Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study
BackgroundThe perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients’ health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2022-11-01
|
| Series: | JMIR Perioperative Medicine |
| Online Access: | https://periop.jmir.org/2022/1/e42341 |
| _version_ | 1797329459222675456 |
|---|---|
| author | Michael D Wood Nicholas C West Rama S Sreepada Kent C Loftsgard Luba Petersen Julie M Robillard Patricia Page Randa Ridgway Neil K Chadha Elodie Portales-Casamar Matthias Görges |
| author_facet | Michael D Wood Nicholas C West Rama S Sreepada Kent C Loftsgard Luba Petersen Julie M Robillard Patricia Page Randa Ridgway Neil K Chadha Elodie Portales-Casamar Matthias Görges |
| author_sort | Michael D Wood |
| collection | DOAJ |
| description |
BackgroundThe perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients’ health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric surgery are already known, the systematic use of preoperative information to guide personalized interventions is not yet widespread in clinical practice.
ObjectiveOur long-term goal is to reduce the incidence of persistent postsurgical pain (PPSP) and long-term opioid use in children by developing personalized pain risk prediction models that can guide clinicians and families to identify targeted prehabilitation strategies. To develop such a system, our first objective was to identify risk factors, outcomes, and relevant experience measures, as well as data collection tools, for a future data collection and risk modeling study.
MethodsThis study used a patient-oriented research methodology, leveraging parental/caregiver and clinician expertise. We conducted virtual focus groups with participants recruited at a tertiary pediatric hospital; each session lasted approximately 1 hour and was composed of clinicians or family members (people with lived surgical experience and parents of children who had recently undergone a procedure requiring general anesthesia) or both. Data were analyzed thematically to identify potential risk factors for pain, as well as relevant patient-reported experience and outcome measures (PREMs and PROMs, respectively) that can be used to evaluate the progress of postoperative recovery at home. This guidance was combined with a targeted literature review to select tools to collect risk factor and outcome information for implementation in a future study.
ResultsIn total, 22 participants (n=12, 55%, clinicians and n=10, 45%, family members) attended 10 focus group sessions; participants included 12 (55%) of 22 persons identifying as female, and 12 (55%) were under 50 years of age. Thematic analysis identified 5 key domains: (1) demographic risk factors, including both child and family characteristics; (2) psychosocial risk factors, including anxiety, depression, and medical phobias; (3) clinical risk factors, including length of hospital stay, procedure type, medications, and pre-existing conditions; (4) PREMs, including patient and family satisfaction with care; and (5) PROMs, including nausea and vomiting, functional recovery, and return to normal activities of daily living. Participants further suggested desirable functional requirements, including use of standardized and validated tools, and longitudinal data collection, as well as delivery modes, including electronic, parent proxy, and self-reporting, that can be used to capture these metrics, both in the hospital and following discharge. Established PREM/PROM questionnaires, pain-catastrophizing scales (PCSs), and substance use questionnaires for adolescents were subsequently selected for our proposed data collection platform.
ConclusionsThis study established 5 key data domains for identifying pain risk factors and evaluating postoperative recovery at home, as well as the functional requirements and delivery modes of selected tools with which to capture these metrics both in the hospital and after discharge. These tools have been implemented to generate data for the development of personalized pain risk prediction models. |
| first_indexed | 2024-03-08T07:04:53Z |
| format | Article |
| id | doaj.art-939e3727b6244292abac103b0affcb79 |
| institution | Directory Open Access Journal |
| issn | 2561-9128 |
| language | English |
| last_indexed | 2024-03-08T07:04:53Z |
| publishDate | 2022-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Perioperative Medicine |
| spelling | doaj.art-939e3727b6244292abac103b0affcb792024-02-03T04:56:01ZengJMIR PublicationsJMIR Perioperative Medicine2561-91282022-11-0151e4234110.2196/42341Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group StudyMichael D Woodhttps://orcid.org/0000-0002-3652-8320Nicholas C Westhttps://orcid.org/0000-0001-8382-5136Rama S Sreepadahttps://orcid.org/0000-0002-6271-6077Kent C Loftsgardhttps://orcid.org/0000-0002-6116-0046Luba Petersenhttps://orcid.org/0000-0002-6468-0776Julie M Robillardhttps://orcid.org/0000-0001-5765-4927Patricia Pagehttps://orcid.org/0000-0001-6570-1631Randa Ridgwayhttps://orcid.org/0000-0002-1226-5327Neil K Chadhahttps://orcid.org/0000-0002-1417-2175Elodie Portales-Casamarhttps://orcid.org/0000-0002-9998-7858Matthias Görgeshttps://orcid.org/0000-0003-2193-178X BackgroundThe perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients’ health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric surgery are already known, the systematic use of preoperative information to guide personalized interventions is not yet widespread in clinical practice. ObjectiveOur long-term goal is to reduce the incidence of persistent postsurgical pain (PPSP) and long-term opioid use in children by developing personalized pain risk prediction models that can guide clinicians and families to identify targeted prehabilitation strategies. To develop such a system, our first objective was to identify risk factors, outcomes, and relevant experience measures, as well as data collection tools, for a future data collection and risk modeling study. MethodsThis study used a patient-oriented research methodology, leveraging parental/caregiver and clinician expertise. We conducted virtual focus groups with participants recruited at a tertiary pediatric hospital; each session lasted approximately 1 hour and was composed of clinicians or family members (people with lived surgical experience and parents of children who had recently undergone a procedure requiring general anesthesia) or both. Data were analyzed thematically to identify potential risk factors for pain, as well as relevant patient-reported experience and outcome measures (PREMs and PROMs, respectively) that can be used to evaluate the progress of postoperative recovery at home. This guidance was combined with a targeted literature review to select tools to collect risk factor and outcome information for implementation in a future study. ResultsIn total, 22 participants (n=12, 55%, clinicians and n=10, 45%, family members) attended 10 focus group sessions; participants included 12 (55%) of 22 persons identifying as female, and 12 (55%) were under 50 years of age. Thematic analysis identified 5 key domains: (1) demographic risk factors, including both child and family characteristics; (2) psychosocial risk factors, including anxiety, depression, and medical phobias; (3) clinical risk factors, including length of hospital stay, procedure type, medications, and pre-existing conditions; (4) PREMs, including patient and family satisfaction with care; and (5) PROMs, including nausea and vomiting, functional recovery, and return to normal activities of daily living. Participants further suggested desirable functional requirements, including use of standardized and validated tools, and longitudinal data collection, as well as delivery modes, including electronic, parent proxy, and self-reporting, that can be used to capture these metrics, both in the hospital and following discharge. Established PREM/PROM questionnaires, pain-catastrophizing scales (PCSs), and substance use questionnaires for adolescents were subsequently selected for our proposed data collection platform. ConclusionsThis study established 5 key data domains for identifying pain risk factors and evaluating postoperative recovery at home, as well as the functional requirements and delivery modes of selected tools with which to capture these metrics both in the hospital and after discharge. These tools have been implemented to generate data for the development of personalized pain risk prediction models.https://periop.jmir.org/2022/1/e42341 |
| spellingShingle | Michael D Wood Nicholas C West Rama S Sreepada Kent C Loftsgard Luba Petersen Julie M Robillard Patricia Page Randa Ridgway Neil K Chadha Elodie Portales-Casamar Matthias Görges Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study JMIR Perioperative Medicine |
| title | Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study |
| title_full | Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study |
| title_fullStr | Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study |
| title_full_unstemmed | Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study |
| title_short | Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study |
| title_sort | identifying risk factors patient reported experience and outcome measures and data capture tools for an individualized pain prediction tool in pediatrics focus group study |
| url | https://periop.jmir.org/2022/1/e42341 |
| work_keys_str_mv | AT michaeldwood identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT nicholascwest identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT ramassreepada identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT kentcloftsgard identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT lubapetersen identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT juliemrobillard identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT patriciapage identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT randaridgway identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT neilkchadha identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT elodieportalescasamar identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy AT matthiasgorges identifyingriskfactorspatientreportedexperienceandoutcomemeasuresanddatacapturetoolsforanindividualizedpainpredictiontoolinpediatricsfocusgroupstudy |