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Research Article|Articles in Press, 102316

The construction and validation of a frailty risk prediction model for older adults with lung cancer: A cross-sectional study

  • Author Footnotes
    1 These authors have contributed equally to this work and share first authorship.
    Ying-ge Hou
    Footnotes
    1 These authors have contributed equally to this work and share first authorship.
    Affiliations
    Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China

    Tianjin Medical University, 300070, China
    Search for articles by this author
  • Author Footnotes
    1 These authors have contributed equally to this work and share first authorship.
    Shu-mei Feng
    Footnotes
    1 These authors have contributed equally to this work and share first authorship.
    Affiliations
    Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China

    Tianjin Medical University, 300070, China
    Search for articles by this author
  • Song-mei Wang
    Affiliations
    Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
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  • Ya-jie Zhao
    Affiliations
    Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
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  • Ling Yan
    Correspondence
    Corresponding author. Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, West Huan-Hu Rd, Ti Yuan Bei, Hexi District, Tianjin, 300060, China.
    Affiliations
    Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
    Search for articles by this author
  • Author Footnotes
    1 These authors have contributed equally to this work and share first authorship.
Published:March 16, 2023DOI:https://doi.org/10.1016/j.ejon.2023.102316

      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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