Thirteen patients were evaluated, anti-NMDAR was the most common antibody found , followed by anti-LGI1 , and anti-AMPAR . Median time to diagnosis was 4 months (range 2–9 months). Among these 13 patients, 6 (46.1%) had inflammatory markers and when compared to those who did not present signs of inflammation, there were no significant differences regarding the age of onset, time to diagnosis and modified Rankin scale score at the last visit. Most of the patients presented partial or complete response to immunotherapy during follow-up. Our findings suggest that the presence of inflammatory markers may not correlate with clinical presentation or prognosis in patients with AE associated with antibodies against neuronal surface. Neurologists should be aware to recognize clinical features of AE and promptly request antibody testing even without evidence of inflammation in CSF or MRI studies.Acute encephalitis is a debilitating neurological disorder associated with brain inflammation and rapidly progressive encephalopathy. Autoimmune encephalitis (AE) is increasingly recognized as one of the most frequent causes of encephalitis, however signs of inflammation are not always present at the onset which may delay the diagnosis. We retrospectively assessed patients with AE associated with antibodies against neuronal surface diagnosed in reference centers in Northeast of Brazil between 2014 to 2017. CNS inflammatory markers were defined as altered CSF (pleocytosis >5 cells/mm Autoimmune encephalitis (AE) is increasingly recognized as one of the most frequent causes of encephalitis , 2. The We retrospectively identified consecutive patients whose serum or CSF samples tested positive for neuronal antibodies in the city of Fortaleza, state of Ceará, northeast Brazil, from January 2014 to March 2017. This study was conducted in the main neurological comprehensive care centers of our city , covering a population of almost 2.7 million. Thus, we believe that most patients diagnosed with AE during this period were included in our cohort.Blood and CSF samples analyses were performed at IDIBAPS, Barcelona, with support from Dr. Josep Dalmau. All samples were screened for reactivity using rat brain sections and then through cell-based assay with HEK293 recombinantly expressing N-methyl-D-aspartate receptor (NMDAR), leucine-rich glioma inactivated-1 (LGI1), contactin-associated protein-like 2 (CASPR2), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), gamma-aminobutyric acid (GABA)b receptor or GABAa receptor as previously reported .3), or magnetic resonance imaging (MRI) features suggestive of encephalitis; and (3) reasonable exclusion of alternative causes  subacute onset (rapid progression <3 months) of working memory deficits (short-term memory loss), altered mental status, or psychiatric symptoms; (2) at least one of the following: new focal CNS findings, seizures not explained by a previously known seizure disorder, CSF pleocytosis (white blood cell count > 5 cells/mm subacute3) and/or any brain MRI abnormality suggestive of encephalitis .Electroencephalogram (EEG) monitoring was performed in all patients. The following EEG variables were collected: presence or absence of electrographic seizures; clinical or subclinical seizures; diffuse slowing; focal slowing; rhythmic delta activity; excessive beta activity; and presence of extreme delta brush. MRI included T1-weighted, T2-weighted, fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI) and gradient echo sequences. CNS inflammatory markers were defined as altered CSF  was used to assess disability at onset, at the worst clinical status and at the last visit. A good outcome was defined as mRS score of 0–2 at the last follow-up and a poor outcome as mRS score > 2. Tumors were screened for at the initial presentation and every 6 months with thoracic and abdominal computed tomography and vaginal ultrasound or ovarian MRI for ovarian teratomas.U test or 2-sample t-test as appropriate for continuous variables, and Fisher's exact test for categorical variables. Two-tailed p-values of < 0.05 were considered statistically significant. Statistical analyses were performed IBM-SPSS version 18.0 .Categorical variables were described as absolute frequencies and percentages, while numerical variables were described as medians and ranges. Comparative analyses between patients with and without inflammatory changes were performed using the Mann-Whitney All patients gave their written consent for the storage and use of clinical samples for research purposes and to be included in this report. The study was approved by the local ethics committee .Among 13 patients identified, 9 (69%) were female and median age was 17 years (range 4–75 years). Anti-NMDAR was the most common antibody found , followed by anti-LGI1 , and anti-AMPAR . Median time to diagnosis was 4 months (range 2–9 months) Before an AE was considered, patients received other diagnosis including herpes simplex encephalitis, primary psychosis, and neuroleptic malignant syndrome.The most common initial presentation was encephalopathy with memory loss and behavioral changes suggestive of AE seen in 11/13 (84%): all 10 patients with anti-NMDAR and 1 patient with anti-AMPAR. Psychiatric symptoms presented before neurologic dysfunction in 4/13 patients. Dyskinesias, typically orofacial, were present in 9/13 patients and hand dyskinesias in 5/13. Dystonia was seen in 3/13 patients. Faciobrachial dystonic seizures (FBDS) followed by encephalopathy and memory loss were present in both anti-LGI1 patients. Only one patient with anti-NMDAR presented with refractory status epilepticus followed by orofacial and hand dyskinesias, dystonia and encephalopathy after resolution. Hyponatremia was present in three patients, one with anti-AMPAR and in both with anti-LGI1, and improved after immunotherapy. Regarding associated tumors, there was only one case of small cell lung carcinoma in the patient with anti-AMPAR. Treatment consisted in intravenous methylprednisolone alone , methylprednisolone with immunoglobulin , or immunoglobulin alone . Most patients  responded to the first-line therapy. In three patients with no significant improvement after 2 weeks, a second-line therapy was done with rituximab, and in one patient combined with cyclophosphamide. Patients were severely impaired at initial presentation, with a median mRS score of 5 (range 4–5). Most showed significant improvement after treatment, where 9/13 (69%) achieved a good outcome (mRS ≤ 2). Median follow up duration was 22 months (range 3–48). Three patients had a mRS ≥ 3 at the last visit, including one patient with anti-NMDAR who died of central line associated sepsis while receiving parenteral nutrition for a drug-induced pancreatitis . Maintenance treatment was conducted at the decision of attending physicians. Most patients received oral steroids for a variable period (median of 5 months). None received chronic immunosuppression. There were no relapses during follow-up.Most patients had normal MRI . From those with MRI abnormalities : two (both with anti-NMDAR) had extralimbic findings with nonspecific cortical hyperintensities; and one had bilateral hippocampal hyperintensities compatible with limbic encephalitis and left basal ganglia hypersignal . Follow up MRI scans were performed in two patients. In one of the anti-LGI1 patients atrophy of right caudate and putamen was observed after 1 year. In an anti-NMDAR patient complete resolution of subcortical abnormalities and no atrophy were observed after 8 months. CSF pleocytosis was seen in less than a half of patients . None of the patients had positive oligoclonal bands. All 13 patients showed EEG abnormalities; they were mostly nonspecific slowing of baseline activity . Two patients, both with anti-NMDAR, showed an “extreme delta brush” pattern. One patient with anti-NMDAR presented with status epilepticus. In both patients with FBDS, there was no EEG correlation with abnormal movements.p = 0.559). No significant differences in age of onset, response to immunotherapy, time to diagnosis and prognosis were observed between patients with or without inflammatory markers. Seven patients (53.8%) had no signs of inflammation on CSF or MRI. No evidence of inflammation was seen in half of patients with anti-NMDAR, in one patient with anti-LGI1 and in the only patient with anti-AMPAR. A good outcome at the last visit was seen in 83.3% of the patients with inflammatory changes and in 57.1% of those without these changes  include the presentation of decreased level of consciousness, neuroimaging findings suggestive of inflammation, and CSF pleocytosis . Yet, a The most common antibody found in our study was anti-NMDAR, as reported in other series of AE patients . A recenNMDAR antibodies bind to an extracellular conformational epitope region close to the amino acid 369 of the GluN1 NMDAR subunit and reduce the receptor density leading to a reversible direct neuronal dysfunction . Other aPossible triggers for autoimmune response are under investigation. Ectopic expression of NMDAR in ovarian teratomas, for instance, is thought to trigger autoimmune response in paraneoplastic anti-NMDAR encephalitis . Recent Concerning anti-LGI1 encephalitis, we observed a good response to initial treatment in the patient with normal MRI, while the one with T2 hyperintensities in putamen and caudate had a poorer response to first-line therapy with only slight improvement of FBDS and required additional therapy with rituximab. It has been suggested that these signal abnormalities are more common in the presence of FBDS and are related to time since disease onset, with T2 hypersignal disappearing with disease progression . WhetherIn our cohort, only two patients had follow-up imaging for analysis. In both cases, associated with anti-LGI1 and anti-NMDAR, no hippocampal atrophy was observed as previously reported . In the The importance of testing new onset refractory status epilepticus (NORSE) patients for neuronal surface antibodies has been previously discussed, as many of these cases might have an autoimmune etiology . In our The lack of difference between patients with and without inflammatory changes in our study might be due to the small sample size, heterogeneity and lack of representativeness, since cases with only three antibodies types were included with most of the cases being anti-NMDAR. However, we believe this cohort is closer to a “real-world” scenario, as our centers are not specialized in autoimmune encephalopathies and we analyzed all patients who tested positive for neuronal antibodies.The present study was the first to provide an estimate of incidence of AE in a developing country, where access to MRI and EEG is very limited and antibody testing is not available commercially. Our findings reinforce the importance of previously reported diagnostic clues like FBDS and a syndrome suggestive of anti-NMDAR encephalitis for diagnosis, as well as the sensitivity of Graus criteria for possible autoimmune encephalitis, allowing for early initiation of immunotherapy before antibody results are available. We also report on a good outcome despite many challenges to diagnosis and treatment in underdeveloped parts of the world.Local Ethics Commitee: Hospital Universitário Walter Cantídio-Universidade Federal do Ceará.We would like to outline that all patients provided written informed consent for the collection of samples and subsequent case report. All authors have read and agree with the manuscript publication. The local ethics committees approved the final version of this article.PN and PB-N: conception and design of the work. PN, LM, FM, MK, CS, NM, MP, MS, and PB-N: acquisition, analysis, or interpretation of data for the work. PN, MP, MS, and PB-N: drafting the work. All authors were involved in critical revision of the manuscript for important intellectual content.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Overweight children with asthma may display impaired response to inhaled corticosteroids (ICS), possibly due to non-eosinophilic inflammation or weight-related lung compression; these mechanisms may differentially affect response to ICS and leukotriene receptor antagonists (LTRAs). We assessed whether weight status modified the response to low-dose ICS and LTRA Step-2 monotherapy.A historical cohort study from clinical data linked to administrative databases was conducted among children aged 2–18 years with specialist-diagnosed asthma who were initiating or continuing a Step-2 monotherapy from 2000 to 2007 at the Montreal Children’s Hospital Asthma Centre. The outcome was time-to-management failure defined as any step-up in therapy, acute care visit, hospitalisation or oral corticosteroids for asthma, whichever occurred first. The independent and joint effects of weight status (body mass index [BMI] percentile) and time-varying treatment on time-to-management failure were estimated with marginal structural Cox models. The likelihood ratio test (LRT) and relative excess risk due to interaction (RERI) were computed to assess treatment effect modification by weight status on the multiplicative and additive scales.Of the 433 and 85 visits with a low-dose ICS and LTRA prescription, respectively, 388 management failures occurred over 14 529 visit-weeks of follow-up. Children using LTRA compared with low-dose ICS tended to have an overall higher risk of early management failure . Irrespective of treatment, the hazard of management failure increased by 5%  for every 10-unit increase in BMI percentile. An additional hazard reduction of 17%  was observed for every 10-unit increase in BMI percentile among LTRA users, but not for ICS . The LRT indicated a departure from exact multiplicativity (p<0.0001), and the RERIs for ICS and LTRA were −0.05 (95% CI −0.14 to 0.05) and −0.52 (95% CI −1.76 to 0.71).Weight status was associated with earlier time-to-management failure in children prescribed Step-2 therapy. This hypothesis-generating study suggests that LTRA response increases in children with higher BMI percentiles, although further research is warranted to confirm findings. Children with the obese-asthma phenotype may exhibit higher degrees of airflow obstruction and non-eosinophilic inflammation, which may respond more favourably to leukotriene receptor antagonists (LTRAs), than inhaled corticosteroids (ICS), due to their different modes of action; however, this has not yet been studied in children.Children with excess weight were more likely to have an early management failure event (step-up in therapy or exacerbation) and respond to LTRA, but not low-dose ICS, when compared with their normal-weight counterparts.These data support LTRA as a possible alternative therapy for mild asthma in children with excess weight who may have not previously responded to low-dose ICS; further prospective research is warranted to confirm findings.Over the last few decades, the prevalence of obesity in paediatric asthma has risen rapidly, with an estimated 20% and 30% of children with asthma reported to be overweight and obese, respectively.Poor response to ICS among obese patients has been hypothesised to be due to altered lung function and inflammatory processes.Leukotriene receptor antagonists (LTRAs) are a second-line Step-2 oral maintenance monotherapy that inhibit both eosinophil-mediated and neutrophil-mediated inflammation and whose action is independent of lung mechanics to achieve therapeutic targets; it may thus serve as an alternative therapy in patients with excess weight.The primary objective of this study was to assess if weight status modifies the response to low-dose ICS and LTRA monotherapy, specifically by evaluating whether the association between Step-2 therapies and time-to-management failure is modified by body mass index (BMI) percentile in children with asthma. We hypothesised that weight-related changes to lung physiology and mechanics would lead to a differential response to Step-2 therapies in children with higher BMI status.A historical cohort study was conducted using a database that linked clinical chart data to Quebec administrative health and drug claims databases for the purposes of evaluating the effectiveness of different asthma maintenance therapies and risk factors for poor outcomes. The Paediatric Asthma Database contained 15 147 clinical records of 4621 children who visited the Asthma Centre of the Montreal Children’s Hospital between January 2000 and December 2007.Because this is a database study, the research question and outcomes measured were not informed by patients, nor were patients involved in the recruitment to or conduct of the study. As all data were anonymised, study results cannot be directly disseminated to the patients included in this study.The Paediatric Asthma Database included detailed information on sociodemographics, asthma diagnosis, severity and control indicators as assessed by the consulting asthma specialist, environmental exposures, lung function test results and medical chart records of all prescribed asthma maintenance and/or rescue treatments, with the indication for medication use clearly labelled, for each visit at the Asthma Centre. Patient records from the Paediatric Asthma Database were linked to the hospital admission (MED-ECHO), Régie de l’assurancemaladie du Québec (RAMQ) medical service and prescription claims administrative databases. The RAMQ medical service database contains information on the type, location of service delivery and diagnostic codes using the International Classification of Diseases 9th and 10th revisions  for all medical services billed by physicians. The MED-ECHO database includes admission and discharge codes for all hospitalisations in Quebec. Children are provided universal access to all healthcare services in Quebec. Lastly, the RAMQ prescription claims database contains data on all drugs dispensed to patients enrolled in the Public Drug Insurance Plan (42% of Quebec residents).Children were included if they were aged 2–18 years, consulted the Asthma Centre during the study period 2000–2007, had an asthma diagnosis confirmed by a specialist, and were prescribed low-dose ICS or LTRA (Step-2) maintenance monotherapies as documented in the medical chart at the index visit date. Patients were excluded if they were diagnosed with bronchopulmonary dysplasia or cystic fibrosis or were not enrolled in the public drug or health insurance plan at least 6 months prior to and 3 months following the visit date. The study population included children who were initiating or continuing a prescription of low-dose ICS or LTRA. Children initiating (‘incident’ users) and continuing therapy  were defined as those who had no claims and at least one claim for low-dose ICS or LTRA, respectively, in the 6 months preceding the index date (‘lookback period’). Children who had claims for medium-dose or high-dose ICS or ICS in combination with long-acting beta-2 agonists (LABAs) or LTRA during the lookback period were excluded. The cohort entry (index) date was defined as the date at which patients met all inclusion criteria. We included all visit dates eligible for cohort entry based on the above criteria; thus, some children contributed multiple entry dates  a step-up in maintenance therapy, that is, higher dose ICS, ICS/LABA or ICS/LTRA combination therapy dispensed during follow-up; (2) a short course of oral corticosteroids; (3) an acute care visit; or (4) a hospital admission for an asthma exacerbation . We instated a lag of 3 days following cohort entry before assessing the outcome to exclude the possibility of pre-existing exacerbations.BMI percentiles were computed using Z-scores with age- and sex-specific WHO growth reference values from the height and weight measurements documented by trained healthcare professionals at the Asthma Centre index visit.A time-varying exposure definition was implemented to classify Step-2 maintenance therapy use throughout follow-up. Children dispensed low-dose ICS, that is, <200 μg/day (<250 μg/day if ≥12 years old) of hydrofluoroalkane-propelled beclomethasone dipropionate or equivalent measured ex-valve,1) test values as they were updated at each visit to the Asthma Centre. In some children, higher dose ICS was prescribed as a rescue therapy by the Asthma Centre physician, which could have confounded treatment effects. Thus, we also controlled for higher dose ICS rescue therapy use, where the child was classified as using the rescue therapy from the date at which he/she claimed the rescue medication  until the end of follow-up, and season to account for viral-induced and allergy-induced worsening of asthma symptoms.Sociodemographic variables , user type , asthma phenotype (persistent vs non-persistent), respiratory comorbidities , exposure to cigarette smoke and morbidity (number of exacerbations in the preceding year) were documented at the index visit and defined as time-fixed confounders. Time-varying confounders included physicians’ global assessment of asthma severity and prebronchodilator percent predicted forced expiratory volume in 1 s (FEV1 (13.1%), exposure to smoking (22.6%) and family income (22.9%) at the index date. We did not test differences in baseline characteristics between treatment groups, as recommended by the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. We fit extended Cox models to obtain conditional associations for Step-2 maintenance therapies and a 10-unit increase in BMI percentile at baseline with respect to the hazard of management failure, while adjusting for all measured confounders. We also fit marginal structural Cox models for time-varying treatments, which have the added advantage of correcting for time-dependent confounding.We used multiple imputations to address missing data for BMI percentile (1.2%), ethnicity (9.3%), percent predicted FEVViolations to the proportional hazards assumption were investigated by visually inspecting the scaled Schoenfeld residuals plot and confirmed with the likelihood ratio test (LRT) for improved fit when an interaction term with time was included in the model. To obtain conditional and marginal treatment HRs for different weight status categories, interaction terms between treatment and BMI percentile at baseline was included. When evaluating whether treatment effects were modified by BMI percentile, we computed the relative excess risk due to interaction (RERI)Based on the data distribution, BMI percentiles were classified ad hoc into normal (BMI ≤80th percentile) and excess weight (BMI >80th percentile) using the median as the cut-off point to improve statistical power; this dichotomisation was used to estimate joint effects and the specific survival curves for each Step-2 therapy and weight category combination, as well as the treatment (ICS or LTRA) HRs stratified by weight category. Sensitivity analyses were conducted to verify (1) potential non-linear relationships between BMI percentile and treatment effects with restricted cubic splines; (2) effects observed only in those with persistent asthma; and (3) the robustness of methods for handling missing data . In addition, subgroup analyses explored whether findings differed in incident and prevalent users. Robust SEs were used to account for the inclusion of multiple ‘person-visits’ and the weights in the marginal model. Data management was performed with SAS V.9.4, and survival curves were illustrated using Stata Statistical Software V.13, while other analyses were performed using RStudio V.1.0.44.Of the total 15 147 visits (N=4621 patients) in the Paediatric Asthma Database, 518 (n=342) person-trials were included in the final cohort , of whom10.1136/bmjresp-2019-000401.supp1Supplementary dataChildren included in the cohort were mostly male (56%), aged 5 years and older (87.5%), with a mean treatment period of 11 weeks (ranging from 0 to 52 weeks). Children prescribed low-dose ICS had more moderate-severe asthma and comorbidities, as well as higher healthcare utilisation in the previous year, than those prescribed LTRA. On average, children prescribed LTRA were older, had higher family income, and reported more exercise-triggered symptoms compared with those prescribed low-dose ICS .There were a total of 338 management failures during the 14 529 trial-weeks of follow-up. The incidence rates for management failure remained relatively stable between prescribed treatment at the index visit (ICS vs LTRA), regardless of the number of visits contributed by the child in the analysis; however, the incidence rate was elevated for children using LTRA during follow-up who contributed two or more visits compared with those contributing a unique visit online . In the 10.1136/bmjresp-2019-000401.supp2Supplementary dataIn the conditional and marginal models that include an interaction term between Step-2 therapy and BMI percentile, a clinically relevant reduction in the risk of management failure was not observed with increasing BMI percentile among ICS users. In contrast, for children treated with LTRA, an additional 22%  and 17% reduction  in the conditional and marginal hazards of management failure were observed for every increase of 10 BMI percentile units, respectively. Of note, inclusion of the interaction term of treatment by BMI percentile significantly improved the fit (LRT p<0.001) in both models, indicating the presence of treatment effect modification on the multiplicative scale; the RERIs suggested a trend towards subadditivity, that is, the joint effects of BMI and treatment were less than the sum of their individual effects on the additive scale, which was most pronounced for LTRA  relative to ICS  . In the The average conditional and marginal HRs for different treatment–weight status categories (≤80th vs >80th percentile) were also estimated in relation to a common reference group, namely normal-weight children who were non-compliant to treatment . CompareThe HRs for different treatment comparisons stratified by normal weight and overweight subgroups are displayed graphically in In this cohort of children with asthma treated with Step-2 therapy, BMI percentile was associated with an overall increased risk of management failure. Our findings suggest a differential response to Step-2 maintenance therapies by weight status for LTRA. Whereas there was an overall greater risk of failure in children treated with LTRA, this was not observed in children with excess weight. No statistically significant interaction with weight was observed in children treated with low-dose ICS, although there was a trend for reduced response in children with higher BMIs.Obesity is an established risk factor for poor control. We observed an overall 5%–6% increased risk of Step-2 monotherapy treatment failure for every 10-unit increase in BMI percentile in the main effects model and up to a 40% higher risk of failure in overweight children who were non-compliant to treatment in the analysis displaying HRs for different treatment–weight status categories. This is consistent with a recent meta-analysis of observational data including 46 070 children, which identified obesity as a significant determinant of asthma exacerbations .et alet alet al1, symptom scores and short-acting beta-2 agonist use across all weight categories, although it was suggested that response to both treatments may be attenuated at higher BMIs.et al,To our knowledge, no previous studies have evaluated the differential response to LTRA relative to low-dose ICS by weight status in children, and the marginal structural models applied in this study are novel to paediatric asthma, although this approach has been previously implemented in a few adult asthma studies.1 and peripheral airway diameter.There are several mechanisms underlying a possible differential response to LTRA and/or ICS by weight status in children. First, obesity has been shown to adversely affect pulmonary physiology by changing the elastic properties of the chest wall, leading to reduced forced vital capacity, FEVThe strengths of this study include specialist-diagnosed asthma (vs self-report), the availability of clearly recorded data on Asthma Centre physician prescriptions , as well as updated information on severity indicators throughout the follow-up period, which enabled the implementation of the marginal structural Cox model. In addition to a more intuitive interpretation, the marginal structural model generates a marginal HR that avoids biases arising from complicated cases of time-dependent confounding and non-collapsibility that are inherent to the conditional HR.In conclusion, our findings provide clinical insight into the association between weight status and treatment response in children initiated on Step-2 maintenance monotherapy. Children with higher BMI percentiles were overall more likely to experience early management failure despite Step-2 maintenance therapythan their normal-weight counterparts. Of interest, LTRA seems to achieve best response at higher BMI percentiles. While higher BMI status appears to be associated with a trend towards a reduced response to low-dose ICS, this association did not reach statistical significance. While addressing weight-reduction approaches to overcome challenges associated with obese-asthma, consideration may be given to LTRAs as an alternative Step-2 monotherapy in children with excess weight or at least in those with suboptimal response to low-dose ICS. However, given the limitations of this retrospective cohort study, further research is warranted to determine whether ICS and LTRA monotherapies are comparable in terms of prolonging time-to-management failure in higher risk obese children with asthma.


Few studies have focused on the potential relationship between secondhand smoke (SHS) exposure and depressive symptoms. This study aimed to explore the potential association between SHS exposure and depressive symptoms and differentiate this association in setting-specific exposure and symptom-specific outcomes. A cross-sectional study was conducted in Guangdong province of China from September to December 2010 using a multistage sampling method to randomly sample adults aged 18 years and older. SHS exposure was defined as inhalation by non-smokers of the smoke exhaled from smokers for at least 1 day a week in the past 30 days. Depressive symptoms were measured using the nine-item Patient Health Questionnaire. The zero-inflate negative binomial regression models were used to explore the associations between SHS exposure and depressive symptoms. A total of 2771 non-smokers were included in this study, with mean age of 49.6 ± 14.0 years and 70.3% of females. The prevalence of depressive symptoms was significantly higher in participants with SHS exposure than in those without exposure (incidence rate ratio (IRR) = 1.32, 95% confidence interval (CI) 1.16–1.51), and there were similar positive associations for SHS exposure in medical facilities  and in schools . Notably, there was a monotonically increasing dose-response relationship between frequency of SHS exposure and depressive symptoms. When differentiating this relationship by the dimensions of depressive symptoms, there were similar dose-response relationships for cognitive-affective and somatic symptoms. When differentiating this relationship by sex, only females showed a significant dose-response relationship. Our findings suggest dose-response relationships between SHS exposure and depressive symptoms in sex-specific and symptom-specific manners. Future longitudinal studies are needed to establish the biological mechanisms of the impact of SHS exposure. It is well established that there is no risk-free level of exposure to secondhand smoke (SHS) . In a reDepressive disorders are the most common mental problems, occurring as early as at three years of age and across all world regions ,6. PreviStrong epidemiological evidence has demonstrated that smoking is strongly associated with depression or major depression ,11. In aThe study was part of the 2010 China Chronic Disease and Risk Factor Surveillance organized by Guangdong Provincial Center for Disease Control and Prevention. All interviewers in each area were trained to ensure that the survey was carried out according to the protocol and that operation procedures were identical across all areas. The details of the study design and quality control have been described elsewhere ,28. BrieThe present study was approved by the Ethics Committee of the Chinese Center for Disease Control and Prevention and was performed in accordance with the approved guidelines (No. 201010).The main outcome variable was self-reported depressive symptoms assessed by a Chinese version of the nine-item Patient Health Questionnaire (PHQ-9). Previous research has shown that a PHQ-9 score ≥10 had a sensitivity of 88% and a specificity of 88% for major depression, the area under the curve in diagnosing major depression was 0.95, and there was a strong association between increasing PHQ-9 scores and worsening function, suggesting good construct and criterion validity . ParticiThe main independent variable was self-reported SHS exposure. To ascertain the SHS status in general, participants were asked the following question: “During the past 30 days, how many days a week have you breathed in smoke (including tobacco or e-cigarettes) exhaled from smokers in any places?”. SHS exposure in general was defined as inhalation by non-smokers of smoke for at least 1 day a week (1 day means that in that day there are smokers smoking up to 15 min or longer) in the past 30 days . FrequenOther influencing factors including potential covariates and confounders were chosen a priori on the basis of a literature review. Other influencing factors in this study included demographic characteristics (age and sex), socioeconomic status (education and per capita family income), physical injury, and disease history, which may affect depression or depressive symptoms ,13,18. Pp-value of <0.05 was defined as being of statistical significance. All statistical analyses were performed using Stata version 14.0 .Notably, the scores of depressive symptoms were positively skewed, and there were many zero values in the data (66.0%), so we used the zero-inflated negative binomial (ZINB) regression to fit the data. Univariable and multivariable ZINB regression models were used to calculate the incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for evaluating the potential association between SHS exposure and depressive symptoms. To explore the potential influences of covariates on the association between SHS exposure and depressive symptoms, two multivariable models were fitted. The multivariable model 1 (basic-adjusted model) was adjusted for sex, age, education, and per capita family income. The multivariable model 2 (fully adjusted model) was further adjusted for physical injury and number of disease history. Linear trends of SHS exposure were assessed by modeling exposure as continuous variables  in the ZINB models. To adjust for sampling design (clustering) in the analysis, the three-level nested model was fitted, but the intraclass correlations were not significant, so the fixed models were used in this study. A two-sided p < 0.001). The prevalence of SHS exposure in general was 33.5%, with no significant differences according to sex . SHS exposure was the highest in restaurants (36.8%), followed by exposure in transport vehicles (23.0%), medical facilities (16.0%), and schools (12.0%). The mean age (± standard deviation) was 49.6 ± 14.0 years, 70.3% were female non-smokers; other sample characteristics are given in As shown in The prevalence of depressive symptoms was significantly higher in participants with SHS exposure in general than in those without exposure , and a similar positive association was observed for SHS exposure in medical facilities  and in schools . However, in males, the association between continuous frequency of SHS exposure (days/week) and depressive symptoms was not statistically significant . These results were unchanged in the fully adjusted models for both females and males.This cross-sectional study revealed that non-smoking adults with setting-specific SHS exposure experienced a significantly higher prevalence of depressive symptoms than those without SHS exposure. The most striking finding from this study is that there was a monotonically increasing dose-response relationship between continuous frequency of SHS exposure and depressive symptoms. When differentiating this relationship by dimensions of depressive symptoms, there were similar dose-response relationships for cognitive-affective and somatic symptoms. When differentiating this relationship by sex, only females showed a significant dose-response relationship.Much attention has been focused on the association between SHS exposure and the risk of depressive symptoms, but the findings are inconsistent ,16,17,18It remains unclear whether there is a dose-response relationship between SHS exposure and depressive symptoms. Some studies revealed linear increasing trends between frequency of SHS exposure and depressive symptoms among adolescents and pregnant women ,34, but To date, it is still uncertain whether there is a sex-specific association between SHS exposure and depressive symptoms. Two Korean studies on non-smoking adults revealed that there was a significant dose-response pattern for household SHS exposure among women, but no significant association was observed for men ,16. ThesThere are potential biological and behavioral mechanisms that may explain the association between SHS exposure and depression. First, SHS exposure may be an indicator of stressful living and working environment, which contribute to the worsening of depressive symptoms through impaired neuroplasticity mechanisms . Second,A novel aspect of this study is the differentiation of the association in setting-specific exposure and symptom-specific outcomes, so as to make exposure and outcomes clearer. However, there are some potential limitations in this study. First, SHS exposure was evaluated by self-reports and not measured by biomarkers . However, self-reported SHS exposure has been used in several population-based surveys and is generally valid ,16. BiocIn conclusion, this study found monotonically increasing dose-response relationships between frequency of SHS exposure and depressive symptoms among adults. In addition, we observed setting-specific and symptom-specific associations between SHS exposure and depressive symptoms. Future longitudinal studies are needed to establish the causal relationship and the biological mechanisms of the impact of SHS exposure.


The FITNET-NHS Trial is a UK, national, trial investigating whether an online cognitive behavioural therapy program (FITNET-NHS) for treating chronic fatigue syndrome/ME in adolescents is clinically effective and cost-effective in the NHS. At the time of writing (September 2019), the trial was recruiting participants. This article presents an update to the planned sample size and data collection duration previously published within the trial protocol.18020851. Registered 8 April 2016.ISRCTN, ID:  Recruitment projections were estimated in advance and were based on reaching national paediatric CFS/ME populations at high volumes – made possible due to our innovative methodology using entirely remote processes for recruitment and treatment delivery. The original sample size target if achieved would have given us sufficient statistical power to detect a true effect of FITNET-NHS within the subgroup of participants with co-morbid mood disorder.The FITNET-NHS Trial is a United Kingdom (UK), national ,randomised controlled trial testing the acceptability, effectiveness and cost-effectiveness of an online cognitive behavioural therapy (CBT) programme, FITNET-NHS , designed to treat adolescents with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) . In the The current article is an amendment to our previously published protocol for the FITNET-NHS Trial: Baos et al. 2018) [8 [1].n = 734) without a substantial extension to the recruitment period. The original sample size was selected to provide 80% power to detect a 0.4-standard deviation (SD) difference at 5% significance with 10% attrition on the primary outcome in a subgroup of participants (estimated to be 30% or n = 220) with co-morbid mood disorders of anxiety and depression. For all participants (with or without co-morbid mood disorders), the original sample size provided 97% power at 1% significance to detect a 0.35-SD difference on the primary outcome  score) at 6 months.Recruitment into the FITNET-NHS Trial began on 1 November 2016. In mid-2018, it became apparent that the recruitment rate would not allow us to achieve our original sample size target  and consulted with the Trial Management Group , the Data Safety Monitoring Committee  and the Trial Steering Committee .In September 2018, we calculated the required sample size for the primary outcome in all participants (with or without co-morbid mood disorders):Data on 266 children will give us 90% power at 5% significance to detect a 0.4-SD difference on the SF-36-PFS. With attrition currently at approximately 15%, we will need to recruit 314 children. This is achievable (based on recruitment rates to date) by the end of October 2020.We considered the issue of co-morbid disorders. On 2 October 2018, we investigated the rate of co-morbid mood disorders in FITNET-NHS participants at baseline. This was higher than our original estimates as the rate of co-morbid mood disorders was 40% . With the revised sample size target of 314 there will be approximately 106 participants with co-morbid mood disorders (53 in each treatment group). This will give 53% power at 5% significance to detect a 0.4-SD difference on the SF-36-PFS between treatment groups within this co-morbid subgroup.From consultation with the NIHR HTA, TSC, DSMC and TMG, the decision was made to agree revised recruitment targets and extend the recruitment time by 6 months to enable the FITNET-NHS Trial to achieve the primary aim of testing the effectiveness (and cost-effectiveness) of the FITNET-NHS treatment compared to Activity Management. The NIHR HTA gave their approval in principle of this change on 30 January 2019, subsequent to provision of documentation. Full NIHR HTA approval for revised recruitment target and the contract variation to include the 6-month extension to the project timeline was received on 24 April 2019. We will, therefore, recruit 314 children and recruitment will finish on 31 October 2020. Follow up will finish on 31 October 2021.We have followed standard procedures to update all relevant organisations regarding these changes, including trial registration , and submitting as a substantial trial amendment to the Research Ethics Committee  and the Health Research Authority .With our revised recruitment target of 314 participants, the FITNET-NHS is still set to be the largest paediatric CFS/ME treatment trial in the UK and globally, the results of which will should the future of paediatric CFS/ME treatment delivery.


Physical activity (PA) has long been an underutilized point of intervention in cardiovascular disease (CVD) prevention, with an estimated 10% of premature mortality and $117 billion healthcare dollars annually linked to inadequate PA . The recA leading feature of the 2018 PA guidelines is the recommendation that adults, including older adults, spend at least 150-300 minutes per week on moderate-intensity aerobic exercise, or 75-150 minutes of vigorous exercise, as defined as 3-5.9 metabolic equivalents (METs) or ≥6 METs, respectively . The ACCUnderstanding that even limited amounts of PA can still be beneficial is of particular importance in the aging population, where comorbidities may make 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity PA more difficult, or even unsafe, to achieve. The guidelines highlight this point specifically, suggesting that those unable to perform 150 minutes of moderate-intensity activity should be as physically active as any limiting medical conditions allow . A furthFurthermore, there does not appear to be an upper bound on the CVD benefits derived from PA in those able to exceed PA recommendations. As cited in the guidelines, there is strong evidence of an inverse dose-response relationship between moderate and/or vigorous PA and CVD mortality ,4. ThereAn area specifically highlighted by the guidelines with respect to older patients is the importance of multi-component activity that includes aerobic in addition to balance and weight-training exercise . This haThe benefits of PA on CVD risk and mortality are clear and growing. In older adults aged ≥70, higher fitness was associated with improved survival; whereas there was no association of traditional risk factors and survival in this older cohort . While m


The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R Monitoring crop growth and predicting yield are essential for proper crop management, agricultural operation improvement, and food-security policy decision making –2. Crop Crop growth models have been developed to simulate crop growth and development, and physiological processes according to environment components at the canopy scale since the mid-1960s –11, and Remote sensing data provide information related to crop growth status –14, and These constraints inherent in crop growth modeling and remote sensing can be overcome by integrating remote sensing data into a crop growth model –20; “forThe objectives of this study were to develop a simple strategy for assimilating MODIS data into a crop growth model using minimum inputs and to evaluate the regional crop yield prediction performance of a simple strategy in a major corn production region, Illinois, USA. In addition, this study focused on examining the possibility of early crop yield predictions using a simple strategy before harvest.Illinois , USA, waCorn yields from 2000 to 2013 in Illinois were obtained from the National Agricultural Statistics Service (NASS) by AD and state to evaluate the reliability of assimilation strategies for predicting regional corn yields. Planted and harvested area in acres and production in bushels were available at the national, state, and county levels. Corn yields, which are measured in bushel per acre in Illinois, were converted to kilogram per hectare.Corn phenology data in Illinois, which were provided weekly by AD and state, were obtained from the NASS-Illinois Field Office (IFO), and the phenology data were used to estimate corn planting date using crop phenology prediction model . Those dThe median DOY on which a given planting stage reached 50% was calculated using linear interpolation because the planting data by AD were surveyed as the planted proportion by week, and the calculated median DOY parameter was used for comparisons with the estimated dates on which a certain phenological stage occurred in the AD.Weather and soil data were generated and obtained to use as input data for the crop growth model.-2 day-1), maximum and minimum temperature (°C), and rainfall (mm) at 10-km spatial resolution from 2000 to 2013 in Illinois.To examine the possibility of early crop yield predictions, weather data were hind-casted using the Pusan National University coupled general circulation model (PNU CGCM) model and downscaled using the dynamic downscaling method , and inchttp://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx) operated by the United States Department of Agriculture, Natural Resources Conservation Service of the USA and the data were produced by the National Cooperative Soil Survey. Representative soils by county were selected based on the map unit symbol, which accounts for the largest area of the county and data of representative soil related to chemical and physical properties were obtained. The soil data were processed using Sbuild program within DSSAT 4.6 for subsequent use in the crop growth model. Variables related to soil water contents , which are dependent on physical soil properties, were calculated by soil layer using Sbuild. Soil organic carbon (OC) which is an input variable of DSSAT 4.6, was calculated with soil organic matter (OM) obtained from Web Soil Survey using the following equation [Soil data were obtained from Web Soil Survey (equation :SoilOC(https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php). Crop cover data in Illinois were obtained from 2000 to 2013. The projection of crop cover data was converted to a Universal Transverse Mercator (UTM) projection and World Geodetic System (WGS)-84 coordinates at 1-km spatial resolution using ENVI .Corn crop cover data  were obthttp://modis.gsfc.nasa.gov/). The h10v04, h10v05, h11v04, and h11v05 tiled grid data for Illinois were collected from DOY 89 to 329.The MODIS surface reflectance data  from 2000 to 2013 were obtained from Reverb operated by the National Aeronautics and Space Administration  and red (band 1) band of MOD09A1 were converted to estimate LAI for corn growing area. First, ban1 and 2 of all tiled data were mosaicked into a single dataset using interface description language . The mosaicked data were converted to UTM projection at 1-km spatial resolution and WGS-84 geographic latitude and longitude coordinates using IDL, which applies the triangulation wrap and nearest neighbor resampling methods. The converted data were resized to fit the size and georeference of crop cover data by year using FWTools, which is a collection of open-source GIS applications. Lastly, the resized data were extracted by corn grid of crop cover data using MATLAB .LAI was calculated from MOD09A1 of red and NIR bands according to the equations suggested by Nguy-Robertson et al. .LAI={(Nere NDVI  and SR [ere NDVI  represenThe CERES-Maize model  in DSSATRS) and water stress factors (i.e. TURFACest and SWFACest) estimated using the MODIS-derived LAD-logistic function for each grid into the CERES-Maize model.Two assimilation strategies were employed for predicting regional corn yields. The first assimilation strategy was to employ planting date and maturity group estimated using MODIS-derived LAD-logistic function for each grid into the CERES-Maize model. The second assimilation strategy was to further employ daily LAI  due to the difference of grid size in LAI derived from MODIS, soil, and weather data.Planting date and maturity group estimated using MODIS-derived LAD-logistic function for each grid were assimilated into the CERES-Maize model to predict corn yield for each grid, as shown in The planting date was estimated using remote sensing data, and the CERES-Maize model coded in Fortran was modified to estimate maturity group. CERES-Maize model was simulated as much as the number of cultivar coefficients to estimate maturity group for a given grid under two water supply conditions , maximum LAI values simulated with each cultivar coefficient compared to the maximum LAI value estimated using remote sensing data, and the cultivar coefficient that had the smallest difference was designated as the maturity group for a given grid. Corn yields with the assimilation of estimated planting date and maturity group by grid were simulated under the rain-fed and auto-irrigation conditions, and the corn yields by grid were aggregated to the AD and state levels.Daily LAI and water stress factors estimated using the MODIS-derived LAD-logistic function for each grid were additionally assimilated into the CERES-Maize model to predict corn yields by a grid, as shown in The estimated planting date and maturity group were used for the CERES-Maize model simulation and the model coded in Fortran was modified to estimate daily water stress factors. The water stress factors on a given day were estimated using growth rate estimated from MODIS-derived LAD-logistic function to that simulated by the CERES-Maize model on a given day, and all daily LAI and water stress factors estimated before a given day were used again as input of CERES-Maize model to estimate water stress factors on a given day. The CERES-Maize model was simulated as much as the number of a growth period to estimate daily water stress factors during a growth period because CERES-Maize model is a daily time-step simulation model. The corn yields by grid were aggregated to the AD and state levels.1 and b2) of the LAD-logistic equation as predictor variables. The data of phonological stage (D) was estimated as follows:1 and b2 represent the rate of LAI growth and the date when the LAI value reaches the maximum, respectively. The τ and ρ values for planting date by the end of the DOY (EOD) were estimated with calibration datasets of Ban et al. [The planting date was estimated via the crop phenology prediction model  using a n et al.  establisn et al.  using thEOD denotes the last date of remote sensing data products used to fit the logistic function and sets to verify the possibility of early crop yield predictions using a simple strategy before harvest. EODs 209, 257, and 321 were selected to evaluate corn yield predictions, and EODs 209, 257, and 321 are near the usual corn flowering DOY in Illinois, the earliest DOY when the LAD-logistic function could be established reliably, and the date on which the corn harvest was completed, respectively. RMSE and normalized RMSE (NRMSE) for validation datasets of 209, 257, and 321 EOD were 7.5 days and 6.1%, 6.9 days and 5.6%, and 7.3 days and 5.9%, respectively.1, b2, and b3) of the LAD-logistic equation were used.1, b2, and b3 represent the rate of LAI growth, the date with maximum LAI, and the cumulative LAI at physiological maturity, respectively, and d indicates a given date. Daily LFWT was calculated using LFWT computation equation of CERES-Maize model [-2).Daily LAI was estimated by differentiating the LAD-logistic equation  in termsze model –45.LFWTThe CERES-Maize model was simulated to estimate corn maturity group by grid under the rain-fed and auto-irrigation simulations. Corn maturity has been classified by the number of days from planting to harvest and is mainly determined by GDD which is accumulated from planting to maturity . Illinoi2)-LAD(b2-1) in The cultivar coefficients for five generic corn hybrids, identified as PC0001–PC0005 according to GDD and included in DSSAT 4.6 , were usThe most crucial limitation for crop model-based crop yield prediction in regions where rain-fed and irrigated areas are mixed, as in Illinois, is to assess water stress as a critical factor for crop growth and yield. Leaf growth is very sensitive to inhibition by water stress , and lea2 plant-1), SENLA is normal leaf senescence today (cm2 plant-1), and PLTPOP is plant population (plants m-2). The PLA was differently computed by ISTAGELAI in CERES-Maize model –45 is co2 plant-1), PLAG is leaf area growth rate (cm2 plant-1 day-1), and is computed as follows:In ISTAGE 1 and 2, PLA is computed as follows:-1), and GROLF is leaf weight growth rate (g plant-1 day-1). GROLF is computed as follows:2 plant-1)). SENLA is computed as follows:2 day-1). PLAS computed as follows:2 plant-1), SENLA is normal leaf senescence today (cm2 plant-1), SLFW is leaf senescence factor due to water stress, SLFC is leaf senescence factor due to competition for light, SLFT is leaf senescence factor due to temperature, SLFN is leaf senescence factor due to nitrogen stress, and SLFP is leaf senescence factor due to phosphorus stress. This paper assumed that plant is only affected by water stress, and SLFW related to water stress is computed as follows:-1), and SWFAC is soil water stress effect on growth. FSLFW is set to 0.05 in CERES-Maize model. Thus, SWFAC can be estimated with daily leaf area senescence rate. If one water stress factor can be estimated by each ISTAGE, the other water stress factor can be also estimated. TURFAC was computed as follows:-1), TRWUP is total root water uptake (mm day-1), and EP1 is potential plant transpiration (mm day-1). SWFAC was computed as follows:In ISTAGE 3, PLA is computed as follows:SWFAC was also calculated as follows using Eqs In CERES-Maize model, RWUEP1 in TURFAC equation is set to 1.5. Therefore, if one water stress factor can be estimated, the other water stress factor can be also estimated. The water stress factors below 0.0 were set to 0.0, and above 1.0 were set to 1.0.2 m-2), RS is estimated value derived from MODIS data, sim is simulated value of CERES-Maize model, d is current day, and d-1 is previous day.In ISTAGE 1 and 2, TURFAC is estimated as follows:-1).In ISTAGE 3, TURFAC is estimated as follows:In ISTAGE 1–3, SWFAC is estimated as follows:In ISTAGE 4–6, SWFAC and TURFAC are estimated as follows:-1) is computed as follows:-1), and EARS is ears per m2. GRNWT is computed as follows:-1). GROGRN is computed as follows:-1) as affected by temperature on relative grain filling rate, GPP is grain number per plant (grains plant-1), and G3 is potential kernel growth rate (mg kernel-1 day-1). Daily growth of the grain  is affected by SWFAC during grain filling period. Finally, corn yield is affected by SWFAC.Water stress factors were calculated from 5 days after planting to 5 days before harvest and these days were considered the emergence date and physiological maturity date, respectively. The daily water stress factors and LAI estimated using the MODIS-derived LAD-logistic function were integrated into the CERES-Maize model for predicting corn growth and yield. In CERES-Maize model, the corn yield  . Althoug2 value of 0.78 and RMSE of 0.75 t ha-1 for the state corn yield prediction at EOD 321. This result would be ascribable to the followings: Though the CERES-Maize model calculates the actual growth rates by multiplying the potential growth rate and the water stress factors ranging from 0.0 to 1.0 together [In this study, a simple data assimilation strategy was developed to improve regional corn yield prediction performance by integrating information on crop management and growth derived from MODIS data into the CERES-Maize model using minimum inputs. This method does not need to estimate the initial conditions and/or parameters of CERES-Maize model. Only planting date, maturity group, daily LAI, and water stress factors, which were estimated using a MODIS-derived LAD-logistic function, were assimilated into the CERES-Maize model to improve accuracy for predicting corn yield. The corn yield simulation at the AD and state levels using only the estimated planting date and maturity group showed under estimation under the rain-fed condition, whereas overestimation was observed under the auto-irrigation condition Figs . This retogether –44, the together  reported-1. Ines et al. [-1 using EnKF to assimilate soil moisture and/or MODIS-LAI into the CERES-Maize model, and Fang et al. [-1 using the Markov model. Although the region and scale in the current study differed from those in previous studies, the RMSE value of the predicted corn yields achieved by additional assimilation was smaller than the RMSE values reported in the previous studies. In addition, the RMSE value at a fairly early stage of EOD 257 was 0.88 t ha-1, being similar to those for the previous studies. The first previous study used a recursive process that calculated the posterior probability density function using EnKF, and the second previous study also used a repetitive process that adjusted the environmental conditions and parameters of the crop growth model by minimizing the difference between remote sensing-derived values and simulated values in the crop growth model. For example, Fang et al. [The RMSE value at EOD 321 for the state-level corn yields predicted with daily LAI and water stress factors estimated using the MODIS-derived LAD-logistic function was 0.75 t has et al.  used preg et al.  predicteg et al.  estimateBy assimilating daily LAI and water stress factors, the predicted yearly trend in the state corn yields was very close to the reported trend of yearly corn yields. However, corn yields were predicted to be much higher than the reported corn yield in 2002 and 2010 , indicatA simple approach to predict regional corn yield was developed by assimilating MODIS product data into the CERES-Maize model using minimum inputs. This method requires only a repetitive process to estimate water stress factors of the CERES-Maize model. Minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data were used to simulate corn growth and yield using CERES-Maize model. Planting date, corn maturity group, daily LAI, and daily water stress factors estimated using the MODIS-derived LAD-logistic function were directly assimilated into the CERES-Maize model to predict regional corn yield in Illinois, USA. The corn yield predictions using only estimated planting date and maturity group performed very poorly under the rain-fed condition at both the AD and state levels, whereas corn yield prediction performance improved by simulation under the auto-irrigation condition. Moreover, adding the daily LAI and water stress factors into the MODIS-derived LAD-logistic function further improved corn yield prediction performance under the both rain-fed and auto-irrigation conditions. However, the simulation by further assimilating daily LAI and water stress factors under the auto-irrigation condition showed the best performance. In addition, earlier corn yield prediction at DOY 257 was possible without degrading accuracy. This simple approach was successful for predicting regional corn yield with considerable accuracy and precision in Illinois, USA. However, this method needs to be examined in regions with more diverse agro-climatic and agro-technology conditions.


Which generic and sepsis-specific patient characteristics, known during index critical care admission for sepsis, are independently associated with long-term mortality in sepsis survivors?In this cohort study of 94 748 adult sepsis survivors, age, male sex, 1 or more severe comorbidities, prehospitalization dependency, nonsurgical status, acute severity of illness, site of infection, and organ dysfunction were independently associated with long-term mortality.Generic and sepsis-specific risk factors, known during index critical care admission for sepsis, could be used to identify a higher-risk sepsis survivor population for targeted strategies aimed at reducing the excess risk of long-term mortality. This cohort study investigates the generic and sepsis-specific patient characteristics that are associated with long-term mortality in patients who survive hospitalization for sepsis. Sepsis survivors, defined as adult patients who survived to hospital discharge following a critical care unit admission for sepsis, are at increased risk of long-term mortality. Identifying factors independently associated with long-term mortality, known during critical care admission for sepsis, could inform targeted strategies to reduce this risk.To assess, in adult sepsis survivors, factors independently associated with long-term mortality, known during their index critical care admission for sepsis, meeting Third International Consensus Definitions for Sepsis and Septic Shock criteria.This cohort study included a nationally representative sample of 94 748 adult sepsis survivors from 192 critical care units in England. Participants were identified from consecutive critical care admissions between April 1, 2009, and March 31, 2014, with survival status ascertained as of March 31, 2015. Statistical analyses were completed in June 2017.Generic patient characteristics  and sepsis-specific patient characteristics  known during index critical care admission for sepsis.Long-term mortality in adult sepsis survivors with maximum follow-up of 6 years. Adjusted hazard ratios (HRs) were estimated using Cox regression for both generic and sepsis-specific patient characteristics.Sepsis survivors had a mean (SD) age of 61.3 (17.0) years, 43 584 (46.0%) were female, and 86 056 (90.8%) were white. A total of 46.3% had respiratory site of infection. By 1 year from hospital discharge, 15% of sepsis survivors had died, with 6% to 8% dying per year over the subsequent 5 years. Age, sex, race/ethnicity, severe comorbidities, dependency, nonsurgical status, and site of infection were independently associated with long-term mortality. Compared with single-organ dysfunction, having 2 or 3 organ dysfunctions was associated with increased risk of long-term mortality , while having 4 organ dysfunctions or more was not associated with increased risk. Unexpectedly, the Acute Physiology and Chronic Health Evaluation acute physiology component score had an incremental association with long-term mortality . The adjusted HR for septic shock was 0.89 .This study suggests that generic and sepsis-specific risk factors, known during index critical care admission for sepsis, could identify a high-risk sepsis survivor population for biological characterization and designing interventions to reduce long-term mortality. The ongoing health care needs of adult sepsis survivors are an emerging global health care challenge.5Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection.9 A systematic review6 that included 59 studies and explored the association between sepsis and long-term mortality indicated that, on average, 16% of sepsis survivors die in the first year following their index hospitalization for sepsis. There are major variations in the sepsis case definition and in the risk factors assessed for this long-term mortality between studies. When studying risk factors for long-term mortality in sepsis survivors, the factors associated with short-term mortality may overwhelm and disguise factors associated with long-term mortality—as observed in many sepsis epidemiology studies included in the recent systematic review.10 The reason for this is that an increase in sepsis severity may worsen cumulative long-term mortality by increasing hospital mortality. This competing risk of hospital mortality on long-term mortality may hide the impact of acute illness characteristics, unless we study a sepsis survivor cohort. Therefore, the following should be considered in the selection of factors to study: (1) potential factors associated with mortality should include both the generic and sepsis-specific factors that resulted in the critical care admission for sepsis11; (2) generic factors should relate to the baseline risk of death irrespective of sepsis ; and (3) sepsis-specific factors should reflect the severity of sepsis. In this context, inferences are not biased when studying a sepsis survivor population (survivorship bias), as the exposure of interest is conditional on surviving to hospital discharge following a critical care admission for sepsis. If long-term mortality risk after sepsis is associated with acute illness characteristics that are known when a sepsis survivor leaves the hospital, those risk factors could be used to identify a target sepsis survivor population for follow-up care, biological characterization, and designing interventions.10It is also well recognized that sepsis survivors have a greater risk of long-term mortality when compared with patients hospitalized for nonsepsis reasons and the general population.3 criteria who survived to hospital discharge in England between April 1, 2009, and March 31, 2014.In this context, we assessed which of the generic  and sepsis-specific  patient characteristics, known during index critical care admission for sepsis, were independently associated with long-term mortality in a sepsis survivor population. To address our research aim, we identified a nationally representative cohort of sepsis survivors from an inception cohort of adults with a critical care admission for sepsis according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)STROBE) guidelines.12 We used data from the Intensive Care National Audit & Research Centre (ICNARC) Case Mix Programme, which is the national clinical audit covering all adult general critical care units in England. For consecutive critical care admissions, trained data collectors collect sociodemographic, comorbidity, and physiological data to precise rules and definitions for the patient’s first 24 hours following admission to critical care. Diagnostic data are determined clinically and coded using a 5-tier, hierarchical ICNARC coding method  and secondary  reasons for critical care admission.3 As baseline organ dysfunction at index critical care admission was unknown for our study cohort, we assumed a baseline organ dysfunction of 0, as recommended by the Sepsis-3 consensus definitions.1 We have published this operationalization,3 which included sensitivity analyses to confirm results by excluding patients with any preexisting severe comorbidity , as per Sepsis-3 criteria.10 Generic patient characteristics included were sepsis discharge year, age, sex, ethnicity, severe comorbidities , prehospitalization dependency, surgical status, and acute severity of illness defined using the APACHE II acute physiology component score.17 The prehospitalization dependency is a subjective assessment of whether the participant receives no, some, or total assistance to complete daily activities such as bathing, dressing, going to the toilet, moving in and out of a bed or chair, continence, and eating prior to critical care admission. Sepsis-specific patient characteristics included were site of infection, number of organ dysfunctions defined as Sequential Organ Failure Assessment score of 1 or greater per organ, and septic shock status.The primary outcome was time to death from any cause during follow-up. Generic and sepsis-specific patient characteristics, known during index critical care admission for sepsis, were considered as risk factors, informed by our systematic reviews.18 We gave equal weights to different comorbidities for interpretability, accounted for clustering by intensive care units using robust standard errors, did not perform any variable selection approaches, and used the Breslow method for handling ties, which considers each event at a given time as distinct and allows all failed subjects to contribute fully to the risk set .First, we generated cumulative time-to-mortality plots for all generic and sepsis-specific patient characteristics and tested equality of mortality functions using a Peto-Peto-Prentice log-rank test. The Peto-Peto-Prentice log-rank test was specifically chosen because it is not affected by differences in censoring patterns across different strata. We then assessed which generic and sepsis-specific patient characteristics were independently associated with long-term mortality in adult sepsis survivors using adjusted hazard ratios (HRs) from proportional hazards (Cox) regression modeling. The categorical variables were sex, race/ethnicity, severe comorbidity, surgical status, preadmission dependency, site of infection, number of organ dysfunctions, and septic shock status. Continuous variables were entry date , age (in decades), APACHE II acute physiology component score (in 5-point increments), and index hospital length of stay. A priori, we included an interaction term between the APACHE II acute physiology component score (in 5-point increments) and severe comorbidities. We also estimated time-varying coefficients for age (in decades), APACHE II acute physiology component score (in 5-point increments), and severe comorbidities using an interaction between the covariate and follow-up time that allowed the strength of association between a risk factor and the event of interest to either increase or decrease during the course of follow-up. For all Cox regression models, we tested for evidence against the proportional hazard assumption on the basis of Schoenfeld residuals after fitting a model with Cox regression.19 Fifth, to provide direct comparison with recently published cohort studies,9 we estimated the excess long-term mortality in sepsis survivors compared with age-, sex-, and index year–matched expected probabilities of death20 in the general population of England between 2009 and 201421 using the relative survival framework with Ederer II estimation.We performed 5 separate sensitivity analyses. First, to account for overlap in variables that contribute toward the APACHE II acute physiology component score and severe comorbidity , we performed a sensitivity analysis replicating the Cox regression model in patients without severe comorbidity. Second, to ascertain whether changes observed were influenced by coverage , we repeated all the analyses on a subpopulation of 117 critical care units that contributed data to the Case Mix Programme for the entire study period. Third, to ascertain whether the primary analyses hold true, as in a 5-year cohort study the adjusted HR could change, we report the adjusted HR overlapping time intervals 0 to 1, 0 to 2, 0 to 3, 0 to 4, and 0 to 5 years following hospital discharge. Fourth, to ascertain whether changes observed were influenced by an unobserved random factor between critical care units that modifies the hazard function of an individual or a group or cluster of individuals, we report a mixed-effects Cox model (shared frailty model).P values are 2-sided, with P values less than .05 considered to represent statistically significant results. Continuous data that were normally distributed were summarized as mean and standard deviation. Data that were not normally distributed were summarized as median and interquartile range (IQR). Categorical data were presented as frequency and percentage. Data completeness was extremely good; therefore, a complete case analysis was used, with less than 0.5% of patients excluded from the final model owing to missing data on risk factors. Statistical analyses were completed in June 2017. All analyses were performed using Stata/SE statistical software version 14.0 (StataCorp LP).Reported Over the 5-year study period , there were 152 383 index critical care admissions for sepsis and 98 732 sepsis survivors. After exclusions for age younger than 16 years (976 patients) and those sepsis survivors who were discharged from the hospital after March 31, 2014 (3008 patients), the sepsis survivor study cohort included 94 748 patients .Overall, these sepsis survivors had a mean (SD) age of 61.3 (17.0) years, 43 584 (46.0%) were female, and 86 056 (90.8%) were white. Sepsis survivors more often had nonsurgical admissions (69.3%). The most common site of infection was respiratory (46.3%), the critical care admission day mean (SD) total APACHE II score was 16.7 (5.9) points, and the median (IQR) critical care length of stay was 4 (2-8) days. Further generic and sepsis-specific patient characteristics are reported in the P < .001). Increasing score on the APACHE II acute physiology component was significantly associated with increased risk of long-term mortality . Patients with surgical status had a lower risk of long-term mortality than those with nonsurgical  status  status .P < .001) for long-term mortality in sepsis survivors , while having 4 or more organ dysfunctions was not. Septic shock at index admission had an adjusted HR of 0.89  in sepsis survivors without severe comorbidities , (2) from 117 critical care units that contributed data over the entire study period , (3) for overlapping survival-time intervals, and (4) for random variation with shared frailty model   status increased the risk of long-term mortality. The risk of long-term mortality differed by site of infection at index critical care admission for sepsis. Novel findings were the incremental increase in risk of long-term death in sepsis survivors associated with increased APACHE II acute physiology component score , lower risk associated with septic shock , and the variation in additional hazard for long-term death associated with number of organ dysfunctions. The greater number of in-hospital deaths in patients with septic shock and in patients with greater organ dysfunction is the most likely explanation for these results,11 and biological22 differences within sepsis cohorts, reinforces the value of how routinely available clinical information on index critical care admission for sepsis could inform follow-up care of sepsis survivors. This reasoning is strengthened when noting the differences in the impact of site of infection on long-term mortality we observed, as site of infection potentially influences immune responses.23 A related argument in this context is that even in 2 patients with similar predicted risk but with differences in relative contributions of generic and sepsis-specific risk components, differences would exist in treatability of long-term sequalae. Thus, when these factors independently associated with long-term mortality from our results are considered with those from the literature,25 it indicates that sepsis survivors both retain risk from index critical care admission for sepsis but also accumulate additional risk following hospital discharge from their worsening preexisting comorbidities26 and/or new comorbidities , both of which are associated with increased risk of long-term mortality. Thus, our study makes a case for understanding how management following discharge could alter sepsis survivors’ risk of long-term adverse outcomes.26One of the key reasons why we studied a sepsis survivor population was to unmask the important risk factors associated with long-term mortality, which are often disguised by the risk factors associated with short-term mortality, such as acute illness severity. The incremental increase in hazard seen with increasing APACHE II acute physiology component score persisted in the sensitivity analysis, excluding patients with chronic severe comorbidity. This finding, when considered in the context of clinical14 We reduced selection bias by studying consecutive critical care admissions for sepsis from 192 critical care units representing the whole critical care population in a country over a 6-year period and commencing patient follow-up from hospital discharge with 96% data linkage for long-term mortality. Long-term mortality from a single health care system is often challenged on external validity owing to differences in clinical care.28 We have maximized the external validity of our study by using a valid Sepsis-3 sepsis case definition in a nationally representative adult sepsis survivor cohort older than 16 years. These are important strengths, as, of the 2 recent long-term sepsis outcome studies, 1 focused on older adults (≥65 years) with insurance coverage admitted between 2002 and 2010,8 and the second reported sepsis cases between 2000 and 2002 from a national health insurance database.7We used the Case Mix Programme database, which is a national database for critical care units in England and has been independently assessed to be of high quality.10 in the association between sepsis and long-term mortality based on systematic review of literature.6 Our study cohort reports the epidemiology of adult sepsis survivors, who all had critical care admissions with sepsis identified in the first 24 hours of admission. Thus, we will underestimate patients with nonsepsis critical care admissions who develop sepsis later in their critical care stay. However, as England has among the lowest per capita critical care bed provision in Europe (3.5-7.4 per 100 000 population),29 probability of underestimation is low, as organ dysfunction will often be present on admission day. We were not able to study the biological mechanisms that could explain our findings. Information bias due to misclassification of sepsis exposure or long-term mortality is a risk that we reduced by deriving sepsis cases using raw physiology and long-term mortality from a high-quality national data source. As our goal was to identify risk factors, we have only reported age- and sex- matched general population controls for comparison with those studies that quantify excess long-term risk of death from sepsis.9 A marker of physiological disturbance closer to discharge would be potentially useful, with the counterpoints that the median critical care length of stay was 4 days and if there was significant physiological disturbance, patients would not be discharged from the hospital. We do not present a risk score to implement at bedside, as this was not our research question. Although we did not estimate a formal sample size calculation, we do report the long-term mortality from the largest adult sepsis survivor cohort to date.This study has limitations. As seen in all follow-up studies, our cohort had different durations of follow-up. We used proportional hazards modeling to account for differences in follow-up duration and adjusted for common confounders31 and early appropriate clinical management.34 Immune abnormalities seen in patients with sepsis31 appear to persist in sepsis survivors.36 New or relapsed infection is one of the common reasons for rehospitalization in sepsis survivors, which is potentially associated with site of infection at index sepsis admission.37 Thus, observational studies identifying biological characteristics and value of parsimonious clinical prediction tools using generic and sepsis-specific risk factors to predict risk of long-term adverse health in sepsis survivors would be valuable. The future interventional trials may include clinical practice interventions such as extended follow-up care either by primary care physicians5 or with targeted follow-up clinics38 or immunological interventions informed by acute sepsis illness biology.40 Our study adds further impetus to the recently adopted World Health Organization resolution calling for further work informing “policy decisions related to preventive, diagnostic and treatment activities and access to relevant health care for sepsis survivors.”41Our observations that acute illness severity and site of infection at index admission are associated with increased risk of long-term mortality in sepsis survivors has biological plausibility and informs future research. Acute illness severity and illness trajectory seen with critical care admissions for sepsis are associated with health before the illness causing hospitalization, immune responses in sepsis,Generic and sepsis-specific risk factors, known during index critical care admission for sepsis, are important risk factors associated with long-term mortality seen in a sepsis survivor cohort. Our research provides validity to target sepsis survivor populations based on index admission characteristics, for biological characterization and designing interventions to reduce long-term mortality.


This systematic literature review identified a lack of studies comparing the use of RECIST (with CE-CT) and PERCIST (with FDG-PET/CT) for response evaluation in metastatic breast cancer. The available sparse literature suggests that PERCIST might be more appropriate than RECIST for predicting prognosis in patients with MBC.18F-fluorodeoxyglucose positron emission tomography with integrated computed tomography (FDG-PET/CT) and contrast-enhanced computed tomography (CT) can be used for response evaluation in metastatic breast cancer (MBC). In this study, we aimed to review literature comparing the PET Response Criteria in Solid Tumors (PERCIST) with Response Evaluation Criteria in Solid Tumors (RECIST) in patients with MBC. We made a systematic search in Embase, PubMed/Medline, and Cochrane Library using a modified PICO model. The population was MBC patients and the intervention was PERCIST or RECIST. Quality assessment was performed using the QUADAS-2 checklist. A total of 1975 articles were identified. After screening by title/abstract, 78 articles were selected for further analysis of which 2 duplicates and 33 abstracts/out of focus articles were excluded. The remaining 43 articles provided useful information, but only one met the inclusion and none of the exclusion criteria. This was a retrospective study of 65 patients with MBC showing one-year progression-free survival for responders versus non-responders to be 59% vs. 27% ( Breast cancer is the second most common cancer in the world, with an estimated 1.7 million new cases diagnosed in 2012 ,4. Not aVarious imaging methods and standardized criteria have been used over the years to monitor tumor response to treatment. Many current guidelines recommend Response Evaluation Criteria in Solid Tumors (RECIST 1.1) based on contrast-enhanced computed tomography (CE-CT)  as, for The aim of this systematic literature review was to find studies directly comparing PERCIST (using FDG-PET/CT) and RECIST (using CE-CT) for response evaluation in metastatic breast cancer.Our review was made in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement . A revieWe structured the search on a modified PICO model. PICO is a widely used framework for developing literature search strategies and is an acronym for Population, Intervention, Comparison, and Outcome.The study population consisted of patients with metastatic breast cancer. While the interventions from the patient’s perspective were FDG-PET/CT and CE-CT, we wanted to compare PERCIST ,15 and RBreast cancer ANDMetastasis ANDResponse evaluation criteria in solid tumors OR PET response criteria in solid tumorsNo limits were applied. A full search strategy is available as supplementary content  and Emtree Thesaurus (Embase) were used plus free-text searches consisting of truncated versions of every associated entry term for MeSH/Emtree in all three databases. The search was conducted on 10 October 2017 using the following terms and their derivatives:ntent in .Two authors (FH and MHH) independently screened titles and abstracts from the databases. In case of disagreement of whether the article should be included or not, full-text versions were read and consensus was reached between the two authors.Studies that focused on or included patients with MBCScans were used to evaluate response to treatmentScans were evaluated by RECIST and PERCISTIncluded patients should have at least one FDG-PET/CT and one CE-CT-scan after diagnosis of metastases and before initiation of a new treatment regimenInclusion criteria were as follows:Patients with locally advanced breast cancerRECIST evaluations by MRIExclusion criteria were as follows:Possible sources of bias in included articles were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool . Four doThe literature search initially provided 1975 articles, of which 1292 were from Embase, 360 from PubMed, and 293 from Cochrane . An addiThe 1975 articles were screened by title/abstract and 78 empirical articles were selected for further screening. Two duplicates were found and removed and 33 records were excluded as they were conference abstracts or considered out of focus for this review, e.g., they focused on other response evaluation criteria or did not address response evaluation. After full-text screening, only one article  met all We found 42 articles that presented data on FDG-PET/CT or CE-CT-scan for response evaluation. These provided useful information regarding use of metrics, ideal number of lesions to be analyzed, and shortcomings of conventional CE-CT and RECIST, and we refer to them in the Introduction and Discussion sections.We assessed the quality of the study by Riedl et al.  using QUThe one comparative study identified in the initial search was a retrospective study by Riedl et al.  that incp = 0.1954) by RECIST compared to 63% and 0% (p = 0.0001) by PERCIST, where PERCIST had a higher concordance index with progression-free survival than RECIST (0.7 vs. 0.6).One-year progression-free survival for responders versus non-responders was 59% vs. 27% (p = 0.003) by RECIST compared to 58% vs. 18% (p = 0.0001) by PERCIST, where PERCIST had a higher concordance index with disease-specific survival than RECIST (0.65 vs. 0.55).Four-year disease-specific survival for responders versus non-responders was 50% vs. 38%  and maximum standardized uptake value (SUVmax) were highly correlated (r = 0.998), and response classification was the same in every case .We were surprised that our search identified only one study that directly compared the use of RECIST (with CE-CT) and PERCIST (with FDG-PET/CT) for response evaluation in patients treated for metastatic breast cancer. This suggests that PERCIST based on FDG-PET/CT is still a relatively new tool in MBC, but it may also reflect the general lack of randomized controlled trials with PET included as a diagnostic arm. Siepe et al.  concludeWe expect publications on the relative performance of the PERCIST tool to increase, however, as we found many more studies describing FDG-PET/CT as a modality for response evaluation but without using specified criteria to assess the degree of response. Several of these indicate that response evaluation with FDG-PET/CT correlates strongly with prognosis in patients with MBC ,25,26,27The RECIST criteria are well defined and documented and have a high degree of repeatability . Its draThe advantages of PERCIST based on FDG-PET/CT include a high degree of repeatability, less inter-observer variability than in measurements with CE-CT, the ability to differentiate between active cancer tissue and post-therapeutic sequelae , and theA useful aspect of the RECIST criteria for longitudinal response evaluation is the use of the nadir scanning as reference . When deTo our knowledge, Mandrekar et al.  have pubSeyal et al.  found maSeveral studies point out problems with CE-CT for evaluating response in bone metastases as metabolic response occurs earlier than morphological changes in the bone, and early response may be misclassified as progression due to osteoblastic activity ,32,33. BCE-CT and the RECIST criteria generally classify patients rather broadly, with stable disease ranging from 30% decrease to 20% increase in the largest tumor diameter , represen = 65) and therapy regimes from multiple protocols including cytotoxic, hormone, target therapies, and a combination of these. Breast cancer subtype and hormone receptor status also varied, giving a highly heterogeneous patient group [n = 36).Regarding the use of FDG-PET/CT, current literature indicates PERCIST to be a valid method for response evaluation in MBC. However, the only study that directly compared PERCIST from FDG-PET/CT and RECIST from CE-CT was a retrospective study with some limitations, such as a relatively small patient group  or RECIST  in metastatic breast cancer. As our search strategy focused on criteria rather than modality (in accordance with our study aim), we may have overlooked potentially relevant studies investigating the efficacy of FDG-PET/CT versus CE-CT, although we think this is unlikely. We chose to apply the response evaluation criteria to ensure uniformity of scan interpretation as well as to investigate the current use of these criteria in MBC. The few relevant empirical studies using response criteria make it difficult to compare the two modalities in a standardized setting, and we thus cannot draw definite conclusions regarding the aim of our study. Nevertheless, the findings in the main article identified  and in Conventional response evaluation in MBC using CE-CT and RECIST has several limitations. This suggests a need for an improved and reproducible method for response evaluation, and the literature indicates that FDG-PET/CT could fulfil this need. We recommend that future studies stay true to PERCIST guidelines, analyze the utility of the metrics SULpeak, SUVmax, and TLG, and investigate the impact of analyzing both one lesion and up to five lesions.We also recommend the use of progression-free survival as primary endpoint, as this seems to be a better surrogate measure for treatment response in MBC than overall survival, mainly because progression-free survival represents a period in which the patient benefits from current treatment, whereas overall survival is influenced by all subsequent treatment regimens, co-morbidities and other courses of disease. For long ongoing trials, overall survival should be used as a secondary endpoint when possible .The literature search also yielded some other interesting and promising methods for response evaluation ,43,44,45This systematic literature review identified a remarkable lack of published studies in the field of FDG-PET/CT and the use of PERCIST for response evaluation and prediction of prognosis in patients with metastatic breast cancer. The limited available literature suggested FDG-PET/CT to be superior to conventional CE-CT and PERCIST to be more appropriate for disease prediction than RECIST in MBC. Our study underlines the need for larger, prospective studies addressing response monitoring and disease prediction with PERCIST compared to RECIST 1.1.