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| Construction and verification of frailty risk prediction model in elderly lung cancer patients |
| 1Xu Mengmeng,1Wang Xiaolan,2Chen Roudi |
| 1Department of Thoracic Surgery People's Hospital of Hai'an Hai'an 226600 Jiangsu China
2 Department of Operating Room
Zhongshan Hospital Affiliated to Fudan University Shanghai 200032 China |
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Abstract Objective To investigate the occurrence and influencing factors of frailty in elderly lung cancer patients establish a
frailty prediction model and verify it. Method In this study 500 patients with lung cancer admitted to our hospital during the period
from January 2022 to March 2024 were selected as research objects. The frailty status and influencing factors were analyzed
statistically. Regression analysis was used to construct frailty risk prediction model and the model was displayed and verified by
column graph. Result In the modeling group of this study 350 elderly lung cancer patients aged 61-79 years old with an average
age of 68. 22 ± 7. 25 years old including 121 females 34. 57% and 229 males 65. 43% participated. Among 350 elderly
patients with lung cancer 123 had frailty 35. 14% . Univariate analysis showed that age Charson comorbidity index nutritional
status duration of disease body mass index BMI hemoglobin D - dimer albumin depression and cancer fatigue were the
independent risk factors for fadility in elderly lung cancer patients P < 0. 05 . Result of multi - factor analysis Age Charson
comorbidity index duration of disease D-dimer depression and cancer fatigue were the risk factors for frailty in elderly lung cancer
patients P < 0. 05 hemoglobin albumin BMI nutritional status were the protective factors for frailty in elderly lung cancer patients
P < 0. 05 . In the verification group AUC was 0. 842 95%CI = 0. 802-0. 886 sensitivity was 83. 5% and specificity was 73. 8%.
Conclusion This study established a risk prediction model for frailty in elderly lung cancer patients. Frailty in elderly lung cancer
patients is affected by age Charson comorbidity index nutritional status duration of disease D-dimer albumin depression and
cancerous fatigue. In the future trajectory studies should be carried out to optimize the prediction model and establish a dynamic
nomogram model to provide a reliable tool for clinical nurses to dynamically predict the frailty of elderly patients with lung cancer with
a view to reducing frailty.
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