A systematic review of validated methods for identifying hypersensitivity reactions other than anaphylaxis (fever, rash, and lymphadenopathy), using administrative and claims data

Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health

Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.

Risk of Allergic Conditions in Children Born to Women With Systemic Lupus Erythematosus

OBJECTIVE

Limited evidence suggests a potentially increased risk of allergic conditions in offspring born to women with systemic lupus erythematosus (SLE). In a large population-based study, we aimed to determine if children born to mothers with SLE have an increased risk of allergic conditions compared to children born to mothers without SLE.

METHODS

Using the Offspring of SLE Mothers Registry, we identified children born live to mothers with SLE and their matched controls, and ascertained the number of allergic conditions (asthma, allergic rhinitis, eczema, urticaria, angioedema, and anaphylaxis) based on ≥1 hospitalization or ≥1 or 2 physician(s) visit(s) with a relevant diagnostic code. We adjusted for maternal age, education, race/ethnicity, obstetrics complications, calendar year of birth, sex of the child, and maternal medication.

RESULTS

There were 509 women with SLE who had 719 children, while 5,824 matched controls had 8,493 children. The mean ± SD followup period was 9.1 ± 5.8 years. Compared to controls, more children born to mothers with SLE had evidence of allergic conditions (43.9% [95% confidence interval (95% CI) 40.4-47.6] versus 38.1% [95% CI 37.0-39.1]). In multivariate analysis (n = 9,212), children born to mothers with SLE had an increased risk of allergic conditions versus control children (odds ratio 1.35 [95% CI 1.13-1.61]).

CONCLUSION

Compared to children from the general population, children born to women with SLE may have an increased risk of allergic conditions. Genetics, shared environmental exposures, as well as in utero exposure to maternal autoantibodies and cytokines may mediate this increased risk.

Identifying multiple myeloma patients using data from the French health insurance databases: Validation using a cancer registry

This study aimed to assess the performance of several algorithms based on hospital diagnoses and the long-term diseases scheme to identify multiple myeloma patients.Potential multiple myeloma patients in 2010 to 2013 were identified using the presence of hospital records with at least 1 main diagnosis code for multiple myeloma (ICD-10 "C90"). Alternative algorithms also considered related and associated diagnoses, combination with long-term conditions, or at least 2 diagnoses. Incident patients were those with no previous "C90" codes in the past 24 or 12 months. The sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) were computed, using a French cancer registry for the corresponding area and period as the criterion standard.Long-term conditions data extracted concerned 11,559 patients (21,846 for hospital data). The registry contained 125 cases of multiple myeloma. Sensitivity was 70% when using only main hospital diagnoses (specificity 100%, PPV 79%), 76% when also considering related diagnoses (specificity 100%, PPV 74%), and 90% with associated diagnoses included (100% specificity, 64% PPV).In relation with their good performance, selected algorithms can be used to study the benefit and risk of drugs in treated multiple myeloma patients.

Utility of ICD-9-CM Codes for Identification of Allergic Drug Reactions

BACKGROUND

The epidemiology of allergic drug reactions is poorly understood due, in large part, to difficulty in identifying true cases in population data sets. Use of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes is a potentially valuable approach that requires formal evaluation.

OBJECTIVE

To better understand the utility of ICD-9-CM codes for identification of allergic drug reactions, including the validation of specific codes by chart review.

METHODS

We reviewed randomly sampled medical records of patients treated in the emergency department (ED) between January 1, 2001, and December 31, 2006, with ICD-9-CM codes for drug allergy and E codes (E930-949) for adverse drug reactions.

RESULTS

During the 6-year period, 11,130 charts were identified by ICD-9-CM and E codes and 1,634 were reviewed. Allergic drug reactions were found in 444 (27%) of the reviewed ED visits. The codes that identified the highest percentage of true allergic drug reactions were dermatitis due to drug (693.0; 87%), adverse reaction to drug (995.2; 52%), and anaphylaxis (995.0; 38%). Patients with both an ICD-9-CM code and an E code had a high likelihood of having an allergic drug reaction (76%). Most allergic drug reactions were attributed to antibiotics (42%), intravenous contrast (7%), and nonsteroidal anti-inflammatory drugs (6%). The estimated frequency of allergic drug reactions increased from 0.49% of ED visits in 2001 to 0.94% in 2012.

CONCLUSIONS

Specific ICD-9-CM and E codes can be used in combination to identify allergic drug reactions. Further study of these codes in the inpatient and outpatient settings is necessary to better understand the utility of diagnosis codes for improving epidemiologic research on drug allergy.

The Validity of Claims-Based Algorithms to Identify Serious Hypersensitivity Reactions and Osteonecrosis of the Jaw

Validation of claims-based algorithms to identify serious hypersensitivity reactions and osteonecrosis of the jaw has not been performed in large osteoporosis populations. The objective of this project is to estimate the positive predictive value of the claims-based algorithms in older women with osteoporosis enrolled in Medicare. Using the 2006-2008 Medicare 5% sample data, we identified potential hypersensitivity and osteonecrosis of the jaw cases based on ICD-9 diagnosis codes. Potential hypersensitivity cases had a 995.0, 995.2, or 995.3 diagnosis code on emergency department or inpatient claims. Potential osteonecrosis of the jaw cases had ≥1 inpatient or outpatient physician claim with a 522.7, 526.4, 526.5, or 733.45 diagnosis code or ≥2 claims of any type with a 526.9 diagnosis code. All retrieved records were redacted and reviewed by experts to determine case status: confirmed, not confirmed, or insufficient information. We calculated the positive predictive value as the number of confirmed cases divided by the total number of retrieved records with sufficient information. We requested 412 potential hypersensitivity and 304 potential osteonecrosis of the jaw records and received 174 (42%) and 84 (28%) records respectively. Of 84 potential osteonecrosis of the jaw cases, 6 were confirmed, resulting in a positive predictive value (95% CI) of 7.1% (2.7, 14.9). Of 174 retrieved potential hypersensitivity records, 95 were confirmed. After exclusion of 25 records with insufficient information for case determination, the overall positive predictive value (95% CI) for hypersensitivity reactions was 76.0% (67.5, 83.2). In a random sample of Medicare data, a claim-based algorithm to identify serious hypersensitivity reactions performed well. An algorithm for osteonecrosis of the jaw did not, partly due to the inclusion of diagnosis codes that are not specific for osteoporosis of the jaw.

Effectiveness and safety of dabigatran and warfarin in real-world US patients with non-valvular atrial fibrillation: a retrospective cohort study

Background

The recent availability of dabigatran, a novel oral anticoagulant, provided a new treatment option for stroke prevention in atrial fibrillation beyond warfarin, the main therapy for years. Little is known about their real‐world comparative effectiveness and safety, even less among patient demographic and clinical subgroups.

Methods and Results

Using a cohort of non‐valvular AF patients initiating anticoagulation from October 2010 to December 2012 drawn from a large US database of commercial and Medicare supplement claims, we applied propensity score weights to Cox proportional hazards regression to assess the comparative effectiveness and safety of dabigatran versus warfarin. Analyses were repeated among clinical and demographic subgroups using stratum‐specific propensity scores as an exploratory analysis. Of the 64 935 patients initiating anticoagulation, 32.5% used dabigatran. Compared with warfarin, dabigatran was associated with a lower risk of ischemic stroke or systemic embolism (composite adjusted Hazard Ratio [aHR], 95% CI: 0.86, 95% CI: 0.79 to 0.93), hemorrhagic stroke (aHR: 0.51, 0.40 to 0.65), and acute myocardial infarction (aHR: 0.88, 95% CI: 0.77 to 0.99), and no relation was seen between dabigatran and the composite harm outcome (aHR: 0.94, 95% CI: 0.87 to 1.01). However, dabigatran was associated with a higher risk of gastrointestinal bleeding (aHR: 1.11, 95% CI: 1.02 to 1.22). Estimates of effectiveness and safety appeared to be mostly similar across subgroups.

Conclusions

Dabigatran could be a safe and potentially more effective alternative to warfarin in patients with atrial fibrillation managed in routine practice settings.

Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory

BACKGROUND AND OBJECTIVE

Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical 'phenotypes' accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one 'gold standard' chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across different definitions.

MATERIALS AND METHODS

We used retrospective queries of hospitalizations (2002-2011) in the Indiana Network for Patient Care with any CHF ICD-9 diagnoses, a primary diagnosis, an echocardiogram performed, a B-natriuretic peptide (BNP) drawn, or BNP >500 pg/mL. We used a hybrid between proportional sampling by Venn zone and over-sampling non-overlapping zones. The acute CHF (presence/absence) outcome was based on expert chart review using a priori criteria.

RESULTS

Among 79,091 hospitalizations, we reviewed 908. A query for any ICD-9 code for CHF had PPV 42.8% (SE 1.5%) for acute CHF and sensitivity 94.3% (1.3%). Primary diagnosis of 428 and BNP >500 pg/mL had PPV 90.4% (SE 2.4%) and sensitivity 28.8% (1.1%). PPV was <10% when there was no echocardiogram, no BNP, and no primary diagnosis. 'False positive' hospitalizations were for other heart disease, lung disease, or other reasons.

CONCLUSIONS

This novel method successfully allowed flexible application and validation of queries for patients hospitalized with acute CHF.

The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction

The Mini-Sentinel is a pilot program that is developing methods, tools, resources, policies, and procedures to facilitate the use of routinely collected electronic healthcare data to perform active surveillance of the safety of marketed medical products, including drugs, biologics, and medical devices. The U.S. Food and Drug Administration (FDA) initiated the program in 2009 as part of its Sentinel Initiative, in response to a Congressional mandate in the FDA Amendments Act of 2007. After two years, Mini-Sentinel includes 31 academic and private organizations. It has developed policies, procedures, and technical specifications for developing and operating a secure distributed data system comprised of separate data sets that conform to a common data model covering enrollment, demographics, encounters, diagnoses, procedures, and ambulatory dispensing of prescription drugs. The distributed data sets currently include administrative and claims data from 2000 to 2011 for over 300 million person-years, 2.4 billion encounters, 38 million inpatient hospitalizations, and 2.9 billion dispensings. Selected laboratory results and vital signs data recorded after 2005 are also available. There is an active data quality assessment and characterization program, and eligibility for medical care and pharmacy benefits is known. Systematic reviews of the literature have assessed the ability of administrative data to identify health outcomes of interest, and procedures have been developed and tested to obtain, abstract, and adjudicate full-text medical records to validate coded diagnoses. Mini-Sentinel has also created a taxonomy of study designs and analytical approaches for many commonly occurring situations, and it is developing new statistical and epidemiologic methods to address certain gaps in analytic capabilities. Assessments are performed by distributing computer programs that are executed locally by each data partner. The system is in active use by FDA, with the majority of assessments performed using customizable, reusable queries (programs). Prospective and retrospective assessments that use customized protocols are conducted as well. To date, several hundred unique programs have been distributed and executed. Current activities include active surveillance of several drugs and vaccines, expansion of the population, enhancement of the common data model to include additional types of data from electronic health records and registries, development of new methodologic capabilities, and assessment of methods to identify and validate additional health outcomes of interest.