Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool
BackgroundLow back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explor...
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JMIR Publications
2024-11-01
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| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2024/1/e64806 |
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| author | Anabela C Areias Robert G Moulder Maria Molinos Dora Janela Virgílio Bento Carolina Moreira Vijay Yanamadala Steven P Cohen Fernando Dias Correia Fabíola Costa |
| author_facet | Anabela C Areias Robert G Moulder Maria Molinos Dora Janela Virgílio Bento Carolina Moreira Vijay Yanamadala Steven P Cohen Fernando Dias Correia Fabíola Costa |
| author_sort | Anabela C Areias |
| collection | DOAJ |
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BackgroundLow back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explored, including those employing artificial intelligence (AI), they still lack scalability and real-time capabilities. Digital care programs (DCPs) facilitate seamless data collection through the Internet of Things and cloud storage, creating an ideal environment for developing and implementing an AI predictive tool to assist clinicians in dynamically optimizing treatment.
ObjectiveThis study aims to develop an AI tool that continuously assists physical therapists in predicting an individual’s potential for achieving clinically significant pain relief by the end of the program. A secondary aim was to identify predictors of pain nonresponse to guide treatment adjustments.
MethodsData collected actively (eg, demographic and clinical information) and passively in real-time (eg, range of motion, exercise performance, and socioeconomic data from public data sources) from 6125 patients enrolled in a remote digital musculoskeletal intervention program were stored in the cloud. Two machine learning techniques, recurrent neural networks (RNNs) and light gradient boosting machine (LightGBM), continuously analyzed session updates up to session 7 to predict the likelihood of achieving significant pain relief at the program end. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curves, specificity, and sensitivity. Model explainability was assessed using SHapley Additive exPlanations values.
ResultsAt each session, the model provided a prediction about the potential of being a pain responder, with performance improving over time (P<.001). By session 7, the RNN achieved an ROC-AUC of 0.70 (95% CI 0.65-0.71), and the LightGBM achieved an ROC-AUC of 0.71 (95% CI 0.67-0.72). Both models demonstrated high specificity in scenarios prioritizing high precision. The key predictive features were pain-associated domains, exercise performance, motivation, and compliance, informing continuous treatment adjustments to maximize response rates.
ConclusionsThis study underscores the potential of an AI predictive tool within a DCP to enhance the management of LBP, supporting physical therapists in redirecting care pathways early and throughout the treatment course. This approach is particularly important for addressing the heterogeneous phenotypes observed in LBP.
Trial RegistrationClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946 and NCT05417685; https://clinicaltrials.gov/ct2/show/NCT05417685 |
| format | Article |
| id | doaj-art-1ce3caeff5ab4f9fb804e1ea4f0c48bc |
| institution | Kabale University |
| issn | 2291-9694 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
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| series | JMIR Medical Informatics |
| spelling | doaj-art-1ce3caeff5ab4f9fb804e1ea4f0c48bc2024-11-19T21:04:23ZengJMIR PublicationsJMIR Medical Informatics2291-96942024-11-0112e6480610.2196/64806Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction ToolAnabela C Areiashttps://orcid.org/0000-0002-1807-7386Robert G Moulderhttps://orcid.org/0000-0001-7504-9560Maria Molinoshttps://orcid.org/0000-0002-9679-4639Dora Janelahttps://orcid.org/0000-0002-8440-2044Virgílio Bentohttps://orcid.org/0000-0001-6025-8511Carolina Moreirahttps://orcid.org/0000-0001-8781-1823Vijay Yanamadalahttps://orcid.org/0000-0002-2456-5888Steven P Cohenhttps://orcid.org/0000-0001-5928-2127Fernando Dias Correiahttps://orcid.org/0000-0001-8028-926XFabíola Costahttps://orcid.org/0000-0001-8981-7049 BackgroundLow back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explored, including those employing artificial intelligence (AI), they still lack scalability and real-time capabilities. Digital care programs (DCPs) facilitate seamless data collection through the Internet of Things and cloud storage, creating an ideal environment for developing and implementing an AI predictive tool to assist clinicians in dynamically optimizing treatment. ObjectiveThis study aims to develop an AI tool that continuously assists physical therapists in predicting an individual’s potential for achieving clinically significant pain relief by the end of the program. A secondary aim was to identify predictors of pain nonresponse to guide treatment adjustments. MethodsData collected actively (eg, demographic and clinical information) and passively in real-time (eg, range of motion, exercise performance, and socioeconomic data from public data sources) from 6125 patients enrolled in a remote digital musculoskeletal intervention program were stored in the cloud. Two machine learning techniques, recurrent neural networks (RNNs) and light gradient boosting machine (LightGBM), continuously analyzed session updates up to session 7 to predict the likelihood of achieving significant pain relief at the program end. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curves, specificity, and sensitivity. Model explainability was assessed using SHapley Additive exPlanations values. ResultsAt each session, the model provided a prediction about the potential of being a pain responder, with performance improving over time (P<.001). By session 7, the RNN achieved an ROC-AUC of 0.70 (95% CI 0.65-0.71), and the LightGBM achieved an ROC-AUC of 0.71 (95% CI 0.67-0.72). Both models demonstrated high specificity in scenarios prioritizing high precision. The key predictive features were pain-associated domains, exercise performance, motivation, and compliance, informing continuous treatment adjustments to maximize response rates. ConclusionsThis study underscores the potential of an AI predictive tool within a DCP to enhance the management of LBP, supporting physical therapists in redirecting care pathways early and throughout the treatment course. This approach is particularly important for addressing the heterogeneous phenotypes observed in LBP. Trial RegistrationClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946 and NCT05417685; https://clinicaltrials.gov/ct2/show/NCT05417685https://medinform.jmir.org/2024/1/e64806 |
| spellingShingle | Anabela C Areias Robert G Moulder Maria Molinos Dora Janela Virgílio Bento Carolina Moreira Vijay Yanamadala Steven P Cohen Fernando Dias Correia Fabíola Costa Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool JMIR Medical Informatics |
| title | Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool |
| title_full | Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool |
| title_fullStr | Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool |
| title_full_unstemmed | Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool |
| title_short | Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool |
| title_sort | predicting pain response to a remote musculoskeletal care program for low back pain management development of a prediction tool |
| url | https://medinform.jmir.org/2024/1/e64806 |
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