Identifying Patients with Rare Disease Using Electronic Health Record Data: The Kaiser Permanente Southern California Membranous Nephropathy Cohort

Epidemiology of cardiac amyloidosis in Germany: a retrospective analysis from 2009 to 2018

BACKGROUND

Improved imaging modalities contributed to increasing awareness of cardiac amyloidosis. Contemporary data on frequency trends in Germany are lacking.

METHODS

In a retrospective study using health claims data of a German statutory health insurance, patients with diagnostic codes of amyloidosis and concomitant heart failure between 2009 and 2018 were identified.

RESULTS

Prevalence increased from 15.5 to 47.6 per 100,000 person-years, and incidence increased from 4.8 to 11.6 per 100,000 person-years, with a continuous steepening in the slope of incidence trend. In patients with amyloidosis and heart failure age and proportion of men significantly increased, whereas the frequency of myeloma and nephrotic syndrome significantly decreased over time. Median (IQR) survival time after first diagnosis was 2.5 years (0.5-6 years), with a 9% (95% CI 2-15%, p = 0.008) reduced risk of death in the second compared to the first 5 years of observation. In the 2 years prior and 1 year after diagnosis, mean total health care costs were 6568 €, 11,872 € and 21,955 € per person and year.

CONCLUSION

The rise in cardiac amyloidosis has continuously accelerated in the last decade. Considering the adverse outcome and high health care burden, further effort should be put on early detection of the disease to implement available treatment.

OARD: Open annotations for rare diseases and their phenotypes based on real-world data

Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated with additional annotations found in published case reports. Despite their potential, real-world data such as electronic health records (EHRs) have not been fully exploited to derive rare disease annotations. Here, we present open annotation for rare diseases (OARD), a real-world-data-derived resource with annotation for rare-disease-related phenotypes. This resource is derived from the EHRs of two academic health institutions containing more than 10 million individuals spanning wide age ranges and different disease subgroups. By leveraging ontology mapping and advanced natural-language-processing (NLP) methods, OARD automatically and efficiently extracts concepts for both rare diseases and their phenotypic traits from billing codes and lab tests as well as over 100 million clinical narratives. The rare disease prevalence derived by OARD is highly correlated with those annotated in the original rare disease knowledgebase. By performing association analysis, we identified more than 1 million novel disease-phenotype association pairs that were previously missed by human annotation, and >60% were confirmed true associations via manual review of a list of sampled pairs. Compared to the manual curated annotation, OARD is 100% data driven and its pipeline can be shared across different institutions. By supporting privacy-preserving sharing of aggregated summary statistics, such as term frequencies and disease-phenotype associations, it fills an important gap to facilitate data-driven research in the rare disease community.

Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank

BACKGROUND

Rare diseases collectively affect up to 10% of the population, but often lack effective treatment, and typically little is known about their pathophysiology. Major challenges include suboptimal phenotype mapping and limited statistical power. Population biobanks, such as the UK Biobank, recruit many individuals who can be affected by rare diseases; however, investigation into their utility for rare disease research remains limited. We hypothesized the UK Biobank can be used as a unique population assay for rare diseases in the general population.

METHODS

We constructed a consensus mapping between ICD-10 codes and ORPHA codes for rare diseases, then identified individuals with each rare condition in the UK Biobank, and investigated their age at recruitment, sex bias, and comorbidity distributions. Using exome sequencing data from 167,246 individuals of European ancestry, we performed genetic association controlling for case/control imbalance (SAIGE) to identify potential rare pathogenic variants for each disease.

RESULTS

Using our mapping approach, we identified and characterized 420 rare diseases affecting 23,575 individuals in the UK Biobank. Significant genetic associations included JAK2 V617F for immune thrombocytopenic purpura (p = 1.24 × 10^-13) and a novel CALR loss of function variant for essential thrombocythemia (p = 1.59 × 10^-13). We constructed an interactive resource highlighting demographic information ( http://www-personal.umich.edu/~mattpat/rareDiseases.html ) and demonstrate transferability by applying our mapping to a medical claims database.

CONCLUSIONS

Enhanced disease mapping and increased power from population biobanks can elucidate the demographics and genetic associations for rare diseases.

Autosomal Dominant Polycystic Kidney Disease Prevalence among a Racially Diverse United States Population, 2002 through 2018

Among a large racially and ethnically diverse US population, the prevalence of diagnosed ADPKD between 2002 and 2018 was 42.6 per 100,000 persons.ADPKD prevalence (per 100,000) was higher in (non-Hispanic) White (63.2) and Black (73.0) patients compared with Hispanic (39.9) and Asian (48.9) patients.Given the variable penetrance of ADPKD, our findings suggest race may be a factor in the clinical presentation and diagnosis of ADPKD.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Different approaches to improve cohort identification using electronic health records: X-linked hypophosphatemia as an example

Electronic Health Records (EHRs) represent a source of high value data which is often underutilized because exploiting the information contained therein requires specialized techniques unavailable to the end user i.e. the physician or the investigator. Here I describe four simple and practical avenues that will allow the standard EHR end user to identify patient cohorts: the use of diagnostic codes from different international catalogues; a search in reports from complementary tests (e.g. radiographs or lab tests) for any result of interest; a free text search; or a drug prescription search in the patient's electronic prescription record. This medical approach is acquiring great importance in the field of rare diseases, and here I demonstrate its application with X-linked hypophosphatemia. The use of these four EHR questioning approaches makes finding a cohort of patients of any condition or disease feasible and manageable, and once each case record is checked, a well-defined cohort can be assembled.