Dynamic machine learning architecture integrating time-phased predictions for acute kidney injury following extracorporeal membrane oxygenation

Figures

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  Figure 1. Flowchart illustrating the study design and analytical procedures in this cohort study.

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  Figure 2. Feature selection and two-phased machine learning (ML) model development and validation. (A, B) Important characteristic variables identified by the Boruta algorithm in pre-ECMO (A) and on-ECMO (B) datasets. The horizontal axis shows the names of variables, and the vertical axis shows the Z-score of variables. The boxplot shows the Z-score of variables during the model calculation process. (C, D) An innovative modeling framework using 12 classical ML-based integrative procedure and further calculated the area under curve (AUC) of each model in derivation and validation cohorts for pre-ECMOAKI (C) and on-ECMOAKI (D) models. AKI, acute kidney injury; ECMO, extracorporeal membrane oxygenation.

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  Figure 3. Comprehensive evaluation of predictive performance, calibration, and clinical utility of pre-ECMOAKI and on-ECMOAKI models in predicting ECMO-AKI. (A–C) Receiver operating characteristic (ROC) curves with bootstrap-adjusted AUC values to evaluate and comapre predictive performance of pre-ECMOAKI and on-ECMOAKI models in derivation (A), validation (B) and overall (C) cohorts. (D, E) precision recall (PR) curves with AUC values to evaluate predictive efficacy of pre-ECMOAKI (D) and on-ECMOAKI (E) models. (F, G) Calibration curves for pre-ECMOAKI (F) and on-ECMOAKI (G) models. X-axis is predicted probability of ECMO-AKI. Y-axis is observed probability of ECMO-AKI. (H) Decision curve analysis (DCA) was applied to evaluate and compare the clinical usefulness of pre-ECMOAKI and on-ECMOAKI models. The Y-axis represents the net benefit. The black line represents the hypothesis that no patients treatment. The X-axis represents the threshold probability. The threshold probability is where the expected benefit of treatment is equal to the expected benefit of avoiding treatment.

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  Figure 4. Global and local explanation by the Shapley Additive Explanations (SHAP) method. (A, B) Summary plot showed the features ranking by mean absolute SHAP values in pre-ECMOAKI (A) and on-ECMOAKI (B) models. (C, D) Global model explanation for pre-ECMOAKI (C) and on-ECMOAKI (D) models. Each variable name is shown on the left-hand side with the variable with the greatest contribution listed at the top. To the right of the variables, there are colored lines, which are individual points that correspond to observations in the population. A higher value for the variable is represented in yellow, while a lower value for the variable will be shown in purple. A value farther to the right (ie, a higher SHAP value) indicates that the variable is contributed to a prediction of a positive target, such as ECMO-AKI. (E, F) An example of risk factor analysis for a patient with ECMO-AKI which represented the individual ECMO towards the “AKI” class in pre-ECMOAKI (E) and on-ECMOAKI (F) models.

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  Figure 5. Comparison of two-stage ECMO-AKI model to other indicators, alongside synergistic prediction framework integrating the two models. (A , B) Multiple ROC analysis was performed to compare the diagnostic performance of the pre-ECMOAKI (A) and on-ECMOAKI (B) models against APACHE II, SOFA-2, EASIX and Cr. (C, D) DCA was applied to compare the clinical usefulness of pre-ECMOAKI (C) and on-ECMOAKI (D) models against APACHE II, SOFA-2, EASIX and Cr. (E) The Sankey plots illustrate the flow of patients across two time points based on the ECMO-AKI models’ risk predictions. Patients are stratified into four risk levels according to the integrated analysis of pre-ECMOAKI and on-ECMOAKI model predictions: consensus high-risk, consensus low-risk, upgraded risk and downgraded risk group. The light red flow represents patients in the consensus high-risk group who developed ECMO-AKI, while the light blue flow represents consensus high-risk patients who did not. The light blue flow indicates consensus low-risk patients who did not develop ECMO-AKI.

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  Figure 6. Potential nonlinear association for the levels of predictors with ECMO-AKI risk measured in pre-ECMOAKI and on-ECMOAKI models by restricted cubic spline regression with optimal knots. (A) EASIX in pre-ECMOAKI model. (B) CPRT in pre-ECMOAKI model. (C) EASIX in on-ECMOAKI model. (D) TyG in on-ECMOAKI model. (E) SHR in on-ECMOAKI model. (F) PNI in on-ECMOAKI model.

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  Figure 7. The dynamic trends and longitudinal trajectories of core predictive variables during the pre-ECMO and on-ECMO periods. (A–G) Paired tests were adopted to compare the changing trends of lactate (A), TBI (B), PT (C), Cr (D), SHR (E), PNI (F), and EASIX (G) between the pre-ECMO and on-ECMO stages among AKI, non-AKI and total populations. (H–K) The latent class growth mixture model (LCGMM) was applied to explore the associations between longitudinal EASIX (H, 3 classes), lactate (I, 3 classes), PT (J, 3 classes), and TBI (K, 2 classes) trajectories and the occurrence of ECMO-AKI. Owing to substantial data dispersion of EASIX, PT and TBI, natural log transformation was applied: ln(EASIX + 1), ln(PT), ln(TBI).