Information for data extraction and subsequent summary and appraisal
| Domain | Key items |
| Source of data | Source of data (eg, cohort, case–control, randomised trial participants, registry data, etc) |
| Participants | Participant eligibility and recruitment method (eg, location, number of centres, setting, inclusion and exclusion criteria) |
| Participant description (age, sex, primary VL or relapse case, comorbidities including HIV coinfection) | |
| Details of treatments received | |
| How VL diagnosis is defined (whether consistent for all participants, using serology and/or microscopy, molecular testing, clinical history and physical signs, etc) | |
| Study dates | |
| Outcome(s) to be predicted | Type of outcome (eg, single or combined endpoints) |
| Definition and method for measurement of outcome (for example, is mortality disease-specific or all-cause, is cure/initial failure/relapse diagnosed based on clinical symptoms and/or diagnostic testing) | |
| Was the same outcome definition (and method for measurement) used in all patients? | |
| Time of outcome occurrence or summary of duration of follow-up | |
| Was the outcome assessed without knowledge of the candidate predictors (ie, blinded)? | |
| Candidate predictors | Number and type of predictors (eg, demographics, patient history, physical examination, laboratory parameters, HIV status, disease characteristics, etc) |
| Definition and method for measurement of candidate predictors (including whether defined and measured in a similar way for all participants) | |
| Timing of predictor measurement (eg, at patient presentation, at diagnosis, at treatment initiation or otherwise) | |
| Handling of predictors in the modelling (eg, continuous, linear, non-linear transformations or categorised) | |
| Sample size | Number of participants and number of outcomes/events |
| Events per candidate predictor | |
| Whether the authors describe a sample size calculation | |
| Missing data | Number of participants with any missing value (including predictors and outcomes) |
| Number of participants with missing data for each predictor | |
| Handling of missing data (eg, complete-case analysis, imputation or other methods) | |
| Model development | Modelling method (eg, logistic, survival or other) |
| Modelling assumptions satisfied | |
| Description of participants that were excluded from the analysis with justification | |
| Method for selection of predictors for inclusion in multivariable modelling (eg, all candidate predictors, preselection based on unadjusted association with the outcome) | |
| Method for selection of predictors during multivariable modelling (eg, full model approach, backward or forward selection) and criteria used (eg, p value, Akaike information criterion) | |
| Shrinkage of predictor weights or regression coefficients (eg, no shrinkage, uniform shrinkage, penalised estimation) | |
| Model performance | Calibration (calibration plot, calibration slope, Hosmer-Lemeshow test), discrimination (C-statistic, D-statistic, log-rank) and overall performance measures with confidence intervals |
| Classification measures (eg, sensitivity, specificity, predictive values, net reclassification improvement) and whether a priori cut points were used | |
| Model evaluation | Method used for testing model performance: development dataset only (apparent performance, random split of data, resampling methods, eg, bootstrap or cross-validation, none) or separate external validation |
| For external validations; data source and participants to be described as per ‘source of data’ and ‘participants’ domains. Definitions and distributions (including missing data) of outcome and candidate predictors | |
| In case of poor external validation, whether model was updated or extended (eg, intercept recalibrated, predictor effects adjusted, or new predictors added) | |
| Results | Final and other multivariable models presented, including predictor weights or regression coefficients, intercept, baseline survival, model performance measures (with SEs or CIs) |
| Any alternative presentation of the final prediction models, for example, sum score, nomogram, score chart, predictions for specific risk subgroups with performance | |
| Comparison of the definition and distribution of predictors (including missing data) for development and validation datasets | |
| Interpretation and discussion | Study authors’ interpretation of presented models (intended use, clinical utility, etc) |
| Study authors’ reported strengths and limitations | |
| Miscellaneous | Source of funding/sponsor |
| Any declared conflicts of interest | |
| Methodological guidelines used |
Adapted from CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST).
CI, confidence interval; HIV, human immunodeficiency virus; SE, standard error; VL, visceral leishmaniasis.