Evidence map›Paper›PMID 42605792›Full record

ArticleEuropean journal of pain (London, England)2026

A 5-Item Prediction Model Incorporating Pain With Neuropathic Characteristics at 2 Weeks for Chronic Postsurgical Pain After Video-Assisted Thoracoscopic Lung Surgery: A Prospective Cohort Study.

Wei Dou, Hai-Jing Shi, Jin-Yi Liu, Yi-Qian Zhang, Hong Liu, Jing-Hui Hu, Ke Peng

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Article in European journal of pain (London, England), 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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5 · Who and what money

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7 authors.

Wei DouDepartment of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Hai-Jing ShiDepartment of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Jin-Yi LiuDepartment of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Yi-Qian ZhangDepartment of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Hong LiuDepartment of Anesthesiology and Pain Medicine, University of California Davis Health, Sacramento, California, USA.
Jing-Hui HuDepartment of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Ke PengDepartment of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.ORCID https://orcid.org/0000-0003-2879-1759

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic postsurgical pain (CPSP) after video-assisted thoracoscopic surgery (VATS) is common, yet clinicians lack tools to identify high-risk patients at routine follow-up. We hypothesized that pain with neuropathic characteristics at 2 weeks, signifying central sensitization, could stratify risk and guide precision analgesia.

methodsThis prospective cohort study enrolled 500 adults undergoing VATS lung resection. Two logistic models were developed: one using variables on postoperative day 1 (POD1), and another adding neuropathic pain features on POD14. The primary outcome was CPSP at 3 months, defined as a Numerical Rating Scale [NRS] score ≥ 1 for pain in the surgical region that was not present preoperatively and was not attributable to malignancy or infection; sensitivity analysis was conducted using NRS ≥ 3. Discrimination (area under the curve [AUC]), calibration and decision curve analysis quantified predictive performance and potential clinical applicability.

resultsOf 483 patients analysed, 247 (51.1%) developed CPSP and 184 (38.1%) had 3-mon NRS ≥ 3. The POD14 model (sex, surgery type, chest tube duration, pain intensity, pain with neuropathic characteristics) was superior (AUC 0.904, 95% CI: 0.872-0.936) to the POD1 model (AUC 0.865, 95% CI: 0.827-0.903). Neuropathic pain at 2 weeks was the strongest predictor (odds ratio 13.98, 95% CI: 2.90-67.38). Temporal validation confirmed discrimination with good calibration. Sensitivity analysis showed an AUC of 0.786 (95% CI: 0.737-0.835) for the POD14 model; pain with neuropathic characteristics retained strong predictive value.

conclusionsThis 5-item tool with neuropathic pain features at 2-week visit identifies patients at high risk for CPSP after VATS. SIGNIFICANCE STATEMENT: Translating central sensitization from a theoretical mechanism to an actionable bedside tool, this study demonstrates that neuropathic pain features captured at routine two-week follow-up stratify risk for CPSP after VATS. This 5-item model is a first step toward individualized CPSP risk stratification after VATS and requires external validation before clinical implementation.

Indexed as

Chronic PainNeuralgiaPostoperative PainThoracic Surgery, Video-AssistedAgedFemaleHumansMaleMiddle AgedPain MeasurementPrediction AlgorithmsProspective Studies

Identifiers

PMID42605792
PMCPMC13479335

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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.