Dataset Information

Accuracy of Algorithms to Identify Pulmonary Arterial Hypertension in Administrative Data: A Systematic Review.

ABSTRACT:

Background

The diagnosis of pulmonary arterial hypertension (PAH) is challenging, and there is significant overlap with the more heterogenous diagnosis of pulmonary hypertension (PH). Clinical and research efforts that rely on administrative data are limited by current coding systems that do not adequately reflect the clinical classification scheme. The aim of this systematic review is to investigate current algorithms to detect PAH using administrative data and to appraise the diagnostic accuracy of these algorithms against a reference standard.

Methods

We conducted comprehensive searches of Medline, Embase, and Web of Science from their inception. We included English-language articles that applied an algorithm to an administrative or electronic health record database to identify PAH in adults.

Results

Of 2,669 unique citations identified, 32 studies met all inclusion criteria. Only four of these studies validated their algorithm against a reference standard. Algorithms varied widely, ranging from single International Classification of Diseases (ICD) codes to combinations of visit, procedure, and pharmacy codes. ICD codes alone performed poorly, with positive predictive values ranging from 3.3% to 66.7%. The addition of PAH-specific therapy and diagnostic procedures to the algorithm improved the diagnostic accuracy.

Conclusions

Algorithms to identify PAH in administrative databases vary widely, and few are validated. The sole use of ICD codes performs poorly, potentially leading to biased results. ICD codes should be revised to better discriminate between PH groups, and universally accepted algorithms need to be developed and validated to capture PAH in administrative data, better informing research and clinical efforts.

SUBMITTER: Gillmeyer KR

PROVIDER: S-EPMC6514429 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Accuracy of Algorithms to Identify Pulmonary Arterial Hypertension in Administrative Data: A Systematic Review.

Gillmeyer Kari R KR Lee Ming-Ming MM Link Alissa P AP Klings Elizabeth S ES Rinne Seppo T ST Wiener Renda Soylemez RS

Chest 20181122 4

<h4>Background</h4>The diagnosis of pulmonary arterial hypertension (PAH) is challenging, and there is significant overlap with the more heterogenous diagnosis of pulmonary hypertension (PH). Clinical and research efforts that rely on administrative data are limited by current coding systems that do not adequately reflect the clinical classification scheme. The aim of this systematic review is to investigate current algorithms to detect PAH using administrative data and to appraise the diagnosti ...[more]

PMID: 30471268

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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:BackgroundAdministrative data algorithms (ADAs) to identify pneumonia cases are commonly used in the analysis of pneumonia burden, trends, etiology, processes of care, outcomes, health care utilization, cost, and response to preventative and therapeutic interventions. However, without a good understanding of the validity of ADAs for pneumonia case identification, an adequate appreciation of this literature is difficult. We systematically reviewed the quality and accuracy of published ADAs to identify adult hospitalized pneumonia cases.MethodsWe reviewed the Medline, EMBase, and Cochrane Central databases through May 2020. All studies describing ADAs for adult hospitalized pneumonia and at least one accuracy statistic were included. Investigators independently extracted information about the sampling frame, reference standard, ADA composition, and ADA accuracy.ResultsThirteen studies involving 24 ADAs were analyzed. Compliance with a 38-item study-quality assessment tool ranged from 17 to 29 (median, 23; interquartile range [IQR], 20 to 25). Study setting, design, and ADA composition varied extensively. Inclusion criteria of most studies selected for high-risk populations and/or increased pneumonia likelihood. Reference standards with explicit criteria (clinical, laboratorial, and/or radiographic) were used in only 4 ADAs. Only 2 ADAs were validated (one internally and one externally). ADA positive predictive values ranged from 35.0 to 96.5% (median, 84.8%; IQR, 65.3 to 89.1%). However, these values are exaggerated for an unselected patient population because pneumonia prevalences in the study cohorts were very high (median, 66%; IQR, 46 to 86%). ADA sensitivities ranged from 31.3 to 97.8% (median, 65.1%; IQR 52.5-72.4).DiscussionADAs for identification of adult pneumonia hospitalizations are highly heterogeneous, poorly validated, and at risk for misclassification bias. Greater standardization in reporting ADA accuracy is required in studies using pneumonia ADA for case identification so that results can be properly interpreted.

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:BackgroundPrevious meta-analyses of treatments for pulmonary arterial hypertension (PAH) have not shown mortality benefit from any individual class of medication.MethodsMEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched from inception through November 2009 for randomized trials that evaluated any pharmacotherapy in the treatment of PAH. Reference lists from included articles and recent review articles were also searched. Analysis included randomized placebo controlled trials of at least eight weeks duration and studies comparing intravenous medication to an unblinded control group.Results1541 unique studies were identified and twenty-four articles with 3758 patients were included in the meta-analysis. Studies were reviewed and data extracted regarding study characteristics and outcomes. Data was pooled for three classes of medication: prostanoids, endothelin-receptor antagonists (ERAs), and phosphodiesterase type 5 (PDE5) inhibitors. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated for mortality, 6-minute walk distance, dyspnea scores, hemodynamic parameters, and adverse effects. Mortality in the control arms was a combined 4.2% over the mean study length of 14.9 weeks. There was significant mortality benefit with prostanoid treatment (RR 0.49, CI 0.29 to 0.82), particularly comparing intravenous agents to control (RR 0.30, CI 0.14 to 0.63). Mortality benefit was not observed for ERAs (RR 0.58, CI 0.21 to 1.60) or PDE5 inhibitors (RR 0.30, CI 0.08 to 1.08). All three classes of medication improved other clinical and hemodynamic endpoints. Adverse effects that were increased in treatment arms include jaw pain, diarrhea, peripheral edema, headache, and nausea in prostanoids; and visual disturbance, dyspepsia, flushing, headache, and limb pain in PDE5 inhibitors. No adverse events were significantly associated with ERA treatment.ConclusionsTreatment of PAH with prostanoids reduces mortality and improves multiple other clinical and hemodynamic outcomes. ERAs and PDE5 inhibitors improve clinical and hemodynamic outcomes, but have no proven effect on mortality. The long-term effects of all PAH treatment requires further study.

Project description:BackgroundLack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims. The objective of this study to evaluate published algorithms to identify patients with soft tissue sarcoma (STS) in administrative claims and to evaluate new algorithms to improved performance.MethodsTwo cancer populations including STS cases and non-STS controls were selected from the MarketScan Explorys Linked Claims-Electronic Medical Record (EMR) Database between January 1, 2000 and July 31, 2018. Eligible cases had a diagnosis on a clinical record for STS in the EMR while controls had no evidence of STS on any EMR records. Both cases and controls were enrolled in administrative claims during a period of observation and were aged???18 years. A split sample was used to test and validate algorithms using data from administrative claims. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated for 14 algorithms. Prior literature validating algorithms in administrative claims across other cancer types report both sensitivity and specificity ranging from as low as 73% to as high as 95%. This was used as a benchmark for defining algorithm success.ResultsThere were 784 STS cases and 249,062 non-STS cancer controls eligible for analysis. Requiring at least two claims with an ICD-CM diagnosis code for STS achieved a sensitivity of 67% but had a specificity of 72%. Algorithms that required NCCN-recommended systemic treatment for STS improved the specificity to over 90% but dropped the sensitivity to below 20%. Other combinations of diagnostic tests, symptoms, and procedures did not improve performance.ConclusionsThe algorithms tested in this study sample did not achieve sufficient performance and suggest the ability to accurately identify the STS population in administrative data is problematic. Difficulties are likely due to the origin of STS in a variety of locations, the non-specific symptoms of STS, and the common diagnostic tests recommended to diagnose the disease. Future research applying machine learning to examine timing and patterns of variables that comprise the diagnostic process may further investigate the ability to accurately identify STS cases in claims databases.

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.

Dataset Information

Accuracy of Algorithms to Identify Pulmonary Arterial Hypertension in Administrative Data: A Systematic Review.

Background

Methods

Results

Conclusions

Publications

Accuracy of Algorithms to Identify Pulmonary Arterial Hypertension in Administrative Data: A Systematic Review.

Similar Datasets

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