Table A1.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Checklist: Prediction Model Development and Validation.

Section/Topic	Item		Checklist Item	Page
Title and abstract
Title	1	D; V	Identify the study as developing and/or validating a multivariable prediction model, the target population, and the outcome to be predicted.	1
Abstract	2	D; V	Provide a summary of objectives, study design, setting, participants, sample size, predictors, outcome, statistical analysis, results, and conclusions.	2
Introduction
Background and objectives	3a	D; V	Explain the medical context (including whether diagnostic or prognostic) and rationale for developing or validating the multivariable prediction model, including references to existing models.	2–3
	3b	D; V	Specify the objectives, including whether the study describes the development or validation of the model or both.	3–5
Methods
Source of data	4a	D; V	Describe the study design or source of data (e.g., randomized trial, cohort, or registry data), separately for the development and validation datasets, if applicable.	6–7
	4b	D; V	Specify the key study dates, including start of accrual; end of accrual; and, if applicable, end of follow-up.	6–7
Participants	5a	D; V	Specify key elements of the study setting (e.g., primary care, secondary care, general population), including number and location of centers.	6–7
	5b	D; V	Describe eligibility criteria for participants.	6–7 Figure 3 and Figure 4
	5c	D; V	Give details of treatments received, if relevant.	n/a
Outcome	6a	D; V	Clearly define the outcome that is predicted by the prediction model, including how and when assessed.	8–12
	6b	D; V	Report any actions to blind assessment of the outcome to be predicted.	9–12
Predictors	7a	D; V	Clearly define all predictors used in developing the multivariable prediction model, including how and when they were measured.	7–8
	7b	D; V	Report any actions to blind assessment of predictors for the outcome and other predictors.	7–8
Sample size	8	D; V	Explain how the study size was arrived at.	7–8
Missing data	9	D; V	Describe how missing data were handled (e.g., complete-case analysis, single imputation, multiple imputation) with details of any imputation method.	7
Statistical analysis methods	10a	D	Describe how predictors were handled in the analyses.	7–8
	10b	D	Specify type of model, all model-building procedures (including any predictor selection), and method for internal validation.	4–5, 8 Figure 1
	10c	V	For validation, describe how the predictions were calculated.	8
	10d	D; V	Specify all measures used to assess model performance and, if relevant, to compare multiple models.	8 Figure 2
	10e	V	Describe any model updating (e.g., recalibration) arising from the validation, if done.	8
Risk groups	11	D; V	Provide details on how risk groups were created, if done.	7
Development vs. validation	12	V	For validation, identify any differences from the development data in setting, eligibility criteria, outcome, and predictors.	8
Results
Participants	13a	D; V	Describe the flow of participants through the study, including the number of participants with and without the outcome and, if applicable, a summary of the follow-up time. A diagram may be helpful.	Figure 3 and Figure 4
	13b	D; V	Describe the characteristics of the participants (basic demographics, clinical features, available predictors), including the number of participants with missing data for predictors and outcome.	Figure 3 and Figure 4
	13c	V	For validation, show a comparison with the development data of the distribution of important variables (demographics, predictors, and outcome).	Table A2 and Table A3
Model development	14a	D	Specify the number of participants and outcome events in each analysis.	7–8
	14b	D	If done, report the unadjusted association between each candidate predictor and outcome.	8–9
Model specification	15a	D	Present the full prediction model to allow predictions for individuals (i.e., all regression coefficients, and model intercept or baseline survival at a given time point).	10
	15b	D	Explain how to use the prediction model.	8–12
Model performance	16	D; V	Report performance measures (with CIs) for the prediction model.	11 Table 4
Model-updating	17	V	If done, report the results from any model updating (i.e., model specification, model performance).	12–13
Discussion
Limitations	18	D; V	Discuss any limitations of the study (such as non-representative sample, few events per predictor, missing data).	15
Interpretation	19a	V	For validation, discuss the results with reference to performance in the development data, and any other validation data.	13–14
	19b	D; V	Give an overall interpretation of the results, considering objectives, limitations, results from similar studies, and other relevant evidence.	14–15
Implications	20	D; V	Discuss the potential clinical use of the model and implications for future research.	14–15
Other information
Supplementary information	21	D; V	Provide information about the availability of supplementary resources, such as study protocol, web calculator, and datasets.	16–18
Funding	22	D; V	Give the source of funding and the role of the funders for the present study.	15

Items relevant only to the development of a prediction model are denoted by D, items relating solely to a validation of a prediction model are denoted by V, and items relating to both are denoted D; V.