Age, Multiple Chronic Conditions, and COVID-19: A literature review

BACKGROUND: Various patient demographic and clinical characteristics have been associated with poor outcomes for individuals with coronavirus disease 2019 (COVID-19). To describe the importance of age and chronic conditions in predicting COVID-19 related outcomes. METHODS: Search strategies were conducted in PubMed/MEDLINE. Daily alerts were created. RESULTS: A total of 28 studies met our inclusion criteria. Studies varied broadly in sample size (n=21 to more than 17,000,000). Participants mean age ranged from 48 years to 80 years and the proportion of male participants ranged from 44%-82%. The most prevalent underlying conditions in patients with COVID-19 were hypertension (range: 15% - 69%), diabetes (8% - 40%), cardiovascular disease (4% - 61%), chronic pulmonary disease (1% - 33%), and chronic kidney disease (range 1% - 48%). These conditions were each associated with an increased in-hospital case fatality rate ranging from 1% to 56%. Overall, older adults have a substantially higher case fatality rate (CFR) as compared with younger individuals affected by COVID-19 (42% for those \u3c 65 vs 65% \u3e 65 years ). Only one study examined the association of chronic conditions and the risk of dying across different age groups; their findings suggested similar trends of increased risk in those \u3c 65 and those \u3e 65 years as compared to those without these conditions. CONCLUSIONS: There has been a traditional, single condition approach to consideration of how chronic conditions and advancing age relate to COVID-19 outcomes. A more complete picture of the impact of burden of multimorbidity and advancing patient age is needed

Validation of an electronic algorithm for Hodgkin and non-Hodgkin lymphoma in ICD-10-CM

PURPOSE: Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claims data. METHODS: We developed a three-component algorithm to identify patients aged \u3e /=15 years who were newly diagnosed with Hodgkin (HL) or non-Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA\u27s Sentinel System. The algorithm identified potential cases as patients with \u3e /=2 ICD-10-CM lymphoma diagnosis codes on different dates within 183 days; \u3e /=1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and \u3e /=1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated. RESULTS: We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%-84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL. CONCLUSIONS: Seventy-seven percent of lymphoma cases identified by an algorithm based on ICD-10-CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost-effective way to target an important health outcome of interest for large-scale drug safety and public health surveillance studies