Objective: The aim of this study is to develop and validate a prediction model of AOHPL to facilitate physicians in identifying patients at higher probability of developing AOHPL.

Background: Levodopa could induce orthostatic hypotension (OH) in Parkinson’s disease (PD) patients. It reduced quality of life, and aggravated burden of disease. Accurate prediction of acute OH post levodopa (AOHPL) is important for not only rational, safe, and effective drug use in PD patients but also for data-based treatment decision-making.

Method: We examined the probability of AOHPL among 497 PD inpatients who underwent levodopa challenge test (LCT) and Supine-to-standing test (STS) during LCT. Patients were classified into two groups, OH occurred after taken levodopa during LCT were involved in AOHPL cohort, otherwise in NON-AOHPL group. The whole dataset was randomly split into training (80%) and independent test data (20%). Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) were trained on the training set for prediction of AOHPL. The leave-one-out cross validation (LOOCV) performance of these models were compared. Independent test data provided an evaluation of the final predictive model which had the best LOOCV performance. Shapley additive explanations (SHAP) values were used to disclose how variables explain the specific prediction on a given observation and associated prediction on independent test data.

Results: RF was selected as our final predictive model as its LOOCV performance [Accuracy 71.6%, Sensitivity 72.3%, Specificity 70.8%] outperformed other models [Accuracy, Sensitivity, Specificity are 69.2%, 69.7%, 68.8% for LR, and 70.6%, 71.6%, 69.4% for SVM, respectively]. 17 variables were included in the constructive RF model. Mean artery pressure drop (ΔMAP) was the most important feature in this predictive model. For independent test data, we achieved a prediction accuracy of 72%. ΔMAP, age and supine hypertension were the top three variables which explain most to the prediction across all individual observation on the independent test data.

Conclusion: A random forest classifier model can be used to predict PD patients to develop AOHPL or not through a routine data-driven approach. This validated classifier could help clinicians to recognize high risk of AOHPL early, and to provide suggestion for treatment decision-making preventing OH, improving the quality of life in PD patients.

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