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Validation of a claims-based antipsychotic polypharmacy measure.

ABSTRACT: Purpose Given the metabolic and neurologic side effects of antipsychotics and concerns about the increased risks associated with concomitant use, antipsychotic polypharmacy is a quality concern. This study assessed the operating characteristics of a Medicaid claims-based measure of antipsychotic polypharmacy. Methods A random sample from 10 public mental health clinics and 312 patients met criteria for this study. Medical record extractors were blind to measure status. We examined the prevalence, sensitivity, specificity, and positive predictive value (PPV) in Medicaid claims, testing nine different definitions of antipsychotic polypharmacy, including >14, >60, or >90?days concurrent use of ?2 antipsychotic agents, each with allowable gaps of up to 0, 14, or 32?days in days' supply of antipsychotic medications. Results All Medicaid claims measure definitions tested had excellent specificity and PPV (>91%). Good to excellent sensitivity was dependent upon use of a 32-day gap allowance, particularly as duration of concurrent antipsychotic use increased. The proposed claims-based measure (90-day concurrent use of ?2 or more antipsychotics, allowing for a 32-day gap) had excellent specificity (99.1%, 95%CI: 98.2-99.6) and PPV (90.9%, 95%CI: 83.1-95.7) with good sensitivity (79.4%, 95%CI: 70.4-86.6). The overall level of concordance between claims and medical record-based categorization of antipsychotic polypharmacy was high (96.4%, n?=?301/312 clients, Cohen's K?=?84.7, 95%CI: 75.9-93.5). Discrepant cases were reviewed, and implications are discussed. Conclusions Administrative claims data can be used to construct valid measures of antipsychotic polypharmacy.

SUBMITTER: Leckman-Westin E

PROVIDER: S-EPMC4486331 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Validation of a claims-based antipsychotic polypharmacy measure.

Leckman-Westin Emily E Kealey Edith E Gupta Nitin N Chen Qingxian Q Gerhard Tobias T Crystal Stephen S Olfson Mark M Finnerty Molly M

Pharmacoepidemiology and drug safety 20140324 6

Purpose Given the metabolic and neurologic side effects of antipsychotics and concerns about the increased risks associated with concomitant use, antipsychotic polypharmacy is a quality concern. This study assessed the operating characteristics of a Medicaid claims-based measure of antipsychotic polypharmacy. Methods A random sample from 10 public mental health clinics and 312 patients met criteria for this study. Medical record extractors were blind to measure status. We examined the prevalence ...[more]

PMID: 24664793

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Project description:Administrative claims studies do not adequately distinguish pulmonary arterial hypertension (PAH) from other forms of pulmonary hypertension (PH). Our aim is to develop and validate a set of algorithms using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and electronic medical records (EMR), to identify patients with PAH. From January 2012 to August 2015, the EMRs of patients with ICD-9-CM codes for PH with an outpatient visit at the University of Texas Medical Branch were reviewed. Patients were divided into PAH or non-PAH groups according to EMR encounter diagnosis. Patient demographics, echocardiography, right heart catheterization (RHC) results, and PAH-specific therapies were assessed. RHC measurements were reviewed to categorize cases as hemodynamically determined PAH or not PAH. Weighted sensitivity, specificity, and positive and negative predictive values were calculated for the developed algorithms. A logistic regression analysis was conducted to determine how well the algorithms performed. External validation was performed at the University of Virginia Health System. The cohort for the development algorithms consisted of 683 patients with PH, PAH group (n = 191) and non-PAH group (n = 492). A hemodynamic diagnosis of PAH determined by RHC was recorded in the PAH (26%) and non-PAH (3%) groups. The positive predictive value for the algorithm that included ICD-9-CM and PAH-specific medications was 66.9% and sensitivity was 28.2% with a c-statistic of 0.66. The positive predictive value for the EMR-based algorithm that included ICD-9-CM, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medication was 69.4% and a c-statistic of 0.87. A validation cohort of 177 patients with PH examined from August 2015 to August 2016 using EMR-based algorithms yielded a similar positive predictive value of 62.5%. In conclusion, claims-based algorithms that included ICD-9-CM codes, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medications better-identified patients with PAH than ICD-9-CM codes alone.

Project description:BackgroundAdministrative healthcare claims data provide a mechanism for assessing and monitoring multiple sclerosis (MS) disease status across large, clinically representative "real-world" populations. The estimation of MS disease status using administrative claims can be a challenge, however, due to a lack of detailed clinical information. Retrospective claims analyses in MS have traditionally used rates of MS relapses to approximate disease status. Healthcare costs may be alternate, broader claims-based indicators of disease activity because costs reflect multiple facets of care of patients with MS, and there is a strong correlation between quality of life of patients with MS and costs of the disease. This study developed, tested, and validated a healthcare cost-based measure to serve as an indicator of overall disease status in patients with MS treated with disease-modifying drugs (DMDs) utilizing administrative claims.MethodsUsing IMS Health Real World Data Adjudicated Claims - US data (January 2006-June 2013), a negative binomial regression predicted annual all-cause medical costs. Coefficients reaching statistical significance (p < 0.05) and increasing costs by ?5% were selected for inclusion into an MS-specific severity score (scale of 0 to 100). Components of the score included rehabilitation services, altered mental state, pain, disability, stiffness, balance disorder, urinary incontinence, numbness, malaise/fatigue, and infections. Coefficient weights represented each predictor's contribution. The predictive model was derived using 50% of a random sample and tested/validated using the remaining 50%.ResultsAverage overall predicted annual total medical cost was $11,134 (development sample, n = 11,384, vs. $10,528 actual) and $11,303 (validation sample, n = 11,385, vs. $10,620 actual). The model had consistent bias (approximately +$600 or +6% of actual costs) for both samples. In the validation sample, mean MS disease status scores were 0.24, 8.95, and 21.77 for low, medium, and high tertiles, respectively. Mean costs were most accurately predicted among less severe patients ($5243 predicted vs. $5233 actual cost for lowest tertile).ConclusionThe algorithm developed in this study provides an initial step to helping understand and potentially predict cost changes for a commercially insured MS population.

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Validation of a claims-based antipsychotic polypharmacy measure.

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Validation of a claims-based antipsychotic polypharmacy measure.

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