Evidence map›Paper›PMID 42404940›Full record

Observational studyFrontiers in public health2026

Development and validation of a predictive model for chronic pain after thoracoscopic pulmonary resection.

Yue Shang, Chenyang Wang, Jiameng Wang, Wenlun Zhong, Changjun Gao

Abstract readValidation StudyObservational Study
In one paragraph

Observational study in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yue ShangDepartment of Anesthesiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Chenyang WangDepartment of Anesthesiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Jiameng WangDepartment of Anesthesiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Wenlun ZhongGraduate School, Xi'an Medical University, Xi'an, Shaanxi, China.
Changjun GaoDepartment of Anesthesiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic postsurgical pain (CPSP) is a major complication following video-assisted thoracoscopic surgery (VATS) for lung resection. Despite its clinical importance, the reported incidence of CPSP varies considerably across studies, and a standardized tool for risk prediction remains lacking. This study aimed to identify predictors associated with CPSP after VATS lung resection within a single-center prospective observational cohort and to develop predictive models to guide personalized analgesic strategies. Methods: Clinical data were analyzed from patients who underwent thoracoscopic pulmonary resection at Tangdu Hospital between August 2024 and July 2025. Eligible patients were randomly allocated into a training cohort (80%) for model development and an internal validation cohort (20%) for model validation. Acute pain was assessed using the highest Numerical Rating Scale (NRS) score recorded within 72 h postoperatively, while CPSP was evaluated at 3 months after surgery. Patients were dichotomized based on the presence of CPSP into the CPSP group and the non-CPSP group. Variables with a Results: A total of 744 patients were included in this study, with 595 assigned to the training set and 149 to the validation set. Among all patients, 284 (38.1%) developed CPSP. The incidence rates in the training and validation cohorts were 37.8 and 36.9%, respectively. Multivariate logistic regression identified gender, postoperative acute pain, postoperative pneumonia, duration of chest tube drainage, and opioid rescue dose (in morphine milligram equivalents) as independent predictors associated with CPSP. The nomogram model constructed based on these predictors demonstrated good performance, with an area under the curve (AUC) of 0.880 (95% CI: 0.850-0.910). Calibration curve analysis confirmed the high predictive accuracy of the model (Hosmer-Lemeshow test, Conclusions: CPSP remains highly prevalent after thoracoscopic pulmonary resection.

Indexed as

Chronic PainPneumonectomyPostoperative PainThoracic Surgery, Video-AssistedAgedFemaleHumansMaleMiddle AgedPrediction AlgorithmsProspective Studiesassociated predictorschronic postsurgical painmultimodal analgesiapredictive modelthoracoscopic pulmonary resection

Identifiers

PMID42404940
PMCPMC13328369

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.