Construction and validation of nomogram to predict the frailty risk of senile stroke patients.

Background: Senile stroke patients with frailty are more likely to have adverse clinical outcomes such as falls, disability and re-admission. Severe frailty may even lead to death. Therefore, early identification of frailty and targeted intervention measures will help to reduce or delay the occurrence and development of frailty in senile stroke patients and avoid related adverse outcomes.

Methods: From September 2022 to March 2023, 415 senile stroke patients from a hospital in Liaoning Province were selected by convenient sampling method. Collect data through questionnaire survey, screen the independent influencing factors of patients' frailty, use R software to draw the nomogram of multivariate logistic regression model, and verify the model according to the area under the ROC curve (AUC), calibration curve and clinical decision curve (DCA).

Results: A total of 415 participants were collected, and the incidence of frailty was 72.5 %. Impaired ADL (OR=5.713, 95 %CI: 1.296-25.194), frequency of incidence(OR=4.694, 95 %CI: 1.505-14.64), anxiety (OR=17.398, 95 %CI: 4.251-71.214), depression(OR=5.578, 95 %CI: 1.659-18.752) and abnormal nutritional status (OR=6.335, 95 %CI: 1.69-23.74) were independent risk factors for senile stroke patients, and social support (OR=0.826, 95 %CI: 0.773-0.882) was protective factor. AUC is 0.957 (95 %CI: 0.967-0.989). The calibration curve shows that the calibration curve fits the ideal curve. The clinical decision curve shows that this model has good clinical practicability.

Conclusions: The frail risk prediction model of senile stroke patients established in this study has a good prediction effect, which is conducive to early detection of frail high-risk groups, so as to guide medical personnel to take targeted treatment measures to reduce the incidence of frailty.