Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks

Abstract

BackgroundAccurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization.MethodsWe retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the length of stay that was collected at admission, and a sensitivity analysis was performed to assess the effect of each predictor. We applied 5-fold cross-validation and used the validation set to evaluate the classification performance of the artificial neural network models.ResultsOverall, 2,240 patients were enrolled in this study. The median length of hospital stay was 9 days. A total of 1,101 patients (49.2%) had a prolonged hospital stay. A prolonged length of stay is associated with worse neurological outcomes at discharge. Univariate analysis identified 14 baseline parameters associated with prolonged length of stay, and with these parameters as input, the artificial neural network model achieved training and validation areas under the curve of 0.808 and 0.788, respectively. The mean accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of prediction models were 74.5, 74.9, 74.2, 75.2, and 73.9%, respectively. The key factors associated with prolonged length of stay were National Institutes of Health Stroke Scale scores at admission, atrial fibrillation, receiving thrombolytic therapy, history of hypertension, diabetes, and previous stroke.ConclusionThe artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.

Beyond diagnosis: Leveraging routine blood and urine biomarkers to predict severity and functional outcome in acute ischemic stroke

“…Demographic data—including age, sex, and the presence of cerebrovascular risk factors—were collected on admission. The overall severity of AIS was assessed by certified stroke specialists on the basis of the NIHSS scores, and functional outcomes 90 days after AIS onset were determined using modified Rankin Scale (mRS) scores [ 6 , 16 , 17 ]. At admission, blood count, prothrombin time, activated partial thromboplastin time (APTT), aspartate aminotransferase (AST), creatinine, and urine specific gravity were measured for each patient.…”

“…Thus, enhancing prognosis capability and identifying factors associated with AIS severity are crucial for targeting high-risk patients and implementing timely interventions to optimize clinical outcomes. Many studies have developed predictive models for AIS prognosis, with stroke severity serving as a widely used variable for predicting AIS outcomes [ [4] , [5] , [6] , [7] ]. The National Institutes of Health Stroke Scale (NIHSS) is a 15-item neurological examination scale commonly employed to assess stroke severity and is one of the common predictors of AIS outcomes in stroke prediction models [ 4 , 6 , [8] , [9] , [10] ].…”

“…Many studies have developed predictive models for AIS prognosis, with stroke severity serving as a widely used variable for predicting AIS outcomes [ [4] , [5] , [6] , [7] ]. The National Institutes of Health Stroke Scale (NIHSS) is a 15-item neurological examination scale commonly employed to assess stroke severity and is one of the common predictors of AIS outcomes in stroke prediction models [ 4 , 6 , [8] , [9] , [10] ]. However, the NIHSS requires trained stroke specialists or neurologists to operate the scoring process.…”

Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients

“…Further research developed a prediction of long-term hospitalization; research in China using the XGBoost algorithm with an AUC value of 0.92 showed the model's ability to predict whether a patient will be hospitalized for more than one day with an accuracy of 85% (R. . The approach using Artificial Neural Network successfully predicted the long-term treatment of stroke patients in hospitals with an AUC of 0.788 (Yang et al, 2023), (Neto et al, 2020).…”

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study

“…For machine learning predicting prolonged LOS in AIS, Kurtz et al (12) accurately predicted the LOS of patients admitted to the ICU with stroke through machine learning methods, but they did not include stroke-specific data, such as the National Institutes of Health Stroke Scale (NIHSS) score or neuroimaging findings. Yang et al (13) found that the artificial neural network model achieved adequate discriminative power for predicting prolonged LOS after AIS and identified crucial factors associated with a prolonged hospital stay. However, they did not include pneumonia or another important onset symptom of stroke, which proved to be strong influencing factors of LOS in AIS patients.…”