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Validation of an administrative claims coding algorithm for serious opioid overdose: A medical chart review.

ABSTRACT: PURPOSE:A standardized definition for serious opioid overdose has not been clearly established for disease surveillance or assessing the impact of risk mitigation strategies. The purpose of this study was to use medical chart review to clinically validate a claims-based algorithm to identify serious opioid overdose events. METHODS:The algorithm for serious opioid overdose required an opioid poisoning or external cause ICD-9-CM code occurring within 1 day of (a) an adverse effect code for serious central nervous system or respiratory depression or (b) a mechanical ventilation or critical care CPT code. The claims coding algorithm identified a sample of 145 individuals 18 years or older among patients that presented to the emergency department of two large hospitals in metropolitan Atlanta, Georgia from January 2014 to August 2015. Claims-defined cases were evaluated against rigorous clinical definitions for serious opioid overdose using (a) literature-based criteria for typical clinical manifestations of opioid overdose and/or (b) clinical response to the opioid-specific reversal agent naloxone. The positive predictive value (PPV) for a serious opioid overdose was calculated as the percentage of clinically confirmed cases (definite or probable). RESULTS:Among 140 evaluable claims-defined cases, 107 fulfilled clinical criteria for a serious opioid overdose [95 definite and 12 probable; PPV of 76.4% (95% CI 69.4%, 83.5%)]. Among 30 nonconfirmed cases, 20 were polyintoxications involving one or more nonopioid psychoactive agents. CONCLUSIONS:An administrative claims coding algorithm for serious opioid overdose had high clinical predictive performance in a medical chart review.

SUBMITTER: Mountcastle SB

PROVIDER: S-EPMC6810754 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Validation of an administrative claims coding algorithm for serious opioid overdose: A medical chart review.

Mountcastle Sally B SB Joyce Andrew R AR Sasinowski Maciek M Costello Nancy N Doshi Snehal S Zedler Barbara K BK

Pharmacoepidemiology and drug safety 20190904 10

<h4>Purpose</h4>A standardized definition for serious opioid overdose has not been clearly established for disease surveillance or assessing the impact of risk mitigation strategies. The purpose of this study was to use medical chart review to clinically validate a claims-based algorithm to identify serious opioid overdose events.<h4>Methods</h4>The algorithm for serious opioid overdose required an opioid poisoning or external cause ICD-9-CM code occurring within 1 day of (a) an adverse effect c ...[more]

PMID: 31483548

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Project description:BackgroundMuscular dystrophies (MDs) are a group of inherited conditions characterized by progressive muscle degeneration and weakness. The rarity and heterogeneity of the population with MD have hindered therapeutic developments as well as epidemiological and health outcomes research. The objective of the study was to develop and validate a case-finding algorithm utilizing administrative claims data to identify and characterize patients with MD.MethodsThis retrospective cohort study used medical chart validation to evaluate an ICD-9/10 coding algorithm in a large commercial claims database. Patients were identified who had ≥2 office visits with a diagnosis of hereditary progressive MDs from January 1, 2013 through December 31, 2016, were male, and younger than 18 years at the time of first MD diagnosis. Cases who met the algorithm were then validated against medical charts. Diagnoses of MD and specific type (Duchenne, Becker, or other MD) were confirmed by medical chart review by trained reviewers. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated using a 2 × 2 contingence table. Patient demographic, clinical, and health utilization characteristics were summarized using basic descriptive statistics.ResultsCharts were obtained and reviewed for 109 patients who met the algorithm. The PPV of the case-identifying algorithm for MD was 95% (95% CI 88-98%). Of the 103 confirmed MD cases, 87 patients (85%, 95% CI 76-91%) had Duchenne or Becker MD; 76 patients (74%, 95% CI 64-82%) had Duchenne MD, and 11 patients (11%, 95% CI 5-18%) had Becker MD. A total of 74 (67.9%) patients had ≥1 pediatric complex chronic condition (other than neurologic/neuromuscular disease); 54 (49.5%) had cardiovascular conditions; 14 (12.8%) had respiratory conditions; 50 (45.9%) had bone-related issues; 11 (10.1%) had impaired growth; and 6 (5.5%) had puberty delay.ConclusionsThe results of this study demonstrate that the case-finding algorithm accurately identified patients with MD, primarily Duchenne MD, within a large administrative database. The algorithm, which was constructed using a few items easily accessible from claims, can be used to facilitate epidemiological and health outcomes research in the Duchenne patient population.

Project description:STUDY DESIGNRetrospective analysis of Medicare claims linked to a multicenter clinical trial.OBJECTIVEThe Spine Patient Outcomes Research Trial (SPORT) provided a unique opportunity to examine the validity of a claims-based algorithm for grouping patients by surgical indication. SPORT enrolled patients for lumbar disc herniation, spinal stenosis, and degenerative spondylolisthesis. We compared the surgical indication derived from Medicare claims with that provided by SPORT surgeons, the "gold standard."SUMMARY OF BACKGROUND DATAAdministrative data are frequently used to report procedure rates, surgical safety outcomes, and costs in the management of spinal surgery. However, the accuracy of using diagnosis codes to classify patients by surgical indication has not been examined.METHODSMedicare claims were link to beneficiaries enrolled in SPORT. The sensitivity and specificity of 3 claims-based approaches to group patients on the basis of surgical indications were examined: (1) using the first listed diagnosis; (2) using all diagnoses independently; and (3) using a diagnosis hierarchy on the basis of the support for fusion surgery.RESULTSMedicare claims were obtained from 376 SPORT participants, including 21 with disc herniation, 183 with spinal stenosis, and 172 with degenerative spondylolisthesis. The hierarchical coding algorithm was the most accurate approach for classifying patients by surgical indication, with sensitivities of 76.2%, 88.1%, and 84.3% for disc herniation, spinal stenosis, and degenerative spondylolisthesis cohorts, respectively. The specificity was 98.3% for disc herniation, 83.2% for spinal stenosis, and 90.7% for degenerative spondylolisthesis. Misclassifications were primarily due to codes attributing more complex pathology to the case.CONCLUSIONStandardized approaches for using claims data to group patients accurately by surgical indications have widespread interest. We found that a hierarchical coding approach correctly classified more than 90% of spine patients into their respective SPORT cohorts. Therefore, claims data seem to be a reasonably valid approach to classifying patients by surgical indication.LEVEL OF EVIDENCE3.

Project description:Administrative claims data have not commonly been used to study the clinical effectiveness of medications for rheumatoid arthritis (RA) because of the lack of a validated algorithm for this outcome. We created and tested a claims-based algorithm to serve as a proxy for the clinical effectiveness of RA medications.We linked Veterans Health Administration (VHA) medical and pharmacy claims for RA patients participating in the longitudinal Department of Veterans Affairs (VA) RA registry (VARA). Among individuals for whom treatment with a new biologic agent or nonbiologic disease-modifying agent in rheumatic disease (DMARD) was being initiated and with registry follow-up at 1 year, VARA and administrative data were used to create a gold standard for the claims-based effectiveness algorithm. The gold standard outcome was low disease activity (LDA) (Disease Activity Score using 28 joint counts (DAS28) ? 3.2) or improvement in DAS28 by > 1.2 units at 12 ± 2 months, with high adherence to therapy. The claims-based effectiveness algorithm incorporated biologic dose escalation or switching, addition of new disease-modifying agents, increase in oral glucocorticoid use and dose as well as parenteral glucocorticoid injections.Among 1,397 patients, we identified 305 eligible biologic or DMARD treatment episodes in 269 unique individuals. The patients were primarily men (94%) with a mean (± SD) age of 62 ± 10 years. At 1 year, 27% of treatment episodes achieved the effectiveness gold standard. The performance characteristics of the effectiveness algorithm were as follows: positive predictive value, 76% (95% confidence interval (95% CI) = 71% to 81%); negative predictive value, 90% (95% CI = 88% to 92%); sensitivity, 72% (95% CI = 67% to 77%); and specificity, 91% (95% CI = 89% to 93%).Administrative claims data may be useful in evaluating the effectiveness of medications for RA. Further validation of this effectiveness algorithm will be useful in assessing its generalizability and performance in other populations.

Project description:BackgroundTo effectively use administrative claims for healthcare research, clinical events must be inferred from coding data according to validated algorithms. In October 2015, the United States transitioned from the International Classification of Diseases Ninth Revision (ICD-9) to the Tenth Revision (ICD-10). We describe our method to derive new ICD-10 codes for outcomes after vascular procedures from our prior, validated ICD-9 codes.MethodsWe began with validated ICD-9 coding lists known to represent outcomes after lower extremity revascularization, thoracic aortic endograft placement, abdominal aortic aneurysm reintervention, and carotid revascularization. We used the publicly available general equivalence mapping tools to derive corresponding ICD-10 codes for each of the ICD-9 codes in our current lists. The resulting lists were then manually reviewed by multiple authors to ensure clinical relevance for appropriate event detection. Clinically nonrelevant and duplicated codes were removed.ResultsA total of 475 ICD-9 codes were translated to ICD-10 with a 98-fold increase (n = 46,630) in the total number of codes. Overall, we found that 77% of codes (n = 35,833) were either duplicated or not clinically relevant upon manual review. For example, for thoracic aortic endograft placement, 97 ICD-9 codes mapped to 14,661 ICD-10 codes in total. A total of 890 codes were removed as duplicates and 9035 codes were removed during manual clinical review. The resultant, reviewed list contained 4736 ICD-10 codes representing a 49-fold increase from the initial ICD-9 list. Findings were similar across the other procedures studied.ConclusionsICD-10 has expanded the number of codes necessary to describe outcomes after vascular procedures. More than 75% of the codes obtained using the general equivalence mapping database were either duplicated or not clinically relevant. Manual review of codes by researchers with clinical knowledge of the procedures is imperative.

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Validation of an administrative claims coding algorithm for serious opioid overdose: A medical chart review.

Publications

Validation of an administrative claims coding algorithm for serious opioid overdose: A medical chart review.

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