Highlights
- •This study analyzed the risk factors of frailty in older adults with lung cancer receiving anti-cancer medications including age, fatigue-related symptom cluster, depression, nutrition, D-dimer, Alb, comorbidity, and disease course.
- •By assigning different weights to those risk factors, the prediction model is displayed using a nomogram.
- •The risk prediction model has a good predictive performance. Base on it, health-care workers can monitor frailty risk regularly and conduct personalized preventive interventions.
Abstract
Objective
We aimed to construct and internally validate a frailty risk prediction model in older
adults with lung cancer.
Method
In total, 538 patients were recruited in a grade A tertiary cancer hospital in Tianjin,
and patients were randomly divided into the training group (n = 377) and the testing
group (n = 166) at a ratio of 7:3. The Frailty Phenotype scale was used to identify
frailty and logistic regression analysis was used to identify the risk factors and
establish a frailty risk prediction model.
Results
In the training group, logistic regression showed that age, fatigue-related symptom
cluster, depression, nutritional status, D-dimer level, albumin level, presence of
comorbidities, and disease course were independent risk factors for frailty. The areas
under the curve (AUCs) of the training and testing groups were 0.921 and 0.872, respectively.
A calibration curve of P = 0.447 validated model calibration. The decision curve analysis demonstrated greater
clinical benefit when the threshold probability was >20%.
Conclusion
The prediction model had a favorable prediction power for determining the risk of
frailty, contributing to the prevention and screening of frailty. Patients with a
frailty risk score of more than 0.374 should be regularly monitored for frailty and
receive personalized preventive interventions.
Keywords
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Article info
Publication history
Accepted:
March 10,
2023
Received in revised form:
February 18,
2023
Received:
December 6,
2022
Publication stage
In Press Accepted ManuscriptFootnotes
☆This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Identification
Copyright
© 2023 Published by Elsevier Ltd.