All nurses involved in performing the 1-hour pad test had received previous training from the primary investigator. One-hour pad test more than 10 gm were defined as severe SUI.,20 Once these women underwent successful pessary fitting for 1 month, and were willing to continue vaginal pessaries, they would be followed up every 3 to 6 months regularly. We recorded their condition to June 2018 by chart review. The medical chart information included initial symptoms and subsequent change, pelvic examination results, any complications of pessary treatment, and whether they continued vaginal pessaries.Prolapse stage assessment was according to the POP-Q system.t test, Kaplan–Meier survival analysis, and Cox regression analysis as appropriate. The Statistical Package for Social Sciences  was used for statistical analysis. A P value of .05 was considered to be significant.The outcome measures and associated clinical variables were analyzed using the chi-square test, Mann–Whitney statistical tests, 2-tailed Student 3Figure P = .019∗) (Table P = .047).The majority of patients who stopped using pessaries underwent surgery instead (73.3%)     (Table P = .002). No other variables, including POP stage, previous hysterectomy or pessary type, predicted noncompliance with follow-up.In order to analyze the factors which contribute to the noncompliance of pessary treatment, we performed multivariable cox regression analysis. We found that patients with severe SUI (1 hour pad test >10 gm) after pessary reduction predicted noncompliance with pessary treatment at 2-years as compared with patients of mild or no SUI symptoms . Elderly women who are more likely to have medical contraindications for surgery would keep good long-term compliance of vaginal pessaries (P = .04). Multivariate Cox regression analysis also revealed similar results. Therefore, we suggest that long-term pessary treatment would be more suitable for elderly patients and for those with no SUI or mild SUI symptoms. Evaluation of the severity of SUI after reduction before vaginal pessary treatment is warranted.Our study found that severe SUI after reduction of POP may decrease long-term compliance of vaginal pessaries at 24 months  of the patients were excluded due to missing basic data, loss to follow up, or previous POP surgery. Additionally, 1-hour pad test was performed to every patient only during initial pessary fitting, and this may underestimate the patients suffered from severe SUI. The results would be more convincing if we completed urodynamic study and regularly followed up 1-hour pad test during every visit. However, because this was a retrospective study, only near half of the patients received complete urodynamic study, which is not sufficient for analysis. Meanwhile, we only repeated urodynamic study or pad test when it was required . Thus, we could not present the change of SUI after long-term pessary treatment. There is still 1 thing to be stressed, although we consider severe SUI after reduction and younger age are factors of poor long-term compliance of vaginal pessaries, pessary treatment should not be discouraged to these women since the satisfaction is case by case.In conclusion, severe SUI after reduction (1-hour pad test >10 gm) or younger age (age ≦60-year-old) were significantly associated with poor long-term compliance in women with POP treated with vaginal pessaries. Objective evaluation of SUI is important to predict patients’ compliance and help us to provide more information during consultation. Additional large, well-designed prospective studies are worthwhile and necessary.MF Hsieh, CC Lo, YF An, and ZH Lin were responsible for extracting the data. MF Hsieh, HW Tsai, and HY Lin were responsible for the analysis and interpretation of data. HW Tsai and HY Lin were drafting the article. WS Liou and HY Lin were responsible for the design of the study and the acquisition of data. HW Tsai and HY Lin were responsible for revising the manuscript and for the final approval of the version to be submitted.Conceptualization: Hsiao-Wen Tsai, Hsin-Yin Lin.Data curation: Ming-Fang Hsieh, Ching-Chuan Lo, Zi-Han Lin, Ya-Fen An.Formal analysis: Ming-Fang Hsieh, Hsin-Yin Lin.Investigation: Wen-Shiung Liou, Ching-Chuan Lo, Zi-Han Lin.Methodology: Ming-Fang Hsieh, Ya-Fen An.Supervision: Wen-Shiung Liou.Writing – original draft: Hsiao-Wen Tsai.Writing – review and editing: Hsiao-Wen Tsai, Hsin-Yin Lin.Hsiao-Wen Tsai orcid: 0000-0003-3483-3943.


To determine the effects of yoga practice on subclinical cardiovascular measures, risk factors and neuro-endocrine pathways in patients undergoing cardiac rehabilitation (CR) following acute coronary events.3-month, two-arm  parallel randomised mechanistic study.One general hospital and two primary care CR centres in London. Assessments were conducted at Imperial College London.80 participants, aged 35–80 years  referred to CR programmes 2012–2014.A certified yoga teacher conducted yoga classes which included exercises in stretching, breathing, healing imagery and deep relaxation. It was pre-specified that at least 18 yoga classes were attended for inclusion in analysis. Participants and partners in both groups were invited to attend weekly a 6- to 12-week local standard UK National Health Service CR programme.2 following a 3 min step-test. Effects on the hypothalamus–pituitary–adrenal axis, autonomic function, body fat, blood lipids and glucose, stress and general health were also explored.(i) Estimated left ventricular filling pressure (E/e′), (ii) distance walked, fatigue and breathlessness in a 6 min walk test, (iii) blood pressure, heart rate and estimated peak VO25 participants in the yoga + usual care group and 35 participants in the usual care group completed the study. Following the 3-month intervention period, E/e′ was not improved by yoga . Exercise testing and secondary outcomes also showed no benefits of yoga.In this small UK-based randomised mechanistic study, with 60 completing participants , we found no discernible improvement associated with the addition of a structured 3-month yoga intervention to usual CR care in key cardiovascular and neuroendocrine measures shown to be responsive to yoga in previous mechanistic studies.NCT01597960; Pre-results. Comprehensive clinical and subclinical cardiovascular measures before and after a yoga intervention ) versus usual CR.Real world setting—older people following an acute coronary event.High level of dropout, particularly in the yoga plus usual CR arm.We can only assess the potential of yoga in addition to usual CR following an acute coronary event.Outcomes limited to 3 monthspost-interventionThe practice of yoga originated in ancient India as a form of exercise which includes breath control, the adoption of bodily postures and meditation which aim to increase strength and flexibility and to aid physical and mental well-being.Cardiac rehabilitation (CR) has been shown to improve cardiovascular mortality and hospital re-admissions in patients with coronary heart disease (CHD).In this UK-based randomised study ), we hypothesised that yoga would be associated primarily with improvements in cardiovascular function and exercise capacity both chronically and acutely in people eligible for CR. The chronic study compared cardiovascular measures at 3 months between two groups randomised either to usual care (including CR) or to usual care plus a programme of yoga classes. For the chronic study, where the emphasis was on rehabilitation following a coronary event, we focused on the ratio between early mitral inflow and mitral annular early diastolic velocity (E/e′) as the primary cardiac measure. E/e′ provides an estimate of left ventricular (LV) filling pressure,In addition to these primary outcome measures, we studied a range of other cardiovascular risk factors and measures which might be expected to improve following CR and provide mechanistic insight into any beneficial effect of yoga; these included markers of the hypothalamic–pituitary axis, measures of autonomic function, measures of cardiac structure and function, brachial and central resting and 24-hour ambulatory BP, markers of atherosclerosis, blood glucose and lipids and self-reported health, lifestyle factors and perceived stress levels.Inclusion criteria included referral to CR programmes in north-west London following an acute coronary syndrome . Pre-specified inclusion criteria were age between 35 and 80 years, male or female, without comorbid disease or mobility limitations that would preclude participation in CR and our investigations, and, given the north-west London area of recruitment, able to understand English or Punjabi. Ethnicity was self-defined, and verified by country of birth of all four grandparents. In all, 80 participants were recruited following discharge from hospital and randomised in equal numbers to the yoga intervention plus their standard CR programme, or to standard CR programme  alone. Randomisation was performed by an independent researcher using a standard computerised algorithm (customised Java web application (srub)) and stratified by ethnicity (South Asian and non-South Asian), gender, 5-year age group and rehabilitation centre. The generated sequence was displayed only to the user at the time of assignment to the yoga intervention or usual care. 75% of participants were recruited from referrals to CR programmes at Ealing Hospital in west London, with the remainder recruited from two primary care CR programmes in north-west London (Harrow and Brent (Flexi-Heart Plan)). Recruitment of the planned 80 participants took place between October 2012 and April 2014, with the final participant seen for 3-month follow-up measures in July 2014.Eligibility criteria were broadened in January 2013 and April 2013, respectively, with ethical approval, to include patients who had undergone coronary artery bypass grafting or who had received medical management only for their acute coronary event. The initial study plans were to recruit only patients referred to a CR programme post-angioplasty as treatment for an acute coronary syndrome. With cardiologist advice, it was felt that the earlier decision to exclude these patients based on safety grounds was unnecessary given the gentle and tailored nature of the exercises.Patients and public were not involved in the study design, conduct, results, evaluation or dissemination.Informed written consent was obtained from all participants.The yoga intervention was delivered on a twice-weekly group session basis for 12 weeks alongside the usual care, 6- to 12-week CR programme. There were 24 yoga classes in total. Participants’ partners were invited to take part in each session as a method of improving adherence. The yoga session was designed and conducted by a teacher certified in yoga and CR, and included gentle exercises in deep relaxation, stretching, breathing, healing imagery and a healthy diet. A prescription of exercises with an accompanying DVD was provided to be performed regularly at home. Each session lasted approximately 75 min, divided into three equal parts: breathing exercises, yogic poses and meditations, education and discussion  and British Association for Cardiac Prevention and Rehabilitation standardshttp://www.theheartmanual.com/Pages/default.aspx, accessed 26/9/2017).Usual care is described in the online Transthoracic two-dimensional and Doppler echocardiography were performed as previously described.Exercise capacity was measured by a 6MWT conducted along a 30 m straight path in an outdoor covered area marked clearly with the beginning and end of each lap. Participants wore appropriate shoes and loose-fitting clothing and rested in a chair for 10 min before the start of the test. Participants were asked to walk briskly as far as possible for a timed 6 min. Fatigue and dyspnoea before and after the walk test were assessed using the Borg scale.2) was calculated based on achieved heart rate in the immediate post-exercise period as described previously.Physical fitness was measured using a Tecumseh step-testMeasures of cardiac structure and function were obtained as described under primary outcomes above and included LV mass index, relative wall thickness, left atrial diameter, ejection fraction, mitral E/A ratio, s′ (peak velocity during systole) and e′ (peak velocity during early diastole).Resting seated brachial and central BP was measured after 5 min seated rest using a Pulsecor BP +device A Vicorder oscillometric device Ambulatory BP monitoring was conducted using the oscillometric Mobilograph device Heart rate variability (HRV) and baroreceptor sensitivity (BRS) were measured according to a published protocol.Fasting bloods were analysed for glucose and lipids at baseline and 3-month follow-up. The HPA axis was assessed by salivary cortisol sampled at five points during the day pre-intervention and at 3-month follow-up as described for the acute study below. Salivary amylase, as a marker of sympathetic activity, was measured at five time points during the day, as described for cortisol.The full extra-cranial carotid artery was examined for the presence of plaque using an iE33 ultrasound machine (Philips) equipped with a linear-array transducer (L11_3) with concurrent recording of three-lead ECG over three–five cardiac cycles. Carotid intima-media thickness (IMT) was measured in the distal 1 cm of the left common carotid artery from three longitudinal planes  in a region free of plaque with a clearly identified double-line pattern. Plaque was defined according to the Mannheim consensus as a focal structure encroaching into the arterial lumen by at least 0.5 mm or 50% of the surrounding IMT value, or a region of IMT having a thickness >1.5 mm. Analyses were performed using a validated semi-automated programme (AMS-II).The GeneActiv wrist-worn waterproof accelerometry device was fitted at the end of the pre-intervention and 3-month follow-up visits and worn for 3 days after each visit. Analysis of the data was performed using a validated algorithm at the University of NewcastleSelf-completion questionnaires were administered pre-interventions and at the 3month follow-up as follows:http://www.ipaq.ki.se/scoring.pdf, accessed 25 August 2017)The international physical activity questionnaire (IPAQ) long version was administered and analysed according to the IPAQ guidelines  is a standardised instrument for use as a measure of health outcome. It provides a simple descriptive profile a visual analogue scale to indicate self-rated health and a health status score based on UK population norms (there is no set of scores based on Indian Asian populations).Euroqol-5 dimension-3 Levels (EQ-5D-3L)  measured immediately post-exercise.BP and heart rate at rest and following a 3 min step-test and estimated peak oxygen consumption  collection kit at five time points during the day pre-intervention . For the acute study, waking, waking plus 12 hours and bedtime samples were taken on the day of the first yoga session. The latter two sampling points therefore occurred after the first yoga session. Samples were analysed using using indirect ELISA kits .Saliva samples for amylase and cortisol were collected by the participants at home using a Salivette .The sample size estimate was based on primary outcome measures for the chronic effects of yoga, that is, E/e′ echocardiography, 6MWT and Tecumseh 3 min step-test. Previous studies have reported at least half a SD benefit associated with yoga on both diastolic function and exercise testingChronic study: summary descriptions of continuous pre-intervention characteristics are shown as means (95% CI) for normally distributed data or as medians (95% CI of the median (CIM)) for non-normally distributed variables or as number (%) for categorical variables. Pre-intervention characteristics are shown for the whole study group  and for 10.1136/bmjopen-2019-030119.supp2Supplementary dataFor the 3 min step-test which was conducted in three stages pre- and post-intervention, repeated measures analysis of variance (ANOVA) models were used to determine differences by intervention arm and timing (pre-intervention and 3-month follow-up for the chronic study) and for the acute study (pre-first and post-first yoga session). Repeated measures ANOVA models were also used for salivary amylase (log transformed) and cortisol measured 5 times on 3 days  or 2 days .2 test.The remaining measures were analysed using robust regression models,For HRV and BRS, we conducted sensitivity analyses that excluded the few participants who were not receiving beta-blocker medication.Statistical analyses were performed using STATA V.15 software.In all, 80 participants were recruited and randomly assigned in equal numbers to the yoga plus usual care and usual care groups. Pre-intervention, average age was 57.1 (95% CI: 54.9 to 59.4), 68% were men and 64% were of South Asian origin. Diabetes was present in 36%. The majority were receiving statins (90%) and/or anti-hypertensive medication (95%) . ConsistIn all, 35 participants in the usual care arm (63% South Asian) and 25 participants in the yoga arm (59% South Asian) completed the study. Greater loss to follow-up occurred in the yoga group, mostly due to unwillingness to continue with yoga classes—participants frequently citing ill health as a reason, although one participant withdrew from the study because of return to work . Charact10.1136/bmjopen-2019-030119.supp3Supplementary dataNo adverse events were reported. There was minimal change in the number and type of medications prescribed over the 3-month course of the study .At 3-month follow-up, E/e′ improved in both groups, but there was no evidence of yoga-related additional benefit in diastolic function   p=0.4 The total distance walked increased in both groups at 3-month follow-up, but there was no evidence of yoga-related additional benefit  m, p=0.7; 2 in both groups at follow-up, but there was no evidence of additional benefit associated with yoga  cholesterol .Both groups had slightly lower waist to hip ratios at follow-up than at baseline, but with no evidence of yoga-related additional benefit compared with usual care alone. .Accelerometry over 3 days showed that the usual care group modestly increased levels and the yoga group maintained levels of physical activity during the follow-up period. Self-reported physical activity (IPAQ) increased in both groups, with no evidence of additional benefit from yoga compared with usual care alone .Similarly, the EQ-5D-3L measures of health status or self-rated health at follow-up did not show any convincing evidence of a treatment effect at follow-up, although there was a small increase in EQ-5D-3L health status based on UK population norms in people randomised to yoga compared with those receiving usual care. The EQ-5D-3L self-rated health thermometer improved to equal extents in both yoga and usual care groups over the 3-month period. The yoga group had lower stress scores than the usual care group both pre-intervention and at follow-up and there was no convincing evidence of change in stress score in either treatment group .There were very few current smokers at baseline or follow-up and there were no between-group differences or within-group changes. There were no between-group differences or significant within-group changes at follow-up in self-reported hours and quality of sleep, in alcohol consumption or in consumption of fresh fruit and vegetables (not shown).Sensitivity analyses of the primary outcomes which added those four participants who did not complete 18 yoga classes, but who did attend the 3-month follow-up clinic, did not alter findings. Likewise, exclusion of the few people who were not receiving beta-blocker medication did not alter the findings for HRV, BRS and salivary amylase. Additional adjustment of primary outcome measures for selected informative baseline covariates ) did not alter conclusions, for example, adjusted between group difference in E/e′ was −0.18  compared with −0.38  when adjusted only for baseline E/e′. The 3 min step-test and 6MWT findings were little changed on adjustment for these baseline covariates2  were similar between interventions.Measures of HRV may be more sensitive to subtle changes than traditional tests of autonomic function, and improvements associated with yoga training or tai chi have been observed in systematic reviews for a number of parametersYoga has been variously shown to reduce fasting glucose and glycated haemoglobin, insulin, total and LDL cholesterol, triglyceride and weight, even in those without diabetes,A 3-month multimodality intervention including yoga improved stress management, stress, depression, hostility and quality of life in a large group of patients with CHD, although this study lacked a control arm.Impacts of yoga on subclinical and clinical cardiovascular disease have been inconsistently studied, making it difficult to place in context our findings of no evidence of improvement in E/e′ due to yoga, although E/e′ appeared lower in both groups at follow-up compared with baseline. Although our participants had pre-intervention levels of E/e′ that could be considered ‘normal’, it is important to note that increased cardiovascular risk increases linearly across the entire range of E/e′ and there is evidence that exercise can improve diastolic function even in healthy individuals.As noted earlier, there is a general difficulty in comparing studies due to the wide variation in study designs and populations. A recent systematic review of 306 randomised controlled trials of yoga found that 91% reached positive conclusions.This is the first study to our knowledge to adopt a comprehensive approach to measuring cardiovascular clinical and subclinical outcomes in response to a yoga intervention. It is also unusual in studying outcomes in a real-world setting in an older group of people eligible for CR following an acute coronary event; however, the trial was not designed to establish whether yoga may have benefits in terms of cardiovascular events or angina, for this much larger studies would be required. Also for some outcomes, such as IMT, it is likely that 3 months is too short a time to observed substantial regression. Dropout in the yoga arm of the study exceeded that in the usual care arm , possibly reflecting the dual burden of attending both yoga training and usual CR. Consequently, the study will not have achieved the planned statistical power, as we had estimated a requirement for 33 in each group to enable detection of a 0.5SD difference in the primary outcomes, although given the measured effect sizes and CIs, we believe if there are benefits of yoga on the measured outcomes, they are likely to be small. We did not adjust for multiple testing as we had identified a priori relevant primary outcomes for the trial, but in practice adjustment for multiple testing would not have altered our interpretation given the null findings. CR is standard care following an acute coronary event in the UK, thus ethical reasons prevented comparison of yoga-based CR directly with usual CR alone—hence, this study cannot tell us about the potential of yoga as an alternative to traditional CR. Our findings also cannot be generalised to other conditions that may benefit from CR, such as heart failure, post-valve replacement, stable angina pectoris or symptomatic peripheral artery disease.In this small UK-based randomised mechanistic study with 60 completing participants , we found no discernible improvement associated with the addition of a structured 3-month yoga intervention to usual CR care in any key cardiovascular or neuroendocrine outcomes shown to be responsive to yoga in previous mechanistic studies.We suggest that usual care CR programmes in the UK, which include exercise, and optimisation of medical therapy may leave little additional scope for added benefits from a further intervention such as yoga.


Individuals who have suffered a stroke are at risk for developing cognitive impairment and dementia. Thus, it is important to identify modifiable risk factor for cognitive decline in this population. Among older adults without a history of stroke, greater muscle strength is associated with better cognitive function. Whether this relationship also exist in older adults with a history of stroke is not known. Thus, we aimed to examine whether cognition, as measured by both the Montreal Cognitive Assessment (MoCA) and the 13-item Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog 13), is associated with lower extremity muscle strength in adults with chronic stroke (> 1 year post stroke). Ninety-one community-dwelling adults, aged 55 years and older, with chronic stroke were included in this analysis. Isometric strength of the quadriceps was measured bilaterally in kilograms. Two linear regression models were constructed to determine the independent association of quadriceps strength (mean kilograms of both legs) with: 1) MoCA; and 2) ADAS-Cog 13, after controlling for age, sex, and mood. Mean quadriceps strength was independently associated with both MoCA and ADAS-Cog scores, after accounting for age, sex, and mood. Specifically, quadriceps strength explained an additional 5.6% of the variable in MoCA scores; total variance explained by the model was 12.0%. For ADAS-Cog 13, quadriceps strength explained an additional 5.4% of the variance; total variance explained by the model was 16.5%. Our current cross-sectional results suggest that the maintenance of muscle strength may be important for cognitive health in older adults who have suffered a stroke.


This study reports the findings of the first large-scale Phase III investigator-driven clinical trial to slow the rate of cognitive decline in Alzheimer disease with a dihydropyridine (DHP) calcium channel blocker, nilvadipine. Nilvadipine, licensed to treat hypertension, reduces amyloid production, increases regional cerebral blood flow, and has demonstrated anti-inflammatory and anti-tau activity in preclinical studies, properties that could have disease-modifying effects for Alzheimer disease. We aimed to determine if nilvadipine was effective in slowing cognitive decline in subjects with mild to moderate Alzheimer disease.n = 498), with the Clinical Dementia Rating Scale sum of boxes (CDR-sb) as a gated co-primary outcome, eligible to be promoted to primary end point conditional on a significant effect on the ADAS-Cog 12. The analysis set had a mean age of 73 years and was 62% female. Baseline demographic and Alzheimer disease–specific characteristics were similar between treatment groups, with reported mean of 1.7 years since diagnosis and mean SMMSE of 20.4. The prespecified primary analyses failed to show any treatment benefit for nilvadipine on the co-primary outcome (p = 0.465). Decline from baseline in ADAS-Cog 12 on placebo was 0.79  at 13 weeks, 6.41 (5.33–7.49) at 52 weeks, and 9.63 (8.33–10.93) at 78 weeks and on nilvadipine was 0.88 (0.02–1.74) at 13 weeks, 5.75 (4.66–6.85) at 52 weeks, and 9.41 (8.09–10.73) at 78 weeks. Exploratory analyses of the planned secondary outcomes showed no substantial effects, including on the CDR-sb or the Disability Assessment for Dementia. Nilvadipine appeared to be safe and well tolerated. Mortality was similar between groups ; higher counts of adverse events (AEs) on nilvadipine , and serious adverse events , were observed. There were 14 withdrawals because of AEs. Major limitations of this study were that subjects had established dementia and the likelihood that non-Alzheimer subjects were included because of the lack of biomarker confirmation of the presence of brain amyloid.NILVAD was an 18-month, randomised, placebo-controlled, double-blind trial that randomised participants between 15 May 2013 and 13 April 2015. The study was conducted at 23 academic centres in nine European countries. Of 577 participants screened, 511 were eligible and were randomised . Participants took a trial treatment capsule once a day after breakfast for 78 weeks. Participants were aged >50 years, meeting National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s disease Criteria (NINCDS-ADRDA) for diagnosis of probable Alzheimer disease, with a Standardised Mini-Mental State Examination (SMMSE) score of ≥12 and <27. Participants were randomly assigned to 8 mg sustained-release nilvadipine or matched placebo. The a priori defined primary outcome was progression on the Alzheimer's Disease Assessment Scale Cognitive Subscale-12 (ADAS-Cog 12) in the modified intention-to-treat (mITT) population (The results do not suggest benefit of nilvadipine as a treatment in a population spanning mild to moderate Alzheimer disease.NCT02017340, EudraCT number 2012-002764-27.Clinicaltrials.gov  In a randomised controlled trial, Brian Lawlor and colleagues investigate whether the blood pressure medication nivaldipine can slow the progression of Alzheimer disease. There are few licensed drug treatments for Alzheimer disease and none are effective in slowing the rate of disease progression.Nilvadipine is a licensed blood pressure medication and has been shown to lower brain amyloid and improve memory function in animal models of Alzheimer disease.If nilvadipine were shown to be effective in slowing the rate of progression of Alzheimer disease, because it is already licensed and available to treat high blood pressure, it would be possible to introduce the drug for use in Alzheimer disease relatively quickly.We carried out an investigator-led clinical trial funded by the European Union across 23 academic university sites and involving 511 patients with mild- and moderate-stage Alzheimer disease, as diagnosed by a clinician.We tested whether a single dose of nilvadipine, compared with placebo, was safe and slowed the progression of Alzheimer disease over a period of 18 months.We found that nilvadipine appeared safe and was well tolerated but did not slow decline in cognition or function in this group of mild- and moderate-stage Alzheimer disease patients.Nilvadipine does not appear to be effective as a treatment for people with mild- or moderate-stage Alzheimer disease.We cannot rule out that this medication may help at an earlier stage of the disease process, before the person experiences loss of function. Observational studies have suggested a benefit of certain blood pressure medications on reducing the risk of developing dementia . ParticuIn clinical studies, nilvadipine stabilised cognitive decline and reduced conversion to Alzheimer disease in a small study of patients with hypertension and mild cognitive impairment . AnotherThese studies are complemented by a number of epidemiological and interventional studies involving different calcium channel blockers that have reported on the potential benefit of this drug class in the prevention of Alzheimer disease. In the treatment of Systolic Hypertension in Europe (Syst-Eur) trial, which involved over 2,400 older participants with systolic hypertension treated with the DHP calcium channel blocker, nitrendipine, there was a reported 55% reduction in the incidence of Alzheimer disease ,13. The To our knowledge, there has been no definitive intervention study with a calcium channel blocker to test for an effect on slowing the rate of cognitive decline in patients with Alzheimer disease.Given the previous preclinical and clinical data suggesting the potential efficacy for nilvadipine and related compounds against Alzheimer disease, the objective of this 78-week randomised, placebo-controlled study was to determine whether treatment with nilvadipine sustained-release 8 mg, once a day, was effective and safe in slowing the rate of cognitive decline in patients with mild to moderate Alzheimer disease.This 18-month Phase III, randomised, placebo-controlled, double-blind, parallel-group study was carried out at 23 academic centres in nine European countries: Ireland (two sites), United Kingdom (one site), Italy (four sites), the Netherlands (three sites), France (seven sites), Greece (three sites), Sweden (one site), Germany (one site), and Hungary (one site) . The triA detailed list of inclusion and exclusion criteria is provided in the published protocol . BrieflyAll participants provided written informed consent before enrolling in the study. The consent form was amended as required in each country to comply with local ethics requirements. All caregivers also provided consent for involvement.Participants were randomly assigned to nilvadipine sustained-release 8 mg or placebo using block randomisation with randomly varying block sizes, stratified by site, using an online system integrated with stock control across sites. Participants, caregivers, and assessors were blinded to treatment assignment.Participants took a trial treatment capsule once a day after breakfast for 78 weeks and returned their used treatment boxes at subsequent dispensing visits, when the number of returned capsules was recorded. Participants were assessed at 6, 13, 26, 39, 52, 65, and 78 weeks after commencing treatment. Participants were followed up 4 weeks after the final, week 78 visit.The co-primary outcome measures were the change from baseline in the 12-item Alzheimer Disease Assessment Scale–Cognitive Subscale (ADAS-Cog 12)  and the The sample size of 250 patients in each group was calculated to allow detection of a 50% reduction in cognitive decline in the nilvadipine group over the 78 weeks of follow-up . This reThe primary and secondary efficacy analyses were conducted in a modified intention-to-treat (mITT) population, including all participants randomised who had both a baseline assessment and at least one later assessment. The safety set included all patients who took at least one dose of the trial treatment.A secondary per-protocol analysis was carried out using only those patients compliant with medication (defined as taking >80% of doses) and with all assessments on schedule.p-value less than 0.05 for a (Visit × Arm) interaction test using change scores from baseline and adjusting for the baseline score. We adopted a gated approach to control the false positive rate over multiple end points. The ordered outcomes were as follows: change from baseline of ADAS-Cog 12 ; followed by the change in CDR-sb; then, in order, ADAS-Cog 12 and CDR-sb were to be tested for a linear improvement over continuous time. The key secondary outcome of DAD was next on the list, followed by the other secondary outcomes. In the case of a nonsignificant result, any further analyses are purely exploratory, with no further tests of a null hypothesis. Full technical details and description of the gated approach and statistical models are given in the The primary and secondary end point analyses consisted of linear mixed-effects models, with country as a random effect and correlated residuals over time. Findings hinged on a APOE) ε4 allele carriers and noncarriers. The latter analysis was limited to the patient subgroup that participated in the blood biomarker study [Responder analyses were conducted on a dichotomised change score from baseline to week 78 using logistic regression, with no imputation for missing values. Preplanned subgroup analyses included examination of a difference in nilvadipine effect size between mild and moderate Alzheimer disease , between males and females, and between Apolipoprotein E gene (er study . SubgrouBaseline and safety end points were tested by standard tests for proportions (Pearson chi-squared test) or rates (Poisson count model), with no corrections applied for multiple testing.An independent Data Safety Monitoring Board, blind to group assignment, reviewed safety data throughout the trial.This trial adhered to the Declaration of Helsinki and International Conference on Harmonisation Good Clinical Practice (ICH GCP) guidelines and was conducted in compliance with the protocol, data protection regulations, and all other regulatory requirements, as appropriate.Between 15 May 2013 and 13 April 2015, 511 eligible participants were randomised; the last outcomes visit was in November 2016. Of the 511 randomised, 498 had at least one post-baseline ADAS-Cog 12 assessment and comprised the mITT population , with 24APOE genotype was available from 161 participants in the nilvadipine group and 167 in the placebo group.Baseline demographic and Alzheimer disease–specific characteristics were similar between treatment groups , Table 2p = 0.465). The nilvadipine difference from placebo, in change from baseline in the ADAS-Cog 12 score, was −0.22   . Similar01–1.57) , Fig 2.Per-protocol analyses showed identical patterns to the primary analysis. The prespecified responder analysis showed no effects of nilvadipine on the proportion of patients maintaining cognition or function as measured by the ADAS-Cog 12: odds ratio 1.09 , the CDR-sb: odds ratio 1.74 , or the DAD: odds ratio 0.90 .APOE ε4 allele carriers showed less decline than noncarriers on nilvadipine . Despite a higher total number of AEs or serious adverse events (SAEs) in the nilvadipine group  was fewer than the overall treatment population. Further exploratory analyses, making use of the sub-study data, will look for correlation between biomarkers , cerebral blood flow, and other brain imaging data to better understand whether specific mechanisms, e.g., via a blood pressure–lowering pathway or changes in Aβ or tau correlate with cognitive change.The findings from the predefined subgroup analyses suggest differential effects of nilvadipine in those at a milder disease stage, in d better , in thesThe strengths of this investigator-driven clinical trial include the successful recruitment and retention of participants and the conduct of the study to a high standard. There are, however, a number of issues related to the study design that could be considered for future trials of this nature that are suggested by our main findings. Firstly, a single-dose strategy was used, and it is possible that an insufficient dose was given to effect a treatment response. The side effect profile for nilvadipine in this older, mild to moderate Alzheimer disease population was favourable and the effect on blood pressure quite modest, so it would probably have been safe to give a higher dose. While we predicted that any effect of nilvadipine on cognition would be via an anti-amyloid rather than a blood pressure–lowering pathway, it is possible that a lack of benefit in the overall population may have been contributed to by the modest blood pressure–lowering effect of nilvadipine in this study. Secondly, the lack of biomarker confirmation of the diagnosis of Alzheimer disease, which could mean that up to 20% of patients included in the trial may not have had significant amyloid pathology , could bThis study of Nilvadipine at a dose of 8 mg found no overall effect on slowing the rate of cognitive decline in a population spanning mild to moderate Alzheimer disease.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 Text(DOCX)Click here for additional data file.S4 Text(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableADAS-Cog 12, Alzheimer's Disease Assessment Scale Cognitive-12.(DOCX)Click here for additional data file.S3 TableAPOE, Apolipoprotein E gene.ε4, epsilon 4 allele; ADAS-Cog 12, Alzheimer's Disease Assessment Scale Cognitive-12; (DOCX)Click here for additional data file.S4 TableADAS-Cog 12, Alzheimer's Disease Assessment Scale Cognitive-12.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableMedDRA, Medical Dictionary for Regulatory Activities.(XLSX)Click here for additional data file.S1 CONSORT Checklist(DOCX)Click here for additional data file.


Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput  Drosophila melanogaster, with large-scale in situ projects that have provided invaluable expression information for many genes, 40% of the genes still lack spatio-temporally resolved expression information. Such data is complemented by transcriptome datasets such as microarray and RNA-seq, which have whole-genome coverage and measure expression levels with greater dynamic range, but they typically lack precise spatio-temporal resolution. To bridge this gap, we developed a machine learning approach that combines the spatio-temporal resolution of in situ data with the accurate quantification and whole-genome coverage of genomic experiments, integrating information from 6,378 expression and chromatin profiling data sets. With this new approach, we present a genome-wide resource of spatio-temporal gene expression predictions for over 200 tissue-developmental stages during Drosophila embryogenesis. This resource is experimentally validated to have high-quality predictions, can guide the discovery of new tissue-specific genes, and provides a new tool to perform genome-wide analyses of spatio-temporal specificity.When and where a gene is expressed is fundamental information for understanding embryonic development. Current knowledge for such expression patterns is typically far from complete. Even for the long-standing model organism,  Spatio-temporal gene expression information is fundamental for understanding the intricate mechanisms of embryonic development, which involves both precisely controlled differentiation into dozens of tissue types, as well as dramatic temporal transitions such as gastrulation and segmentation.Drosophila melanogaster, decade-long efforts in systematically characterizing developmental and tissue-specific expression patterns have greatly advanced the understanding of embryonic development and tissue formation [in situ hybridization and imaging, most representatively the Berkeley Drosophila Genome Project (BDGP) [Drosophila embryonic development one of the most comprehensively characterized systems of spatio-temporal transcriptional regulation.In the model organism ormation –3. Larget (BDGP) , have ret (BDGP) . These mvia classical in situ hybridization and imaging are typically qualitative or grossly semi-quantitative [in situ hybridization protocols that are typically used in large-scale efforts restricts the detection of lowly expressed genes.However, the current compendium of embryonic expression is not without limitations, even for this deeply investigated model organism. For many tissues only a small number of gene’s have been annotated to be expressed, with a median of 80 genes for each tissue, which is likely an underrepresentation of the true number of tissue-expressed genes. In addition, more than 4,000 protein-coding genes, as well as many non-coding genes, have not been annotated at all . Moreovein situ data can be complemented by integrating them with the ever-growing transcriptome data measured by microarray or RNA-seq. With their whole-genome coverage and quantitative nature, the advantages of transcriptome profiles are highly complementary to those of in situ data. Even for cases where the transcriptome profiles are not tissue- or stage-specific, they are an information-rich source of co-regulated gene expression patterns, such as those of genes within the same regulatory or biochemical pathways. In these datasets, as gene expression is measured in a diversity of genetic or environmental conditions, functionally related genes are often co-expressed or perturbed together [D. melanogaster embryonic development, the well-annotated resource of gene expression across tissues and developmental time, provide a unique opportunity to systematically predict not only tissue-specific expression but also the temporal stage of gene expression. Prior work has demonstrated the feasibility of making such predictions in a small set of 15 tissue-stages [D. melanogaster development [To address these limitations, together ,6. Usingtogether  from none-stages . MoreoveD. melanogaster based on integrating the tissue specificity of in situ hybridization data with the quantitative and comprehensive measurements from public genomic data compendium. The algorithm, which we refer to as structured in silico nano-dissection (SIND), improves individual tissue predictions with a cell lineage-aware prediction integration stage. This stage learns and applies transcriptional dependencies between developmentally and anatomically related tissue-stages, with lineage knowledge encoded in the structure of a probabilistic graphical model based on the tissue developmental ontology.Here, we present an approach for the prediction of gene expression during embryonic development in in situ gene annotations and a compendium of 6,378 genome-wide expression and chromatin immunoprecipitation (ChIP) datasets, we predicted global expression patterns in 282 tissue-developmental stages, including >4000 genes without prior in situ data. The predictions were validated both computationally, via hold-out validation, and experimentally in the case of new predictions without prior literature evidence. We verified predictions in muscle and brain for 13 novel genes with no prior in situ data or literature evidence to the extent of our knowledge, for both tissue-specificity and temporal specificity, including co-expression patterns in multiple tissues, demonstrating the accuracy of our predictions. The complete and quantitative nature of the predictions enables previously infeasible analysis of tissue specificities, such as analyzing tissue-specific signatures in whole-animal differential expression datasets and mapping human diseases to D. melanogaster tissue/stages for aiding the development of human disease models in the fly. We provide all our predictions and exploratory tools in a user-friendly web interface FIND  that allows the user to query predicted gene expression pattern and perform tissue-specificity prediction task.Using our SIND algorithm, together with Drosophila tissue-developmental stage gene expression prediction system FIND.As an overview , our appConceptually, in the first stage, our method discovers and exploits the co-regulatory patterns of cell lineage-specific genes from high-throughput measurements of samples from diverse tissue origins and under a wide range of genetic, chemical, or environmental perturbations ,6. The cin situ annotations and use it for integrating predictions. For each gene, the probabilistic graphical model treats expression in each tissue-stage term as a binary random variable. The value of each tissue-stage variable is dependent on both the input to the model, which are the SVM predictions from individual classifiers, and the values of other tissue-stage variables that are connected in the tissue developmental ontology. As the expression for each tissue is globally predicted considering dependencies between all tissue-stage terms, this approach allows for borrowing information from relevant tissues.In the second stage, we use prior developmental cell lineage knowledge to help integrate individual tissue-stage specific predictions with a conditional random field (CRF) probabilistic graphical model. Developmental lineage knowledge is expected to be informative for improving spatial-temporal predictions. This is both because gene expression in a precursor tissue usually provides informative insight into gene expression patterns of the tissue it develops into, and because related tissues, e.g. different cell types in the nervous system, are likely to share expression patterns for many genes. Specifically, we estimate the quantitative gene expression dependencies between developmentally related tissues empirically from Drosophila embryogenesis has been extensively studied via large-scale in situ hybridization projects, such as the BDGP in situ database [in situ hybridization data in multiple ways: our approach makes predictions for plenty of genes  unmeasured or undetected by in situ, computes quantitative scores comparable across genes and tissues and predict expression probabilities across all tissues, reducing the bias of manual annotation towards the most prominent or easy to recognize tissues.Spatio-temporal gene expression during database  and Fly-database . These sin situ annotations for 282 tissue-stage terms spanning 6 time periods of fly embryonic development  model is a major factor for enhancing performance, in addition to utilizing a wide range of datasets and data types . Using tCG12792, which is a ubiquitously expressed genes at early developmental stages, with more restricted expression at 13 hours after egg laying (AEL)  . This deng (AEL) . Based oeriments .in situ and to the tissue-stages with no detectable in situ expression. This is another important dimension of prediction performance, besides correctly ranking genes for each tissue. The distribution of AUROC for genes is shown in  experiments for genes where no prior tissue-developmental mapping was available.We examined the ability of the model to predict expression patterns (outside of the training set) by both collecting gene expression information from the literature and performing fluorescent in situ ontology categories) that had both a large number of genes with high scoring predictions  and are easy to assess in in situ images: brain primordium (stages 11–12) and embryonic muscle system (stages 13 and later). We found that all genes newly predicted to be expressed in theses tissues with no prior annotations were confirmed. For brain primordium, all 5 genes with detectable FISH signals as predicted by FIND were indeed highly expressed in brain at stages 11 or 12  and five  genes with more than 20-fold enrichment above background, and in all cases we were able to detect expression on the respective tissues . Thus, wPpn (Papilin). The main cell type where this gene is expressed is the plasmatocyte  can be a very valuable tool to guide the investigation of complex spatial-temporal expression patterns for individual genes or groups of genes.In addition to using spatiotemporal expression pattern predictions to generate hypotheses for individual genes, these predictions can also enable new spatio-temporal specificity analysis of high-throughput experimental data. In case studies below, we showcase two new directions of such applications.High-throughput expression profiling and differential expression analysis are common and important techniques for exploratory analysis of molecular responses to different perturbations. While perturbations such as genetic ablation of a specific gene are expected to affect certain tissues preferentially, this is usually difficult to assess since, for practical reasons, expression profiling experiments are often performed on the whole body or tissue homogenates. FIND’s quantitative score for every tissue and stage for every gene in the genome can detect tissue-specificity signals from whole-organism differential expression experiments, even in the absence of reliable tissue-specific markers. This is particularly useful for tissues with few annotated marker genes. As a proof-of-concept, we used three differential gene expression datasets comparing whole-embryo mutant vs. wild type to predict the tissues most affected by these perturbations independently of any prior knowledge other than FIND predictions .Myocyte enhancer factor 2 (Mef2) loss-of-function mutants have defects in myoblast fusion and terminal muscle differentiation [Twist loss-of-function mutants lead to a block in gastrulation and a loss of mesodermal cells (E-TABM-162). Accordingly, our method predicts the loss of a wide range of mesoderm derivatives in twist loss-of-function mutants, and at earlier stages than those predicted for Mef2  mutant versus wildtype whole embryo samples. The trh gene codes for an essential transcription factor in the development of the tracheal system [trh loss-of-function mutants [For example, ntiation , consistntiation . Twist lfor Mef2 . Finallyl system   expression datasets and identification of most relevant tissues for study of human disease.http://find.princeton.edu). This web tool allows for querying tissue-gene predictions, discovering genes with similar tissue-specific expression patterns and predicting tissue-stage specificity from a user specified list of genes .To facilitate the exploration of our spatio-developmental gene expression predictions and downstream analyses, we provide a user-friendly web interface . Non-trNNImpute . The datin situ database (insitu.fruitfly.org) and FlyFISH database (fly-fish.ccbr.utoronto.ca) on May 2014. Genes included in both microarray platforms were used. Since BDGP in situ annotation are more comprehensive in fine tissue structures, we use BDGP as the main standards while using FlyFISH as assistance standards that were only used to train first-stage classifiers. The subset of genes mapped to microarray platforms were used, with a holdout set strictly not used in any steps of algorithm design and the decision of hyperparameters for evaluation.Tissue- and developmental stage- specific expression standards were obtained from BDGP Our Drosophila embryonic development tissue developmental ontology is modified from  with a fOur machine learning algorithm uses a cell-lineage aware approach to predict gene expression with spatio-developmental specificity based on whole-genome scale expression data. The cell lineage information is critical for specific tissues and stages that can benefit from considering developmental context including its precursor and descendant tissues.in situ gene expression annotations and functional genomics data as input and performs prediction in the stages. In the first stage, one individual classifier was trained for each individual tissue-stage term. Specifically, for each tissue-stage term, a linear structural SVM model was trained to predict in situ annotations (whether a gene is annotated to a tissue-stage) from functional genomic datasets including transcriptome profiling data (microarray and RNA-seq) and chromatin profiling data (ChIP-seq). The model was trained using the SVMperfer tool in the Sleipnir library [Our model takes  library .In the second stage, we use a conditional random field classifier, which integrates individual tissue-stage predictions using learned dependencies of gene expression between tissue-stage terms. The conditional random field model represents each tissue-stage as a node and cell-lineage relations as edges connecting the nodes. Tissue-stage nodes are connected with the following rules:If they are in a developmental precursor-and-descendent relationship and in the same or adjacent stages.If they are directly connected in the tissue developmental ontology by the “part of “relationship and in the same or adjacent stages.If they are the same tissue type in adjacent stages.The “no staining” terms were connected to all other terms in the same stage (because of the expected negative dependencies).The CRF model that directly provides probability output for each gene and each tissue is formulated as the followingm(WmX + αm)Ym and a cell lineage dependency term Ym = 1 indicates expression in the m-th tissue-stage and Ym = 0 indicates no-expression in the tissue-stage indexed by m. X represents a matrix containing all SVM predictions from the first stage classifiers, in which a column corresponds to a classifier. The model is also illustrated by a diagram in P(Y│X) describes the conditional probability model. The conditional probability is determined by a term that describes dependencies on first-stage individual classifiers output ∑W, J, a are estimated from the input (first stage predictions and in situ annotations). Specifically in the first term Wm represents weight the m-th tissue-stage classifier from the first stage, which is analogous to coefficients of a logistic regression model and αm is the intercept. We allow a tissue-stage to be informed by predictions for any tissue-stage classifiers in the first term, which integrate expression signals over expression predictions for multiple tissue-stage-specific classifiers based on annotations from both BDGP in situ and FlyFISH.Jmn represents interaction between tissue-stage m and n and only edges in the tissue developmental ontology are allowed to be non-zero. Gontology represent the ontology based graph structure which is a set of edges built with rules described above. Prediction for a tissue-stage can either increase or decrease probability of a connected tissue-stage term depending on the estimated dependency scores J. Z is the normalizing constant which ensures the sum of probabilities equals 1, and it does not need to be explicitly computed in our learning algorithm.In the second term, λ|J|. The L1 regularization λ|J| is used to avoid overfitting to tissue-stage dependencies by encouraging J to be 0 if it contributes little to the likelihood. |J| represents the L1 norm of interactions J which equals sum of absolute values of all entries of J. λ is the L1-regularization parameter.The model can be trained by minimizing the L1 Penalized likelihood ∑log P(Y│X) + in situ annotations, for the training of the second stage model, all predictions from the first stage were made on holdout genes with 5 fold cross-validation. Specifically, for each fold, 4/5 of the genes were used for training and the trained model was used to predict the rest 1/5 holdout genes. The concatenated predictions on the holdout set for each fold, which covers all the training set genes, were used as input to the second stage classifier. As the concatenated predictions were all predicted using models that have not seen the gene before, it minimizes the bias due to overfitting in the second stage.To prevent overfitting to the Next, we describe the optimization algorithm for minimizing the objective function. Computing the exact gradient of the CRF likelihood over the ontology-based graph is intractable since the graph is not tree or chain structured. Approximation inference algorithms, such as pseudo-likelihood-based method is biased but computationally efficient, while Markov chain Monte Carlo (MCMC) sampling-based inference methods converge to the exact gradient in the limit of large sample size, but is computationally expensive to obtain low variance gradient estimate.Z which is intractable, and initialize the second step optimization which optimizes the true objective with the estimators from first step. The L1 regularized optimization problem was solved with Projected scaled sub-gradient (Gafni-Bertsekas variant) as implemented in (https://www.cs.ubc.ca/~schmidtm/Software/thesis.html).To address both speed and accuracy, we choose to use a hybrid of both. We first obtain a fast approximation of parameters by optimizing a modified pseudo-likelihood objective,For the second step, we estimate the stochastic gradient of the model objective by Gibbs sampling and employ stochastic gradient descent with momentum for optimization. For Gibbs sampling based gradient estimation, we first rewrite the likelihood asm) and E(YmYn) are estimated by MCMC sampling. For each iteration, we run a separate Markov chain for each gene and average the stochastic gradients obtained from each chain over all genes. To address efficiency and burn-in time of MCMC, we initialize MCMC chains from the last sample of the previous iteration , and for each iteration, we only run one sweep of Gibbs sampling which sample each node exactly once and use the single sample Y* to compute estimator To compute the stochastic gradient for the log-likelihood when conditioned on X, we used the fact (ignored writing down conditioning on X for simplicity)For each iteration, we increment model parameters based on stochastic gradient descent with specified step size. We use a learning rate annealing scheme of 100,000 iterations of learning rate 1e-6, followed by 50,000 iterations of 1e-7 and 50,000 iterations of 1e-8. Additionally, a momentum term was used for stabilizing the update and improving convergence. L1 regularization was applied to control overfitting. We set momentum to 0.9 and L1-regularization parameter to 8.in situ annotations, we predict their expression with 5 fold cross-validation.After the model is trained, for prediction from the second-stage CRF model we ran 80,000 rounds of Gibbs sampling and average over the samples. For genes with Drosophila melanogaster using functional knowledge transfer (FKT) method with p-value cutoff 0.1 [Human disease gene sets were obtained from OMIM. We selected the functional ortholog of human genes in toff 0.1 . For eactoff 0.1 .in situ hybridizations were performed as previously described [Fluorescent escribed . Brieflyescribed  for prepescribed  and CG34Imaging was performed in a Zeiss LSM 780 confocal microscope, using a Plan-Apochromat 20x/0.8 objective. For fluorescein detection, we used the 488nm wavelength of a multiline Argon laser, a MSB 488/561 dichroic mirror and a spectral detector set for a 500-600nm detection range. For Cy3 detection, samples were excited with a DPSS laser at. For DAPI detection, samples were excited with a diode at 405nm and the emission fluorescence was detected with a MBS405 dichroic mirror. Other acquisition parameters, such as PMT gain and offset, were set independently for each probe, dependent on the signal. After the acquisition, images were processed using ImageJ 1.46r . Althoughttps://github.com/FunctionLab/find_scripts. The gene expression prediction dataset is available at http://find.princeton.edu/predictions/download/ or https://doi.org/10.5281/zenodo.3408411.The code is available at S1 FigClassifiers trained using a single data type, ChIP or RNA-seq, from a large collaborative project, modENCODE, was compared to classifiers using all public data from NCBI GEO for a single data type, and classifiers using all public NCBI GEO data sets and all data types. Integrating prediction utilizing tissue relationships information by ontology-CRF method further improved prediction performances. The left panel compares AUCs for each tissue-stage category with and without using ontology-CRF integration. For every input type, the cross-validation performances for all tissue-stage categories measured by area under ROC curves (AUC) were shown with boxplot.(TIFF)Click here for additional data file.S2 FigThe two dimensional projections of high-dimensional gene expression probability predictions were obtained with linear discriminant analysis (LDA) to best separate the stages. Each point corresponds to a tissue-stage, and the color represent the developmental stage.(TIFF)Click here for additional data file.S3 FigThe two dimensional projections of high-dimensional gene expression probability predictions were obtained with multidimensional scaling (MDS). The first two principal coordinates approximately correspond to variation across stages (a) and the third and fourth principal coordinates approximately correspond to variation across tissue types (b). Each point corresponds to a tissue-stage, and the color represents developmental stage in (a) and tissue type in (b).(TIFF)Click here for additional data file.S4 FigThe gene expression tissue specificity prediction performances for all genes are measured by AUROC based on cross-validation. The distribution of AUROCs for genes with different gene expression annotation complexities were compared, showing better prediction performance for low complexity genes but retaining good performance even for high complexity genes.(TIFF)Click here for additional data file.S5 Figa. Four genes with more than 20-fold enrichment above background in the term "6.embryonic Malpighian tubule" were selected for FISH on late embryos (> stage 13). Images show Z-projections of 2–3 focal planes from entire embryos, with the signal for the specific gene probe in red. mt: Malpighian tubules; hg: hindgut; mg: midgut. b. Five genes with more than 20-fold enrichment above background in the term "6.embryonic salivary gland" were selected for FISH on late embryos (> stage 13). Images show Z-projections of 2–3 focal planes from zoomed anterior sections of embryos. Signal for the specific gene probe is shown in red and signal for the salivary gland marker Gld is shown in green. For clarity, in some cases we pointed at the salivary gland cells with arrowheads based on the Gld signal. DAPI staining is included  to show the shape of the embryo. Scale bar corresponds to 20μm in all images shown.(TIFF)Click here for additional data file.S6 Figα and network connectivity parameter J are learned during training on tissue-stage labels Y.The structured in-silico nano-dissection(SIND) algorithm make prediction based on dependencies of each tissue-stage term on SVM prediction, represented by X, and network connectivities between each term and the terms connected in the developmental ontology, represented by J. The SVM prediction dependency parameters W, (TIFF)Click here for additional data file.S1 Tableth BDGP category corresponds to stages 11 and 12, we inform the range of modENCODE RNA-seq expression values for time points 6-8h and 8-10h, and we inform a positive result when expression in the predicted tissue is seen in stage 11 or 12 embryos (last column). Evidence from literature encompasses all references listed in FlyBase for each gene. Evidence from hold-out in situ data includes FlyFISH expression patterns as well as BDGP in situs that have not been included in the training set for any reason. In all cases, we report “yes” if the reported expression includes expression in the brain during stages 11 or 12. Related evidence is also stated.List of the 17 genes selected to validate the expression prediction for the BDGP term “5.brain primordium”. Since 5(DOCX)Click here for additional data file.S2 Tableth BDGP category corresponds to stages 13 and later, we inform the range of modENCODE RNA-seq expression values for time points from 10-12h to the end of embryogenesis. Similarly, we inform a positive result when expression in the predicted tissue is seen in stage 13 or later (last column). Evidence from literature encompasses all references listed in FlyBase for each gene. Evidence from hold-out in situ data includes FlyFISH expression patterns as well as BDGP in situs that have not been included in the training set for any reason. In all cases, we report “yes” if the reported expression includes expression in the brain during stages 13 or later. Related or contradictory evidence (in red) is also stated.List of the 17 genes selected to validate the expression prediction for the BDGP term “6.embryonic larval muscle system”. Since 6(DOCX)Click here for additional data file.S3 TableTable summarizing both the results of our FISH experiments (first three rows) and the whole evidence available to support the expression of the genes in the predicted tissue (last four rows).(DOCX)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(TXT)Click here for additional data file.S3 File(XLSX)Click here for additional data file.


To estimate the risk of self-harm in people with epilepsy and identify factors which influence this risk.We identified people with incident epilepsy in the Clinical Practice Research Datalink, linked to hospitalization and mortality data, in England (01/01/1998–03/31/2014). In Phase 1, we estimated risk of self-harm among people with epilepsy, versus those without, in a matched cohort study using a stratified Cox proportional hazards model. In Phase 2, we delineated a nested case–control study from the incident epilepsy cohort. People who had self-harmed (cases) were matched with up to 20 controls. From conditional logistic regression models, we estimated relative risk of self-harm associated with mental and physical illness comorbidity, contact with healthcare services and antiepileptic drug (AED) use.Phase 1 included 11,690 people with epilepsy and 215,569 individuals without. We observed an adjusted hazard ratio of 5.31 (95% CI 4.08–6.89) for self-harm in the first year following epilepsy diagnosis and 3.31 (95% CI 2.85–3.84) in subsequent years. In Phase 2, there were 273 cases and 3790 controls. Elevated self-harm risk was associated with mental illness , multiple general practitioner consultations, treatment with two AEDs versus monotherapy  and AED treatment augmentation .People with epilepsy have elevated self-harm risk, especially in the first year following diagnosis. Clinicians should adequately monitor these individuals and be especially vigilant to self-harm risk in people with epilepsy and comorbid mental illness, frequent healthcare service contact, those taking multiple AEDs and during treatment augmentation.The online version of this article (10.1007/s00415-018-9094-2) contains supplementary material, which is available to authorized users. People with epilepsy are twice as likely to die by suicide compared to those without epilepsy . NonfataRisk of hospitalization for self-harm in people with epilepsy has been estimated in two studies , 5. SingThe World Health Organization (WHO) recommends that people with epilepsy should be asked about self-harming thoughts and behaviours in certain, specific circumstances . HoweverWe, therefore, aimed to: (1) estimate self-harm risk in persons with epilepsy versus those without; and (2) identify risk factors for self-harm among individuals with epilepsy.We extracted an incident epilepsy cohort from the Clinical Practice Research Datalink (CPRD), linked to Hospital Episode Statistics (HES) and Office for National Statistics (ONS) mortality data. The CPRD is a primary care dataset that contains routinely collected electronic health records capturing information on patient demographics, diagnoses and treatments in general practice. It has been shown to be representative of the UK population . All of The study was approved by the Independent Scientific Advisory Committee (protocol 17_063R) of the CPRD. Informed consent is not required for studies that use anonymized data from the CPRD.From the CPRD, we extracted the incident epilepsy cohort that formed the basis for both Phase 1 and Phase 2. The study observation period was 01/01/1998–31/03/2014 to correspond with linkage availability. We used our previously published definition to identify people with epilepsy . We defiWe matched each person with incident epilepsy to up to 20 individuals without epilepsy on gender, year of birth (± 2 years) and general practice. Individuals sampled for the comparison cohort had not received a diagnostic code for epilepsy or self-harm in the look-back period, and had been registered for at least 12 months at the practice. Individuals were followed up until the earliest date of: first self-harm event, death, patient transferred out of practice, latest date of data collection from the practice, or end of the study’s observation period.From the cohort of people with incident epilepsy, we identified first recorded cases of self-harm during the study window—the self-harm case date. We matched these cases to up to 20 control individuals from within the incident epilepsy cohort, without history of self-harm on the self-harm case date, using incidence-density sampling . We matcWe included both fatal (suicide) and nonfatal self-harm in our definition. We identified self-harm from primary care records using clinician-verified Read codes  and fromhttp://www.clinicalcodes.org [In Phase 2, we investigated multiple exposures. We identified level of deprivation by quintiles of Index of Multiple Deprivation IMD-2010), and compared to the least deprived quintile (1st quintile). We identified mental illness diagnoses  from primary care data and HES using previously published codes , 18 thatodes.org . We iden10, and codes.org  and ICD-odes.org . We measodes.org , we defip value < 0.05 indicating non-proportionality [In Phase 1, we estimated the relative risk of self-harm in the incident epilepsy versus comparison cohorts using a stratified Cox proportional hazards model. We adjusted for level of deprivation because both epilepsy  and selfionality , and by We matched 11,690 people with incident epilepsy  to 215,569 persons without epilepsy. Compared to the matched cohort, the epilepsy cohort was more deprived and more likely to have been diagnosed with any mental illness, treated with psychotropic medication or opioids (Table p = 0.007); therefore, we divided follow-up time to first year after diagnosis and subsequent years. There was an excess risk of self-harm during the first year of follow-up  compared to subsequent years , although elevated risk persisted throughout the follow-up period.There were 273 first self-harm events in the epilepsy cohort and 1547 in the comparison cohort. The overall incidence rates for first self-harm event  and matched them to 3790 control patients with epilepsy and without history of self-harm on the self-harm case date . 65.9% of cases and 35.6% of controls had a history of mental illness. Having one or more mental illness diagnoses increased self-harm risk compared to having no such diagnoses  and this risk increased markedly among individuals who had received three or more mental illness diagnoses .All mental illnesses examined were associated with an increased self-harm risk, but the magnitude varied across the diagnostic categories. Depression was the most common diagnosis and was associated with approximately a fourfold elevation in self-harm risk . In a post hoc sensitivity analysis, we included depression symptom codes as well as diagnoses in the definition of depression. This did not alter the estimated risk .In the 12 months prior to self-harm case date, 12.8% of cases and 3.8% of controls were referred to specialist psychiatric services . In the same timeframe, 45.8% of self-harm cases and 29.1% of controls were hospitalized at least once for any reason . The median number of face-to-face consultations with a GP in the 12 months preceding the self-harm case date was nine (IQR 5–15) for cases and six (IQR 3–11) for controls. Compared to individuals who had 0–4 consultations in the previous year, individuals who had five or more consultations were at a two- to fivefold increased self-harm risk.In the 90 days prior to self-harm case date, compared to individuals who were prescribed a single AED, those prescribed no AED , two  or three or more AEDs  were at an increased risk of self-harm . On entry to the incident epilepsy cohort, all individuals were prescribed an AED. Therefore, those individuals without AED prescription on the self-harm case date may have gradually stopped taking AEDs because they became seizure free. In the UK, the National Institute for Health and Care Excellence (NICE) recommends that AED withdrawal should only be considered following a 2-year absence of seizures . Given tHealthcare professionals involved in the care of people with epilepsy could instigate conversations about self-harm risk, especially if the described risk factors are present. These include mental health problems, and extend to both the clinicians responsible for the mental health services and those working in general primary care settings. Furthermore, GPs should consider discussing self-harm risk management in people who consult frequently. Further research could investigate whether any technological prompts could aid this during consultations.This is the first published study to estimate self-harm risk among people with epilepsy in a large, linked primary care patient cohort, including 11,690 people with incident epilepsy. Linkage to HES maximized self-harm case ascertainment. The Read codes used to identify self-harm cases were verified by clinicians and have been used in other studies , 29. To It is not possible to accurately determine the type of epilepsy from UK general practice data; therefore, this is something we could not examine in this study. Epilepsy type may influence risk of self-harm . It woulIn conclusion, clinicians should be aware that people with epilepsy are at increased risk of self-harm, compared to those without the condition, especially during the first year post-diagnosis. These patients should, therefore, be routinely monitored. Additionally, we recommend that clinicians are particularly vigilant for self-harm thoughts and behaviours in people with epilepsy and comorbid mental illness, those who consult regularly, those prescribed AED polytherapy and during periods of AED treatment augmentation.Below is the link to the electronic supplementary material.Supplementary material 1 (DOCX 20 KB)