Validation of claims-based algorithms for pulmonary arterial hypertension

Clinical evaluation of code-based algorithms to identify patients with pulmonary arterial hypertension in healthcare databases

Pulmonary arterial hypertension (PAH) is a rare subgroup of pulmonary hypertension (PH). Claims and administrative databases can be particularly important for research in rare diseases; however, there is a lack of validated algorithms to identify PAH patients using administrative codes. We aimed to measure the accuracy of code-based PAH algorithms against the true clinical diagnosis by right heart catheterization (RHC). This study evaluated algorithms in patients who were recorded in two linkable data assets: the Stanford Healthcare administrative electronic health record database and the Stanford Vera Moulton Wall Center clinical PH database (which records each patient's RHC diagnosis). We assessed the sensitivity and specificity achieved by 16 algorithms (six published). In total, 720 PH patients with linked data available were included and 558 (78%) of these were PAH patients. Algorithms consisting solely of a P(A)H-specific diagnostic code classed all or almost all PH patients as PAH (sensitivity >97%, specificity <12%) while multicomponent algorithms with well-defined temporal sequences of procedure, diagnosis and treatment codes achieved a better balance of sensitivity and specificity. Specificity increased and sensitivity decreased with increasing algorithm complexity. The best-performing algorithms, in terms of fewest misclassified patients, included multiple components (e.g., PH diagnosis, PAH treatment, continuous enrollment for ≥6 months before and ≥12 months following index date) and achieved sensitivities and specificities of around 95% and 38%, respectively. Our findings help researchers tailor their choice and design of code-based PAH algorithms to their research question and demonstrate the importance of including well-defined temporal components in the algorithms.

Validation of diagnostic coding for bronchiectasis in an electronic health record system in Hong Kong

BACKGROUND AND OBJECTIVE

Electronic medical record (EMR) databases can facilitate epidemiology research in various diseases including bronchiectasis. Given the diagnostic challenges of bronchiectasis, the validity of the coding in EMR requires clarification. We aimed to assess the validity of International Classification of Diseases, 9th Revision (ICD-9) code algorithms for identifying bronchiectasis in the territory-wide electronic medical health record system of Clinical Data Analysis and Reporting System (CDARS) in Hong Kong.

MATERIALS AND METHODS

Adult patients who had the diagnosis of bronchiectasis input from Queen Mary Hospital in 2011-2020 were identified using the ICD-9 code of 494 by CDARS. All patients who had high resolution computed tomography (HRCT) were reviewed by respiratory specialists to confirm the presence of bronchiectasis on HRCT.

RESULTS

A total of 19 617 patients who had the diagnostic code of bronchiectasis among all public hospitals in Hong Kong and 1866 in Queen Mary Hospital in the same period. Six hundred and forty-eight cases were randomly selected and validated using medical record and HRCT review by a respiratory specialist. The overall positive predictive value (PPV) was 92.7% (95% CI 90.7-94.7).

CONCLUSIONS

This was the first ICD-9 coding validation for bronchiectasis in Hong Kong CDARS. Our study demonstrated that using ICD-9 code of 494 was reliable to support utility of CDARS database for further clinical research on bronchiectasis.

Real-world clinical practice of pulmonary arterial hypertension in Japan: Insights from a large administrative database

Pulmonary arterial hypertension (PAH) is a fatal disease that often occurs at an early age. In recent years, aggressive treatment with multiple drugs from the early-stage diagnosis is expected to improve the prognosis. Indeed, a high rate of initial combination therapy and excellent treatment outcomes have been reported from specialized centers for PAH in Japan. However, information on PAH epidemiology, including non-PAH specialized centers in Japan, is unclear. To address the above, we conducted a retrospective observational cohort study from April 2008 to September 2020 using real-world evidence from a large-scale administrative database (Medical Data Vision) to examine baseline characteristics, comorbidities, and treatment profiles of Japanese patients with PAH. Five hundred and eighteen patients with PAH (treatment-naive PAH, age 67.2 ± 15.9) were identified through our comprehensive approach which combined PAH disease codes, medications, and diagnostic procedures. Moreover, we showed that a larger proportion of patients received monotherapy in their initial treatment (66%) compared to those receiving combination therapy (34%). During the 1-year follow-up after PAH diagnosis, 13% of patients increased their PAH medications while other patients either decreased their PAH medications (6%) or discontinued PAH treatment (27%). The 3- and 5-year event-free survival rates of all-cause death were 72% and 64%, respectively. This is the first large-scale administrative database study that provides insights into real-world PAH management in Japan. This study highlighted a different PAH clinical landscape which included a larger portion of the elderly population, higher initial monotherapy treatment, and lower survival rates than previous studies.

Validation of Diagnostic Coding for Asthma in an Electronic Health Record System in Hong Kong

Background

Electronic health record (EHR) databases can facilitate epidemiology research into various diseases including asthma. Given the diagnostic challenges of asthma, the validity of the coding in EHR requires clarification. We aimed to assess the validity of International Classification of Diseases, 9th Revision (ICD-9) code algorithms for identifying asthma in the territory-wide electronic medical health record system of the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong.

Methods

Adult patients who had the diagnosis of asthma input from all public hospitals in Hong Kong and those from Queen Mary Hospital in 2011-2020 were identified using the ICD-9 code of 493 (493.0, 493.1, 493.2, and 493.9) by CDARS. Patients' clinical record and spirometry were reviewed by two respiratory specialists to confirm the presence of asthma in the randomly selected cases.

Results

There were 43,454 patients who had the diagnostic code of asthma among all public hospitals in Hong Kong and 1852 in Queen Mary Hospital in the same period. A total of 200 cases were randomly selected and validated using medical record and spirometry review by a respiratory specialist. The overall positive predictive value (PPV) was 85.0% (95% CI 80.1-89.9%).

Conclusion

This was the first ICD-9 code validation for CDARS (EHR) in Hong Kong on asthma. Our study demonstrated that using ICD-9 code (493.0, 493.1, 493.2 and 493.9) to identify asthma can result in a PPV that was reliable to support the utility of the CDARS database for further research on asthma among the Hong Kong population.

Epidemiology of PAH in Korea: An Analysis of the National Health Insurance Data, 2002–2018

BACKGROUND AND OBJECTIVES

Pulmonary arterial hypertension (PAH) is a rare but fatal disease. Recent advances in PAH-specific drugs have improved its outcomes, although the healthcare burden of novel therapeutics may lead to a discrepancy in outcomes between developing and developed countries. We analyzed how the epidemiology and clinical features of PAH has changed through the rapidly advancing healthcare infrastructure in South Korea.

METHODS

PAH was defined according to a newly devised 3-component algorithm. Using a nationwide health insurance claims database, we delineated annual trends in the prevalence, incidence, medication prescription pattern, and 5-year survival of PAH in Korea. Cumulative survival and potential predictors of mortality were also assessed among 2,151 incident PAH cases.

RESULTS

Between 2002 or 2004 and 2018, the prevalence and incidence of PAH increased 75-fold (0.4 to 29.9 per million people) and 12-fold (0.5 to 6.3 per million person-years), respectively. The proportion of patients on combination PAH-specific drug therapy has also steadily increased up to 29.0% in 2018. Among 2,151 incident PAH cases (median [interquartile range] age, 50 [37-62] years; 67.2% female), the 5-year survival rate and median survival duration were 71.8% and 13.1 years, respectively. Independent predictors of mortality were age, sex, etiology of PAH, diabetes, dyslipidemia, and chronic kidney disease.

CONCLUSIONS

This nationwide study delineated that the prevalence and incidence of PAH have grown rapidly in Korea since the early 2000s. The use of combination therapy has also increased, and the 5-year survival rate of PAH in Korea was similar to those in western countries.

An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record

BACKGROUND

Study of pulmonary arterial hypertension (PAH) in claims-based (CB) cohorts may facilitate understanding of disease epidemiology, however previous CB algorithms to identify PAH have had limited test characteristics. We hypothesized that machine learning algorithms (MLA) could accurately identify PAH in an CB cohort.

METHODS

ICD-9/10 codes, CPT codes or PAH medications were used to screen an electronic medical record (EMR) for possible PAH. A subset (Development Cohort) was manually reviewed and adjudicated as PAH or "not PAH" and used to train and test MLAs. A second subset (Refinement Cohort) was manually reviewed and combined with the Development Cohort to make The Final Cohort, again divided into training and testing sets, with MLA characteristics defined on test set. The MLA was validated using an independent EMR cohort.

RESULTS

194 PAH and 786 "not PAH" in the Development Cohort trained and tested the initial MLA. In the Final Cohort test set, the final MLA sensitivity was 0.88, specificity was 0.93, positive predictive value was 0.89, and negative predictive value was 0.92. Persistence and strength of PAH medication use and CPT code for right heart catheterization were principal MLA features. Applying the MLA to the EMR cohort using a split cohort internal validation approach, we found 265 additional non-confirmed cases of suspected PAH that exhibited typical PAH demographics, comorbidities, hemodynamics.

CONCLUSIONS

We developed and validated a MLA using only CB features that identified PAH in the EMR with strong test characteristics. When deployed across an entire EMR, the MLA identified cases with known features of PAH.

Validation of diagnostic coding for interstitial lung diseases in an electronic health record system in Hong Kong

OBJECTIVE

Large electronic medical record (EMR) databases can facilitate epidemiology research into uncommon diseases such as interstitial lung disease (ILD). Given the rarity and diagnostic difficulty of ILD, the validity of the coding in EMR requires clarification. We aimed to assess the validity of International Classification of Diseases, 9th Revision (ICD-9) code algorithms for identifying ILD in the territory-wide electronic medical health record system of Clinical Data Analysis and Reporting System (CDARS) in Hong Kong.

METHOD

Patients who visited the Queen Mary Hospital in 2005-18 with ILD were identified using the following ICD-9 codes: post-inflammatory pulmonary fibrosis (PPF; ICD-9: 515), idiopathic fibrosing alveolitis (IFA; ICD-9: 516.3), connective tissue disease-associated interstitial lung disease (CTD-ILD; ICD-9: 517.2, 517.8, 714.81), sarcoidosis (ICD-9: 135) and extrinsic allergic alveolitis (EAA; ICD-9: 495). A random selection was conducted in cases with diagnostic code of PPF and IFA, where a relative higher case number was identified. All the cases of CTD-ILD, sarcoidosis and EAA were included in validation for relatively small case number.

RESULTS

269 cases were validated using medical record review by a respiratory specialist. The overall positive predictive value (PPV) was 79% (95% CI, 74 to 84%). In subgroup analysis, true positive case numbers of PPF, IFA, CTD-ILD, sarcoidosis and EAA were 74 / 100 (74%), 95 / 100 (95%), 11 / 15 (73%), 27 / 32 (84%) and 6 / 22 (27%), respectively.

CONCLUSIONS

This was the first ICD-9 coding validation for ILD in Hong Kong CDARS. Our study demonstrated that using ICD-9 algorithms 515, 516.3, 517.2, 517.8, 714.81 and 135 enhanced identifications of ILDs with PPV that was reliable to support utility of CDARS database for further clinical research on ILDs. The validity is particularly high with 516.3. This article is protected by copyright. All rights reserved.

Population-Based Penetrance of Deleterious Clinical Variants

IMPORTANCE

Population-based assessment of disease risk associated with gene variants informs clinical decisions and risk stratification approaches.

OBJECTIVE

To evaluate the population-based disease risk of clinical variants in known disease predisposition genes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 72 434 individuals with 37 780 clinical variants who were enrolled in the BioMe Biobank from 2007 onwards with follow-up until December 2020 and the UK Biobank from 2006 to 2010 with follow-up until June 2020. Participants had linked exome and electronic health record data, were older than 20 years, and were of diverse ancestral backgrounds.

EXPOSURES

Variants previously reported as pathogenic or predicted to cause a loss of protein function by bioinformatic algorithms (pathogenic/loss-of-function variants).

MAIN OUTCOMES AND MEASURES

The primary outcome was the disease risk associated with clinical variants. The risk difference (RD) between the prevalence of disease in individuals with a variant allele (penetrance) vs in individuals with a normal allele was measured.

RESULTS

Among 72 434 study participants, 43 395 were from the UK Biobank (mean [SD] age, 57 [8.0] years; 24 065 [55%] women; 2948 [7%] non-European) and 29 039 were from the BioMe Biobank (mean [SD] age, 56 [16] years; 17 355 [60%] women; 19 663 [68%] non-European). Of 5360 pathogenic/loss-of-function variants, 4795 (89%) were associated with an RD less than or equal to 0.05. Mean penetrance was 6.9% (95% CI, 6.0%-7.8%) for pathogenic variants and 0.85% (95% CI, 0.76%-0.95%) for benign variants reported in ClinVar (difference, 6.0 [95% CI, 5.6-6.4] percentage points), with a median of 0% for both groups due to large numbers of nonpenetrant variants. Penetrance of pathogenic/loss-of-function variants for late-onset diseases was modified by age: mean penetrance was 10.3% (95% CI, 9.0%-11.6%) in individuals 70 years or older and 8.5% (95% CI, 7.9%-9.1%) in individuals 20 years or older (difference, 1.8 [95% CI, 0.40-3.3] percentage points). Penetrance of pathogenic/loss-of-function variants was heterogeneous even in known disease predisposition genes, including BRCA1 (mean [range], 38% [0%-100%]), BRCA2 (mean [range], 38% [0%-100%]), and PALB2 (mean [range], 26% [0%-100%]).

CONCLUSIONS AND RELEVANCE

In 2 large biobank cohorts, the estimated penetrance of pathogenic/loss-of-function variants was variable but generally low. Further research of population-based penetrance is needed to refine variant interpretation and clinical evaluation of individuals with these variant alleles.

Anesthesia in Children With Pulmonary Hypertension: Clinically Significant Serious Adverse Events Associated With Cardiac Catheterization and Noncardiac Procedures

OBJECTIVES

To determine the incidence of clinically significant serious adverse events in a contemporary population of pediatric patients with pulmonary hypertension who require anesthesia and identify factors associated with adverse outcomes.

DESIGN

A retrospective, cross-sectional study.

SETTING

A single-center quaternary-care freestanding children's hospital in the northeastern United States.

PARTICIPANTS

Pediatric patients with pulmonary hypertension based on hemodynamic criteria on cardiac catheterization during a 3-year period from 2015 to 2018.

INTERVENTIONS

Anesthesia care for cardiac catheterization, noncardiac surgery, and diagnostic imaging.

MEASUREMENTS AND MAIN RESULTS

Two hundred forty-nine children underwent 862 procedures, 592 for cardiac catheterization and 278 for noncardiac surgery and diagnostic imaging. The median age was 1.6 years, and the weight was 9.5 lbs. On index catheterization, median pulmonary artery pressure was 36 mmHg, and the pulmonary vascular resistance was 5.1 indexed Wood units. Ten percent of anesthetics were performed with a natural airway, and 80% used volatile anesthetics. Serious adverse events occurred in 26% of procedures (confidence interval [CI], 22%-30%). The rate of periprocedural cardiac arrest was 8 per 1,000 anesthetic administrations. In multivariate analysis, younger age (adjusted odds ratio [aOR], 1.4 per year; CI, 1.1-1.9; p = 0.01), location in the catheterization laboratory (aOR, 5.1; CI, 1.7-16; p = 0.004), and longer procedure duration (aOR, 1.3 per 30 minutes; CI, 1.1-1.4; p = 0.001) were associated with serious adverse events. Patients with a tracheostomy in place were less likely to experience an adverse event (aOR, 0.1; CI, 0.04-0.5; p = 0.001). The primary anesthetic technique was not associated with adverse events. Interventional cardiac catheterization was associated with an increased incidence of adverse events compared with diagnostic catheterization (42% v 21%; OR, 2.23; CI, 1.5-3.3; p < 0.001).

CONCLUSIONS

Serious adverse events were common in this cohort. Careful planning to minimize anesthesia time in young children with pulmonary hypertension should be undertaken, and these factors considered in designing risk mitigation strategies.

Impact of Pulmonary Hypertension on In-Hospital Outcomes and 30-Day Readmissions Following Percutaneous Coronary Interventions

OBJECTIVE

To evaluate the impact of pulmonary hypertension (PH) on percutaneous coronary intervention (PCI) outcomes and 30-day all-cause readmissions by analyzing a national database.

METHODS

We queried the 2014 National Readmissions Database to identify patients undergoing PCI using International Classification of Diseases, Ninth Revision, Clinical Modification codes. These patients were then subcategorized based on the coded presence or absence of PH and further analyzed to determine the impact of PH on clinical outcomes, health care use, and 30-day readmissions.

RESULTS

Among 599,490 patients hospitalized for a PCI in 2014, 19,348 (3.2%) had concomitant PH. At baseline, these patients were older with a higher burden of comorbidities. Patients with PH had longer initial hospitalizations and higher 30-day readmission rates and mortality than their non-PH counterparts. This was largely driven by cardiac causes, most commonly heart failure (20.3% vs 9.0%, P<.001) and non-ST-segment elevation myocardial infarction. Recurrent coronary events (17.5% vs 9.5%, P<.05) including ST-segment elevation myocardial infarction predominated in the non-PH group.

CONCLUSION

Patients with PH undergoing PCI are a high-risk group in terms of mortality and 30-day readmission rates. Percutaneous coronary intervention in patients with PH is associated with higher rates of recurrent heart failure and non-ST-segment elevation myocardial infarction, rather than recurrent coronary events or ST-segment elevation myocardial infarction. This perhaps indicates a predominance of demand ischemia and heart failure syndromes rather than overt atherothrombosis in the etiology of chest pain in these patients.

Burden of pulmonary arterial hypertension in England: retrospective HES database analysis

BACKGROUND

The clinical and economic burden of pulmonary arterial hypertension (PAH) is poorly understood outside the United States. This retrospective database study describes the characteristics of patients with PAH in England, including their healthcare resource utilisation (HCRU) and associated costs.

METHODS

Data from 1 April 2012 to 31 March 2018 were obtained from the National Health Service (NHS) Digital Hospital Episode Statistics database, which provides full coverage of patient events occurring in NHS England hospitals. An adult patient cohort was defined using an algorithm incorporating pulmonary hypertension (PH) diagnosis codes, PAH-associated procedures, PH specialist centre visits and PAH-specific medications. HCRU included inpatient admissions, outpatient visits and Accident and Emergency (A&E) attendances. Associated costs, calculated using national tariffs inflation-adjusted to 2017, did not include PAH-specific drugs on the High Cost Drugs list.

RESULTS

The analysis cohort included 2527 patients (68.4% female; 63.6% aged ⩾50 years). Mean annual HCRU rates ranged from 2.9 to 3.2 for admissions (21-25% of patients had ⩾5 admissions), 9.4-10.3 for outpatient visits and 0.8-0.9 for A&E attendances. Costs from 2013 to 2017 totalled £43.2M (£33.9M admissions, £8.3M outpatient visits and £0.9M A&E attendances). From 2013 to 2017, mean cost per patient decreased 13% (from £4400 to £3833) for admissions and 13% (from £1031 to £896) for outpatient visits, but increased 52% (from £81 to £123) for A&E attendances.

CONCLUSION

PAH incurs a heavy economic burden on a per-patient basis, highlighting the need for improved treatment strategies able to reduce disease progression and hospitalisations.The reviews of this paper are available via the supplemental material section.

Development and Validation of Algorithms to Identify Pulmonary Arterial Hypertension in Administrative Data

BACKGROUND

Pulmonary arterial hypertension (PAH) is a rare disease, and much of our understanding stems from single-center studies, which are limited by sample size and generalizability. Administrative data offer an appealing opportunity to inform clinical, research, and quality improvement efforts for PAH. Yet, currently no standardized, validated method exists to distinguish PAH from other subgroups of pulmonary hypertension (PH) within this data source.

RESEARCH QUESTION

Can a collection of algorithms be developed and validated to detect PAH in administrative data in two diverse settings: all Veterans Health Administration (VA) hospitals and Boston Medical Center (BMC), a PAH referral center.

STUDY DESIGN AND METHODS

In each setting, we identified all adult patients with incident PH from 2006 through 2017 using International Classification of Diseases PH diagnosis codes. From this baseline cohort of all PH subgroups, we sequentially applied the following criteria: diagnosis codes for PAH-associated conditions, procedure codes for right heart catheterizations (RHCs), and pharmacy claims for PAH-specific therapy. We then validated each algorithm using a gold standard review of primary clinical data and calculated sensitivity, specificity, positive predictive values (PPVs), and negative predictive values.

RESULTS

From our baseline cohort, we identified 12,012 PH patients in all VA hospitals and 503 patients in BMC. Sole use of PH diagnosis codes performed poorly in identifying PAH (PPV, 16.0% in VA hospitals and 36.0% in BMC). The addition of PAH-associated conditions to the algorithm modestly improved PPV. The best performing algorithm required ICD diagnosis codes, RHC codes, and PAH-specific therapy (VA hospitals: specificity, 97.1%; PPV, 70.0%; BMC: specificity, 95.0%; PPV, 86.0%).

INTERPRETATION

This set of validated algorithms to identify PAH in administrative data can be used by the PAH scientific and clinical community to enhance the reliability and value of research findings, to inform quality improvement initiatives, and ultimately to improve health for PAH patients.

Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases

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.

Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches

Background

Real‐world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts—a crucial first step underpinning the validity of research results—remains a challenge. We developed and evaluated claims‐based case ascertainment algorithms for pulmonary hypertension (PH), comparing conventional decision rules with state‐of‐the‐art machine‐learning approaches.

Methods and Results

We analyzed an electronic health record‐Medicare linked database from two large academic tertiary care hospitals (years 2007–2013). Electronic health record charts were reviewed to form a gold standard cohort of patients with (n=386) and without PH (n=164). Using health encounter data captured in Medicare claims (including patients’ demographics, diagnoses, medications, and procedures), we developed and compared 2 approaches for identifying patients with PH: decision rules and machine‐learning algorithms using penalized lasso regression, random forest, and gradient boosting machine. The most optimal rule‐based algorithm—having ≥3 PH‐related healthcare encounters and having undergone right heart catheterization—attained an area under the receiver operating characteristic curve of 0.64 (sensitivity, 0.75; specificity, 0.48). All 3 machine‐learning algorithms outperformed the most optimal rule‐based algorithm (P<0.001). A model derived from the random forest algorithm achieved an area under the receiver operating characteristic curve of 0.88 (sensitivity, 0.87; specificity, 0.70), and gradient boosting machine achieved comparable results (area under the receiver operating characteristic curve, 0.85; sensitivity, 0.87; specificity, 0.70). Penalized lasso regression achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity, 0.70; specificity, 0.68).

Conclusions

Research‐grade case identification algorithms for PH can be derived and rigorously validated using machine‐learning algorithms. Simple decision rules commonly applied in published literature performed poorly; more complex rule‐based algorithms may potentially address the limitation of this approach. PH research using claims data would be considerably strengthened through the use of validated algorithms for cohort ascertainment.

Identifying Patients with Pulmonary Arterial Hypertension Using Administrative Claims Algorithms

Retrospective administrative claims database studies provide real-world evidence about treatment patterns, healthcare resource use, and costs for patients and are increasingly used to inform policy-making, drug formulary, and regulatory decisions. However, there is no standard methodology to identify patients with pulmonary arterial hypertension (PAH) from administrative claims data. Given the number of approved drugs now available for patients with PAH, the cost of PAH treatments, and the significant healthcare resource use associated with the care of patients with PAH, there is a considerable need to develop an evidence-based and systematic approach to accurately identify these patients in claims databases. A panel of pulmonary hypertension clinical experts and researchers experienced in retrospective claims database studies convened to review relevant literature and recommend best practices for developing algorithms to identify patients with PAH in administrative claims databases specific to a particular research hypothesis.

Medication adherence, hospitalization, and healthcare resource utilization and costs in patients with pulmonary arterial hypertension treated with endothelin receptor antagonists or phosphodiesterase type-5 inhibitors

Adherence to therapy for pulmonary arterial hypertension is essential to optimize patient outcomes, but data on real-world adherence to different pulmonary arterial hypertension drug classes are limited. This retrospective database analysis evaluated relationships between adherence, hospitalization, and healthcare costs in pulmonary arterial hypertension patients treated with endothelin receptor antagonists or phosphodiesterase type-5 inhibitors. From the IQVIA Adjudicated Health Plan Database, patients with pulmonary arterial hypertension were identified based on diagnostic codes and prescriptions for endothelin receptor antagonists (ambrisentan, bosentan, macitentan) or phosphodiesterase type-5 inhibitors (sildenafil, tadalafil) approved for pulmonary arterial hypertension. Patients were assigned to the class of their most recently initiated (index) pulmonary arterial hypertension therapy between 1 January 2009 and 30 June 2015. Medication adherence was measured by proportion of days covered; patients with proportion of days covered ≥80% were considered adherent. The proportion of adherent patients was higher for endothelin receptor antagonists (571/755; 75.6%) than for phosphodiesterase type-5 inhibitors (970/1578; 61.5%; P < 0.0001). In both groups, hospitalizations declined as proportion of days covered increased. Among adherent patients, those on endothelin receptor antagonists had a significantly lower hospitalization rate than those on phosphodiesterase type-5 inhibitors (23.1% versus 28.5%, P = 0. 0218), fewer hospitalizations (mean (standard deviation) 0.4 (0.8) versus 0.5 (0.9); P = 0.02), and mean hospitalization costs during the six-month post-index ($9510 versus $15,726, P = 0.0318). Increasing adherence reduced hospitalization risk more for endothelin receptor antagonists than for phosphodiesterase type-5 inhibitors (hazard ratio 0.176 versus 0.549, P = 0.001). Rates and numbers of rehospitalizations within 30 days post-discharge were similar between groups. Mean total costs were higher with endothelin receptor antagonists than phosphodiesterase type-5 inhibitors in all patients ($91,328 versus $72,401, P = 0.0003) and in adherent patients ($88,867 versus $56,300, P < 0.0001), driven by higher drug costs.

Combining population-based administrative health records and electronic medical records for disease surveillance

BACKGROUND

Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence.

METHODS

Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers.

RESULTS

The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model.

CONCLUSIONS

The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Cardiovascular Outcomes of Patients With Pulmonary Hypertension Undergoing Noncardiac Surgery

Pulmonary hypertension (PH), defined by a mean pulmonary artery pressure of >25mm Hg at rest, is strongly associated with morbidity and mortality in the perioperative period. The prevalence and outcomes of PH among patients referred for major noncardiac surgery in the United States are unknown. Patients ≥18 years of age hospitalized for noncardiac surgery were identified from Healthcare Cost and Utilization Project's National Inpatient Sample data from 2004 to 2014. Pulmonary hypertension was defined by International Classification of Diseases, Ninth Revision diagnosis codes. The primary outcome was perioperative major adverse cardiovascular events (MACCE), defined as in-hospital death, myocardial infarction, or ischemic stroke. Among 17,853,194 hospitalizations for major noncardiac surgery, 143,846 (0.81%) had PH. MACCE occurred in 8.3% of hospitalizations with any diagnosis of PH in comparison to 2.0% of those without PH (p <0.001), driven by an increased frequency of death (4.4% vs 1.1%, p <0.001) and nonfatal myocardial infarction (3.2% vs 0.6%, p <0.001). After adjusting for demographics, clinical covariates, and surgery type, PH remained independently associated with MACCE (aOR 1.43, 95% CI 1.40 to 1.46). In conclusion, PH is associated with perioperative major adverse cardiovascular events. Careful patient selection, recognition of perioperative risks, and appropriate intraoperative hemodynamic monitoring may improve perioperative cardiovascular outcomes.

Practical management of riociguat in patients with pulmonary arterial hypertension

Riociguat is one of several approved therapies available for patients with pulmonary arterial hypertension (PAH). Treatment should be initiated and monitored at an expert center by a physician experienced in treating PAH, and the dose adjusted in the absence of signs and symptoms of hypotension. In certain populations, including patients with hepatic or renal impairment, the elderly, and smokers, riociguat exposure may differ, and dose adjustments should therefore be made with caution according to the established scheme. Common adverse events are often easily managed, particularly if they are discussed before starting therapy. Combination therapy with riociguat and other PAH-targeted agents is feasible and generally well tolerated, although the coadministration of phosphodiesterase type 5 inhibitors (PDE5i) and riociguat is contraindicated. An open-label, randomized study is currently ongoing to assess whether patients who do not achieve treatment goals while receiving PDE5i may benefit from switching to riociguat. In this review, we provide a clinical view on the practical management of patients with PAH receiving riociguat, with a focus on the opinions and personal experience of the authors. The reviews of this paper are available via the supplemental material section.

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

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.