A prehabilitation-enhanced nomogram for predicting early pulmonary recovery failure after lung tumor surgery: development and multicenter validation - Report - MDSpire

A prehabilitation-enhanced nomogram for predicting early pulmonary recovery failure after lung tumor surgery: development and multicenter validation

  • By

  • Sanhua Lian

  • Xihua Lian

  • Zhixing Zhu

  • Chunping Shi

  • Fengyu Chen

  • July 13, 2026

Share

Clinical Report: Nomogram for Predicting Early Pulmonary Recovery Failure

Overview

This study developed and validated a prehabilitation-enhanced nomogram to predict early postoperative pulmonary recovery failure within 7 days after lung tumor surgery.

Background

Postoperative pulmonary complications are common after lung tumor resection, leading to increased morbidity and healthcare costs. Traditional risk assessment methods often fail to capture dynamic perioperative changes.

Data Highlights

CohortAUCBrier Score
Training0.921 (95% CI 0.891–0.952)0.097
Internal Validation0.885 (95% CI 0.826–0.943)0.122
External Test0.845 (95% CI 0.778–0.912)0.143

Key Findings

  • The prehabilitation-enhanced nomogram included five predictors: intraoperative blood loss, DLCO% predicted, preoperative resting SpO2, incentive spirometry target-achieved days, and breathing training target-achieved days.
  • Adding prehabilitation indicators improved discrimination in all cohorts, with AUCs increasing significantly.
  • The nomogram outperformed any single predictor in terms of predictive capability.
  • Calibration was acceptable across cohorts.
  • Decision curve analysis supported the clinical usefulness of the nomogram.

Clinical Implications

The nomogram provides a tool for individualized risk stratification of early postoperative pulmonary recovery failure, which can guide targeted interventions and resource allocation. Incorporating prehabilitation indicators may enhance postoperative care strategies.

Conclusion

The development of a prehabilitation-enhanced nomogram represents a tool for predicting early postoperative pulmonary recovery failure.

Related Resources & Content

  1. Frontiers in Surgery, 2026 -- Development and validation of a clinical prediction model for postoperative atrial fibrillation after lung cancer surgery: a machine-learning–based study
  2. Predicting Severe Postoperative Complications in Pancreatic Head Resection: A Nomogram-Based Approach for Clinical Decision-Making
  3. Creation and assessment of a radiomics nomogram utilizing [18F]FDG PET/CT to forecast prognostic risks in patients with pretreatment diffuse large B cell lymphoma
  4. European Respiratory Society and European Society of Thoracic Surgeons clinical practice guideline on fitness for curative intent treatment of lung cancer
  5. Guidelines for preoperative pulmonary function assessment in patients with lung cancer who will undergo surgery (The Japanese Association for Chest Surgery)
  6. Frontiers in Oncology — Development and Validation of a Novel Hepato-Metabolic-Renal Score Nomogram for Predicting Disease-Free Survival in Head and Neck Squamous Cell Carcinoma
  7. Efficacy of preoperative pulmonary rehabilitation in lung cancer patients: a systematic review and meta-analysis of randomized controlled trials
  8. Postoperative outcomes of preoperative exercise training in patients with operable non-small cell lung cancer: a systematic review and meta-analysis
  9. European Respiratory Society and European Society of Thoracic Surgeons clinical practice guideline on fitness for curative intent treatment of lung cancer
  10. Guidelines for preoperative pulmonary function assessment in patients with lung cancer who will undergo surgery (The Japanese Association for Chest Surgery)
  11. Checking your browser - reCAPTCHA
  12. Prediction models for postoperative pulmonary complications: a systematic review and meta-analysis - ScienceDirect
  13. Development and validation of a nomogram for predicting postoperative pulmonary complications in older patients undergoing noncardiac thoracic surgery: a prospective, bicentric cohort study - PMC
  14. Checking your browser - reCAPTCHA

Original Source(s)

Related Content