Predicting relapse or recurrence of depression: systematic review of prognostic models

2022 ◽  
pp. 1-11
Author(s):  
Andrew S. Moriarty ◽  
Nicholas Meader ◽  
Kym I. E. Snell ◽  
Richard D. Riley ◽  
Lewis W. Paton ◽  
...  
Keyword(s):  
High Risk ◽  
Validation Studies ◽  
Prediction Models ◽  
Assessment Tool ◽  
External Validation ◽  
Model Development ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Cochrane Library ◽  
Sustained Remission

Background Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence. Aims To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder. Method We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST). Results We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility. Conclusions Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

scholarly journals A systematic review of prediction models used in tuberculosis contact tracing

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
F Kidy ◽  
E Bruno-McClung ◽  
S Shantikumar ◽  
W Proto ◽  
O Oyebode
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
High Income ◽  
Contact Tracing ◽  
Cochrane Library ◽  
Social Risk ◽  
Low Incidence ◽  
Prognostic Prediction

Abstract Background Contact tracing forms a key part of tuberculosis (TB) control in high-income, low-incidence settings. It aims to reduce morbidity, mortality and onward transmission of TB. Contact tracing is a complex and resource intensive intervention. Risk assessment of contacts is needed to ensure appropriate allocation of resources and greatest possible impact. Current prioritisation procedures are based on expert opinion and consensus. Prognostic prediction models offer a way to synthesise evidence about this decision. Methods We searched Medline, Embase, BNI, CINAHL, HMIC, and the Cochrane Library for peer reviewed publications in English about TB contact tracing prediction models. Studies were included if there was statistical combination of predictors. No date, age or other restrictions were applied. Study selection was carried out by two independent reviewers. Data were extracted using the CHARMS checklist and studies evaluated for risk of bias using PROBAST. Results Five reports were selected from a total of 16,585 non-identical returns. Each study was carried out in demographically distinct settings (Peru, USA, France, Taiwan). The choice and definition of outcomes and predictors varied. All the models included external validation and some included internal validation. Calibration and discrimination measures were variably reported. The models were at high risk of bias due to challenges in defining TB disease and due to statistical approaches taken: there was poor reporting of sample size considerations, universal use of univariable analysis to select predictors, and dichotomisation of data. There were some concerns about applicability due to differing populations and diagnostic approaches. None of the models included social risk factors. Conclusions The use of existing models is problematic. There are constraints upon resources which means that contact tracing needs to be carried out efficiently. A robust prediction model is urgently needed to achieve this. Key messages Contact tracing for tuberculosis would benefit from more robust prioritisation tools to save resources and increase impact. Existing prognostic prediction models are at high risk of bias and there are concerns about applicability in high-income, low-incidence settings.

scholarly journals Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

2021 ◽  
Vol 10 (1) ◽  
pp. 93
Author(s):  
Mahdieh Montazeri ◽  
Ali Afraz ◽  
Mitra Montazeri ◽  
Sadegh Nejatzadeh ◽  
Fatemeh Rahimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Discriminative Performance ◽  
Internal Validation ◽  
Diagnostic Models

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool.Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation.Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed. 

P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Youssef
Keyword(s):  
Systematic Review ◽  
Supportive Care ◽  
Prediction Model ◽  
Pregnancy Outcome ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

scholarly journals Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e034564
Author(s):  
Ralph K Akyea ◽  
Jo Leonardi-Bee ◽  
Folkert W Asselbergs ◽  
Riyaz S Patel ◽  
Paul Durrington ◽  
...  
Keyword(s):  
Secondary Prevention ◽  
Cardiovascular Events ◽  
Prediction Models ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Major Adverse Cardiovascular Events ◽  
Bibliographic Databases ◽  
Analysis Model

IntroductionCardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models for stratifying disease severity risk in patients with established CVD for effective secondary prevention strategies are inadequate. Multivariable prognostic models to stratify CVD risk may allow personalised treatment interventions. This review aims to systematically review the existing multivariable prognostic models for the recurrence of CVD or major adverse cardiovascular events in adults with established CVD diagnosis.Methods and analysisBibliographic databases (Ovid MEDLINE, EMBASE, PsycINFO and Web of Science) will be searched, from database inception to April 2020, using terms relating to the clinical area and prognosis. A hand search of the reference lists of included studies will also be done to identify additional published studies. No restrictions on language of publications will be applied. Eligible studies present multivariable models (derived or validated) of adults (aged 16 years and over) with an established diagnosis of CVD, reporting at least one of the components of the primary outcome of major adverse cardiovascular events (defined as either coronary heart disease, stroke, peripheral artery disease, heart failure or CVD-related mortality). Reviewing will be done by two reviewers independently using the pre-defined criteria. Data will be extracted for included full-text articles. Risk of bias will be assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Prognostic models will be summarised narratively. If a model is tested in multiple validation studies, the predictive performance will be summarised using a random-effects meta-analysis model to account for any between-study heterogeneity.Ethics and disseminationEthics approval is not required. The results of this study will be submitted to relevant conferences for presentation and a peer-reviewed journal for publication.PROSPERO registration numberCRD42019149111.

scholarly journals Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e035045
Author(s):  
Morris Ogero ◽  
Rachel Jelagat Sarguta ◽  
Lucas Malla ◽  
Jalemba Aluvaala ◽  
Ambrose Agweyu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Low Income ◽  
Assessment Tool ◽  
Data Extraction ◽  
Model Development ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Risk Scores ◽  
Eligibility Criteria ◽  
Bias Assessment

ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.ResultsOur search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.ConclusionThis review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration numberCRD42018088599.

scholarly journals Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

2019 ◽  
Vol 4 (5) ◽  
pp. e001759 ◽  
Author(s):  
Tessa Heestermans ◽  
Beth Payne ◽  
Gbenga Ayodele Kayode ◽  
Mary Amoakoh-Coleman ◽  
Ewoud Schuit ◽  
...  
Keyword(s):  
Low Income ◽  
Perinatal Death ◽  
External Validation ◽  
Perinatal Outcomes ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Cochrane Library ◽  
Middle Income ◽  
Middle Income Countries ◽  
Adverse Pregnancy

IntroductionNinety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC.MethodsA systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised.Results1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item ‘quality of analysis’ performing the least.ConclusionPrognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged.PROSPERO registration numberCRD42017058044.

scholarly journals Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Rapid Systematic Review

2021 ◽  
Author(s):  
Jamie L. Miller ◽  
Masafumi Tada ◽  
Michihiko Goto ◽  
Nicholas Mohr ◽  
Sangil Lee
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Signs ◽  
Risk Of Bias ◽  
Low Risk ◽  
Prognostic Models ◽  
Organ Support ◽  
Public Data

ABSTRACTBackgroundThroughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.ObjectiveThis systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.MethodsSearches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and July 2020 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.ResultsA primary review found 292 articles relevant based on title and abstract. After further review, 246 were excluded based on the defined inclusion and exclusion criteria. Forty-six articles were included in the qualitative analysis. Inter observer agreement on inclusion was 0.86 (95% confidence interval: 0.79 - 0.93). When the PROBAST tool was applied, 44 of the 46 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Two studied reported prediction models, 4C Mortality Score from hospital data and QCOVID from general public data from UK, and were rated as low risk of bias and low concerns for applicability.ConclusionSeveral prognostic models are reported in the literature, but many of them had concerning risks of biases and applicability. For most of the studies, caution is needed before use, as many of them will require external validation before dissemination. However, two articles were found to have low risk of bias and low applicability can be useful tools.

scholarly journals Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e044687
Author(s):  
Lauren S. Peetluk ◽  
Felipe M. Ridolfi ◽  
Peter F. Rebeiro ◽  
Dandan Liu ◽  
Valeria C Rolla ◽  
...  
Keyword(s):  
Systematic Review ◽  
Treatment Outcomes ◽  
Prediction Models ◽  
Assessment Tool ◽  
Data Extraction ◽  
External Validation ◽  
Included Study ◽  
Risk Of Bias ◽  
Pulmonary Tb ◽  
Tb Treatment

ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.Results14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.ConclusionsTB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.Trial registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)

scholarly journals Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study

2021 ◽  
Author(s):  
Jet M. J. Vonk ◽  
Jacoba P. Greving ◽  
Vilmundur Gudnason ◽  
Lenore J. Launer ◽  
Mirjam I. Geerlings
Keyword(s):  
High Risk ◽  
General Population ◽  
Large Scale ◽  
Prediction Models ◽  
External Validation ◽  
Predictive Performance ◽  
Population Based ◽  
Cognitive Testing ◽  
Prognostic Models ◽  
Dementia Risk

AbstractWe aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76–.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67–.75) did not. Calibration ranged from good to poor across all models, including systematic risk overestimation or overestimation for particularly the highest risk group. Models that overestimate risk may be acceptable for exclusion purposes, but lack the ability to accurately identify individuals at higher dementia risk. Both updating existing models or developing new models aimed at identifying high-risk individuals, as well as more external validation studies of dementia prediction models are warranted.

scholarly journals A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Médéa Locquet ◽  
Anh Nguyet Diep ◽  
Charlotte Beaudart ◽  
Nadia Dardenne ◽  
Christian Brabant ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
High Risk ◽  
Prediction Models ◽  
Assessment Tool ◽  
Risk Of Bias ◽  
Medical Setting ◽  
Eligibility Criteria ◽  
Hospital System ◽  
Model Study

Abstract Background The COVID-19 pandemic is putting significant pressure on the hospital system. To help clinicians in the rapid triage of patients at high risk of COVID-19 while waiting for RT-PCR results, different diagnostic prediction models have been developed. Our objective is to identify, compare, and evaluate performances of prediction models for the diagnosis of COVID-19 in adult patients in a health care setting. Methods A search for relevant references has been conducted on the MEDLINE and Scopus databases. Rigorous eligibility criteria have been established (e.g., adult participants, suspicion of COVID-19, medical setting) and applied by two independent investigators to identify suitable studies at 2 different stages: (1) titles and abstracts screening and (2) full-texts screening. Risk of bias (RoB) has been assessed using the Prediction model study Risk of Bias Assessment Tool (PROBAST). Data synthesis has been presented according to a narrative report of findings. Results Out of the 2334 references identified by the literature search, 13 articles have been included in our systematic review. The studies, carried out all over the world, were performed in 2020. The included articles proposed a model developed using different methods, namely, logistic regression, score, machine learning, XGBoost. All the included models performed well to discriminate adults at high risks of presenting COVID-19 (all area under the ROC curve (AUROC) > 0.500). The best AUROC was observed for the model of Kurstjens et al (AUROC = 0.940 (0.910–0.960), which was also the model that achieved the highest sensitivity (98%). RoB was evaluated as low in general. Conclusion Thirteen models have been developed since the start of the pandemic in order to diagnose COVID-19 in suspected patients from health care centers. All these models are effective, to varying degrees, in identifying whether patients were at high risk of having COVID-19.

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