Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

Abstract

Background: Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcome, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT.Methods: Among 2,349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥20 mmHg from the initial value within 6 hours. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), and light gradient boosting machine (LGBM), were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II.Results: The DNN model showed the highest AUROC (0.822 [0.789–0.856]), and the LGBM and SVM models followed with AUROCs of 0.810 (0.776–0.845) and 0.807 (0.772–0.842), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs <0.6). Although different definitions of hypotension were used such as a reduction of MAP ≥30 mmHg or a reduction occurring within 1 hour, the AUROCs of machine learning models were higher than those of disease-severity scores. These machine learning models were well calibrated.Conclusion: Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.

Machine learning-based prediction of intraoperative hypoxemia for pediatric patients

“…Recent advances in machine learning techniques enable the construction of accurate prediction models for various medical applications [10][11][12][13][14]. Such techniques identify hidden patterns in large datasets and analyze correlations among variables or features.…”

Nursing Countermeasures of Continuous Renal Replacement Treatment in End-Stage Renal Disease with Refractory Hypotension in the Context of Smart Health

“…Understand the patient's usual living habits, in addition to behaviors that have a negative impact on the effect of surgery, should try to adapt to these habits when formulating a recovery plan, so as to reduce the patient's resistance to the recovery plan. At the same time, the recovery status and quality of life of patients are regularly and objectively assessed, and the plan is adjusted in time [16]. Therefore, the routine nursing and nursing strategies (i.e., individualized nursing) were adopted for ESRD patients with refractory hypotension using CRRT in this work.…”