Construction and Validation of a Prediction Model for Postoperative Fatigue Syndrome in Chinese Patients with Lung Cancer
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View abstract on PubMed
Summary
This summary is machine-generated.Postoperative fatigue syndrome (POFS) is common in lung cancer patients after surgery. A new nomogram predicts POFS risk using sleep quality, pain, and lung function, aiding early intervention.
Area Of Science
- Oncology
- Surgical Recovery
- Pulmonary Medicine
Background
- Postoperative fatigue syndrome (POFS) significantly impacts lung cancer patients' recovery.
- POFS is often under-recognized, leading to suboptimal patient care.
- A lack of predictive models hinders proactive management of POFS in lung cancer surgery patients.
Purpose Of The Study
- To develop and validate a predictive model for POFS in lung cancer patients.
- To identify key factors influencing POFS development after lung cancer surgery.
- To improve clinical recognition and management of POFS.
Main Methods
- Analysis of data from 203 lung cancer surgery patients.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for predictor screening.
- Developed a nomogram using multivariate regression and validated it with ROC, calibration curves, and decision curve analysis (DCA).
Main Results
- 57.1% of patients developed POFS.
- Key predictors identified: sleep quality, pain, activated partial thromboplastin time, FVC, and FEV1/FVC ratio.
- The nomogram demonstrated high accuracy (AUC=0.870) and clinical utility above 13% POFS probability.
Conclusions
- High prevalence of POFS observed in lung cancer surgery survivors.
- A validated nomogram incorporating five key factors can predict POFS risk.
- The developed tool aids in identifying patients at risk for POFS, facilitating timely interventions.
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