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Validation of claims-based algorithms for pulmonary arterial hypertension.

ABSTRACT: 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.

SUBMITTER: Papani R

PROVIDER: S-EPMC5833187 | biostudies-literature | 2018 Apr-Jun

REPOSITORIES: biostudies-literature

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Validation of claims-based algorithms for pulmonary arterial hypertension.

Papani Ravikanth R Sharma Gulshan G Agarwal Amitesh A Callahan Sean J SJ Chan Winston J WJ Kuo Yong-Fang YF Shim Yun M YM Mihalek Andrew D AD Duarte Alexander G AG

Pulmonary circulation 20180401 2

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 o ...[more]

PMID: 29480064

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Project description:Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases. A systematic literature review was performed to find publications detailing code-based algorithms used to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients. PheValuator, a diagnostic predictive modelling tool, was applied to three US claims databases, yielding models that estimated the probability of a patient having the disease. These models were used to evaluate the performance characteristics of selected pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension algorithms. With increasing algorithm complexity, average positive predictive value increased (pulmonary arterial hypertension: 13.4-66.0%; chronic thromboembolic pulmonary hypertension: 10.3-75.1%) and average sensitivity decreased (pulmonary arterial hypertension: 61.5-2.7%; chronic thromboembolic pulmonary hypertension: 20.7-0.2%). Specificities and negative predictive values were high (≥97.5%) for all algorithms. Several of the algorithms performed well overall when considering all of these four performance parameters, and all algorithms performed with similar accuracy across the three claims databases studied, even though most were designed for patient identification in a specific database. Therefore, it is the objective of a study that will determine which algorithm may be most suitable; one- or two-component algorithms are most inclusive and three- or four-component algorithms identify most precise pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension populations, respectively.

Project description:Rationale: Event-driven primary endpoints are increasingly used in pulmonary arterial hypertension clinical trials, substantially increasing required sample sizes and trial lengths. The U.S. Food and Drug Administration advocates the use of prognostic enrichment of clinical trials by preselecting a patient population with increased likelihood of experiencing the trial's primary endpoint.Objectives: This study compares validated clinical scales of risk (Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension, the French score, and Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management [REVEAL] 2.0) to identify patients who are likely to experience a clinical worsening event for trial enrichment.Methods: Baseline data from three pulmonary arterial hypertension clinical trials (AMBITION [a Study of First-Line Ambrisentan and Tadalafil Combination Therapy in Subjects with Pulmonary Arterial Hypertension], SERAPHIN [Study of Macitentan on Morbidity and Mortality in Patients with Symptomatic Pulmonary Arterial Hypertension], and GRIPHON [Selexipag in Pulmonary Arterial Hypertension]) were pooled and standardized. Receiver operating curves were used to measure each algorithm's performance in predicting clinical worsening within the pooled placebo cohort. Power simulations were conducted to determine sample size and treatment time reductions for multiple enrichment strategies. A cost analysis was performed to illustrate potential financial savings by applying enrichment to GRIPHON.Measurements and Main Results: All risk algorithms were compared using area under the receiver operating curve and substantially outperformed prediction per New York Heart Association Functional Class. The REVEAL 2.0's risk grouping provided the greatest time and sample size savings in AMBITION and GRIPHON for all enrichment strategies but lacked appropriate inputs (i.e., N-terminal-proB-type natriuretic peptide) to perform as well in SERAPHIN. Cost analysis applied to GRIPHON demonstrated the greatest financial benefit by enrolling patients with a REVEAL score ≥8.Conclusions: This preliminary study demonstrates the feasibility of risk algorithms for pulmonary arterial hypertension trial enrichment and a need for further investigation.

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Validation of claims-based algorithms for pulmonary arterial hypertension.

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Validation of claims-based algorithms for pulmonary arterial hypertension.

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