Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

Tuomo J Meretoja (Corresponding Author), Kenneth Geving Andersen, Julie Bruce, Lassi Haasio, Reetta Sipilä, Neil W Scott, Samuli Ripatti, Henrik Kehlet, Eija Kalso

Research output: Contribution to journalArticle

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Abstract

PurposePersistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool.
Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort.
ResultsModerate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area (P < .001), high body mass index (P = .039), axillary lymph node dissection (P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day (P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively.
ConclusionOur validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.
Original languageEnglish
Pages (from-to)1660-1667
Number of pages8
JournalJournal of Clinical Oncology
Volume35
Issue number15
Early online date13 Mar 2017
DOIs
Publication statusPublished - 20 May 2017

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Breast Neoplasms
Pain
Area Under Curve
Scotland
Denmark
ROC Curve
Acute Pain
Finland
Postoperative Pain
Lymph Node Excision
Body Mass Index
Logistic Models
Regression Analysis
Research Personnel

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Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery. / Meretoja, Tuomo J (Corresponding Author); Andersen, Kenneth Geving ; Bruce, Julie; Haasio, Lassi ; Sipilä, Reetta ; Scott, Neil W; Ripatti, Samuli; Kehlet, Henrik ; Kalso, Eija .

In: Journal of Clinical Oncology, Vol. 35, No. 15, 20.05.2017, p. 1660-1667.

Research output: Contribution to journalArticle

Meretoja, TJ, Andersen, KG, Bruce, J, Haasio, L, Sipilä, R, Scott, NW, Ripatti, S, Kehlet, H & Kalso, E 2017, 'Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery', Journal of Clinical Oncology, vol. 35, no. 15, pp. 1660-1667. https://doi.org/10.1200/JCO.2016.70.3413
Meretoja, Tuomo J ; Andersen, Kenneth Geving ; Bruce, Julie ; Haasio, Lassi ; Sipilä, Reetta ; Scott, Neil W ; Ripatti, Samuli ; Kehlet, Henrik ; Kalso, Eija . / Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery. In: Journal of Clinical Oncology. 2017 ; Vol. 35, No. 15. pp. 1660-1667.
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title = "Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery",
abstract = "PurposePersistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15{\%} to 20{\%} of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool.Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort.ResultsModerate to severe persistent pain occurred in 13.5{\%}, 13.9{\%}, and 20.3{\%} of the patients in the three studies, respectively. Preoperative pain in the operative area (P < .001), high body mass index (P = .039), axillary lymph node dissection (P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day (P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20{\%} risk level, the model had 32.8{\%} and 47.4{\%} sensitivity and 94.4{\%} and 82.4{\%} specificity in the Danish and Scottish cohorts, respectively.ConclusionOur validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.",
author = "Meretoja, {Tuomo J} and Andersen, {Kenneth Geving} and Julie Bruce and Lassi Haasio and Reetta Sipil{\"a} and Scott, {Neil W} and Samuli Ripatti and Henrik Kehlet and Eija Kalso",
note = "This study was supported by grants from the Academy of Finland (grant numbers 110489 & 217028), the Research Funds of the Helsinki and Uusimaa Hospital District (TYH2008225 & TYH2010210), Signe and Ane Gyllenberg Foundaton, The European Commission FP7 (#HEALTH-F2-2013-602891, NeuroPain) and the Danish Cancer Foundation. The Scottish Recovery Study was funded by Cancer Research UK (Project Grant G23143) and the Chief Scientist Office Scotland (Project Grant: CZG/2/488). The first author of this study was supported by a grant from the Finnish Medical Foundation.",
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T1 - Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

AU - Meretoja, Tuomo J

AU - Andersen, Kenneth Geving

AU - Bruce, Julie

AU - Haasio, Lassi

AU - Sipilä, Reetta

AU - Scott, Neil W

AU - Ripatti, Samuli

AU - Kehlet, Henrik

AU - Kalso, Eija

N1 - This study was supported by grants from the Academy of Finland (grant numbers 110489 & 217028), the Research Funds of the Helsinki and Uusimaa Hospital District (TYH2008225 & TYH2010210), Signe and Ane Gyllenberg Foundaton, The European Commission FP7 (#HEALTH-F2-2013-602891, NeuroPain) and the Danish Cancer Foundation. The Scottish Recovery Study was funded by Cancer Research UK (Project Grant G23143) and the Chief Scientist Office Scotland (Project Grant: CZG/2/488). The first author of this study was supported by a grant from the Finnish Medical Foundation.

PY - 2017/5/20

Y1 - 2017/5/20

N2 - PurposePersistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool.Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort.ResultsModerate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area (P < .001), high body mass index (P = .039), axillary lymph node dissection (P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day (P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively.ConclusionOur validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.

AB - PurposePersistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool.Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort.ResultsModerate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area (P < .001), high body mass index (P = .039), axillary lymph node dissection (P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day (P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively.ConclusionOur validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.

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DO - 10.1200/JCO.2016.70.3413

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VL - 35

SP - 1660

EP - 1667

JO - Journal of Clinical Oncology

JF - Journal of Clinical Oncology

SN - 0732-183X

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