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Developing a visual model for predicting depression in patients with lung cancer.

Yanqing Xing1, Wenxiao Zhao1, Chenchen Duan1

  • 1School of Nursing, Shandong University of Traditional Chinese Medicine, Jinan, China.

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|August 11, 2022
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Summary

A new visual prediction model accurately identifies depression risk in lung cancer patients. This tool helps clinicians provide early, targeted interventions, improving patient quality of life.

Keywords:
depressioninfluencing factorslung cancernomogramprediction model

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Area of Science:

  • Oncology
  • Psychiatry
  • Clinical Prediction Modeling

Background:

  • Depression is a common comorbidity in lung cancer patients, increasing suicide risk.
  • Current assessment tools lack the ability to integrate multiple risk factors for quantitative depression risk prediction.
  • Early identification and intervention are crucial for managing depression in this population.

Purpose of the Study:

  • To determine the prevalence of depression in lung cancer patients.
  • To identify risk factors associated with depression in this cohort.
  • To develop a visual, non-invasive clinical prediction model for quantitative depression risk assessment.

Main Methods:

  • A cross-sectional study involving 297 lung cancer patients in China.
  • Data analysis included Chi-square tests and logistic regression to construct a clinical prediction model.
  • Model performance was evaluated using discrimination (AUC), calibration, and decision curve analysis, visualized with a nomogram.

Main Results:

  • The prevalence of depressive symptoms was 43.77% (130 patients).
  • A visual prediction model incorporating age, disease duration, exercise, stigma, and resilience was developed.
  • The model demonstrated good discrimination (AUC = 0.842), calibration, and clinical utility.

Conclusions:

  • The developed visual prediction model accurately predicts depression risk in lung cancer patients.
  • This non-invasive nomogram aids clinicians in early risk assessment and personalized preventive care.
  • Early intervention can improve the quality of life for lung cancer patients experiencing depression.