Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.

PURPOSE: To evaluate the validity of ICD-10-CM code-based algorithms as proxies for influenza in inpatient and outpatient settings in the USA. METHODS: Administrative claims data (2015-2018) from the largest commercial insurer in New Jersey (NJ), USA, were probabilistically linked to outpatient and inpatient electronic health record (EHR) data containing influenza test results from a large NJ health system. The primary claims-based algorithms defined influenza as presence of an ICD-10-CM code for influenza, stratified by setting (inpatient/outpatient) and code position for inpatient encounters. Test characteristics and 95% confidence intervals (CIs) were calculated using test-positive influenza as a reference standard. Test characteristics of alternative outpatient algorithms incorporating CPT/HCPCS testing codes and anti-influenza medication pharmacy claims were also calculated. RESULTS: There were 430 documented influenza test results within the study period (295 inpatient, 135 outpatient). The claims-based influenza definition had a sensitivity of 84.9% (95% CI 72.9%-92.1%), specificity of 96.3% (95% CI 93.1%-98.0%), and PPV of 83.3% (95% CI 71.3%-91.0%) in the inpatient setting, and a sensitivity of 76.7% (95% CI 59.1%-88.2%), specificity of 96.2% (95% CI 90.6%-98.5%), PPV of 85.2% (95% CI 67.5%-94.1%) in the outpatient setting. Primary inpatient discharge diagnoses had a sensitivity of 54.7% (95% CI 41.5%-67.3%), specificity of 99.6% (95% CI 97.7%-99.9%), and PPV of 96.7% (95% CI 83.3%-99.4%). CPT/HCPCS codes and anti-influenza medication claims were present for few outpatient encounters (sensitivity 3%-10%). CONCLUSIONS: In a large US healthcare system, inpatient ICD-10-CM codes for influenza, particularly primary inpatient diagnoses, had high predictive value for test-positive influenza. Outpatient ICD-10-CM codes were moderately predictive of test-positive influenza.

Validation of claims-based algorithms for rheumatoid arthritis in Japan: Results from the VALIDATE-J study.

AIM: Validity of Algorithms in Large Databases: Infectious Diseases, Rheumatoid Arthritis, and Tumor Evaluation in Japan (VALIDATE-J) study examined algorithms for identifying rheumatoid arthritis (RA) in Japanese claims data. METHODS: VALIDATE-J was a multicenter, cross-sectional retrospective study. Disease-identifying algorithms were used to detect RA diagnosed between January 2012 and December 2016 using claims data from two Japanese hospitals. An RA diagnosis was confirmed using one of four gold standard definitions. Positive predictive values (PPVs) were calculated for prevalent (regardless of baseline RA-free period) and incident (preceded by a 12-month RA-free period) cases. RESULTS: Of patients identified using claims-based algorithms, a random sample of 389 prevalent and 134 incident cases of RA were included. Cases identified by an RA diagnosis, no diagnosis of psoriasis, and treatment with any disease-modifying antirheumatic drugs (DMARDs) resulted in the highest PPVs versus other claims-based treatment categories (29.0%-88.3% [prevalent] and 41.0%-78.2% [incident]); cases identified by an RA diagnosis, no diagnosis of psoriasis, and glucocorticoid-only treatment had the lowest PPVs. Across claims-based algorithms, PPVs were highest when a physician diagnosis or decision by adjudicators (confirmed and probable cases) was used as the gold standard and were lowest when American College of Rheumatology/European Alliance of Associations for Rheumatology 2010 criteria were applied. PPVs of claims-based algorithms for RA in patients aged ≥66 years were slightly higher versus a USA Medicare population (maximum PPVs of 95.0% and 88.9%, respectively). CONCLUSION: VALIDATE-J demonstrated high PPVs for most claims-based algorithms for diagnosis of prevalent and incident RA using Japanese claims data. These findings will help inform appropriate RA definitions for future claims database research in Japan.

Ten-Year Trends of Persistent Mortality With Gallstone Disease: A Retrospective Cohort Study in New Jersey.

BACKGROUND AND AIMS: Recent trends in mortality with gallstone disease remain scarce in the United States. Yet multiple changes in clinical management, such as rates of endoscopy, cholecystectomy, and cholecystostomy, and insurance access at the state level, may have occurred. Thus, we evaluated recent secular trends of mortality with gallstone disease in New Jersey. METHODS: We performed a retrospective, cohort study of mortality from 2009 to 2018 using the National Center for Health Statistics, Restricted Mortality Files. The primary outcome was any death with an International Classifications of Disease, 10th Revision, Clinical Modification diagnosis code of gallstone disease in New Jersey. Simple linear regression was used to model trends of incidence of death. RESULTS: 1580 deaths with diagnosed gallstone disease (dGD) occurred from 2009 to 2018. The annual trend of incidence of death was flat over 10 years. The incidence of death with dGD relative to all death changed only from 0.21% to 0.20% over 10 years. These findings were consistent also in 18 of 20 subgroup combinations, although the trend of death with dGD in Latinos 65 years or older increased [slope estimate 0.93, 95% confidence limit 0.42-1.43, P = .003]. CONCLUSION: The rate of death with dGD showed little change over the recent 10 years in New Jersey. This needs to be reproduced in other states and nationally. A closer examination of the changes in clinical care and insurance access is needed to help understand why they did not result in a positive change in this avoidable cause of death.

Validity of claims-based diagnoses for infectious diseases common among immunocompromised patients in Japan.

BACKGROUND: To validate Japanese claims-based disease-identifying algorithms for herpes zoster (HZ), Mycobacterium tuberculosis (MTB), nontuberculous mycobacteria infections (NTM), and Pneumocystis jirovecii pneumonia (PJP). METHODS: VALIDATE-J, a multicenter, cross-sectional, retrospective study, reviewed the administrative claims data and medical records from two Japanese hospitals. Claims-based algorithms were developed by experts to identify HZ, MTB, NTM, and PJP cases among patients treated 2012-2016. Diagnosis was confirmed with three gold standard definitions; positive predictive values (PPVs) were calculated for prevalent (regardless of baseline disease-free period) and incident (preceded by a 12-month disease-free period for the target conditions) cases. RESULTS: Of patients identified using claims-based algorithms, a random sample of 377 cases was included: HZ (n = 95 [55 incident cases]); MTB (n = 100 [58]); NTM (n = 82 [50]); and PJP (n = 100 [84]). PPVs ranged from 67.4-70.5% (HZ), 67.0-90.0% (MTB), 18.3-63.4% (NTM), and 20.0-45.0% (PJP) for prevalent cases, and 69.1-70.9% (HZ), 58.6-87.9% (MTB), 10.0-56.0% (NTM), and 22.6-51.2% (PJP) for incident cases, across definitions. Adding treatment to the algorithms increased PPVs for HZ, with a small increase observed for prevalent cases of NTM. CONCLUSIONS: VALIDATE-J demonstrated moderate to high PPVs for disease-identifying algorithms for HZ and MTB using Japanese claims data.

Validation of a claims-based algorithm to identify cases of ulcerative colitis in Japan.

BACKGROUND AND AIM: The prevalence of ulcerative colitis (UC) is increasing in Japan. Validated claims-based definitions are required to investigate the epidemiology of UC and its treatment and disease course in clinical practice. This study aimed to develop a claims-based algorithm for UC in Japan. METHODS: A committee of epidemiologists, gastroenterologists, and internal medicine physicians developed a claims-based definition for UC, based on diagnostic codes and claims for UC treatments, procedures (cytapheresis), or surgery (postoperative claims). Claims data and medical records for a random sample of 200 cases per site at two large tertiary care academic centers in Japan were used to calculate the positive predictive value (PPV) of the algorithm for three gold standards of diagnosis, defined as physician diagnosis in the medical records, adjudicated cases, or registration in the Japanese Intractable Disease Registry (IDR). RESULTS: Overall, 1139 claims-defined UC cases were identified. Among 393 randomly sampled cases (mean age 44; 48% female), 94% had received ≥ 1 systemic treatment (immunosuppressants, tumor necrosis factor inhibitors, corticosteroids, or antidiarrheals), 7% had cytapheresis, and 7% had postoperative claims. When physician diagnosis was used as a gold standard, PPV was 90.6% (95% confidence interval [CI]: 87.7-93.5). PPV with expert adjudication was also 90.6% (95% CI: 87.7-93.5). PPVs with enrollment in the IDR as gold standard were lower at 41.5% (95% CI: 36.6-46.3) due to incomplete case registration. CONCLUSIONS: The claims-based algorithm developed for use in Japan is likely to identify UC cases with high PPV for clinical studies using administrative claims databases.

Validity of claims-based algorithms for selected cancers in Japan: Results from the VALIDATE-J study.

PURPOSE: Real-world data from large administrative claims databases in Japan have recently become available, but limited evidence exists to support their validity. VALIDATE-J validated claims-based algorithms for selected cancers in Japan. METHODS: VALIDATE-J was a multicenter, cross-sectional, retrospective study. Disease-identifying algorithms were used to identify cancers diagnosed between January or March 2012 and December 2016 using claims data from two hospitals in Japan. Positive predictive values (PPVs), specificity, and sensitivity were calculated for prevalent (regardless of baseline cancer-free period) and incident (12-month cancer-free period; with claims and registry periods in the same month) cases, using hospital cancer registry data as gold standard. RESULTS: 22 108 cancers were identified in the hospital claims databases. PPVs (number of registry cases) for prevalent/incident cases were: any malignancy 79.0% (25 934)/73.1% (18 119); colorectal 84.4% (3519)/65.6% (2340); gastric 87.4% (3534)/76.8% (2279); lung 88.1% (2066)/79.9% (1636); breast 86.4% (4959)/59.9% (3185); pancreatic 87.1% (582)/80.4% (508); melanoma 48.7% (46)/42.9% (36); and lymphoma 83.6% (1457)/77.8% (1035). Specificity ranged from 98.3% to 100% (prevalent)/99.5% to 100% (incident); sensitivity ranged from 39.1% to 67.6% (prevalent)/12.5% to 31.4% (incident). PPVs of claims-based algorithms for several cancers in patients ≥66 years of age were slightly higher than those in a US Medicare population. CONCLUSIONS: VALIDATE-J demonstrated high specificity and modest-to-moderate sensitivity for claims-based algorithms of most malignancies using Japanese claims data. Use of claims-based algorithms will enable identification of patient populations from claims databases, while avoiding direct patient identification. Further research is needed to confirm the generalizability of our results and applicability to specific subgroups of patient populations.

Targeting abnormal metabolism in Alzheimer's disease: The Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study.

Drug discovery for disease-modifying therapies for Alzheimer's disease and related dementias (ADRD) based on the traditional paradigm of experimental animal models has been disappointing. We describe the rationale and design of the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, an innovative multidisciplinary alternative to traditional drug discovery. First, we use a systems biology perspective in the "hypothesis generation" phase to identify metabolic abnormalities that may either precede or interact with the accumulation of ADRD neuropathology, accelerating the expression of clinical symptoms of the disease. Second, in the "hypothesis refinement" phase we propose use of large patient cohorts to test whether drugs approved for other indications that also target metabolic drivers of ADRD pathogenesis might alter the trajectory of the disease. We emphasize key challenges in population-based pharmacoepidemiologic studies aimed at quantifying the association between medication use and ADRD onset and outline robust causal inference principles to safeguard against common pitfalls. Candidate ADRD treatments emerging from this approach will hold promise as plausible disease-modifying therapies for evaluation in randomized controlled trials.