Leveraging healthcare utilization to explore outcomes from musculoskeletal disorders: methodology for defining relevant variables from a health services data repository
Abstract:BackgroundLarge healthcare databases, with their ability to collect many variables from daily medical practice, greatly enable health services research. These longitudinal databases provide large cohorts and longitudinal time frames, allowing for highly pragmatic assessment of healthcare delivery. The purpose of this paper is to discuss the methodology related to the use of the United States Military Health System Data Repository (MDR) for longitudinal assessment of musculoskeletal clinical outcomes, as well a… Show more
“…1 ). Additional details of the extraction for cohort have been published and available (Rhon et al 2018 ). Fig.…”
Section: Methodsmentioning
confidence: 99%
“…A recent systematic review identified nine predictive models for determining opioid abuse, and the majority were based on diagnosis codes (ICD-9) (Alzeer et al 2018 ). Specific details for how these variables were extracted and their relevance to prognosis in individuals with musculoskeletal outcomes have been published (Rhon et al 2018 ). Seventeen variables were identified as potential predictors.…”
Section: Methodsmentioning
confidence: 99%
“…Care-oriented predictors included three or more visits of rehabilitation for the hip, occurrence of a hip infection, surgical procedures of femoroplasty (cam lesion), acetabuloplasty (pincer lesion), and arthroscopic repair of labrum. Specific diagnosis and procedure codes used for each category in this cohort have been published (Rhon et al 2018 ).…”
BackgroundPrescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic surgery.MethodsIndividuals were ages between 18 and 50 years and undergoing arthroscopic hip surgery between 2004 and 2013. Two categories of chronic opioid use were calculated based on individuals (1) having three or more unique opioid prescriptions within 2 years and (2) still receiving opioid prescriptions > 1 year after surgery. Univariate elationships were identified for each predictor variable, then significant variables (P > 0.15) were entered into a multivariate logistic regression model to identify the most parsimonious group of predictor variables for each chronic opioid use classification. Likelihood ratios were derived from the most robust groups of variables.ResultsThere were 1642 participants (mean age 32.5 years, SD 8.2, 54.1% male). Nine predictor variables met the criteria after bivariate analysis for potential inclusion in each multivariate model. Eight variables: socioeconomic status (from enlisted rank family), prior use of opioid medication, prior use of non-opioid pain medication, high health-seeking behavior before surgery, a preoperative diagnosis of insomnia, mental health disorder, or substance abuse were all predictive of chronic opioid use in the final model (seven variables for three or more opioid prescriptions; four variables for opioid use still at 1 year; all< 0.05). Post-test probability of having three or more opioid prescriptions was 93.7% if five of seven variables were present, and the probability of still using opioids after 1 year was 69.6% if three of four variables were present.ConclusionA combination of variables significantly predicted chronic opioid use in this cohort. Most of these variables were mediators, indicating that modifying them may be feasible, and the potential focus of interventions to decrease the risk of chronic opioid use, or at minimum better inform opioid prescribing decisions. This clinical prediction rule needs further validation.
“…1 ). Additional details of the extraction for cohort have been published and available (Rhon et al 2018 ). Fig.…”
Section: Methodsmentioning
confidence: 99%
“…A recent systematic review identified nine predictive models for determining opioid abuse, and the majority were based on diagnosis codes (ICD-9) (Alzeer et al 2018 ). Specific details for how these variables were extracted and their relevance to prognosis in individuals with musculoskeletal outcomes have been published (Rhon et al 2018 ). Seventeen variables were identified as potential predictors.…”
Section: Methodsmentioning
confidence: 99%
“…Care-oriented predictors included three or more visits of rehabilitation for the hip, occurrence of a hip infection, surgical procedures of femoroplasty (cam lesion), acetabuloplasty (pincer lesion), and arthroscopic repair of labrum. Specific diagnosis and procedure codes used for each category in this cohort have been published (Rhon et al 2018 ).…”
BackgroundPrescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic surgery.MethodsIndividuals were ages between 18 and 50 years and undergoing arthroscopic hip surgery between 2004 and 2013. Two categories of chronic opioid use were calculated based on individuals (1) having three or more unique opioid prescriptions within 2 years and (2) still receiving opioid prescriptions > 1 year after surgery. Univariate elationships were identified for each predictor variable, then significant variables (P > 0.15) were entered into a multivariate logistic regression model to identify the most parsimonious group of predictor variables for each chronic opioid use classification. Likelihood ratios were derived from the most robust groups of variables.ResultsThere were 1642 participants (mean age 32.5 years, SD 8.2, 54.1% male). Nine predictor variables met the criteria after bivariate analysis for potential inclusion in each multivariate model. Eight variables: socioeconomic status (from enlisted rank family), prior use of opioid medication, prior use of non-opioid pain medication, high health-seeking behavior before surgery, a preoperative diagnosis of insomnia, mental health disorder, or substance abuse were all predictive of chronic opioid use in the final model (seven variables for three or more opioid prescriptions; four variables for opioid use still at 1 year; all< 0.05). Post-test probability of having three or more opioid prescriptions was 93.7% if five of seven variables were present, and the probability of still using opioids after 1 year was 69.6% if three of four variables were present.ConclusionA combination of variables significantly predicted chronic opioid use in this cohort. Most of these variables were mediators, indicating that modifying them may be feasible, and the potential focus of interventions to decrease the risk of chronic opioid use, or at minimum better inform opioid prescribing decisions. This clinical prediction rule needs further validation.
“…These comorbid conditions were chosen because they have an established relationship with opioid use, and they have been shown to impact the prognosis of musculoskeletal disorders. 34 The specific International ICD codes used to identify these comorbidities are listed in the Supplemental Appendix . Based on guidelines used by the Armed Forces Health Surveillance Branch (central epidemiologic resource for the U.S. Armed Forces), 35 at least 2 separate visits for each comorbidity needed to be present for it to be counted.…”
Objectives Due to their potentially deleterious effects, minimizing the use of opioids for musculoskeletal pain is a priority for healthcare systems. The objective of this study was to examine the risk of future opioid prescription use based on prior opioid use within a non-surgical cohort with musculoskeletal knee pain. We also examined the risk of pre-existing comorbidities on future opioid use, and the risk of prior opioid use on future comorbidities (sleep, mental health, cardiometabolic disorders). Methods Data came from the Military Health System Data Repository for 80 290 consecutive beneficiaries with an initial episode of care for patellofemoral pain from January 1, 2010 through December 31, 2011. Risk was calculated using 2 × 2 tables based on pre- and post-opioid utilization and comorbid diagnosis. Risk ratios, relative and absolute risk increases, and numbers needed to harm were calculated, all with 95% confidence intervals. Results Prior opioid use resulted in a risk ratio of 18.0 (95 CI 17.1, 19.0) and an absolute risk increase of 61.6% for future opioid use (numbers needed to harm = 2). The presence of all comorbidities (except cardiometabolic syndrome) were associated with a significant relative risk for future opioid use (RR range 1.2-1.5), but the absolute risk increase was trivial (range 0.7%-2.2%). The relative risk for a chronic pain diagnosis, traumatic brain injury/concussion, insomnia, depression, and PTSD were all significantly higher in those with prior opioid use (1.3-1.6), but absolute risk increase was minimal (1.1%-6.5%). Discussion Prior opioid use was a strong risk factor for future opioid use in non-surgical patients with knee pain. These findings show that history of prior opioid use is important when assessing the risk of future opioid use, whereas prior comorbidities may not be as important. Opioid history assessment should be standard practice for all patients with patellofemoral pain in whom an opioid prescription is considered.
“…Identifying the onset of OA after traumatic injury is challenging because surveillance is needed in systems that can track comprehensive health services utilization for patients over long periods. The Military Health System (MHS) is one of very few large closed systems that exist in the United States, with very little missing data (Rhon, Clewley, Young, Sissel, & Cook, ).…”
Purpose: The aims of the present study were: (a) to identify the incidence of osteoarthritis (OA) after a traumatic knee injury; (b) identify the risk of post-traumatic osteoarthritis (PTOA) based on the type of injury; and (c) identify the time from injury to OA diagnosis.Patients and methods: The Expeditionary Medical Encounter Database, containing healthcare utilization for all deployment injuries sustained by military service members, was queried for traumatic knee injuries between 2001 and 2016. Subsequent diagnosis of knee OA was identified, defined as PTOA. Time to knee PTOA diagnosis was determined and logistic regression was used to obtain odds ratios (ORs) (95% confidence interval [CI]) between knee injury type and development of PTOA.Results: A total of 345 (9.57%) of the 3,605 subjects were diagnosed with PTOA.The median time to diagnosis was 4.10 years. Four primary diagnoses remained significantly associated with PTOA after adjusting for age and injury severity score: fracture (adjusted OR [aOR] = 1.36; 95% CI 1.02, 1.82), sprain (aOR = 1.59; 95% CI 1.23, 2.06), dislocation (aOR = 3.70; 95% CI 2.09, 6.55) and derangement (aOR = 2.38; 95% CI 1.33, 4.28). Subjects were significantly less likely to develop PTOA after a softtissue injury (aOR = 0.44; 95% CI 0.41, 0.75).
Conclusions:A substantial number of individuals with a traumatic knee injury developed early PTOA (9.6%). Certain knee injuries have a greater association with PTOA. Future studies should implement longer surveillance periods and identify other healthcare variables associated with the risk of developing PTOA, to include appropriate and timely interventions.
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