Yanjie Dong, Huolin Zeng, Lei Yang, Huan Song, Qian Li
{"title":"成年患者慢性术后疼痛的预后预测模型：系统回顾和荟萃分析","authors":"Yanjie Dong, Huolin Zeng, Lei Yang, Huan Song, Qian Li","doi":"10.1007/s44254-025-00093-7","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Chronic postsurgical pain (CPSP) presents a significant impact in the postoperative recovery, affecting patients’ outcomes and quality of life. Numerous prognostic prediction models have been developed to predict the risk of CPSP, however, the clinical utility remains variable. This systematic review and meta-analysis aimed to critically assessed and synthesize the existing CPSP prognostic prediction models in adult patients.</p><h3>Methods</h3><p>A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane library up to August 2024. A total of 22 models were included in the systematic review, with 19 models subsequently integrated into the meta-analysis.</p><h3>Results</h3><p>The overall pooled C-index of the models was 0.79 (95% confidence interval [CI]: 0.75, 0.83; I<sup>2</sup> = 88.6%). For studies evaluating CPSP at 3 months postoperatively, the pooled C-index was 0.80 (95% CI: 0.73, 0.87; I<sup>2</sup> = 82.1%). At 4 months, the pooled C-index was 0.75 (95% CI: 0.62, 0.87; I<sup>2</sup> = 82.8%), while studies considered CPSP at 6 months showed a pooled C-index of 0.81 (95% CI: 0.73, 0.89; I<sup>2</sup> = 93.8%). For 12 months post-surgery, the C-index was 0.77 (95% CI: 0.74, 0.79; I<sup>2</sup> = 0%). Among models with external validation, the C-index was 0.76 (95% CI: 0.70, 0.82; I<sup>2</sup> = 68.2%). For orthopedic surgery, the C-index was 0.82 (95% CI: 0.74, 0.91; I<sup>2</sup> = 92.7%). For breast surgery, the C-index was 0.78 (95% CI: 0.75, 0.81; I<sup>2</sup> = 0%). For studies reported C-index, the C-index was 0.70 (95% CI: 0.66, 0.73; I<sup>2</sup> = 0%) while the C-index was 0.81 (95% CI: 0.77, 0.85; I<sup>2</sup> = 88%) for studies reported area under receiver operating characteristic curve.</p><h3>Conclusions</h3><p>While prognostic prediction models demonstrated promising discriminative performance, the high overall risk of bias raises concerns about their quality and generalizability. These findings underscore the urgent need for rigorously designed and externally validated models to improve CPSP risk prediction in clinical practice.</p></div>","PeriodicalId":100082,"journal":{"name":"Anesthesiology and Perioperative Science","volume":"3 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44254-025-00093-7.pdf","citationCount":"0","resultStr":"{\"title\":\"Prognostic prediction model for chronic postsurgical pain among adult patients: a systematic review and meta-analysis\",\"authors\":\"Yanjie Dong, Huolin Zeng, Lei Yang, Huan Song, Qian Li\",\"doi\":\"10.1007/s44254-025-00093-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Chronic postsurgical pain (CPSP) presents a significant impact in the postoperative recovery, affecting patients’ outcomes and quality of life. Numerous prognostic prediction models have been developed to predict the risk of CPSP, however, the clinical utility remains variable. This systematic review and meta-analysis aimed to critically assessed and synthesize the existing CPSP prognostic prediction models in adult patients.</p><h3>Methods</h3><p>A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane library up to August 2024. A total of 22 models were included in the systematic review, with 19 models subsequently integrated into the meta-analysis.</p><h3>Results</h3><p>The overall pooled C-index of the models was 0.79 (95% confidence interval [CI]: 0.75, 0.83; I<sup>2</sup> = 88.6%). For studies evaluating CPSP at 3 months postoperatively, the pooled C-index was 0.80 (95% CI: 0.73, 0.87; I<sup>2</sup> = 82.1%). At 4 months, the pooled C-index was 0.75 (95% CI: 0.62, 0.87; I<sup>2</sup> = 82.8%), while studies considered CPSP at 6 months showed a pooled C-index of 0.81 (95% CI: 0.73, 0.89; I<sup>2</sup> = 93.8%). For 12 months post-surgery, the C-index was 0.77 (95% CI: 0.74, 0.79; I<sup>2</sup> = 0%). Among models with external validation, the C-index was 0.76 (95% CI: 0.70, 0.82; I<sup>2</sup> = 68.2%). For orthopedic surgery, the C-index was 0.82 (95% CI: 0.74, 0.91; I<sup>2</sup> = 92.7%). For breast surgery, the C-index was 0.78 (95% CI: 0.75, 0.81; I<sup>2</sup> = 0%). For studies reported C-index, the C-index was 0.70 (95% CI: 0.66, 0.73; I<sup>2</sup> = 0%) while the C-index was 0.81 (95% CI: 0.77, 0.85; I<sup>2</sup> = 88%) for studies reported area under receiver operating characteristic curve.</p><h3>Conclusions</h3><p>While prognostic prediction models demonstrated promising discriminative performance, the high overall risk of bias raises concerns about their quality and generalizability. These findings underscore the urgent need for rigorously designed and externally validated models to improve CPSP risk prediction in clinical practice.</p></div>\",\"PeriodicalId\":100082,\"journal\":{\"name\":\"Anesthesiology and Perioperative Science\",\"volume\":\"3 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s44254-025-00093-7.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anesthesiology and Perioperative Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s44254-025-00093-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anesthesiology and Perioperative Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s44254-025-00093-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prognostic prediction model for chronic postsurgical pain among adult patients: a systematic review and meta-analysis
Purpose
Chronic postsurgical pain (CPSP) presents a significant impact in the postoperative recovery, affecting patients’ outcomes and quality of life. Numerous prognostic prediction models have been developed to predict the risk of CPSP, however, the clinical utility remains variable. This systematic review and meta-analysis aimed to critically assessed and synthesize the existing CPSP prognostic prediction models in adult patients.
Methods
A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane library up to August 2024. A total of 22 models were included in the systematic review, with 19 models subsequently integrated into the meta-analysis.
Results
The overall pooled C-index of the models was 0.79 (95% confidence interval [CI]: 0.75, 0.83; I2 = 88.6%). For studies evaluating CPSP at 3 months postoperatively, the pooled C-index was 0.80 (95% CI: 0.73, 0.87; I2 = 82.1%). At 4 months, the pooled C-index was 0.75 (95% CI: 0.62, 0.87; I2 = 82.8%), while studies considered CPSP at 6 months showed a pooled C-index of 0.81 (95% CI: 0.73, 0.89; I2 = 93.8%). For 12 months post-surgery, the C-index was 0.77 (95% CI: 0.74, 0.79; I2 = 0%). Among models with external validation, the C-index was 0.76 (95% CI: 0.70, 0.82; I2 = 68.2%). For orthopedic surgery, the C-index was 0.82 (95% CI: 0.74, 0.91; I2 = 92.7%). For breast surgery, the C-index was 0.78 (95% CI: 0.75, 0.81; I2 = 0%). For studies reported C-index, the C-index was 0.70 (95% CI: 0.66, 0.73; I2 = 0%) while the C-index was 0.81 (95% CI: 0.77, 0.85; I2 = 88%) for studies reported area under receiver operating characteristic curve.
Conclusions
While prognostic prediction models demonstrated promising discriminative performance, the high overall risk of bias raises concerns about their quality and generalizability. These findings underscore the urgent need for rigorously designed and externally validated models to improve CPSP risk prediction in clinical practice.
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