Development and electronic health record validation of an algorithm for identifying patients with Duchenne muscular dystrophy in US administrative claims.

BACKGROUND: Muscular dystrophies (MDs) comprise a heterogenous group of genetically inherited conditions characterized by progressive muscle weakness and increasing disability. The lack of separate diagnosis codes for Duchenne MD (DMD) and Becker MD, 2 of the most common forms of MD, has limited the conduct of DMD-specific real-world studies. OBJECTIVE: To develop and validate administrative claims-based algorithms for identifying patients with DMD and capturing their nonambulatory and ventilation-dependent status. METHODS: This was a retrospective cohort study using the statistically deidentified Optum Market Clarity Database (including patient claims linked with electronic health records [EHRs] data) to develop and validate the following algorithms: DMD diagnosis, nonambulatory status, and ventilation-dependent status. The initial study sample consisted of US patients in the database who had a diagnosis code for Duchenne/Becker MD (DBMD) between October 1, 2018, and September 30, 2020, who were male, aged 40 years or younger on their first DBMD diagnosis, and met continuous enrollment and 1-day minimal clinical activities requirement in a 12-month measurement period between October 1, 2017, and September 30, 2020. The algorithms, developed by a cross-functional team of DMD specialists (including patient advocates), were based on administrative claims data with International Classification of Diseases, Tenth Revision, Clinical Modifications coding, using information of diagnosis codes for DBMD, sex, age, treatment, and disease severity (eg, evidence of ambulation assistance/support and/or evidence of ventilation support or dependence). Patients who met each algorithm and had EHR notes available were then validated against structured fields and unstructured provider notes from their own linked EHR to confirm patients' DMD diagnoses, nonambulatory status, and ventilation-dependent status. Algorithm performance was assessed by positive predictive value with 95% CIs. RESULTS: A total of 1,300 patients were included in the initial study sample. Of these, EHR were available and reviewed for 303 patients. The mean age of the 303 patients was 14.8 years, with 61.7% being non-Hispanic White. A majority had a Charlson comorbidity index score of 0 (59.4%) or 1-2 (27.7%). Positive predictive value (95% CI) was 91.6% (85.8%-95.6%) for the DMD diagnosis algorithm, 88.4% (80.2%-94.1%) for the nonambulatory status algorithm, and 77.8% (62.9%-88.8%) for the ventilation-dependent status algorithm. CONCLUSIONS: This work provides the means to more accurately identify patients with DMD from administrative claims data without a specific diagnosis code. The algorithms validated in this study can be applied to assess treatment effectiveness and other outcomes among patients with DMD treated in clinical practice. DISCLOSURES: This study was funded by Pfizer, which contracted with Optum to perform the study and provide medical writing assistance. Ms Schrader reports being an employee of Parent Project Muscular Dystrophy. Mr Posner reports being an employee and stockholder of Pfizer and receiving support from Pfizer for attending conferences not related to this manuscript. Dr Dorling reports being an employee and stockholder of Pfizer at the time the study was conducted and is a current employee of Chiesi USA, Inc. Ms Senerchia reports being an employee of Optum and owning stock in Pfizer and UnitedHealth Group, the parent company of Optum. Dr Chen reports being an employee and stockholder of Pfizer. Ms Beaverson reports being an employee of Pfizer and owning stock in Pfizer and Amicus Therapeutics. Dr Seare reports being an employee of Optum at the time the study was conducted. Dr Garnier and Ms Merla report being employees of Pfizer. Ms Walker reports being an employee of Optum. Dr Alvir reports being an employee and stockholder of Pfizer. Dr Mahn reports being an employee and stockholder of Pfizer. Dr Zhang reports being an employee of Optum. Ms Landis reports being an employee of Optum. Ms Buikema reports being an employee of Optum and holding stock in UnitedHealth Group, the parent company of Optum.

Use of electronic health data to identify patients with moderate-to-severe osteoarthritis of the hip and/or knee and inadequate response to pain medications.

BACKGROUND: No algorithms exist to identify important osteoarthritis (OA) patient subgroups (i.e., moderate-to-severe disease, inadequate response to pain treatments) in electronic healthcare data, possibly due to the complexity in defining these characteristics as well as the lack of relevant measures in these data sources. We developed and validated algorithms intended for use with claims and/or electronic medical records (EMR) to identify these patient subgroups. METHODS: We obtained claims, EMR, and chart data from two integrated delivery networks. Chart data were used to identify the presence or absence of the three relevant OA-related characteristics (OA of the hip and/or knee, moderate-to-severe disease, inadequate/intolerable response to at least two pain-related medications); the resulting classification served as the benchmark for algorithm validation. We developed two sets of case-identification algorithms: one based on a literature review and clinical input (predefined algorithms), and another using machine learning (ML) methods (logistic regression, classification and regression tree, random forest). Patient classifications based on these algorithms were compared and validated against the chart data. RESULTS: We sampled and analyzed 571 adult patients, of whom 519 had OA of hip and/or knee, 489 had moderate-to-severe OA, and 431 had inadequate response to at least two pain medications. Individual predefined algorithms had high positive predictive values (all PPVs ≥ 0.83) for identifying each of these OA characteristics, but low negative predictive values (all NPVs between 0.16-0.54) and sometimes low sensitivity; their sensitivity and specificity for identifying patients with all three characteristics was 0.95 and 0.26, respectively (NPV 0.65, PPV 0.78, accuracy 0.77). ML-derived algorithms performed better in identifying this patient subgroup (range: sensitivity 0.77-0.86, specificity 0.66-0.75, PPV 0.88-0.92, NPV 0.47-0.62, accuracy 0.75-0.83). CONCLUSIONS: Predefined algorithms adequately identified OA characteristics of interest, but more sophisticated ML-based methods better differentiated between levels of disease severity and identified patients with inadequate response to analgesics. The ML methods performed well, yielding high PPV, NPV, sensitivity, specificity, and accuracy using either claims or EMR data. Use of these algorithms may expand the ability of real-world data to address questions of interest in this underserved patient population.

Medication use and comorbidities in an increasingly younger osteoarthritis population: an 18-year retrospective open-cohort study.

OBJECTIVES: As understanding of the pathogenesis and treatment strategies for osteoarthritis (OA) evolves, it is important to understand how patient factors are also changing. Our goal was to examine demographics and known risk factors of patients with OA over time. DESIGN: Open-cohort retrospective study using electronic health records. SETTING: Large US integrated health system with 7 hospitals, 2.6 million outpatient clinic visits and 97 300 hospital admissions annually in a mostly rural geographic region. PARTICIPANTS: Adult patients with at least two encounters and a diagnosis of OA or OA-relevant surgery between 2001 and 2018. Because of geographic region, over 96% of participants were white/Caucasian. INTERVENTIONS: None. PRIMARY AND SECONDARY OUTCOME MEASURES: Descriptive statistics were used to examine age, sex, body mass index (BMI), Charlson Comorbidity Index, major comorbidities and OA-relevant prescribing over time. RESULTS: We identified 290 897 patients with OA. Prevalence of OA increased significantly from 6.7% to 33.5% and incidence increased 37% (from 3772 to 5142 new cases per 100 000 patients per year) (p<0.0001). Percentage of females declined from 65.3% to 60.8%, and percentage of patients with OA in the youngest age bracket (18-45 years) increased significantly (6.2% to 22.7%, p<0.0001). The percentage of patients with OA with BMI ≥30 remained above 50% over the time period. Patients had low comorbidity overall, but anxiety, depression and gastro-oesophageal reflux disease showed the largest increases in prevalence. Opioid use (tramadol and non-tramadol) showed peaks followed by declines, while most other medications increased slightly in use or remained steady. CONCLUSIONS: We observe increasing OA prevalence and a greater proportion of younger patients over time. With better understanding of how characteristics of patients with OA are changing over time, we can develop better approaches for managing disease burden in the future.

Problematic opioid use among osteoarthritis patients with chronic post-operative pain after joint replacement: analyses from the BISCUITS study.

OBJECTIVES: Opioids are commonly used to manage pain, despite an increased risk of adverse events and complications when used against recommendations. This register study uses data of osteoarthritis (OA) patients with joint replacement surgery to identify and characterize problematic opioid use (POU) prescription patterns. METHODS: The study population included adult patients diagnosed with OA in specialty care undergoing joint replacement surgery in Denmark, Finland, Norway, and Sweden during 1 January 2011 to 31 December 2014. Those with cancer or OA within three years before the first eligible OA diagnosis were excluded. Patients were allocated into six POU cohorts based on dose escalation, frequency, and dosing of prescription opioids post-surgery (definitions were based on guidelines, previous literature, and clinical experience), and matched on age and sex to patients with opioid use, but not in any of the six cohorts. Data on demographics, non-OA pain diagnoses, cardiovascular diseases, psychiatric disorders, and clinical characteristics were used to study patient characteristics and predictors of POU. RESULTS: 13.7% of patients with OA and a hip/knee joint replacement were classified as problematic users and they had more comorbidities and higher pre-surgery doses of opioids than matches. Patients dispensing high doses of opioids pre-surgery dispensed increased doses post-surgery, a pattern not seen among patients prescribed lower doses pre-surgery. Being dispensed 1-4,500 oral morphine equivalents in the year pre-surgery or having a non-OA pain diagnosis was associated with post-surgery POU (OR: 1.44-1.50, and 1.11-1.20, respectively). CONCLUSIONS: Based on the discovered POU predictors, the study suggests that prescribers should carefully assess pain management strategies for patients with a history of comorbidities and pre-operative, long-term opioid use. Healthcare units should adopt risk assessment tools and ensure that these patients are followed up closely. The data also demonstrate potential areas for further exploration in improving patient outcomes and trajectories.

Associations of Healthcare Utilization and Costs with Increasing Pain and Treatment Intensity Levels in Osteoarthritis Patients: An 18-Year Retrospective Study.

INTRODUCTION: Osteoarthritis (OA) is a complex disease, and prior studies have documented the health and economic burdens of patients with OA compared to those without OA. Our goal was to use two strategies to further stratify OA patients based on both pain and treatment intensity to examine healthcare utilization and costs using electronic records from 2001 to 2018 at a large integrated health system. METHODS: Adult patients with ≥1 pain numerical rating scale (NRS) and diagnosis of OA were included. Pain episodes of ≥90 days were defined as mild (0-3), moderate (4-6), or severe (7-10) based on initial NRS. Patients were initially classified as mild and moved to moderate-severe OA if any of eight treatment-based criteria were met. Outpatient visits (OP), emergency department visits (ED), inpatient days, and healthcare costs (both all-cause and OA-specific) were compared among pain levels and OA severity levels as frequencies and per-member-per-year rates, using generalized linear regression models adjusting for age, sex, and body mass index, with contrasts of p < 0.05 considered significant. RESULTS: We identified 127,656 patients, 92,576 with pain scores. Moderate and severe pain were associated with significantly higher rates of OA-related utilization and costs, and all-cause ED visits and pharmacy costs. Moderate-severe OA patients had significantly higher OA-related utilization and costs, and all-cause OP, ED and pharmacy costs. CONCLUSIONS: Pain and treatment intensity were both strongly associated with OA-related utilization but not consistently with all-cause utilization. Our results provide promising evidence of better criteria and approaches for predicting disease burden and costs in the future.