Comprehensive Summary
Zhao et al. studies the prediction of intradialytic hypotension (IDH), which is a prevalent complication of hemodialysis, with machine learning models .that could supplement clinical experience. With a broad dataset consisting of 26,690 hemodialysis sessions for testing various clinical scenarios and 12,293 for validation, IDH was predicted using five distinct definitions and the novel addition of echocardiography based techniques, like the left ventricular mass index (LVMI) and ejection fraction (EF), to improve predictions. Ten different machine learning algorithms were used for model construction, and ultimately, the CatBoost machine learning model generated the most predictive results with the highest receiver operating characteristics, as it exhibited high ROC-AUC values. Age, systolic blood pressure, heart rate, and more features were used to better streamline machine learning models. Overall, machine learning is reliable for IDH prediction with the most effective algorithm being CatBoost, and LVMI was found to be the most effective feature to measure with. These models and features can be adapted to create a more user friendly website to make prediction data more accurate and precise for clinical applications.
Outcomes and Implications
This study incorporated applied machine learning models (ML) with the superior CatBoost algorithm to predict intradialytic hypotension across five unique clinical definitions. With this, the machine learning tool CatBoost could be adapted further to aid patients and doctors with the detection and prediction process. However, confounding variables may not be as well accounted for in these models and more testing would need to be done with a broader database to ensure the technology's efficacy in the community. Other than that, the predictability of the IDF would allow healthcare providers the means to make their own precise decisions, potentially decreasing kidney and cardiovascular complications to treatable conditions like IDH.