Dataset Information

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.

ABSTRACT:

Objective

To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases.

Study setting

An existing electronic medical record project in Montreal, Quebec.

Study design

One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record.

Data collection/extraction methods

Therapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma-specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross-validation evaluated the diagnostic ability of each predictive model using 50 percent of sample.

Principal findings

The best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best-fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims-specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined.

Conclusions

Our algorithm using asthma-specific markers from administrative claims databases provided moderate sensitivity and high specificity.

SUBMITTER: Kawasumi Y

PROVIDER: S-EPMC3097410 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.

Kawasumi Yuko Y Abrahamowicz Michal M Ernst Pierre P Tamblyn Robyn R

Health services research 20110128 3

<h4>Objective</h4>To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases.<h4>Study setting</h4>An existing electronic medical record project in Montreal, Quebec.<h4>Study design</h4>One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record.<h4>Data collection/extraction methods</h4>Therapeutic indication of asthma in an electronic ...[more]

PMID: 21275988

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Project description:BackgroundConfounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.MethodsData from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts.ResultsIn the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79).ConclusionsA prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.

Dataset Information

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.

Objective

Study setting

Study design

Data collection/extraction methods

Principal findings

Conclusions

Publications

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.

Similar Datasets

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