Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings

Lucinda Archer; Kym I E Snell; Siobhán Stynes; Iben Axén; Kate M Dunn; Nadine E Foster; Gwenllian Wynne-Jones; Daniëlle A van der Windt; Jonathan C Hill

doi:10.1093/ptj/pzad128

Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings

Lucinda Archer, Kym I E Snell, Siobhán Stynes, Iben Axén, Kate M Dunn, Nadine E Foster, Gwenllian Wynne-Jones, Daniëlle A van der Windt, Jonathan C Hill^*

^*Corresponding author for this work

Applied Health Research

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Abstract

OBJECTIVE: The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings.

METHODS: Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance.

RESULTS: Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]).

CONCLUSION: Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance.

IMPACT: External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.

Original language	English
Article number	pzad128
Journal	Physical Therapy
Volume	103
Issue number	11
Early online date	26 Sept 2023
DOIs	https://doi.org/10.1093/ptj/pzad128
Publication status	Published - 4 Nov 2023

Bibliographical note

© The Author(s) 2023. Published by Oxford University Press on behalf of the American Physical Therapy Association.

Funding
This study presents work conducted as part of a project funded by the European Horizon 2020 Research and Innovation Program (Grant Agreement No. 777090). This study uses data collected as part of independent research funded by the National Institute for Health Research under its Programme Grants for Applied Research scheme (STarT MSK program RP-PG-1211-20010) as well as by Centre of Excellence funding from Versus Arthritis (Grant No. 20202). L. Archer and K. Snell were supported by funding from the Evidence Synthesis Working Group, which was funded by the National Institute for Health and Care Research School for Primary Care Research (Project No. 390) and by funding from the National Institute for Health and Care Research Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. K. Snell was funded by the National Institute for Health and Care Research School for Primary Care Research (NIHR SPCR Launching Fellowship). N. Foster was a National Institute for Health and Care Research senior investigator and supported through a National Institute for Health and Care Research Research Professorship (NIHR-RP-011-015).

Keywords

Adult
Humans
Neck Pain
Low Back Pain
Pain Measurement
Prognosis
Primary Health Care

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Archer, L., Snell, K. I. E., Stynes, S., Axén, I., Dunn, K. M., Foster, N. E., Wynne-Jones, G., van der Windt, D. A., & Hill, J. C. (2023). Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings. Physical Therapy, 103(11), Article pzad128. https://doi.org/10.1093/ptj/pzad128

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title = "Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings",

abstract = "OBJECTIVE: The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings.METHODS: Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance.RESULTS: Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]).CONCLUSION: Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance.IMPACT: External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.",

keywords = "Adult, Humans, Neck Pain, Low Back Pain, Pain Measurement, Prognosis, Primary Health Care",

author = "Lucinda Archer and Snell, {Kym I E} and Siobh{\'a}n Stynes and Iben Ax{\'e}n and Dunn, {Kate M} and Foster, {Nadine E} and Gwenllian Wynne-Jones and {van der Windt}, {Dani{\"e}lle A} and Hill, {Jonathan C}",

note = "{\textcopyright} The Author(s) 2023. Published by Oxford University Press on behalf of the American Physical Therapy Association. Funding This study presents work conducted as part of a project funded by the European Horizon 2020 Research and Innovation Program (Grant Agreement No. 777090). This study uses data collected as part of independent research funded by the National Institute for Health Research under its Programme Grants for Applied Research scheme (STarT MSK program RP-PG-1211-20010) as well as by Centre of Excellence funding from Versus Arthritis (Grant No. 20202). L. Archer and K. Snell were supported by funding from the Evidence Synthesis Working Group, which was funded by the National Institute for Health and Care Research School for Primary Care Research (Project No. 390) and by funding from the National Institute for Health and Care Research Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. K. Snell was funded by the National Institute for Health and Care Research School for Primary Care Research (NIHR SPCR Launching Fellowship). N. Foster was a National Institute for Health and Care Research senior investigator and supported through a National Institute for Health and Care Research Research Professorship (NIHR-RP-011-015).",

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doi = "10.1093/ptj/pzad128",

language = "English",

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Archer, L , Snell, KIE, Stynes, S, Axén, I, Dunn, KM, Foster, NE, Wynne-Jones, G, van der Windt, DA & Hill, JC 2023, 'Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings', Physical Therapy, vol. 103, no. 11, pzad128. https://doi.org/10.1093/ptj/pzad128

TY - JOUR

T1 - Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings

AU - Archer, Lucinda

AU - Snell, Kym I E

AU - Stynes, Siobhán

AU - Axén, Iben

AU - Dunn, Kate M

AU - Foster, Nadine E

AU - Wynne-Jones, Gwenllian

AU - van der Windt, Daniëlle A

AU - Hill, Jonathan C

N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of the American Physical Therapy Association. Funding This study presents work conducted as part of a project funded by the European Horizon 2020 Research and Innovation Program (Grant Agreement No. 777090). This study uses data collected as part of independent research funded by the National Institute for Health Research under its Programme Grants for Applied Research scheme (STarT MSK program RP-PG-1211-20010) as well as by Centre of Excellence funding from Versus Arthritis (Grant No. 20202). L. Archer and K. Snell were supported by funding from the Evidence Synthesis Working Group, which was funded by the National Institute for Health and Care Research School for Primary Care Research (Project No. 390) and by funding from the National Institute for Health and Care Research Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. K. Snell was funded by the National Institute for Health and Care Research School for Primary Care Research (NIHR SPCR Launching Fellowship). N. Foster was a National Institute for Health and Care Research senior investigator and supported through a National Institute for Health and Care Research Research Professorship (NIHR-RP-011-015).

PY - 2023/11/4

Y1 - 2023/11/4

N2 - OBJECTIVE: The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings.METHODS: Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance.RESULTS: Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]).CONCLUSION: Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance.IMPACT: External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.

AB - OBJECTIVE: The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings.METHODS: Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance.RESULTS: Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]).CONCLUSION: Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance.IMPACT: External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.

KW - Adult

KW - Humans

KW - Neck Pain

KW - Low Back Pain

KW - Pain Measurement

KW - Prognosis

KW - Primary Health Care

U2 - 10.1093/ptj/pzad128

DO - 10.1093/ptj/pzad128

M3 - Article

C2 - 37756617

SN - 0031-9023

VL - 103

JO - Physical Therapy

JF - Physical Therapy

IS - 11

M1 - pzad128

ER -

Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings

Abstract

Bibliographical note

Keywords

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