Methodological characteristics of included studies
| Characteristics | Number (%) or median (IQR) |
| Sample size (n) | 165 (103–348) |
| Death events (n) | 35 (23–72) |
| Multicentre study | |
| Yes | 9 (28.1) |
| No | 23 (71.9) |
| Epidemiological design | |
| Prospective cohort | 13 (40.6) |
| Retrospective cohort | 19 (59.4) |
| Data sources | |
| Cohort study | 5 (15.6) |
| EMR data | 22 (68.8) |
| Registry | 5 (15.6) |
| Did the study clearly describe inclusion/exclusion criteria for participants? | |
| Yes | 31 (96.9) |
| No | 1 (3.1) |
| Consistent definition/diagnostic criteria of predictors used in all participants | |
| Yes | 32 (100.0) |
| No | 0 (0) |
| Consistent measurement of predictors used in all participants | |
| Yes | 32 (100.0) |
| No | 0 (0) |
| Consistent definition/diagnostic criteria of outcomes used in all participants | |
| Yes | 31 (96.9) |
| No | 1 (3.1) |
| Consistent measurement of outcomes used in all participants | |
| Yes | 31 (96.9) |
| No | 1 (3.1) |
| Were all enroled participants included in the analysis? | |
| Yes | 22 (68.8) |
| No | 10 (31.2) |
| Was missing outcome data reported, and the methods for handling missing outcome | |
| Yes, complete-case analysis | 1 (3.1) |
| No | 30 (93.8) |
| Not reported | 1 (3.1) |
| Was any missing predictor data reported, and the methods for handling missing predictor | |
| Yes, complete-case analysis | 5 (15.6) |
| No | 1 (3.1) |
| Not reported | 26 (81.3) |
| Prognostic factors (n=18) prediction models | |
| Number of outcomes/events in relation to the number of predictors for assessing prognostic factors (EPVs) | |
| <10 | 1 (5.6) |
| 10–20 | 8 (44.4) |
| ≥20 | 9 (50.0) |
| Model structure used in the study | |
| Logistic regression | 11 (61.1) |
| Cox regression | 5 (27.8) |
| ROC analyses (not report regression) | 2 (11.1) |
| Relevant model performance measures evaluated for addressing prognostic factors | |
| AUC | 2 (11.1) |
| AUC, sensitivity, specificity | 15 (83.3) |
| Sensitivity, specificity | 1 (5.6) |
| Prediction models (n=14) | |
| Number of outcomes/events in relation to the number of predictors in multivariable analysis (EPVs) | |
| <10 | 3 (21.4) |
| 10–20 | 8 (57.1) |
| ≥20 | 3 (21.4) |
| Model structure used in the study | |
| Logistic regression | 10 (71.4) |
| Cox regression | 1 (7.1) |
| ROC analyses (not report regression) | 1 (7.1) |
| Logistic regression and support vector machines | 1 (7.1) |
| Logistic regression and neural networks | 1 (7.1) |
| Relevant model performance measures evaluated for addressing prediction models | |
| AUC, p value of Hosmer-Lemeshow test | 5 (35.7) |
| AUC | 4 (28.6) |
| AUC, sensitivity, specificity | 2 (14.3) |
| P value of Hosmer-Lemeshow test | 1 (7.1) |
| Expected and observed | 1 (7.1) |
| Sensitivity, specificity | 1 (7.1) |
| Develop prediction models (n=11) | |
| Statistical method for selecting predictors during addressing prediction models | |
| Univariate analysis of predictors by P value | 3 (27.3) |
| Univariate analysis of predictors by p value and other specific predictors | 3 (27.3) |
| Stepwise selection | 2 (18.1) |
| Not reported | 3 (27.3) |
| Handling the predictors for addressing prediction models | |
| Continuous predictor was transformed into categories | 4 (36.4) |
| Not reported | 7 (63.6) |
.EMRs, electronic medical records; EPV, events per variable.