Background Early identification of high-risk factors and populations for short-term adverse events in stroke patients is crucial for improving prognosis and recovery outcomes.
Objective To analyze the factors influencing short-term prognosis in stroke patients and develop a predictive model and nomogram, so as to provide references for formulating targeted rehabilitation interventions.
Methods A retrospective case-control study was conducted, including stroke patients who admitted to our hospital from January 2018 to December 2020 and met the inclusion criteria. Modified Barthel Index was applied to evaluate short-term prognosis, and key predictors were identified from clinically relevant variables (age, sex, past medical history, comorbidities) using multivariate logistic regression. A prognostic prediction model was developed based on the identified variables, followed by the construction of a clinical nomogram. The predictive performance of the model versus clinical scales was assessed using the area under the ROC curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and calibration curves to evaluate discrimination and calibration accuracy. Stroke patients who met the inclusion criteria from January 2021 to January 2023 were selected as a validation cohort to verify the predictive model.
Results The training set of this study included 120 patients and the validation set included 65 patients. The results of multifactorial logistic regression showed that age (OR=1.033, 95% CI: 0.990 - 1.062, P=0.041), lung infection (OR=1.724, 95% CI: 1.652 - 1.880, P=0.007), modified Rankin Scale (mRS) score (OR=1.970, 95% CI: 1.353 - 2.872, P < 0.001) and Medical Research Council scale total score (healthy limb) (MRC sum scores, MRC-SS) (OR=0.854, 95% CI: 0.744 - 0.982, P=0.012) were independently correlated with poor short-term prognosis of stroke. A prediction model was constructed accordingly, and the area under the ROC curve (AUC) was 0.809 (95% CI: 73.7% - 89.2%), with a sensitivity of 0.803 and a specificity of 0.763. The Hosmer-Lemeshow test value was P=0.385; and the calibration curve showed a significant concordance between the predicted and actual values. The model showed significantly higher predictive power compared with the National Institutes of Health Stroke Scale score (AUC: 0.809 vs 0.613, P=0.004). The model was validated against the validation set and showed similar predictive value to the training set results (AUC=0.784, 95% CI: 0.665 - 0.902), the short-term prognostic clinical prediction model for stroke had good stability.
Conclusion This study develops a prediction model utilizing four variables (age, pulmonary infection status, modified Rankin Scale score, and contralateral Medical Research Council Scale score) to forecast short-term prognosis at two weeks post-admission in stroke patients. The model demonstrates favorable predictive performance, potentially facilitating early clinical rehabilitation interventions and improving long-term patient outcomes.
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