Main findings
death, critically ill, Stroke, stroke, neurologic deficit
CRITICALLY ILL, STROKE, STROKE, DISEASE, ISCHEMIC STROKE
Ischemic stroke is the most common type of stroke, which predominantly affects older adults and has a high short-term mortality rate after admission to the ICU. Therefore, establishing a death prediction model is essential for the risk stratification of these elderly patients with IS.The present study is the first to use machine learning (ML) models to predict the short-term prognosis of elderly patients with IS admitted to the ICU, which provides a basis for identifying those critically ill older IS patients early. The present study compares the performance of multiple supervised machine learning (ML) algorithms with that of traditional LR methods to explore the best model for predicting short-term death in elderly patients with IS. Each model's prediction performance on the test set resembles that on the training set, indicating that the model has good robustness. In particular, xgboost model based on machine learning shows the best short-term death prediction accuracy compared with those of the traditional LR method and another common machine learning algorithm.The existing scores for 30-day mortality prediction are PLAN (pre-admission comorbidities, level of consciousness, age, and neurologic deficit) (In addition, the present study provides a simpler prediction tool that displays clinically useful discrimination of in-hospital risk of mortality for elderly patients with IS in the ICU. Our model is based on readily available variables of the eICU-CRD database, including patient demographics, history, and examination information. The database did not include variables about disease severity such as the Canadian Neurological Scale (CNS) and the National Institutes of Health Stroke Scale (NIHSS). A more widespread use of stroke severity assessments is probably held back by the time needed to complete even a short assessment. NIHSS strongly influences mortality and improvement. When the NIHSS score was added to the model, the C statistic can improve by 0.12 (Some ML models for IS patients (We found elevated oxygen saturation, AST, Polys, heart rate, WBC, and creatinine levels and decreased BUN level, and the percentage of lymphocytes could be used as predictors of the prediction model for 28-day in-hospital mortality in elderly patients with IS admitted to the ICU. Bhatia et al. (