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To investigate the prevalence and associated influencing factors of potentially inappropriate medications (PIM) in elderly hospitalized patients with ischemic stroke, and to construct a risk prediction model for this population. This study aimed to provide decision support for identifying high-risk patients and ensuring rational clinical medication use.
Retrospective analysis was performed on the medical records of elderly hospitalized patients with ischemic stroke admitted to our hospital from January 2021 to December 2023. The 2023 version of the Beers Criteria was adopted to assess PIM. Univariate and multivariate Logistic regression analyses were applied to identify risk factors associated with PIM, and the optimal model was ascertained via the Akaike information criterion (AIC) followed by nomogram development. The discriminative ability, calibration, and clinical utility of the model were evaluated systematically.
A total of 1 797 patients were included, among whom 866 (48.19%) had PIM, involving 1 756 PIM episodes. Multivariate Logistic regression analysis revealed that female gender, number of medications≥10 types, National Institutes of Health Stroke Scale (NIHSS) score of >5 points, personal history of cerebral infarction, type 2 diabetes mellitus, coronary heart disease, and atrial fibrillation were independent risk factors for PIM (P<0.05, P<0.001). Validation of the nomogram model showed an area under curve (AUC) of the receiver operating characteristic of 0.73. The Hosmer-Lemeshow goodness-of-fit test showed a P-value >0.05, meaning the model fit well, and there was good agreement between the calibration curve and the ideal curve. Clinical decision curve analysis (DCA) demonstrated that when the threshold probability ranged from 15% to 87%, the model achieved a high net benefit, confirming its clinical practical value.
The constructed nomogram model exhibits good discriminative ability, calibration, and clinical applicability. It can accurately predict the risk of PIM in elderly patients with ischemic stroke, facilitating early clinical risk identification and targeted intervention implementation.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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