What are the incidence, risk factors, and outcomes associated with in-hospital acute myocardial infarction (AMI)?This cohort study of 1.3 million patients hospitalized in US Veterans Health Administration facilities found an incidence of in-hospital AMI of 4.27 per 1000 admissions, and risk factors associated with in-hospital AMI included history of coronary artery disease, elevated heart rate, low hemoglobin level, and elevated white blood cell count. Compared with a matched control group, mortality was significantly higher for in-hospital AMI.In-hospital AMI is common and is associated with prior cardiovascular disease, physiological disturbances, and poor survival. This cohort study investigates the incidence, risk factors, and outcomes associated with in-hospital acute myocardial infarction (AMI) among a population of patients hospitalized in US Veterans Health Administration facilities. Studies of acute myocardial infarction (AMI) occurring outside the hospital have informed approaches to addressing risk, treatment, and patient outcomes. Similar insights for in-hospital AMI are lacking.To determine the incidence, risk factors, and outcomes associated with in-hospital AMI.Cohort, nested case-control, and matched cohort study of patients hospitalized in US Veterans Health Administration facilities between July 2007 and September 2009. The incidence of in-hospital AMI was determined from a complete cohort of in-hospital AMI relative to the total number of inpatient admissions. From the in-hospital AMI cohort, detailed medical record review was performed on 687 cases and 687 individually matched controls. Risk factors and outcomes associated with in-hospital AMI were determined from matched comparison of in-hospital AMI cases to hospitalized controls.Candidate risk factors for in-hospital AMI included characteristics at the time of admission and in-hospital variables prior to the index date.In the determination of the incidence and risk factors associated with in-hospital AMI, the outcome of interest was in-hospital AMI. All-cause mortality was the main outcome of interest following in-hospital AMI.A total of 5556 patients with in-hospital AMI  were identified among 1.3 million admissions, with an incidence of 4.27 in-hospital AMI events per 1000 admissions. Independent risk factors associated with in-hospital AMI included intensive care unit setting, history of coronary artery disease, heart rate greater than 100 beats/min, hemoglobin level less than 8 g/dL, and white blood cell count 14 000/μL or greater. Compared with the matched control group, mortality was significantly higher for patients with in-hospital AMI .In-hospital AMI was common and associated with common cardiovascular risk factors and markers of acute illness. Patient outcomes following in-hospital AMI were poor, with 1-year mortality approaching 60%. Further study of in-hospital AMI may yield opportunities to reduce in-hospital AMI risk and improve patient outcomes. Insights from these studies have informed risk factors and optimal treatment of AMI, which have led to subsequent reductions in AMI incidence and mortality.8 Although these comparisons have identified potential treatment gaps in the care of in-hospital STEMI,9 less is known about the patient characteristics and long-term outcomes associated with in-hospital AMI. Additionally, prior studies have compared in-hospital AMI outcomes with those of individuals with outpatient onset AMI who survive to hospital admission, potentially biasing comparisons of patient characteristics and outcomes.11 Finally, STEMI reflects less than 25% of all myocardial infarctions2 and few studies of in-hospital AMI have included non–ST-segment elevation myocardial infarctions (NSTEMI).13 Little is known about the full spectrum of in-hospital AMI.Patients with in-hospital ST-segment elevation myocardial infarction (STEMI) have been shown to experience delays in revascularization and worse short-term outcomes when compared with patients experiencing STEMI onset in the outpatient setting.12 With this is in mind, we used a case-control design to identify risk factors associated with developing in-hospital AMI and to compare subsequent 1-year mortality rates.The objectives of this study were to describe the incidence, risk factors, and long-term mortality outcomes associated with in-hospital AMI. We determined the incidence of in-hospital AMI in the Veterans Administration (VA) health care system using data from the Veterans Health Administration (VHA) External Peer Review Program (EPRP), which includes a 100% sample of all AMIs. Reports of the differences between patients with in-hospital AMI and similar patients who were admitted to the hospital but did not develop AMI are limited.International Classification of Diseases, Ninth Revision (ICD-9), diagnosis codes (410.xx). Working with the EPRP abstraction contractor, West Virginia Medical Institute, Charleston, the VHA Office of Quality and Performance generated a list of all patients with ICD-9 diagnosis codes of 410.xx from administrative data housed at the Austin Automation Center in Austin, Texas. This patient list was transmitted to VHA hospitals, where both paper and electronic medical records were manually abstracted by trained abstractors using standard reporting forms. Abstracted data were then entered into a database maintained by the contractor. The total number of inpatient admissions in the VHA for the period of study was determined from in-patient bed section files on the Austin mainframe. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for observational studies. This study was approved by the VA Puget Sound Human Research Protection Program and waiver of informed consent was granted.To determine the incidence of in-hospital AMI, we identified patients with in-hospital AMI from data collected as part of the VHA EPRP for quality monitoring and improvement. This program captures information for a variety of medical conditions and procedures, including all hospitalized patients with AMI according to We identified patients with in-hospital AMI, defined as AMI diagnosis more than 24 hours after admission to the same hospital based on coding by the EPRP abstractor, occurring between July 2007 and September 2009.We performed a nested case-control and matched cohort study of cases and controls to determine risk factors and outcomes associated with in-hospital AMI. We identified cases from the cohort of patients with in-hospital AMI identified by the VHA EPRP. Information on patient controls for comparison with in-hospital AMI cases was obtained from in-patient bed section files on the Austin mainframe.ICD-9 diagnosis codes (410-414). Classification as to whether AMI occurred in-hospital was initially based on coding by the EPRP abstractor and the admitting diagnosis from VHA bed section files. The final determination was made by study abstractors who reviewed the electronic health record for criteria of AMI in accordance with universal criteria.14 These criteria included a typical increase and decrease of cardiac-specific enzyme levels plus 1 of the following: typical ischemic symptoms (chest pain or dyspnea); electrocardiographic changes indicative of ischemia (ST-segment elevation or depression or development of new pathologic Q waves); and/or evidence of a significant coronary lesion  on coronary angiography. For each case, the timing of AMI was determined by the date and time of the first elevated troponin value or first electrocardiogram automated interpretation showing electrocardiographic changes indicative of ischemia, whichever occurred first. As troponin assays and reference ranges vary by facility, an elevated troponin level consistent with AMI was defined in accordance with the local facility assay and reference range. Patients with AMI onset within 24 hours of admission were excluded to ensure cases represented AMIs that truly occurred in-hospital. Characterization of STEMI and NSTEMI was on the basis of ICD-9 coding without additional medical record review as prior work suggested our study would have inadequate sample size for stratified analysis on this characteristic.3 Accordingly, analysis by STEMI vs NSTEMI was not planned beyond descriptive findings.To permit comparison with patients in the control group and provide increased specificity in case definition, cases included patients with in-hospital AMI who were aged 50 years or older at the time of the event and admitted to a medical bed service with a diagnosis other than ischemic heart disease by Two additional exclusion criteria were applied. First, patients who were transferred to VHA hospitals from outside medical facilities were excluded because key baseline variables were not available. Second, we excluded patients with perioperative AMI as defined by admission to a surgical bed unit or a surgical procedure within 72 hours of the event, given the extensive literature, risk indices, and practice guidelines that exist for the perioperative AMI population.ICD-9 diagnosis codes 410-414) and did not receive a diagnosis of AMI at any time during their hospitalization. Patients who were transferred in from outside hospitals, admitted to surgical bed sections, or hospitalized for less than 3 days were excluded. In addition, we excluded individuals without a diagnosis of AMI during the hospitalization despite evidence of troponin elevation that was identified in medical record review. The day after admission was used as the enrollment date for the control group. Using VHA bed section files, we constructed a sampling frame for control patients; cells for sampling were constructed according to hospital, 10-year age categories, and 6-day admission categories. Within each cell, 1 control individual was randomly selected, such that in comparison with the case patient, he or she was from the same hospital, was within 5 years of age, and was admitted within 90 days. The electronic medical records of control individuals were reviewed by trained study abstractors to ensure there was no occurrence of AMI during the admission.The control group included patients aged 50 years or older who were admitted to a medical bed section with a diagnosis other than ischemic heart disease , medical history, and clinical risk factors , which were determined from review of the medical history in the medical record on admission. In-hospital variables included bed section at the time of event , the most recent vital signs within 24 hours of the event , and the most recent laboratory values within 48 hours of the enrollment date  for in-hospital laboratory testing that was performed in more than 90% of case and control participants . All candidate risk factors were subsequently used as covariates in risk-adjusted outcome models. There were no missing data on covariates with the exception of laboratory testing (<1%) and vital sign data . Stochastic regression methods were used to impute missing laboratory and vital sign data.Full-time medical record abstractors were trained on 10 sample cases and their results were compared and discussed in preparatory study meetings to resolve discrepancies and develop and revise an abstracting algorithm. After completion of this phase, each abstractor independently screened a test data set consisting of 50 potential cases (37 eligible and 13 ineligible) and 50 potential controls (40 eligible and 10 ineligible). The abstractors demonstrated 100% agreement on both eligibility and the reasons for ineligibility .The outcome was abstractor-validated in-hospital AMI for our case-control study of risk factors associated with in-hospital AMI. The primary outcome for our matched cohort study of outcomes following in-hospital AMI was all-cause mortality occurring in the year following the date of the index event. Secondary outcomes included in-hospital mortality, 30-day mortality, 1-year all-cause readmission, and admission for AMI. Death was ascertained from the VA Information Resource Center Vital Status File, which compiles data from the Beneficiary Identification Records Locator Subsystem Death file, VA Medicare Vital Status File, and the Social Security Administration Death Master File. In the period following discharge, we identified 1-year all-cause readmission and admission for AMI from VA administrative inpatient data records and To determine the incidence of in-hospital AMI, we report the number of in-hospital AMIs as identified by EPRP relative to the total number of inpatient admissions in the VHA for the period of study.2 statistic for categorical variables and the 2-sample t test for continuous variables. We then sought to identify risk factors associated with in-hospital AMI using conditional logistic regression. Given the large number of potential risk factors under consideration, we used sequential model building and retained variables at each step that maintained statistical significance at the 0.10 level. In our first model, we included patient demographic characteristics, past medical history and clinical risk factors, and bed section details. We then added covariates for vital signs in a second model and covariates for laboratory values in a third model. A fourth model was constructed consisting of variables significant at the 0.10 level in any of the 3 prior models.In the comparison of cases and controls, we first compared patient characteristics at admission and in-hospital variables prior to the event using the χTo compare outcomes between in-hospital AMI cases and controls, we report in-hospital mortality, 30-day mortality, and 1-year mortality rates following the index event for the matched cohort. Kaplan-Meier estimates of the survival function were calculated with cases stratified by STEMI vs NSTEMI designation. We then assessed the unadjusted association between in-hospital AMI and 1-year outcomes from a null multiple logistic regression model. We then included all covariates described above in a fully adjusted model. These models were repeated for the outcomes of in-hospital mortality, 30-day mortality, and 1-year all-cause readmission. Events were too few to permit risk-adjusted models for the outcome of AMI-specific readmission.P < .05 was considered statistically significant. All statistical analyses were performed using SAS statistical software version 9.4 (SAS Institute Inc).All tests for statistical significance were 2-tailed and We identified 5556 patients  with in-hospital AMI, as defined by an AMI diagnosis more than 24 hours after admission to the same hospital based on coding by the EPRP abstractor, which occurred between July 2007 and September 2009. Data on race/ethnicity were not available from EPRP. This represents an incidence of 4.27 AMI events per 1000 admissions based on 1.3 million admissions to VA hospitals during this period.ICD-9 codes designating 587 (85.4%) as NSTEMI and 100 (14.6%) as STEMI. Comparison of case and control patient demographic characteristics, history and clinical risk factors, admission bed type, and pre-event vital signs and laboratory values are shown in Of the 5556 patients identified with in-hospital AMI by EPRP, 1213 patients met sampling criteria and were randomly selected for IHAMI medical record review. Subsequent application of case criteria identified 834 total cases of in-hospital AMI for medical record review, with 687 cases of in-hospital AMI matched to control participants  with ICDThe sequential model development for the identification of risk factors associated with in-hospital AMI is shown in eTables 1 to 3 in the In-hospital mortality among all eligible patients with in-hospital AMI was 25.3% and increased to 32.3% at 30 days and 58.4% at 1 year. In analyses of the matched cohort, cases experienced significantly higher mortality in-hospital than controls (26.4% vs 4.2%), at 30 days (33.0% vs 10.0%), and at 1 year (59.2% vs 34.4%), and risk-adjusted mortality was significantly higher for cases at all points . Kaplan-15 and 25% at 1 year,16 we observed mortality for in-hospital AMI that exceeded 25% at 30 days and approached 60% at 1 year. This study highlights the importance of in-hospital AMI as a common and high-risk clinical condition among hospitalized patients.In this study from the VA health care system, we determined the incidence, risk factors, and outcomes associated with in-hospital AMI. A total of 5556 patients had in-hospital AMI during the study period, equating to a prevalence of 4.27 AMI events per 1000 admissions to VA hospitals. Risk factors associated with an increased risk of in-hospital AMI included a history of atherosclerotic disease and cardiovascular risk factors in addition to markers of physiological stress . While contemporary studies suggest the mortality for AMI that begins outside the hospital is approximately 13% at 30 days17 Prior research has sought to inform the optimal treatment of patients who experience in-hospital cardiac arrest.21 In addition, significant clinical resources are dedicated to in-hospital cardiac arrest, with estimates exceeding $300 million nationally to equip, train, and accredit clinicians and hospitals in resuscitation care.23 This attention to in-hospital cardiac arrest may be contributing to improvements in survival of this condition.26 Similar emphasis on research to understand the modifiable risks of in-hospital AMI and optimal treatment are lacking, despite a similar prevalence and poor long-term survival outcome of in-hospital AMI.Compared with other in-hospital acquired conditions, in-hospital AMI occurs frequently. For example, studies of in-hospital cardiac arrest in VA populations have estimated event rates of 4 per 1000 hospital admissions.31 The present study suggests a history of atherosclerosis and risk factors for atherosclerosis contribute to the risk of in-hospital AMI, potentially reflecting a common pathway of spontaneous coronary plaque disruption and intraluminal thrombus formation 32 for some in-hospital AMI. The present study also highlights the potential for physiological disturbances, such as severely low hemoglobin level, elevated heart rate, elevated serum urea nitrogen level, and elevated white blood count, to contribute to in-hospital AMI risk. This may reflect AMI events in the setting of myocardial injury resulting from an imbalance of myocardial oxygen supply relative to demand that is unrelated to coronary plaque disruption. These non–plaque disruption MIs, classified as type 2 MIs in current typology,32 have been associated with anemia, sepsis, and arrhythmia in prior studies of patients presenting with MI.35 The present study expands on this prior work by comparing with a non-AMI control group and identifying risk factors among patients with AMI that occurs during hospitalization. As the differentiation of type 1 MI (acute plaque disruption) and type 2 MI  is challenging and often uncertain even at the point of care,36 no formal attempt was made in the present study to differentiate between these types of events from medical record review. Furthermore, the revision of MI classification to include type 2 MIs was first published in 2007,14 during the period under study.Decades of research have helped to clarify the risk factors associated with atherosclerosis and AMI onset out of the hospital.Of the covariates associated with lower risk of in-hospital AMI, lower heart rate and history of anemia are worth particular mention. Lower heart rate may reflect homeostasis and further confirms the potential for physiological perturbation to contribute to in-hospital AMI risk. Medical record review of history of anemia was not further categorized into patients with persistent (chronic) anemia or those with resolved anemia. As such, the association between reduced hemoglobin level and in-hospital AMI risk may be more reflective of acute change rather than chronically low hemoglobin levels.26 and the 1 year mortality for bronchus and lung cancer is approximately 50% in US surveillance programs.37 Although significant work remains to determine the extent to which in-hospital AMI outcomes can be modified by addressing cardiovascular risk and concurrent illness, these findings should prompt clinicians to recognize patients with in-hospital AMI as having a high mortality risk. Importantly, our study excluded patients with incidentally elevated troponin levels in the absence of concurrent signs and symptoms of myocardial ischemia. As such, our findings pertain to patients with clinical evidence of myocardial ischemia, as determined by symptoms and/or electrocardiographic changes in addition to a typical increase and decrease in cardiac enzymes.The nearly 60% mortality at 1 year following in-hospital AMI is striking. In comparison, mortality at 1 year after in-hospital cardiac arrest is 85%Our study has several strengths, including the use of a complete AMI cohort to estimate the incidence of in-hospital AMI events and a nested case-control design to allow identification of in-hospital AMI risk factors and outcomes as compared with hospitalized non-AMI controls and eliminate selection bias, which is a risk of non-nested case-control design. The use of trained abstractors to extract data from medical records provided more complete and reliable information than complete reliance on computerized databases.ICD-9 codes. Although the sensitivity (94%) and specificity (99%) of ICD-9 codes for identification of patients with a discharge diagnosis of AMI is established,38 it is unclear whether these characteristics apply to the identification of in-hospital AMI. For the present study, issues of specificity were addressed through medical record review and case validation, but we cannot exclude the possibility of underdiagnosis of in-hospital AMI on the basis of ICD-9 codes. Third, the effort required to complete medical record review along with unanticipated regulatory delays related to investigator transitions between institutions resulted in a delay between data acquisition and final analysis. However, this effort allowed for the completion of a case-control design that offered insights not provided from prior studies of in-hospital AMI, and the findings remain clinically relevant despite this latency. Fourth, although we attempted to obtain details on candidate risk factors as proximal to the event as possible, vital signs and laboratory values were often not available without evaluating at least 24 hours prior to the event. Fifth, our analyses of incidence and risk factors did not differentiate between STEMI and NSTEMI as our sample size was insufficient for stratified analyses. Furthermore, we did not attempt to differentiate between type 1 (acute plaque rupture) and type 2 (supply demand mismatch) because the differentiation between these events is often uncertain, even with access to the patient. Sixth, our incidence determination and subsequent nested case identification was dependent on ICD-9 codes with uncertain sensitivity and specificity characteristics for events occurring in-hospital. Regardless, our study highlights the incidence, risk factors, and poor outcomes of patients who experience in-hospital AMI.Limitations of our study include the VA health care setting, which reflects an older male population and may limit generalizability. Furthermore, our case-control study was restricted to patients aged 50 years and older with in-hospital AMI more than 24 hours after admission and excluded postsurgical patients, which also limits generalizability of our findings to medical admissions in older patients. It is worth noting that among the 1213 potential in-hospital AMI cases, only 26 (2.1%) were excluded from detailed medical record review for surgery in the preceding 72 hours. Second, case identification leveraged a quality-monitoring program that captures all hospitalized patients with AMI on the basis of In-hospital AMI is not infrequent, occurring in 4.27 of 1000 hospital admissions. From matched case-control design, factors associated with an increased risk of in-hospital AMI included history of atherosclerosis, traditional atherosclerotic risk factors, and markers of physiological stress. Outcomes following in-hospital AMI are poor, with nearly 60% mortality at 1 year after the event. Additional research to define risk reduction and optimal treatment strategies of in-hospital AMI are needed to address this common and high-risk condition.


European Society of Cardiology guidelines for the treatment of heart failure (HF) prescribe uptitration of angiotensin-converting enzyme inhibitors (ACE-I) and β-blockers to the maximum-tolerated, evidence-based dose. Although HF prognosis can drastically improve when correctly implementing these guidelines, studies have shown that they are insufficiently implemented in clinical practice.The aim of this study was to verify whether supplementing the usual care with the CardioCoach follow-up tool is feasible and safe, and whether the tool is more efficient in implementing the guideline recommendations for β-blocker and ACE-I.A total of 25 HF patients were randomly assigned to either the usual care control group (n=10) or CardioCoach intervention group (n=15), and observed for 6 months. The CardioCoach follow-up tool is a two-way communication platform with decision support algorithms for semiautomatic remote medication uptitration. Remote monitoring sensors automatically transmit patient’s blood pressure, heart rate, and weight on a daily basis.Patients’ satisfaction and adherence for medication intake  and vital sign measurements  were excellent. However, the number of technical issues that arose was large, with 831 phone contacts  in total. The semiautomatic remote uptitration was safe, as there were no adverse events and no false positive uptitration proposals. Although no significant differences were found between both groups, a higher number of patients were on guideline-recommended medication dose in both groups compared with previous reports.The CardioCoach follow-up tool for remote uptitration is feasible and safe and was found to be efficient in facilitating information exchange between care providers, with high patient satisfaction and adherence.ClinicalTrials.gov NCT03294811; https://clinicaltrials.gov/ct2/show/NCT03294811 (Archived by WebCite at http://www.webcitation.org/6xLiWVsgM) Heart failure (HF) is a major health problem affecting more than 10% in the elderly over the age of 70 years . MortaliImprovements in treatment strategies have reduced mortality and (re)hospitalization rates. In 2016, the updated guidelines of the European Society of Cardiology (ESC) concerning the diagnosis and treatment of acute and chronic HF with reduced ejection fraction were published -17. UnfoThe addition of remote monitoring combined with integrated clinical decision support in this aspect could provide added value for both the health care provider and the patient. Remote monitoring of vital parameters and other patient information could allow care givers to evaluate and adjust patients’ medication schemes remotely according to ESC guidelines . Remote The CardioCoach study combines a two-way communication platform with decision support algorithms together with remote monitoring sensors for active medication uptitration. The study will verify whether supplementing the guideline-driven usual care with this two-way communication platform can implement the guideline recommendations for β-blocker and ACE-I more efficiently. This paper focusses on the feasibility of the communication platform for adjusting HF medication remotely and for detecting early deterioration by monitoring blood pressure, heart rate, and weight changes. Patient’s vital measurements and therapy adherence were actively encouraged by the smartphone app and were evaluated together with the patient’s satisfaction of the CardioCoach tool.This is a prospective single-center randomized control feasibility trial conducted in a Belgian tertiary care center  with a specialized HF disease clinic. Newly diagnosed patients with HF and initiation of β-blocker and/or ACE-I therapy or patients with known HF but on suboptimal dosage of β-blocker and/or ACE-I therapy were approached. Upon inclusion, block randomization was used to divide patients in either the usual care control group or the CardioCoach intervention group . All patients provided written informed consent and were followed for 6 months after study enrolment. The study complies with the Declaration of Helsinki, and the study protocol was approved by the local committee on human research.ESC guidelines on uptitration of β-blocker and/or ACE-I therapy are primarily intended to be used by physicians. Therefore, medication dose adaptions were performed during occasional outpatient visits to the cardiologist or general practitioner. Medication doses were determined based on patient’s vital sign measurements, overall well-being, and symptoms. Besides an additional follow-up visit at 3 months, we did not modify the usual care as per standard practice organized in the institution where patients have a scheduled follow-up visit at 6 months.Patients allocated to the CardioCoach intervention group also had a scheduled follow-up visit at 3 months and 6 months. For these patients, the usual care was supplemented with the CardioCoach follow-up tool to proactively uptitrate β-blocker and ACE-I treatment and improve medication adherence for β-blocker, ACE-I, and diuretic treatment. In terms of diuretic treatment, only medication adherence was monitored because it was not part of the active uptitration protocol. This intervention included a two-way communication platform connected to remote monitoring devices such as a weighing scale and blood pressure monitor to collect vital measurements , in which patients were followed by technical and clinical call centers. Medical follow-up  was done by the clinical call center in the hospital, whereas technical follow-up  was done by Remedus . Both call centers were active during working hours; notifications received during the weekend were read on Monday.The two-way communication platform consisted of a smartphone with the preinstalled CardioCoach app, a blood pressure monitor, weighing scale, and a Web-based health management server (Remecare) with a clinical dashboard for the care provider (HF nurse). An overview of the CardioCoach follow-up tool can be found in The CardioCoach app was used to trigger the patient to conduct different actions, such as record a vital sign measurement, complete a questionnaire, and confirm medication intake by sending reminders at predefined time points. For each action, a 5-hour time window was set in which the patient could record all necessary data. This time window could be customized for each patient and was made available an hour before and 4 hours after the ideal recording time. All vital sign measurements were transmitted automatically to the CardioCoach app without manual patient input. The patient-specific medication scheme for β-blocker, ACE-I, and diuretic treatment was automatically uploaded to the patients’ smartphones every morning to inform them about their actual medication dose for that day. When changes were applied to the medication scheme, the patient was notified via a pop-up message, which he/she had to confirm. In addition, a daily education tip was pushed by the smartphone app to the patient covering different HF disease aspects . Screenshots of the CardioCoach app are shown in All information gathered via the CardioCoach smartphone app  was automatically transmitted to a secured Web-based health management server  without patient input. On this server, the completeness of patient data and possible deviations of vital signs based on predefined thresholds were verified.no registration tag was recorded in the clinical database and the patient was contacted by the technical call center within 12 hours. In case of missed medication intake, the technical call center asked whether the patient forgot to register the medication intake or whether the patient forgot to take the required medication. In case of vital sign thresholds crossings for 3 consecutive days , the active uptitration algorithm was deactivated and was followed by a less intensive follow-up phase during which medication intake and vital sign parameters were still monitored and medication uptitration on the discretion of the HF nurse could still proceed.Finally, at 6 months of follow-up, patients from the CardioCoach intervention group were provided with a CardioCoach user experience questionnaire to gain feedback on the use of the CardioCoach smartphone app, the remote monitoring sensors, the contact with HF nurses and technical follow-up team.Outcome measures included CardioCoach user experience, (therapeutic) adherence, call center statistics, algorithm performance, and the number of patients on guideline-recommended medication dose for β-blocker and ACE-I  at both t test and Mann-Whitney U test were used for normally and not normally distributed continuous variables, respectively. The chi-square test and Fisher exact test were used accordingly for categorical variables. To define statistical differences between New York Heart Association class, the Kruskal-Wallis test was used. The significance level for tests was two-sided with an alpha of .05. All statistical analyses were performed using the Statistical Package for Social Sciences version 24.0 .Demographic and functional characteristics were compared using descriptive statistics. Continuous variables were expressed as mean ± standard deviation if normally distributed, or otherwise as median . To define statistical differences between both groups, the independent samples student In total, 25 patients were included in the CardioCoach study. One patient dropped out before 3 months of follow-up and was therefore excluded from analysis. After 3 months of follow-up, 2 more patients dropped out but were still included in the analysis until 3 months of follow-up, because they completed the active medication uptitration phase. The final study population consisted of 24 patients: 14 patients were included in the CardioCoach intervention group and 10 patients were included in the usual care control group. Baseline characteristics of the study population at the time of inclusion are provided in On the basis of gathered data, the CardioCoach algorithm generated 72 medication uptitration proposals in total. In 7% (5/72) of the cases, the algorithm generated a conclusive proposal, whereas in 93% (67/72) of cases, the decision was left up to the HF nurse. This was mainly due to aberrant  or incomplete  data. After each automatic uptitration proposal from the algorithm, the HF nurses in the clinical call center received a notification, which they had to consider. Nurses could respond in different ways to the uptitration proposal . The algOverall, therapeutic adherence as confirmed by the patient via the smartphone app  or via the technical call center after contacting the patient  for the 3 drug treatments was 92.55% , with, respectively, 97.12% (3239/3335) for β-blockers, 94.89% (3549/3740) for ACE-I, and 86.13% (3230/3750) for diuretics. In 1 out of 5 cases, patients did not record medication intake into the CardioCoach smartphone app, and the technical call center had to contact the patients to verify medication intake . In termFor the 14 CardioCoach patients, the Remedus call center made 831 phone calls in total, with a median of 41 phone calls per patient (IQR 32-65). Phone calls were initiated in case of missed vital sign measurements (n=136), missed medication intake , or missing questionnaires (n=34). Due to the limited technical skills of the study participants, technical problems could hardly be solved remotely, and therefore, a device swap was performed in 10 patients: 4 patients had 1 device swap, 5 patients had 2 device swaps, and 1 patient had 3 device swaps.Among the CardioCoach user experience questionnaire, 4 questionnaires were missing: 3 due to early study termination and 1 due to an issue with the Web-based questionnaire platform. Detailed results of these questionnaires can be found in P>.99) and 6 months  of follow-up  and 6 months  of follow-up  and vital sign measurements . In spite of the frequent reminders via the smartphone, the CardioCoach follow-up tool was well accepted by the patients as compared with remote monitoring strategies used in previous studies ,35. UnfoIn this study, the algorithm was built with a large safety margin to avoid false positive uptitration proposals, which has led to a low number of conclusive proposal by the algorithm . In addition, every proposal had to be validated by a dedicated HF nurse. In 69% (50/72) of the cases, the HF nurse confirmed the algorithm proposal. This shows that parameter thresholds can be confined. In this sense, the current feasibility study was very useful for the future development and improvement of an optimal two-way communication system between patients and caregivers. On the basis of feedback from both patients and HF nurses, improvements can be made to the next generation, which will take into account the work efficiency of the HF nurses and enable a customized approach for patients . The CardioCoach follow-up tool is very efficient in facilitating information exchange between the different care providers  and enables a safe way for medication uptitration, as there were no adverse events or false positive uptitration proposals reported. The use of the CardioCoach follow-up tool has been shown to be feasible when combined with a technical call center to handle technical issues and reduce the workload of the clinical call center. This study was unable to demonstrate a significant improvement of the CardioCoach follow-up tool on the number of patients on maximum guideline-recommended β-blocker and ACE-I dose. Probably, this is related to the fact that patients in the control group were also enrolled in a dedicated HF outpatient disease management program, where HF medication dosages were being optimized by intensive follow-up by specialized HF nurses and HF specialists. Hence, the CardioCoach follow-up tool might be more suitable in centers with less intensive HF disease management programs.This feasibility study should be interpreted in the light of some limitations to place the study findings into a correct context. First, the small sample size and the single-center character may impact its external validity. Therefore, these results should be interpreted as hypothesis generating, and an additional multi-center study is necessary to confirm these results. In this study, the control group received the usual care as per standard practice organized in the institution and received no remote monitoring sensors. This is a general issue in multiple remote monitoring studies, which should be taken into account when interpreting study findings as relevant information from the control group may be missing. An alternative control group could be a group in the same setting , but without a physician reviewing the data. Next, technical improvements  are necessary to improve the efficiency of the CardioCoach follow-up tool. Finally, the patient population used to conduct the feasibility study was recruited in a tertiary care center with a specialized HF clinic. Due to the high quality of the usual care provided  with intensive outpatient follow-up, the institution under study may not have been the optimal choice to demonstrate a potential benefit of the CardioCoach follow-up tool on medication uptitration.This study shows the feasibility and safety of a novel two-way communication platform with decision support algorithms in combination with remote monitoring sensors in implementing guideline recommendations concerning β-blocker and ACE-I uptitration. In addition, the CardioCoach follow-up tool was found to be efficient in facilitating information exchange and improving coordination among different care providers. Patients’ satisfaction was reported to be high, which has led to excellent adherence rates during a relative long follow-up period of 6 months. Many technical issues arose, clearly indicating the need for a technical call center. A larger multicenter randomized controlled trial needs to be conducted in centers with minimal usual care follow-up to assess the potential benefits of guideline-recommended medication dose.


A wide variation on the HTLV-1 pVL levels among asymptomatic HTLV-1 carriers was observed with some pVL as high as those observed in symptomatic patients. The asymptomatic HTLV-1 carriers were divided according to the place of birth and the highest levels of pVL were detected among patients from endemics areas from the North of Argentina. Our results reinforce the usefulness of the proviral load would be a prognostic marker of HTLV-1 disease progression. Moreover, host, viral or socio-environmental factors cannot be excluded as determinant of high proviral load.HTLV-1 proviral load (pVL) in peripheral blood mononuclear cell (PBMCs) is proposed as a marker of disease progression but its role still remains controversial. The aim of this study was to evaluate the levels of HTLV-1 pVL in symptomatic patients and asymptomatic HTLV-1 carriers. In this cross-sectional study the pVL was measured by Real Time PCR in 102 asymptomatic carriers and 22 symptomatic patients . We observed that the HTLV-1 pVL was significantly higher in symptomatic patients (median = 4.99 log10 HTLV-1 copies /106 PBMCs) compared to asymptomatic HTLV-1 carriers (median = 4.38 log A. A, . I. I, ..,  or or+ cellcells , . T. T+ cell, , . A. A, [, [, [, [, , no diff10 copies / 106 CMT) are in a high risk for the development of the disease. We consider this finding reinforces the idea that the quantification of the proviral load would be a prognostic marker of disease progression. For all these reasons, we consider that medical and laboratory follow-up by pVL may provide early information of clinical value in the progression from asymptomatic to symptomatic stage; as well as for detecting and treating the early manifestations the HTLV-1 associated disease. In this sense, other studies have revealed that pVL in asymptomatic patients remained stable over time with slight fluctuations [, [In contrast, among the asymptomatic patients, 27% of them had low pVL and 56% had medium pVL levels, still 17% of the patients in this group showed high values similar to those detected among the symptomatic patients. These results suggest that asymptomatic patients with a high level of pVL  in the present study did not have any symptoms or any clinical condition associated with HTLV-I infection moreover the main source in this group were blood donors, who once the diagnosis was performed they received clinical evaluation.In summary, our results indicate that the level of proviral load of HTLV-1 in PBMCs is associated with clinical disease, as HAM/TSP and ATLL. These levels remain low in asymptomatic carriers and high in symptomatic patients. Eventhough, asymptomatic patients from endemic areas in Argentina showed higher levels than those from non-endemic areas from our country. Our data support the hypothesis that HTLV-1 proviral load plays an important role in the pathogenesis of HAM / TSP and ATLL and as a biomarker in the clinical follow up. However, the presence of co-morbidities is another aspect to consider when evaluating the pVL; given that the presence of parasitic and viral co-infections may contribute to the clonal expansion of infected lymphocytes, leading this to an increase in the pVL levels. For these reasons, we consider that it is important to monitor the pVL levels over time and to evaluate the intra- and inter-individual variability. Further studies investigating other factors that might impact on proviral load levels may open new insights on HTLV pathogenesis.S1 STROBE Checklist(DOC)Click here for additional data file.


A good understanding of age-dependent changes and modifications in brain networks is crucial for fully exploring the effects of aging on the human brain. Few reports have been found in studies of functional brain networks using functional near-infrared spectroscopy (fNIRS). Moreover, little is known about the feasibility of using fNIRS to assess age-related changes in brain connectomes. This study applied whole brain fNIRS measurement, combined with graph theory analysis, to assess the age-dependent changes in resting-state brain networks. Five to eight minutes of resting-state brain hemodynamic signals were recorded from 48 participants (18 young adults and 30 older adults) with 133 optical channels covering the majority of the cortical regions. Both local and global graph metrics were computed to identify the age-related changes of topographical brain networks. Older adults showed an overall decline of both global and local efficiency compared to young adults, as well as the decline of small-worldness. In addition, young adults showed the abundance of hubs in the prefrontal cortex, whereas older adults revealed the hub shifts to the sensorimotor cortex. These obvious shifts of hubs may potentially indicate decreases of the decision-making, memory, and other high-order functions as people age. Our results showed consistent findings with published literature and also demonstrated the feasibility of whole-head fNIRS measurements to assess age-dependent changes in resting-state brain networks. We hypothesized that (1) both young and older adults present distinct global network metrics and small-world features in the functional brain connectivity and (2) the reorganization of network hubs occurs in the older adults with respect to the younger group. This study aimed to prove our working hypotheses by performing 133-channel fNIRS measurements, leading to quantitative characterization of age-dependent changes in resting-state, whole-cortex graphical networks.22.1A total of 48 participants participated in this study, including 18 young adults [In this study, we employed a continuous wave, 133-channel, high-performance, fNIRS system , which consists of 40 sets of colocated NIR semiconductor lasers at three wavelengths  digitizer that measured four cranial landmarks and all optodes from four randomly selected participants to estimate the fNIRS optode locations.2.3Time sequences of light intensity changes from 133 channels were recorded for all participants during a resting-state period (5 min for older adults and 8 min for young adults) at a sampling frequency of 8.13 Hz. The reason that we designed 5-min resting-state measurements (instead of 8 min) for older adults was to minimize the drossiness or sleepiness in the older population. It was reported that the 5-min resting-state fNIRS measurements were dynamically stable enough to give rise to reliable functional connectivity.–,All the raw data were visually inspected to reject motion artifacts and other large noise.,,–We calculated relative changes in concentrations of oxygenated hemoglobin  . The loc–,Former studies using fNIRS-GTA to investigate brain networks suggest that the brain functional connectivity could be represented by the correlations of brain hemodynamic changes or fluctuations among different brain regions.trix see . Note thwise see . The twoThe connecting line (or edge) between two connected nodes was used to graphically mark functional connectivity across the whole human brain/cortex. In principle, different selections of thresholds on cross-correlation coefficient will result in a different binarized matrix. In this study, we applied different thresholds to the adjacency matrix to obtain a sequence of binary matrixes. Specifically, the sparsity-based approach was utilized as suggested by Niu et al.2.5,Based on the adjacency matrices, we further quantified the resting-state brain network parameters using GTA. In general, graphical metrics for functional brain networks are calculated based on global and local network characteristics.2.6Statistical analysis was performed to quantify interregional correlations for bilateral nodal regions in both young and older adult groups. Totally, 133 pairs of brain nodal regions were compared by the 33.13.1.1We quantified and compared the global network and/or small-world features of the brain networks between young and older adults. The respective values of The group-level age-related changes in 0.45 see . Meanwhi0.29 see . There w0.30 see . Both gr3.1.2,Functional networks of the human brain have small-world characteristics;05<S<0.5 , and so .3<S<0.5 . At a wi0.5 [σ>1 , which i3.2,We quantified such local graphical parameters in this study as nodal degree , we were able to cover most of the cortical regions of each human participant. This whole-head coverage ensured the cerebral hemodynamic measurements with both abundant distal and local connectomic information. Our findings for age-related RSFC changes were consistent with what have been reported by MEG and fMRI. Specifically, this study successfully observed that: (1) the functional networks in both age groups held well small-world , and this mostly happened in the default mode (7 versus 2), and was relatively obvious in the FP (7 versus 4) networks. Specifically, the nodal betweenness in the default mode regions  was diminished in the older adults. Because the hubs play the central role of integrative processes and communication,,In addition, although the total number of hubs had significantly decreased in the older group, the ratio of FP hubs over the total hubs was kept the same, and the ratios of SM, OC, and undefined hubs were slightly increased in the older group. This interesting finding indicates that the older brain is trying to preserve certain numbers of hubs in primary processing centers, such as SM and OC areas. In sum, the reduction of hubs and changes of symmetrical characteristics should lead to a behavioral shift in the older brain. The abundancy of the network hubs in the primary information-processing region (such as SM networks) and the reduction of hubs within networks supporting higher-level cognitive functions (such as default and FP networks) coincide with the phenomenon that older adults often show the decline of cognitive functions.4.4,–,,,,,Resting state of the human brain is an uncontrolled condition, and it can be studied according to temporal or frequency features of the brain,4.5In any study of this type, there are often a few limitations that should be noted. First, it is known that the NIR light can penetrate through the human scalp, skull, and a portion of gray matter, but the light could not go deeper than 3 cm below the human scalp.Second, there is a trade-off between covering a larger portion of the head versus the time-cost of the experimental setup. A whole-head optode setup can provide an abundancy of collected data from the whole head, but it increases a significant amount of time for placing the optodes and adds much more weight to the participant’s head, which is more likely to cause the participant too uncomfortable, motion artifacts, and even more physiological noises. To overcome these drawbacks, future work can include development of: (1) quicker or more efficient ways to place optodes through hair; (2) a light-weight helmet with a thinner and more flexible layer; (3) a fiber-supporting frame/stand to reduce the setup time and the weight on the participant’s head. Nevertheless, this study demonstrated the feasibility of using fNIRS to assess resting-state functional cortical networks and their age-related changes.Third, a potential confounding factor to the findings resulted from the possibility that the participants, especially the older adults, could go to non-rapid-eye-movement (NREM) sleep during the measurements, which could give rise to certain spatial changes of the resting-state network during the transition from wakefulness to light NREM sleep, as reported by Refs. 57Finally, in this study, we did not record the ethnicity information in the demographic data. The young adult participants were recruited from graduate students on campus of the University of Texas at Arlington. The older adults were recruited from local community of senior centers, with majority being Caucasian population. To avoid potential bias on the results caused by ethnic difference, we will collect ethnicity information and consider the corresponding effect in future studies.5In this present investigation, we introduced/combined graphical theory analysis with the whole-head fNIRS measurements to assess age-related changes in the resting-state functional brain networks. We successfully proved two working hypotheses, namely, we quantified and identified distinct global and local graphical metrics of the cortical brain networks between young and older adults, and revealed reorganization of network hubs in the older adults compared to the younger group. A decrease in global efficiency and increases in the clustering coefficient and shortest path length were observed as characteristic features of older participants, which reflected brain network changes due to aging. In addition, we observed the age-related decline of major functional connectivity hubs in the default mode network, which might underlie the decline of short-time memory and integration of information. Our present findings are also consistent with the literature. All of these strongly demonstrated the feasibility of whole-head fNIRS measurements to assess age-dependent changes in resting-state brain networks, which could have a variety of clinical applications in the near future.


There is growing evidence that patients can provide feedback on the safety of their care. The 44-item Patient Measure of Safety (PMOS) was developed for this purpose. While valid and reliable, the length of this questionnaire makes it potentially challenging for routine use. Our study aimed to produce revised, shortened versions of PMOS (PMOS-30 and PMOS-10), which retained the psychometric properties of the longer version.To produce a shortened diagnostic measure, we analysed data from 2002 patients who completed PMOS-44, and examined the reliability of the revised measure (PMOS-30) in a sample of 751 patients. To produce a brief standalone measure, we again analysed data from 2002 patients who completed PMOS-44, and tested the reliability and validity of the brief standalone measure (PMOS-10) in a sample of 165 patients.The process of shortening the questionnaire involved a combination of secondary data analysis  and a consensus group exercise to produce PMOS-30 and examine face validity. Analysis of PMOS-30 data examined reliability . Further secondary data analysis  produced PMOS-10, and primary data collection assessed its reliability and validity .Fourteen items were removed to produce PMOS-30 and the percentage of negatively worded items was reduced from 57% to 33%. PMOS-30 demonstrated good internal reliability (α=0.89). The 10 items with the highest corrected item-total correlations across both PMOS-44 and PMOS-30 composed PMOS-10. PMOS-10 had good internal reliability (α=0.79), demonstrated convergent validity; however, discriminant validity was not established.Two revised, shortened versions of the original PMOS-44 (PMOS-30 and PMOS-10) were produced to capture patient feedback about safety in hospital. The measures demonstrated good reliability and validity, and preserved the psychometric properties of the original measure. Reliability testing of PMOS-30 took place in a variety of wards across three hospital trusts.The involvement of patient representatives is a key strength of the PMOS-10 development phase.A limitation of our work is that our studies only included patient participants who could understand English.Measurement has posed an ongoing challenge in patient safety improvement. The difficulties and tensions associated with developing and implementing measures of patient safety are well recognised, and there is a need for measures to be relevant to multiple stakeholders, have a scientific basis, as well as being feasible and usable.The collection of feedback on patient satisfaction and experiences of care is now commonplace,The original measure included a minimum of two items per domain, across nine domains, known to contribute to safety incidents in hospitals: (1) communication and teamworking; (2) organisation and care planning; (3) access to resources; (4) ward type and layout; (5) information flow; (6) staff roles and responsibilities; (7) staff training; (8) equipment (design and functioning) and (9) delays. Within a randomised controlled trial (RCT) of the PRASE intervention,While in the PRASE RCTThe objectives of this research were to produce two measures that are practical, feasible and simple for hospital inpatients to complete. As it is currently conceived, the PMOS has a diagnostic function. It can be used to identify those factors (see domains above) where attention might most usefully be focused when planning improvement. The first objective therefore was to produce a revised version of PMOS that was shorter with improved acceptability, while at the same time preserving the psychometric properties, conceptual underpinnings and diagnostic function of the longer version. This version would continue to be used, alongside the PIRT tool as a basis for the PRASE intervention. Our second objective was to develop a version of PMOS that was much shorter and that could be used as a brief standalone measure for ongoing monitoring of ward safety performance not focused on planning improvement. Potentially, this measure could be used to prompt additional information gathering effort using the refined diagnostic measure or to monitor safety performance over time.Patients and the public were involved in this study in the shortening and rewording of the questionnaires. More information is provided in the method.First, we focused on the diagnostic measure and aimed to reduce the number of PMOS items, rephrase items where appropriate, consider face and content validity, and establish the internal reliability of the revised diagnostic measure. Next we focused on producing a brief standalone measure for on-going monitoring of ward safety performance and establishing its internal reliability, face validity, convergent validity and discriminant validity.The shortening exercise included the analysis of PMOS data collected using the original measure (44 items) within the RCT of the PRASE intervention.Subsequently, the internal reliability of the revised PMOS was tested in a sample of 751 patients. Patients were recruited by hospital volunteers across 22 wards from three hospitals, at three hospital trusts in the UK between September 2015 and May 2016, within the hospital volunteers project. The characteristics of the patient samples are presented in The shortening exercise to produce a brief standalone measure took a statistical approach, which involved analyses of PMOS data collected using the original measure within the RCT of the PRASE intervention.Prior to data collection, members of the YQSR Group’s patient panel reviewed the measure resulting from the statistical exercise to ensure it was acceptable . The subsequent reliability and validity analyses included 165 patient participants who were recruited across nine wards in one hospital trust in the UK between June 2017 and January 2018. Patient eligibility criteria were those reported in the RCT of the PRASE intervention.Patients were invited to complete the questionnaire by hospital volunteers within a quality improvement project; therefore, informed consent was not required. The hospital volunteer training package was based on the training for researchers and research nurses who facilitated patient data collection within the RCT of the PRASE intervention.Patients were recruited by a research nurse and an undergraduate industrial placement student. Posters were placed on wards, and staff were encouraged to inform patients that researchers were recruiting. Researchers liaised with the nurse in charge to identify patients who had capacity and were considered well enough to take part. Researchers then approached patients to give them information about the study, both written (in the form of a participant information sheet) and verbally. If the patient agreed to take part and gave informed consent, they were recruited into the study.https://www.improvementacademy.org/tools-and-resources/patient-reporting-and-action-for-a-safe-environment.html (freely available).The response options for PMOS-44 (44 items), PMOS-30 (30 items) and PMOS-10 (10 items) are as follows: 1=strongly disagree, 2=disagree, 3=neither agree or disagree, 4=agree, 5=strongly agree, not applicable, and I prefer not to answer. Responses are recoded such that a high score indicates more favourable perceptions. PMOS-44 and PMOS-30 questionnaires were completed using a laptop/handheld device. PMOS-10 questionnaires were collected on paper. For all versions of the PMOS questionnaire, patients could complete the questionnaire themselves, or completion could be facilitated . PMOS-44, PMOS-30, PMOS-10 and ‘easy read’ versions which have subsequently been produced can be requested via To examine the convergent validity of PMOS-10, the Friends and Family Test (FFT)All analyses were performed using SPSS V.23.For the PMOS-30 and PMOS-10 reliability and validity analyses, the sample sizes were informed by both statistical and pragmatic considerations, based on a minimum subject to item ratio of at least 10:1.Researchers and research nurses who collected PMOS data from patients within the RCT of the PRASE intervention met to discuss the content of the original measure. Potential items for removal were highlighted, for example, based on patient difficulties in understanding of the item or patient feedback that items were repetitive. Prior to a series of consensus meetings analyses were agreed, the results of which would be brought to the consensus meetings. These analyses included item response variation  and inter-item correlations using Pearson’s correlation.Research suggests that for a 5-point scale items which demonstrate reasonable variability (SD of 1.00 or higher) are potential items to retain and that items with limited variance are candidates for removal.Subsequently, in a series of consensus meetings with the wider research group, items which had been identified as problematic, based on either researcher/research nurse perceptions or the item analyses, were discussed in greater detail. For the purpose of construct validity, the group considered each potential item for removal, and focused on whether each item reliably tapped what it was intended to measure. For the purpose of content validity, we also endeavoured to ensure a minimum of two items per domain were retained. We used multiple criteria, rather than only a statistical approach when considering items for removal to safeguard against key items being removed.We examined the internal consistency reliability  of PMOS-30 data collected from 751 hospital in-patients within the hospital volunteers project. In line with guidance, a reliability coefficient α of >0.7 was deemed acceptable.Following a method described by Marteau and Bekker,Taking the same approach as the original PMOS validation study, an overall PMOS-10 ‘positive index’ was constructed by summing the number of items that patients responded to by using one of the two positive response options (strongly agree or agree) for a positively worded item and (strongly disagree or disagree) for a negatively worded item. Therefore, patients could have a score of 10 where higher responses equate to more favourable safety perceptions. If patients had missing responses, the ‘positive index’ was constructed from the available data.We assessed the internal consistency reliability of PMOS-10 , and in line with guidance, a reliability coefficient α of >0.7 was deemed acceptable.To determine the convergent validity of the 10-item questionnaire , we examined the association between the PMOS-10 ‘positive index’ and the FFT scores using Pearson’s correlation. In line with Cohen’s guidelines,A one-way analysis of variance (ANOVA) was conducted to assess the extent to which PMOS-10 discriminated among the nine wards. Ward was entered as a fixed factor, and the PMOS-10 ‘positive index’ as the dependent factor.Of the 44 items in the original measure, 14 items were removed (32%). The revised measure (PMOS-30) demonstrated strong reliability, with a coefficient α of 0.89. Cronbach’s alphas and average inter-item correlations were calculated for domains with three or more items, and inter-item correlations (Pearson) were calculated for domains with two items . CronbacThe mean PMOS-10 ‘positive index’ index score for the entire sample was 8.37 (SD=1.65), meaning that on average, patients responded positively to around eight of the PMOS-10 items. The recruitment response rate was 92%.The PMOS-10 measure demonstrated good reliability, with a coefficient α of 0.79.A significant negative correlation was established between the PMOS-10 ‘positive index’ and the FFT scores , indicating that the more positive PMOS scores among patients, the more positive FFT scores were.F =1.59, p=0.130).A one-way between-groups ANOVA was performed to assess the extent to which the PMOS positive index discriminated among the nine wards. There was no significant main effect of ward  and PMOS-30 formed the briefer standalone measure (PMOS-10), which was ratified in collaboration with patient representatives to establish face validity. A good alpha coefficient demonstrated the internal reliability of PMOS-10, and convergent validity was established. The strength of the correlation (moderate) suggests that although PMOS-10 ‘converges’ with another known measure of care experience from the patient perspective (FFT), it is indeed measuring something different. To determine discriminant validity, we examined whether PMOS-10 was able to discriminate meaningfully at the ward level, with a view to PMOS-10 being used as a tool to identify those wards which might benefit most from further data gathering efforts  and improvement as part of the PRASE intervention. There was no significant main effect of ward and no significant differences between wards. Therefore, based on our analyses of nine wards, we cannot purport that PMOS-10 is able to meaningfully discriminate between wards.There has been substantial research and policy focus on patient involvement in patient safety.A strength of the development of PMOS-30 is the involvement of six researchers/research nurses with an extensive amount of experience of facilitating PMOS  data collection, and the involvement of two hospital volunteers and two patient representatives. This provided a rich source of data regarding potential rephrasing of items; leading to considerable face validity. A further strength is that the reliability testing of PMOS-30 took place in a variety of wards across three hospital trusts. The involvement of two patient representatives in the development of PMOS-10 is a key strength.A limitation of our work is that our studies only included patient participants who could understand English. Therefore, further testing would be required if PMOS-30 and PMOS-10 were to be translated into other languages. For PMOS-30, some of the domains failing to achieve Cronbach’s alpha recommendation of 0.70 is a limitation, even though the average inter-item and inter-item correlations were within the optimum range. For PMOS-10, although the wards recruited were from a variety of medical and surgical specialities, a limitation is the involvement of only one hospital trust. A further limitation is that discriminant validity was not established for PMOS-10. Finally, PMOS-30 had a high amount of missing data which were mainly not applicable responses; therefore, it is possible that a listwise deletion approach may have implications for generalisation.By further refining an existing measure, the findings respond to previous research which called for the acceptability of PMOS to be enhanced. The length and some content of the original measure were problematic, and posed issues relating to participant fatigue and burden. The output of a highly reliable and valid shortened diagnostic version of PMOS, which retains the psychometric properties and conceptual underpinnings of the original measure, is integral to the wider adoption and ongoing implementation of the PRASE intervention. The high recruitment response rate (92%) for PMOS-10 strengthens the argument that the measure is acceptable to patients, and these findings position PMOS-10 as a reliable and valid brief standalone measure for ongoing monitoring of ward safety performance from the patient perspective. To further validate PMOS-10, research is needed to assess the discriminant validity PMOS-10 on larger scale, in addition to further exploring convergent validity by examining associations with other indicators of patient safety.Two revised, shortened versions of the original PMOS-44 (PMOS-30 and PMOS-10) were produced to capture patient feedback about safety in hospital, and these measures demonstrated acceptable reliability and validity. PMOS-30 was produced to serve a diagnostic function to be used, alongside the PRASE intervention as a whole. PMOS-10 was developed to be used as a brief standalone measure for ongoing monitoring of ward safety performance.


The complex nature and structure of the human immunodeficiency virus has rendered the cure for HIV infections elusive. The advances in antiretroviral treatment regimes and the development of highly advanced anti-retroviral therapy, which primarily targets the HIV enzymes, have dramatically changed the face of the HIV epidemic worldwide. Despite this remarkable progress, patients treated with these drugs often witness inadequate efficacy, compound toxicity and non-HIV complications. Considering the limited inventory of druggable HIV proteins and their susceptibility to develop drug resistance, recent attempts are focussed on targeting HIV-host interactomes that are essential for viral reproduction. Noticeably, unlike other viruses, HIV subverts the host nuclear pore complex to enter into and exit through the nucleus. Emerging evidence suggests a crucial role of interactions between HIV-1 proteins and host nucleoporins that underlie the import of the pre-integration complex into the nucleus and export of viral RNAs into the cytoplasm during viral replication. Nevertheless, the interaction of HIV-1 with nucleoporins has been poorly described and the role of nucleoporins during nucleocytoplasmic transport of HIV-1 still remains unclear. In this review, we highlight the advances and challenges in developing a more effective antiviral arsenal by exploring critical host-HIV interactions with a special focus on nuclear pore complex (NPC) and nucleoporins. HIV (Human Immunodeficiency Virus), a major killer worldwide was first recognised in the early 1980s in the United States as a strange retrovirus causing an entirely new disease called AIDS (Acquired Immunodeficiency Syndrome) [HIV arose through zoonotic transmissions of simian immunodeficiency viruses (SIV) during the past century in East and Central Africa ,3. TodayOne of the surprising facets of the HIV/AIDS pandemic is the unequal distribution of HIV. In the year 2018, an estimated 37.9 million individuals were living with HIV worldwide with highest burden witnessed in southern and eastern Africa. In the same year, an estimated 1.7 million new HIV cases were reported [ART mainly targets the enzymatic processes of the HIV-1 replication cycle. The current conventional antiretroviral drugs are classified into six classes of therapeutic mechanisms  25,26,2,226,27. Antiretroviral drugs are suppressive, but cannot cure or eradicate the HIV-1 infection completely, i.e., the patients still carry HIV in their bodies in latent viral reservoirs, ready to rebound in case of HAART interruption ,37,38. TTo control, and eventually eliminate, HIV globally, there is a need of widely accessible and efficient HIV prevention tools like vaccines. However, in the case of HIV, various attempts to develop a protective vaccine (for prevention) as well as a therapeutic vaccine (for treatment) have been severely compromised by the complex structure and nature of the virus. Additionally, the inability of the host to induce a sufficiently potent immune response against HIV is poorly understood . Thus, tAdvances are being made to develop prevention tools to protect people who are at substantial risk for HIV infection ,47. ThesViruses are obligate intracellular parasites relying on host machinery for their survival. They are capable of engaging with numerous cellular proteins for successful viral replication. To achieve this, one of the ways virus use is the direct interaction of viral proteins with the host factors via binding sites. Alternatively, they have evolved intrinsically disordered, short protein regions called as eukaryotic linear motifs (ELMs) or short linear motifs (SLiMs) which mediate interactions with their host . The mecApparently, majority of viral replication steps are supported by interacting host proteins leading to the manipulation of cellular processes by viruses. Therefore, the interpretation of these physical interactions between viral and host proteins (V-H interactome) allows the identification of cellular proteins/functions that are essential in the virus life-cycle and thereby can be considered as new antiviral targets . The stud,l-lysine-acetylsalicylate glycine) against the Influenza virus. This compound with new antiviral mode of action inhibits signal transduction module which is essential for viral replication, has successfully completed Phase II clinical trials [The conventional antiviral (anti-HIV) drugs mainly target the three viral enzymes: Reverse transcriptase, integrase and protease, as shown in l trials . Anotherl trials . This isDuring an HIV attack, the host cell does not offer a friendly environment to the virus. HIV faces several host defence factors which interfere with retroviral replication at different steps ,62,63. TThese antiviral defence mechanisms would have been quite successful in the past encounters; however, HIV-1 has evolved the ability to counteract these by several accessory viral proteins b like viThe initial steps of HIV-1 infection have been studied in substantial detail with a large number of attempts made to block the well-defined interactions occurring during the HIV-1 entry. The first step of HIV-1 infection is the entry of virus which takes place in three sub-steps. First being the ‘attachment’ to the cell surface when gp120  engages with a principal cell surface CD4 receptor. This interaction results in exposure of a ‘co-receptor binding’ site which interacts with an auxiliary co-receptor CCR5 or CXCR4. Subsequently ‘fusion’ occurs where gp41 exposes the fusion peptide to destabilize the host cell membrane and drives membrane fusion . HIV-1 eA variety of strategies have been pursued to develop entry inhibitors ,74. AttaAnother strategy for co-receptor binding inhibition which worked well was the development of small molecules that bind to a hydrophobic pocket in CCR5 inducing conformational changes rather than directly occupying the gp120 binding site . One of lens epithelium-derived growth factor) leads to impaired viral replication. Targeting the interaction interface, thus, offers hope for a new target. Moreover, post-integration, different viral accessory proteins interact with cellular factors to achieve successful replication. For instance, during elongation of viral transcripts, HIV-1 Tat binds to transactivation response element  which in turn interacts with host factor p-TEFb (positive transcription elongation factor b). So, Tat/TAR/P-TEFb interaction seems to be a promising target [HIV-1 can be tackled and/or inhibited at each step of its lifecycle in order to block its spread. Post-entry steps like uncoating, reverse transcription, nuclear entry, integration, transcription, nuclear exit, maturation, assembly and release from the cell membrane, all represent valid targets and are currently being investigated. For instance, during integration, disruption of the interaction between HIV-1 IN (integrase) and LEDGF/p75  and the London patients shared similar medical circumstances. They both were HIV positive and receiving ART. Later, both developed cancer  and required bone marrow transplants to replenish the stem cells which were destroyed by chemotherapy. Doctors in both instances used bone marrow cells from a CCR5 (Δ32/Δ32) donor. A homozygous mutation (Δ32/Δ32) in the CCR5 gene encoding the defective HIV co-receptor CCR5 confers resistance to HIV-1 infection. This Δ32/Δ32 allogenic stem cell transplantation in both the patients cured them with no viral detection for years and months. While these two cases have provided important insight for HIV research, bone marrow transplantation is a very risky procedure with a 50% chance of success and, hence, not a feasible strategy to cure HIV infection. The impetus obtained from these two cured patients led to the development of potent inhibitors of HIV co-receptors, but their clinical development was halted due to undesired side effects. Therefore, though the inhibition or knock-down of Diverse independent genome-wide RNA interference-based screens evaluated more than 842 genes that reduce HIV-1 infection when knocked-down ,87,88. AHIV is capable of infecting terminally differentiated and non-dividing cells . The lonDuring its whole lifecycle, HIV-1 encounters the nuclear membrane via NPC twice: (1) entry of HIV-1 PIC into the nucleus and (2) exit of viral RNAs after transcription into the cytoplasm. The dependency of HIV-1 on the nuclear envelope is reflected by the changes in the expression of nucleocytoplasmic shuttling proteins . VariousReverse transcription of the viral RNA genome into viral DNA takes place in the cytoplasm followed by translocation to the nucleus. The newly synthesized viral DNA remains associated with viral and cellular proteins in a large nucleoprotein complex called the pre-integration complex (PIC) ,103,104.reverse transcription complex) where reverse transcription of viral RNA takes place into cDNA. However, the precise location and mechanism of CA core uncoating remains controversial [cis-to-trans isomerization which lead to conformational changes in the capsid and thereby promote uncoating [Nup358 assist HIV-1 core to dock at the NPC via interaction with viral CA protein. Nup358/RanBP2 is the largest Nup situated in the cytoplasmic fibrils of NPC a. It is oversial ,116. Whioversial . The Cypoversial . Reportsncoating ,120,121.ncoating . HoweverNup153 is a nucleoplasmically oriented Nup that predominantly located to the nuclear side of the NPC as a member of nuclear basket . While tNup62, a glycosylated FG-Nup is a component of the central transport channel (CTC) of the NPC . This NuIt is noteworthy that NPC components not only facilitate nuclear import of the PIC but also plays a role during the integration of the proviral cDNA into the host chromosome. Few Nups, particularly Nup62, Nup153 and Nup98, can be found in the nucleoplasm and regulate the expression of certain cellular genes . These NUpon integration of the vDNA into the host chromosome, the viral 5′-LTR acts as a weak promoter for the transcription of viral mRNA. The viral mRNA is spliced using host splicing mechanisms and results in three different mature RNAs, viz. completely spliced, partially spliced and unspliced RNAs. The latter two are too big to be exported into the cytoplasm. Hence, only the spliced vRNA expressing the viral proteins Rev, Tat and Nef are readily translocated to cytoplasm ,135. TheOverall, HIV-1 utilizes components of the NPC for both PIC import and Rev-vRNA RNP export. Hence, inhibiting the interactions between HIV-1 proteins and Nups might be helpful in developing novel anti-viral strategies. The nuclear pore complex plays a significant role as HIV-1 host partner and helps in the nuclear translocation of viral DNA, RNA and proteins. Nups are the major components of the NPC which not only contribute to NPC assembly and overall architecture and but also maintains a strict permeability barrier by allowing selective active translocation of macromolecules through the NPC. Additionally, Nups are also involved in the regulation of cellular gene expression and interaction with chromatin in the nucleoplasm . Any dysA number of reports have also pointed towards the compositional variability of the NPC both within and between the cells ,152. FurThe physiological and pathophysiological significance of the nuclear membrane and nucleocytoplasmic transport has been recently reported ,156. TreHIV still remains a major global concern. Although there is no sterilising cure or preventive vaccine at present, a functional cure for HIV-1 might be more realistic to achieve. To date, all the antiviral drugs in clinical use for HIV-1 have been designed either to inhibit one of the three viral enzymes or to block the viral entry into the cell during attachment and fusion with the cellular membrane. The ART have certainly resulted in an appreciable decrease in HIV infections. However, development of HIV-1 resistance to these drugs has become one of the greatest challenges in identifying an appropriate combination of drug regimes with the fewest side effects for the treatment of patients. Given that the interaction between HIV-1 and its human host is far more complex than previously anticipated, a major challenge to the field will be the identification of essential interactions between viral and host factors which can be blocked without important physiological side-effects/consequences. Moreover, the drugability of the identified potential targets poses another serious challenge. In the past decades, structure-guided drug discovery has led to the development of various molecules which are being tested for clinical trials. The structural information available for HIV-1 is far more than any other virus; yet we are limited by the structural knowledge of host-viral protein complexes in both infected and uninfected conditions. The in-depth understanding of the structural and functional aspects of cellular proteins, such as Nups and other nuclear factors, which are important in HIV-1 life cycle is needed, which can then be exploited to speculate disease conditions. Even though a large number of cellular protein-protein interactions have been described, the way to the development of clinical drugs is still far. Nevertheless, significant progress is being made in the field and new targets are being discovered to keep the journey of developing novel antivirals going and as well as keep the hope to win over HIV/AIDS alive.


The retention of laser-sintered cobalt-chromium (Co-Cr)-based crowns were examined after dentin pretreatment with desensitizing paste containing 8% arginine and calcium carbonate (DP-ACC). Forty lower first molars were prepared using a standardized protocol. The Co-Cr crowns were produced using selective laser melting. The teeth were either pretreated with the desensitizing paste or not pretreated. After one week, each group was cemented with glass ionomer cement (GIC) or zinc phosphate cement (ZPC). Surface areas of the teeth were measured before cementation. After aging, a universal testing machine was used to test the retentive strength of the cemented crown-tooth assemblies. The debonded surfaces of the teeth and crowns were examined at 2.7× magnification. Pretreating the dentin surfaces with the desensitizing paste before cementation with GIC or ZPC did not affect the retention of the Co-Cr crowns. The retention of the GIC group (6.04 ± 1.10 MPa) was significantly higher than that of the ZPC group (2.75 ± 1.25 MPa). The predominant failure mode for the ZPC and the nontreated GIC group was adhesive cement-dentin failure; for the treated GIC group, it was adhesive cement-crown failure. The desensitizing paste can be safely used to reduce post-cementation sensitivity without reducing the retentive strength of Co-Cr crowns cemented with GIC or ZPC. Dentin hypersensitivity following tooth preparation and cementation of fixed partial dentures (FPDs) has been a common phenomenon . Post-ceIn an effort to control postoperative sensitivity, various desensitizing agents have been used to seal dentinal tubules before crown cementation; however, the literature is inconsistent regarding the effects of these agents on the retentive strength of FPDs. Sailer et al. ,9 and StRecently, a new in-office Colgate Sensitive Pro-Relief Desensitizing Paste containing 8% arginine and calcium carbonate (DP-ACC) was shown to provide immediate and lasting relief from dentin hypersensitivity ,19,20,21Recently, a new additive manufacturing technology operated by computer-aided design and computer-aided manufacturing (CAD/CAM), referred to as selective laser melting (SLM) technology, has been introduced for fabricating cobalt-chromium (Co-Cr) frameworks for PFM crowns. Co-Cr crowns produced with SLM exhibit a marginal and internal accuracy that is comparable to that of conventional production procedures but save time and facilitate laboratory procedures ,28,29.No information has been found in the literature concerning the influence of pretreating dentin with DP-ACC on the retentive strength of Co-Cr crowns produced by the SLM technology and cemented by zinc phosphate cement (ZPC) and glass ionomer cement (GIC), which are the most frequently used cements for luting metal-based restorations.The aim of this in vitro study was to evaluate the effect of the pretreatment of dentin with DP-ACC on the retentive strength of SLM Co-Cr copings cemented by ZPC and GIC. The null hypotheses were as follows: (1) the retentive strength of the SLM Co-Cr copings cemented by ZPC and GIC to human extracted teeth is not affected by DP-ACC, and (2) the retentive strength of the two cements is similar.p = 0.780). The retention obtained with Fuji I capsules (GIC) was significantly (p = 0.001) higher than that obtained with Harvard Cement OptiCaps (ZPC). The interaction between cement and dentin treatment was not significant (p = 0.208).The retentive strength  of the treated and untreated cementation groups are presented in Examination by magnifying glasses of the failure mode after the dislodgment of the crown revealed that for ZPC, the predominant failure mode was adhesive cement-dentin failure. In 85% of the surfaces, all (53%) or part (32%) of the surface of the crown was covered with cement, and the rest were detected on the dentin . This moScanning electron microscopy (SEM) images of the dentin surfaces illustrating the various modes of failure are presented for the ZPC a–c and GColgate Sensitive Pro-Relief Desensitizing Paste contains arginine, bicarbonate, and calcium carbonate and is highly effective in occluding dentin tubules, as was previously demonstrated by confocal laser scanning microscopy (CLSM) and SEM . This paIn the current study, the retention of cobalt-chromium-based (Co-Cr) copings was tested one week after dentin pretreatment with DP-ACC in order to resemble a period of reevaluation prior to final cementation. The luting agents used in this study were GIC and ZPC. Both cements are acid-base materials associated with post-cementation sensitivity, which can last for a week . GIC andThe current results support the first null hypothesis of the study, implying that pretreatment with DP-ACC would have no effect on the retentive strength of SLM Co-Cr crown copings cemented to human extracted teeth with GIC or ZPC. Our results are in agreement with those of another study that demonstrated that pretreating dentin surfaces with DP-ACC prior to cementation did not affect the retention of complete cast metal crowns luted with GIC . MoreoveOur second null hypothesis was rejected because the retentive strength of GIC was significantly higher than that of ZPC. This conclusion is in accordance with a previous study of Wiskott et al.  demonstrThe lack of effect on the retentive strength of ZPC from DP-ACC was also verified by an absence of change in failure mode, which was mainly adhesive cement-dentin failure. This finding implies that all or most of the crown was covered by ZPC, probably due to surface irregularities of the intaglio of the Co-Cr copings. In the GIC group, the predominant failure mode was adhesive cement-crown failure. This finding implies that all or most of the dentin was covered by GIC, probably due to the chemical interaction between the calcium in the mineral phase at the surface of the dentin and carboxylate groups in the cement, implying that in spite of the surface coverage by the DP-ACC, many calcium ions are still available for bonding. Although the DP-ACC did not affect the retentive strength, the failure mode varied between the groups; in the nontreated group, most of the cement remained on the crown, while in the treated group, most of the dentin was covered with cement. These differences might be explained by chelation between the polyalkenoic chains in GIC also with the calcium carbonate contained in the DP-ACC, which physically plugs and seals exposed dentin tubules.This is an in vitro study, hence, the subjective desensitizing effect of the DP-ACC on vital teeth prepared for FPD must be validated in clinical studies before being recommended for use.The study sample comprised forty freshly extracted, caries-free, intact lower first molars that were extracted for periodontal reasons (age range 40–60). Approval from the Ethics Committee of Tel Aviv University was obtained (#21-08-16), and all individuals signed an informed consent.The teeth were stored in a germ-free 0.1% thymol tap water solution at room temperature for a maximum of two weeks until experimentation. Each tooth was suspended in the middle of an aluminum ring and was mounted 2 mm apical to the cementoenamel junction (CEJ) in poly(methyl methacrylate) resin  after notching the roots for retention purposes. The mounted teeth were stored in tap water at room temperature at all times.A standardized protocol yielding an axial height of 5 mm and a 10° taper was followed for preparation. The occlusal surface was sectioned perpendicular to the long axis with a water-cooled precision saw . A 0.4-mm, 360° chamfer finish line located 1 mm above the CEJ with a 10° taper preparation was obtained by a rigidly secured, high-speed handpiece equipped with a diamond bur  mounted on a custom-designed, surveyor-like apparatus. A new diamond bur was used for each tooth.The prepared teeth were digitally scanned by a laboratory scanner  operated by blue light and equipped with five axes of rotation, and STL files were obtained. Forty Co-Cr copings were produced using an SLM system  at a commercial dental laboratory . The CAD-CAM Co-Cr cores were 1.0 mm thick with a 50 µm virtual cement spacer layer. To facilitate tensile loading, an occlusal loop (4-mm outer diameter and 2-mm inner diameter) was designed extending coronally from the occlusal surface . The teeThe cemented crown-tooth assemblies were stored in tap water at 37 °C for two weeks, followed by thermal cycling between water temperatures of 5 and 55 °C for 5000 cycles with a 10 s dwell time . After thermal cycling, the crown-tooth assemblies were subjected to dislodgment forces through a 1.2-mm diameter metal cable entangled through the occlusal loop along the apico-occlusal axis using a universal testing machine  at a crosshead speed of 1 mm/min until failure. The force at dislodgment was recorded and divided by the total surface area of each prepared sample to yield the retention value (Pa).The debonded surfaces of the teeth and crowns were examined with magnifying glasses at 2.7× magnification . Each surface of the dentin-crown interface was analyzed separately (five surfaces per tooth). Failure was classified based on the criteria presented in A separate analysis was performed for each matched Co-Cr tooth surface . For each category, the number of surfaces was counted and presented as a percentage of all the surfaces for the specific cement.To analyze the dentin surfaces, the debonded surfaces of some teeth representing different failure categories from each group were examined under an SEM (Quantum 2000) in high vacuum mode following gold sputter-coating. The acquisition conditions were as follows: 25 kV, 90 µA and 40–1000× magnification.n = 2) and pretreatment (n = 2) were the independent variables. The level of significance was 0.05.Retentive strength was evaluated using two-way analysis of variance (ANOVA) with repeated measures; cement (An 8.0% arginine and calcium carbonate in-house desensitizing paste can be safely used on dentin to reduce post-cementation sensitivity without compromising the retention of SLM Co-Cr crowns cemented with either ZPC or GIC.