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 FINDRISC in Latin America: a systematic review of diagnosis and prognosis models

2020 ◽  
Vol 8 (1) ◽  
pp. e001169 ◽  
Author(s):  
Rodrigo M Carrillo-Larco ◽  
Diego J Aparcana-Granda ◽  
Jhonatan R Mejia ◽  
Antonio Bernabé-Ortiz
Keyword(s):  
Systematic Review ◽  
Latin America ◽  
Assessment Tool ◽  
Data Extraction ◽  
External Validation ◽  
Population Based ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Receiver Operating Curve ◽  
Modelling Studies

This review aimed to assess whether the FINDRISC, a risk score for type 2 diabetes mellitus (T2DM), has been externally validated in Latin America and the Caribbean (LAC). We conducted a systematic review following the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) framework. Reports were included if they validated or re-estimated the FINDRISC in population-based samples, health facilities or administrative data. Reports were excluded if they only studied patients or at-risk individuals. The search was conducted in Medline, Embase, Global Health, Scopus and LILACS. Risk of bias was assessed with the PROBAST (Prediction model Risk of Bias ASsessment Tool) tool. From 1582 titles and abstracts, 4 (n=7502) reports were included for qualitative summary. All reports were from South America; there were slightly more women, and the mean age ranged from 29.5 to 49.7 years. Undiagnosed T2DM prevalence ranged from 2.6% to 5.1%. None of the studies conducted an independent external validation of the FINDRISC; conversely, they used the same (or very similar) predictors to fit a new model. None of the studies reported calibration metrics. The area under the receiver operating curve was consistently above 65.0%. All studies had high risk of bias. There has not been any external validation of the FINDRISC model in LAC. Selected reports re-estimated the FINDRISC, although they have several methodological limitations. There is a need for big data to develop—or improve—T2DM diagnostic and prognostic models in LAC. This could benefit T2DM screening and early diagnosis.

scholarly journals 4298 Prediction models for pulmonary tuberculosis treatment outcomes: a systematic review

2020 ◽  
Vol 4 (s1) ◽  
pp. 34-34
Author(s):  
Lauren Saag Peetluk ◽  
Felipe Ridolfi ◽  
Valeria Rolla ◽  
Timothy Sterling
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Treatment Outcomes ◽  
Outcome Prediction ◽  
Web Of Science ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Google Scholar ◽  
Bias Assessment ◽  
Tb Treatment

OBJECTIVES/GOALS: Many clinical prediction models have been developed to guide tuberculosis (TB) treatment, but their results and methods have not been formally evaluated. We aimed to identify and synthesize existing models for predicting TB treatment outcomes, including bias and applicability assessment. METHODS/STUDY POPULATION: Our review will adhere to methods that developed specifically for systematic reviews of prediction model studies. We will search PubMed, Embase, Web of Science, and Google Scholar (first 200 citations) to identify studies that internally and/or externally validate a model for TB treatment outcomes (defined as one or multiple of cure, treatment completion, death, treatment failure, relapse, default, and lost to follow-up). Study screening, data extraction, and bias assessment will be conducted independently by two reviewers with a third party to resolve discrepancies. Study quality will be assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). RESULTS/ANTICIPATED RESULTS: Our search strategy yielded 6,242 articles in PubMed, 10,585 in Embase, 10,511 in Web of Science, and 200 from Google Scholar, totaling 27,538 articles. After de-duplication, 14,029 articles remain. After screening titles, abstracts, and full-text, we will extract data from relevant studies, including publication details, study characteristics, methods, and results. Data will be summarized with narrative review and in detailed tables with descriptive statistics. We anticipate finding disparate outcome definitions, contrasting predictors across models, and high risk of bias in methods. Meta-analysis of performance measures for model validation studies will be performed if possible. DISCUSSION/SIGNIFICANCE OF IMPACT: TB outcome prediction models are important but existing ones have not been rigorously evaluated. This systematic review will synthesize TB outcome prediction models and serve as guidance to future studies that aim to use or develop TB outcome prediction models.

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

2021 ◽  
Author(s):  
Patricia Pauline M. Remalante-Rayco ◽  
Evelyn Osio-Salido
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Early Warning ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Good Prediction ◽  
Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

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 Safety and efficiency of gasless laparoscopy: a systematic review protocol

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Haitham Shoman ◽  
Simone Sandler ◽  
Alexander Peters ◽  
Ameer Farooq ◽  
Magdalen Gruendl ◽  
...  
Keyword(s):  
Systematic Review ◽  
Assessment Tool ◽  
Data Extraction ◽  
Meta Analysis ◽  
A Priori ◽  
Risk Of Bias ◽  
Conventional Laparoscopy ◽  
Eligibility Criteria ◽  
Gasless Laparoscopy ◽  
Resource Setting

Abstract Background Gasless laparoscopy, developed in the early 1990s, was a means to minimize the clinical and financial challenges of pneumoperitoneum and general anaesthesia. It has been used in a variety of procedures such as in general surgery and gynecology procedures including diagnostic laparoscopy. There has been increasing evidence of the utility of gasless laparoscopy in resource limited settings where diagnostic imaging is not available. In addition, it may help save costs for hospitals. The aim of this study is to conduct a systematic review of the available evidence surrounding the safety and efficiency of gasless laparoscopy compared to conventional laparoscopy and open techniques and to analyze the benefits that gasless laparoscopy has for low resource setting hospitals. Methods This protocol is developed by following the Preferred Reporting Items for Systematic review and Meta-Analysis–Protocols (PRISMA-P). The PRISMA statement guidelines and flowchart will be used to conduct the study itself. MEDLINE (Ovid), Embase, Web of Science, Cochrane Central, and Global Index Medicus (WHO) will be searched and the National Institutes of Health Clinical Trials database. The articles that will be found will be pooled into Covidence article manager software where all the records will be screened for eligibility and duplicates removed. A data extraction spreadsheet will be developed based on variables of interest set a priori. Reviewers will then screen all included studies based on the eligibility criteria. The GRADE tool will be used to assess the quality of the studies and the risk of bias in all the studies will be assessed using the Cochrane Risk assessment tool. The RoB II tool will assed the risk of bias in randomized control studies and the ROBINS I will be used for the non-randomized studies. Discussion This study will be a comprehensive review on all published articles found using this search strategy on the safety and efficiency of the use of gasless laparoscopy. The systematic review outcomes will include safety and efficiency of gasless laparoscopy compared to the use of conventional laparoscopy or laparotomy. Trial registration The study has been registered in PROSPERO under registration number: CRD42017078338

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 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.

scholarly journals Prognostic Models for Predicting Remission of Diabetes Following Bariatric Surgery: A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Pushpa Singh ◽  
Nicola J Adderley ◽  
Jonathan Hazlehurst ◽  
Malcolm Price ◽  
Abd A Tahrani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bariatric Surgery ◽  
Prediction Models ◽  
Data Extraction ◽  
External Validation ◽  
Model Development ◽  
Diabetes Remission ◽  
Development Studies ◽  
Cochrane Central Register

<p>Background</p> <p>Remission of type 2 diabetes following bariatric surgery is well established but identifying patients who will go into remission is challenging. </p> <p>Purpose</p> <p>To perform a systematic review of currently available diabetes remission prediction models, compare their performance, and evaluate their applicability in clinical settings.</p> <p>Data sources</p> <p>A comprehensive systematic literature search of MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE and Cochrane Central Register of Controlled Trials was undertaken. The search was restricted to studies published in the last 15 years and in the English language. </p> <p>Study selection and data extraction</p> <p>All studies developing or validating a prediction model for diabetes remission in adults after bariatric surgery were included. The search identified 4165 references of which 38 were included for data extraction. We identified 16 model development and 22 validation studies. </p> <p>Data synthesis</p> <p>Of the 16 model development studies, 11 developed scoring systems and 5 proposed logistic regression models. In model development studies, 10 models showed excellent discrimination with area under curve (AUC) ≥ 0.800. Two of these prediction models, ABCD and DiaRem, were widely externally validated in different populations, a variety of bariatric procedures, and for both short- and long-term diabetes remission. Newer prediction models showed excellent discrimination in test studies, but external validation was limited.</p> <p>Limitations and Conclusions</p> Amongst the prediction models identified, the ABCD and DiaRem models were the most widely validated and showed acceptable to excellent discrimination. More studies validating newer models and focusing on long-term diabetes remission are needed.

scholarly journals Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

2022 ◽  
Author(s):  
Ursula W. de Ruijter ◽  
Z. L. Rana Kaplan ◽  
Wichor M. Bramer ◽  
Frank Eijkenaar ◽  
Daan Nieboer ◽  
...  
Keyword(s):  
Systematic Review ◽  
Care Management ◽  
Prediction Models ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Model Performance ◽  
Case Finding ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Healthcare Use

Abstract Background In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme’s success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability. Methods Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST). Results Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601–174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients’ risk to become part of some top percentage of the cost distribution (top-1–20%) within a mean time horizon of 16 months (range 12–60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as ‘high’ in 40 studies (67%), mostly due to a ‘high’ risk of bias in the subdomain ‘Analysis’ (n = 37; 62%). Discussion This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient’s risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

scholarly journals Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e025579 ◽  
Author(s):  
Mohammad Ziaul Islam Chowdhury ◽  
Fahmida Yeasmin ◽  
Doreen M Rabi ◽  
Paul E Ronksley ◽  
Tanvir C Turin
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Data Extraction ◽  
Meta Analysis ◽  
External Validation ◽  
Predictive Performance ◽  
Eligibility Criteria ◽  
C Statistic ◽  
Increased Risk ◽  
Patients With Diabetes

ObjectiveStroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis.DesignSystematic review and meta-analysis.Data sourcesA detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models.Eligibility criteriaAll studies that developed stroke prediction models in populations with diabetes were included.Data extraction and synthesisTwo reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic.ResultsOur search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68.ConclusionsOverall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models’ predictive ability and further external validation of the existing models in diverse population to improve generalisability.

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