Creation of a novel algorithm to identify patients with Becker and Duchenne muscular dystrophy within an administrative database and application of the algorithm to assess cardiovascular morbidity

Novel Cardiac Imaging Risk Score for Mortality Prediction in Duchenne Muscular Dystrophy

Cardiovascular disease is the leading cause of death in patients with Duchenne Muscular Dystrophy (DMD), but there is significant cardiomyopathy phenotypic variability. Some patients demonstrate rapidly progressive disease and die at a young age while others survive into the fourth decade. Criteria to identify DMD subjects at greatest risk for early mortality could allow for increased monitoring and more intensive therapy. A risk score was created describing the onset and progression of left ventricular dysfunction and late gadolinium enhancement in subjects with DMD. DMD subjects prospectively enrolled in ongoing observational studies (which included cardiac magnetic resonance [CMR]) were used to validate the risk score. A total of 69 subjects had calculable scores. During the study period, 12 (17%) died from complications of DMD. The median risk score was 3 (IQR [2,5]; range [0,9]). The overall risk score applied at the most recent imaging age was associated with mortality at a median age of 17 years (IQR [16,20]) (HR 2.028, p < 0.001). There were no deaths in subjects with a score of less than two. Scores were stable over time. An imaging-based risk score allows risk stratification of subjects with DMD. This can be quickly calculated during a clinic visit to identify subjects at greatest risk of early death.

Subspecialty Health Care Utilization in Pediatric Patients With Muscular Dystrophy in the United States

Background and Objectives

Standards of care exist to optimize outcomes in Duchenne and Becker muscular dystrophy (DBMD), caused by alterations in the DMD gene; however, there are limited data regarding health care access in these patients. This study aims to characterize outpatient subspecialty care utilization in pediatric patients with DBMD.

Methods

This retrospective cohort study used administrative claims data from IBM MarketScan Medicaid and Commercial Claims and Encounters Research Databases (2013-2018). Male patients 1-18 years with an ICD-9/10 diagnosis code for hereditary progressive muscular dystrophy between January 1, 2013, and December 31, 2017, were included. Participants were stratified into 3 age cohorts: 1-6 years, 7-12 years, and 13-18 years. The primary outcome was rate of annual neurology visits. Secondary outcomes included annual follow-up rates in other subspecialties and proportion of days covered (PDC) by corticosteroids.

Results

A total of 1,386 patients met inclusion-347 (25.0%) age 1-6 years, 502 (36.2%) age 7-12 years, and 537 (38.7%) age 13-18 years. Heart failure, respiratory failure, and technology dependence increased with age (p for all<0.05). The rate of neurology visits per person-year was 0.36 and did not differ by age. Corticosteroid use was low; 30% of person-years (1452/4829) had a PDC ≥20%. Medicaid insurance was independently associated with a lower likelihood of annual neurology follow-up (OR 0.23; 95% CI 0.18-0.28).

Discussion

The rate of annual neurology follow-up and corticosteroid use in patients with DBMD is low. Medicaid insurance status was independently associated with a decreased likelihood of neurology follow-up, while age was not, suggesting that factors other than disease severity influence neurology care access. Identifying barriers to regular follow-up is critical in improving outcomes for patients with DBMD.

Development and electronic health record validation of an algorithm for identifying patients with Duchenne muscular dystrophy in US administrative claims

BACKGROUND: Muscular dystrophies (MDs) comprise a heterogenous group of genetically inherited conditions characterized by progressive muscle weakness and increasing disability. The lack of separate diagnosis codes for Duchenne MD (DMD) and Becker MD, 2 of the most common forms of MD, has limited the conduct of DMD-specific real-world studies. OBJECTIVE: To develop and validate administrative claims-based algorithms for identifying patients with DMD and capturing their nonambulatory and ventilation-dependent status. METHODS: This was a retrospective cohort study using the statistically deidentified Optum Market Clarity Database (including patient claims linked with electronic health records [EHRs] data) to develop and validate the following algorithms: DMD diagnosis, nonambulatory status, and ventilation-dependent status. The initial study sample consisted of US patients in the database who had a diagnosis code for Duchenne/Becker MD (DBMD) between October 1, 2018, and September 30, 2020, who were male, aged 40 years or younger on their first DBMD diagnosis, and met continuous enrollment and 1-day minimal clinical activities requirement in a 12-month measurement period between October 1, 2017, and September 30, 2020. The algorithms, developed by a cross-functional team of DMD specialists (including patient advocates), were based on administrative claims data with International Classification of Diseases, Tenth Revision, Clinical Modifications coding, using information of diagnosis codes for DBMD, sex, age, treatment, and disease severity (eg, evidence of ambulation assistance/support and/or evidence of ventilation support or dependence). Patients who met each algorithm and had EHR notes available were then validated against structured fields and unstructured provider notes from their own linked EHR to confirm patients' DMD diagnoses, nonambulatory status, and ventilation-dependent status. Algorithm performance was assessed by positive predictive value with 95% CIs. RESULTS: A total of 1,300 patients were included in the initial study sample. Of these, EHR were available and reviewed for 303 patients. The mean age of the 303 patients was 14.8 years, with 61.7% being non-Hispanic White. A majority had a Charlson comorbidity index score of 0 (59.4%) or 1-2 (27.7%). Positive predictive value (95% CI) was 91.6% (85.8%-95.6%) for the DMD diagnosis algorithm, 88.4% (80.2%-94.1%) for the nonambulatory status algorithm, and 77.8% (62.9%-88.8%) for the ventilation-dependent status algorithm. CONCLUSIONS: This work provides the means to more accurately identify patients with DMD from administrative claims data without a specific diagnosis code. The algorithms validated in this study can be applied to assess treatment effectiveness and other outcomes among patients with DMD treated in clinical practice. DISCLOSURES: This study was funded by Pfizer, which contracted with Optum to perform the study and provide medical writing assistance. Ms Schrader reports being an employee of Parent Project Muscular Dystrophy. Mr Posner reports being an employee and stockholder of Pfizer and receiving support from Pfizer for attending conferences not related to this manuscript. Dr Dorling reports being an employee and stockholder of Pfizer at the time the study was conducted and is a current employee of Chiesi USA, Inc. Ms Senerchia reports being an employee of Optum and owning stock in Pfizer and UnitedHealth Group, the parent company of Optum. Dr Chen reports being an employee and stockholder of Pfizer. Ms Beaverson reports being an employee of Pfizer and owning stock in Pfizer and Amicus Therapeutics. Dr Seare reports being an employee of Optum at the time the study was conducted. Dr Garnier and Ms Merla report being employees of Pfizer. Ms Walker reports being an employee of Optum. Dr Alvir reports being an employee and stockholder of Pfizer. Dr Mahn reports being an employee and stockholder of Pfizer. Dr Zhang reports being an employee of Optum. Ms Landis reports being an employee of Optum. Ms Buikema reports being an employee of Optum and holding stock in UnitedHealth Group, the parent company of Optum.

Frequency-based rare diagnoses as a novel and accessible approach for studying rare diseases in large datasets: a cross-sectional study

BACKGROUND

Up to 8% of the general population have a rare disease, however, for lack of ICD-10 codes for many rare diseases, this population cannot be generically identified in large medical datasets. We aimed to explore frequency-based rare diagnoses (FB-RDx) as a novel method exploring rare diseases by comparing characteristics and outcomes of inpatient populations with FB-RDx to those with rare diseases based on a previously published reference list.

METHODS

Retrospective, cross-sectional, nationwide, multicenter study including 830,114 adult inpatients. We used the national inpatient cohort dataset of the year 2018 provided by the Swiss Federal Statistical Office, which routinely collects data from all inpatients treated in any Swiss hospital. Exposure: FB-RDx, according to 10% of inpatients with the least frequent diagnoses (i.e.1.decile) vs. those with more frequent diagnoses (deciles 2-10). Results were compared to patients having 1 of 628 ICD-10 coded rare diseases.

PRIMARY OUTCOME

In-hospital death.

SECONDARY OUTCOMES

30-day readmission, admission to intensive care unit (ICU), length of stay, and ICU length of stay. Multivariable regression analyzed associations of FB-RDx and rare diseases with these outcomes.

RESULTS

464,968 (56%) of patients were female, median age was 59 years (IQR: 40-74). Compared with patients in deciles 2-10, patients in the 1. were at increased risk of in-hospital death (OR 1.44; 95% CI: 1.38, 1.50), 30-day readmission (OR 1.29; 95% CI 1.25, 1.34), ICU admission (OR 1.50; 95% CI 1.46, 1.54), increased length of stay (Exp(B) 1.03; 95% CI 1.03, 1.04) and ICU length of stay (1.15; 95% CI 1.12, 1.18). ICD-10 based rare diseases groups showed similar results: in-hospital death (OR 1.82; 95% CI 1.75, 1.89), 30-day readmission (OR 1.37; 95% CI 1.32, 1.42), ICU admission (OR 1.40; 95% CI 1.36, 1.44) and increased length of stay (OR 1.07; 95% CI 1.07, 1.08) and ICU length of stay (OR 1.19; 95% CI 1.16, 1.22).

CONCLUSION(S)

This study suggests that FB-RDx may not only act as a surrogate for rare diseases but may also help to identify patients with rare disease more comprehensively. FB-RDx associate with in-hospital death, 30-day readmission, intensive care unit admission, and increased length of stay and intensive care unit length of stay, as has been reported for rare diseases.

Health Care Use of Cardiac Specialty Care in Children With Muscular Dystrophy in the United States

Background

Duchenne and Becker muscular dystrophy are progressive disorders associated with cardiac mortality. Guidelines recommend routine surveillance; we assess cardiac resource use and identify gaps in care delivery.

Methods and Results

Male patients, aged 1 to 18 years, with Duchenne and Becker muscular dystrophy between January 2013 and December 2017 were identified in the IBM MarketScan Research Database. The cohort was divided into <10 and 10 to 18 years of age. The primary outcome was rate of annual health care resource per person year. Resource use was assessed for place of service, cardiac testing, and medications. Adjusted incidence rate ratios (IRRs) were estimated using a Poisson regression model. Medication use was measured by proportion of days covered. There were 1386 patients with a median follow‐up time of 3.0 years (interquartile range, 1.9–4.7 years). Patients in the 10 to 18 years group had only 0.40 (95% CI, 0.35–0.45) cardiology visits per person year and 0.66 (95% CI, 0.62–0.70) echocardiography/magnetic resonance imaging per person year. Older patients had higher rates of inpatient admissions (IRR, 1.46; 95% CI, 1.03–2.09), outpatient cardiology visits (IRR, 2.0; 95% CI, 1.66–2.40), cardiac imaging (IRR, 1.59; 95% CI, 1.40–1.80), and Holter monitoring (IRR, 3.33; 95% CI, 2.35–4.73). A proportion of days covered >80% for angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers was observed in 13.6% (419/3083) of total person years among patients in the 10 to 18 years group.

Conclusions

Children 10 to 18 years of age have higher rates of cardiac resource use compared with those <10 years of age. However, rates in both age groups fall short of guidelines. Opportunities exist to identify barriers to resource use and optimize cardiac care for patients with Duchenne and Becker muscular dystrophy.

The role of imaging in characterizing the cardiac natural history of Duchenne muscular dystrophy

Duchene muscular dystrophy (DMD) is a rare but devastating disease resulting in progressive loss of ambulation, respiratory failure, DMD-associated cardiomyopathy (DMD-CM), and premature death. The use of corticosteroids and supportive respiratory care has improved outcomes, such that DMD-CM is now the leading cause of death. Historically, most programs have focused on skeletal myopathy with less attention to the cardiac phenotype. This omission is rather astonishing since patients with DMD possess an absolute genetic risk of developing cardiomyopathy. Unfortunately, heart failure signs and symptoms are vague due to skeletal muscle myopathy leading to limited ambulation. Traditional assessment of cardiac symptoms by the New York Heart Association American College of Cardiology/American Heart Association Staging (ACC/AHA) classification is of limited utility, even in advanced stages. Echocardiographic assessment can detect cardiac dysfunction late in the disease course, but this has proven to be a poor surrogate marker of early cardiovascular disease and an inadequate predictor of DMD-CM. Indeed, one explanation for the paucity of cardiac therapeutic trials for DMD-CM has been the lack of a suitable end-point. Improved outcomes require a better proactive treatment strategy; however, the barrier to treatment is the lack of a sensitive and specific tool to assess the efficacy of treatment. The use of cardiac imaging has evolved from echocardiography to cardiac magnetic resonance imaging to assess cardiac performance. The purpose of this article is to review the role of cardiac imaging in characterizing the cardiac natural history of DMD-CM, highlighting the prognostic implications and an outlook on how this field might evolve in the future.

Validation of diagnostic codes and epidemiologic trends of Huntington disease: a population-based study in Navarre, Spain

BACKGROUND

There is great heterogeneity on geographic and temporary Huntington disease (HD) epidemiological estimates. Most research studies of rare diseases, including HD, use health information systems (HIS) as data sources. This study investigates the validity and accuracy of national and international diagnostic codes for HD in multiple HIS and analyses the epidemiologic trends of HD in the Autonomous Community of Navarre (Spain).

METHODS

HD cases were ascertained by the Rare Diseases Registry and the reference Medical Genetics Centre of Navarre. Positive predictive values (PPV) and sensitivity with 95% confidence intervals (95% CI) were estimated. Overall and 9-year periods (1991-2017) HD prevalence, incidence and mortality rates were calculated, and trends were assessed by Joinpoint regression.

RESULTS

Overall PPV and sensitivity of combined HIS were 71.8% (95% CI: 59.7, 81.6) and 82.2% (95% CI: 70.1, 90.4), respectively. Primary care data was a more valuable resource for HD ascertainment than hospital discharge records, with 66% versus 50% sensitivity, respectively. It also had the highest number of "unique to source" cases. Thirty-five per cent of HD patients were identified by a single database and only 4% by all explored sources. Point prevalence was 4.94 (95% CI: 3.23, 6.65) per 100,000 in December 2017, and showed an annual 6.1% increase from 1991 to 1999. Incidence and mortality trends remained stable since 1995-96, with mean annual rates per 100,000 of 0.36 (95% CI: 0.27, 0.47) and 0.23 (95% CI: 0.16, 0.32), respectively. Late-onset HD patients (23.1%), mean age at onset (49.6 years), age at death (66.6 years) and duration of disease (16.7 years) were slightly higher than previously reported.

CONCLUSION

HD did not experience true temporary variations in prevalence, incidence or mortality over 23 years of post-molecular testing in our population. Ascertainment bias may largely explain the worldwide heterogeneity in results of HD epidemiological estimates. Population-based rare diseases registries are valuable instruments for epidemiological studies on low prevalence genetic diseases, like HD, as long as they include validated data from multiple HIS and genetic/family information.

Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims

Background

Muscular dystrophies (MDs) are a group of inherited conditions characterized by progressive muscle degeneration and weakness. The rarity and heterogeneity of the population with MD have hindered therapeutic developments as well as epidemiological and health outcomes research. The objective of the study was to develop and validate a case-finding algorithm utilizing administrative claims data to identify and characterize patients with MD.

Methods

This retrospective cohort study used medical chart validation to evaluate an ICD-9/10 coding algorithm in a large commercial claims database. Patients were identified who had ≥2 office visits with a diagnosis of hereditary progressive MDs from January 1, 2013 through December 31, 2016, were male, and younger than 18 years at the time of first MD diagnosis. Cases who met the algorithm were then validated against medical charts. Diagnoses of MD and specific type (Duchenne, Becker, or other MD) were confirmed by medical chart review by trained reviewers. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated using a 2 × 2 contingence table. Patient demographic, clinical, and health utilization characteristics were summarized using basic descriptive statistics.

Results

Charts were obtained and reviewed for 109 patients who met the algorithm. The PPV of the case-identifying algorithm for MD was 95% (95% CI 88–98%). Of the 103 confirmed MD cases, 87 patients (85%, 95% CI 76–91%) had Duchenne or Becker MD; 76 patients (74%, 95% CI 64–82%) had Duchenne MD, and 11 patients (11%, 95% CI 5–18%) had Becker MD. A total of 74 (67.9%) patients had ≥1 pediatric complex chronic condition (other than neurologic/neuromuscular disease); 54 (49.5%) had cardiovascular conditions; 14 (12.8%) had respiratory conditions; 50 (45.9%) had bone-related issues; 11 (10.1%) had impaired growth; and 6 (5.5%) had puberty delay.

Conclusions

The results of this study demonstrate that the case-finding algorithm accurately identified patients with MD, primarily Duchenne MD, within a large administrative database. The algorithm, which was constructed using a few items easily accessible from claims, can be used to facilitate epidemiological and health outcomes research in the Duchenne patient population.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0816-7) contains supplementary material, which is available to authorized users.