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Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development

INTRODUCTION: Chronic postsurgical pain (CPSP) is a condition that affects an estimated 10%–50% of adults, depending on the surgical procedure. CPSP often interferes with activities of daily living and may have a negative impact on quality of life, emotional and physical well-being. Clinical predict...

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Autores principales: Papadomanolakis-Pakis, Nicholas, Haroutounian, Simon, Christiansen, Christian Fynbo, Nikolajsen, Lone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718417/
http://dx.doi.org/10.1136/bmjopen-2021-053618
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author Papadomanolakis-Pakis, Nicholas
Haroutounian, Simon
Christiansen, Christian Fynbo
Nikolajsen, Lone
author_facet Papadomanolakis-Pakis, Nicholas
Haroutounian, Simon
Christiansen, Christian Fynbo
Nikolajsen, Lone
author_sort Papadomanolakis-Pakis, Nicholas
collection PubMed
description INTRODUCTION: Chronic postsurgical pain (CPSP) is a condition that affects an estimated 10%–50% of adults, depending on the surgical procedure. CPSP often interferes with activities of daily living and may have a negative impact on quality of life, emotional and physical well-being. Clinical prediction models can help clinicians target preventive strategies towards patients at high-risk of CPSP. Therefore, the objective of this study is to develop a clinically applicable and generalisable prediction model for CPSP in adults. METHODS AND ANALYSIS: This research will be a prospective single-centre observational cohort study in Denmark spanning approximately 1 year or until a predefined number of patients are recruited (n=1526). Adult patients aged 18 years and older scheduled to undergo surgery will be recruited at Aarhus University Hospital. The primary outcome is CPSP 3 months after surgery defined as average pain intensity at rest or on movement ≥3 on numerical rating scale (NRS) within the past week, and/or average pain interference ≥3 on NRS among any of seven short-form Brief Pain Inventory items in the past week (general activity, mood, walking ability, normal work (including housework), relations with other people, sleep and enjoyment of life). Logistic regression will be used to conduct multivariate analysis. Predictive model performance will be evaluated by discrimination, calibration and model classification. ETHICS AND DISSEMINATION: This research has been approved by Central Region Denmark and will be conducted in accordance with the Danish Data Protection Act and Declaration of Helsinki. Study findings will be disseminated through conference presentations and peer-reviewed publication. A CPSP risk calculator (CPSP-RC) will be developed based on predictors retained in the final models. The CPSP-RC will be made available online and as a mobile application to be easily accessible for clinical use and future research including validation and clinical impact assessments. TRIAL REGISTRATION NUMBER: NCT04866147.
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spelling pubmed-87184172022-01-12 Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development Papadomanolakis-Pakis, Nicholas Haroutounian, Simon Christiansen, Christian Fynbo Nikolajsen, Lone BMJ Open Surgery INTRODUCTION: Chronic postsurgical pain (CPSP) is a condition that affects an estimated 10%–50% of adults, depending on the surgical procedure. CPSP often interferes with activities of daily living and may have a negative impact on quality of life, emotional and physical well-being. Clinical prediction models can help clinicians target preventive strategies towards patients at high-risk of CPSP. Therefore, the objective of this study is to develop a clinically applicable and generalisable prediction model for CPSP in adults. METHODS AND ANALYSIS: This research will be a prospective single-centre observational cohort study in Denmark spanning approximately 1 year or until a predefined number of patients are recruited (n=1526). Adult patients aged 18 years and older scheduled to undergo surgery will be recruited at Aarhus University Hospital. The primary outcome is CPSP 3 months after surgery defined as average pain intensity at rest or on movement ≥3 on numerical rating scale (NRS) within the past week, and/or average pain interference ≥3 on NRS among any of seven short-form Brief Pain Inventory items in the past week (general activity, mood, walking ability, normal work (including housework), relations with other people, sleep and enjoyment of life). Logistic regression will be used to conduct multivariate analysis. Predictive model performance will be evaluated by discrimination, calibration and model classification. ETHICS AND DISSEMINATION: This research has been approved by Central Region Denmark and will be conducted in accordance with the Danish Data Protection Act and Declaration of Helsinki. Study findings will be disseminated through conference presentations and peer-reviewed publication. A CPSP risk calculator (CPSP-RC) will be developed based on predictors retained in the final models. The CPSP-RC will be made available online and as a mobile application to be easily accessible for clinical use and future research including validation and clinical impact assessments. TRIAL REGISTRATION NUMBER: NCT04866147. BMJ Publishing Group 2021-12-30 /pmc/articles/PMC8718417/ http://dx.doi.org/10.1136/bmjopen-2021-053618 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Surgery
Papadomanolakis-Pakis, Nicholas
Haroutounian, Simon
Christiansen, Christian Fynbo
Nikolajsen, Lone
Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
title Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
title_full Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
title_fullStr Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
title_full_unstemmed Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
title_short Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
title_sort prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718417/
http://dx.doi.org/10.1136/bmjopen-2021-053618
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