Imagine you’re planning a trip across the country from New York City to Los Angeles. You want to make sure you have a smooth journey with no unexpected detours or roadblocks along the way. Similarly, when elderly patients undergo surgery, medical professionals want to predict and prevent postoperative stroke, which can be a serious complication. Researchers tackled this challenge by developing machine learning prediction models using data from the MIMIC database. These models analyzed various risk factors and utilized techniques like data balancing and imputation to address the complexities of the dataset. The best-performing model, called XGB (extreme gradient boosting), achieved an impressive area under the curve (AUC) of 0.78 in predicting postoperative stroke. This means it has a high accuracy in identifying those at risk. Interestingly, hypertension emerged as a top predictor, even surpassing laboratory test results. These findings suggest that healthcare providers should pay close attention to a patient’s history of hypertension when considering their risk of postoperative stroke. To learn more about this study and its implications for stroke prevention in elderly patients, dive into the full research article!

ObjectiveWith the aging of populations and the high prevalence of stroke, postoperative stroke has become a growing concern. This study aimed to establish a prediction model and assess the risk factors for stroke in elderly patients during the postoperative period.MethodsML (Machine learning) prediction models were applied to elderly patients from the MIMIC (Medical Information Mart for Intensive Care)-III and MIMIC-VI databases. The SMOTENC (synthetic minority oversampling technique for nominal and continuous data) balancing technique and iterative SVD (Singular Value Decomposition) data imputation method were used to address the problem of category imbalance and missing values, respectively. We analyzed the possible predictive factors of stroke in elderly patients using seven modeling approaches to train the model. The diagnostic value of the model derived from machine learning was evaluated by the ROC curve (receiver operating characteristic curve).ResultsWe analyzed 7,128 and 661 patients from MIMIC-VI and MIMIC-III, respectively. The XGB (extreme gradient boosting) model got the highest AUC (area under the curve) of 0.78 (0.75–0.81), making it better than the other six models, Besides, we found that XGB model with databalancing was better than that without data balancing. Based on this prediction model, we found hypertension, cancer, congestive heart failure, chronic pulmonary disease and peripheral vascular disease were the top five predictors. Furthermore, we demonstrated that hypertension predicted postoperative stroke is much more valuable.ConclusionStroke in elderly patients during the postoperative period can be reliably predicted. We proved XGB model is a reliable predictive model, and the history of hypertension should be weighted more heavily than the results of laboratory tests to prevent postoperative stroke in elderly patients regardless of gender.

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