Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials

Figures

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  Fig. 1. Model building workflow. The figure displays the model building workflow used to identify responders to spironolactone treatment. Steps 1 to 3 represent the steps of the reiterative cluster allocating permutation. Hereby in a first step patients are randomly grouped into pairs with respect to their initial treatment regime (i.e. spironolactone or placebo). This produces a large number of samples within all possible combinations. In the second step, odds ratios are calculated with regards to the primary outcome (i.e. improvement in E/e’) in order to identify response. In a third step, those groups are sorted according to their within group baseline differences (variance). The groups with the lowest variance, yet highest respective odds ratio is further labelled as responders and the other group as non-responders. Finally, a supervised machine learning approach, in this case an XGBoost model, is used to identify features of the responder group. Of note, any other supervised machine learning approach could be used for this approach. The features extracted by the supervised machine learning approach can now be used on new cohorts and individual patients to predict their respective…

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  Fig. 2. Study flow-chart. Abbreviations: XGBoost denominates extreme gradient boosting.

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  Central Illustration. Study overview of model derivation and validation. The figure represents the study main findings, including relevance of identified responder features, number of patients proposed to be responders and non-responders in the validation cohort, as well as their respective outcome. Abbreviations: NNT denominates number needed to treat.

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  Fig. 3. Examples of patient individual feature importance. For better understanding of the variability of the relationships, the 10 variables of four individuals of the cohort with different response allocations were plotted. On the x-axis, the cumulative relative effect of the variable on the allocation with features favouring response (positive, blue, values) and features favouring non-response (negative, red, values) for every individual are shown. Notably, this figure illustrates how differently variables with seemingly comparable values might be interpreted by the model, an E-wave of 0.5 m/s in patient number one is seen as a factor highly predisposing for patients to be a responder, an E-wave of 0.6 m/s in patient number two is suggesting a higher chance of non-response. This underlines the strength of decision tree based algorithms were variables can be interpreted beyond mere linear assumptions and their model influence changes in accordance to accompanying covariables. Abbreviations: BMI denominates body mass index; BNP, brain natriuretic peptide; HCT, haematocrit; K+, potassium; Hb, haemoglobin; WBc, white blood cell count; A-Wave transmitral E-Wave on continuous wave doppler…