Development of a Nomogram Prediction Model for Activities of Daily Living Improvement After Acupuncture Combined With Physiotherapy and Occupational Therapy in Patients With Ischaemic Stroke

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Objective To develop a nomogram for predicting ADL improvement among patients with ischaemic stroke receiving acupuncture combined with physiotherapy (PT) and occupational therapy (OT) and to identify independent predictors of poor ADL improvement. Methods A total of 253 patients with ischaemic stroke were retrospectively included, comprising a development cohort of 190 patients admitted between April 2024 and April 2026 and an earlier, nonoverlapping temporal validation cohort of 63 patients admitted between December 2023 and March 2024 at the same institution. All patients received a standardised comprehensive rehabilitation programme combining acupuncture with PT and OT. Based on the degree of improvement in the Barthel Index (BI) score following intervention, they were divided into the good group (BI score improvement ≥ 15 points, n = 118) and the poor group (BI score improvement < 15 points, n = 72). Demographic and baseline clinical data, rehabilitation intervention indicators and laboratory indicators were collected. Multivariate logistic regression analysis was used to screen independent predictors of poor ADL improvement. Based on the regression results, a nomogram prediction model was constructed. The receiver operating characteristic curve, calibration curve and decision curve (DCA) were used to evaluate the predictive efficacy, calibration and clinical application value of the model. Results The age, National Institutes of Health Stroke Scale (NIHSS) score and course of infarction in the poor group were higher than those in the good group, and the albumin (ALB) level was lower than that in the good group. Age, NIHSS score and course of infarction were independent risk factors for poor ADL improvement, and ALB was an independent protective factor. The nomogram model constructed based on the above four indicators was internally verified by bootstrap. The area under the curve (AUC) of the original model was 0.853, and the AUC after bootstrap correction was 0.763. The calibration curve shows that the prediction probability is in good agreement with the actual observation probability, and the DCA shows that the model has a good net benefit in a wide threshold probability range. In an earlier, nonoverlapping retrospective temporal validation cohort from the same institution of 63 patients, the nomogram achieved an AUC of 0.936 (95% confidence interval [CI]: 0.878–0.993), which was significantly higher than that of the NIHSS-only model (AUC = 0.814, 95% CI: 0.741–0.902; DeLong P = 0.005). Conclusion Age, admission NIHSS score, infarction course and ALB were independent predictors of poor ADL improvement in patients with ischaemic stroke. The nomogram prediction model based on the above indicators has good discrimination, calibration and clinical practicability.

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