Automatic HIV phenotyping is needed for HIV research based on electronic health records (EHRs). MIMIC-IV, an extension of MIMIC-III, contains more than 520,000 hospital admissions and has become a valuable EHR database for secondary medical research. However, there was no prior phenotyping algorithm to extract HIV cases from MIMIC-IV, which requires a comprehensive knowledge of the database. Moreover, previous HIV phenotyping algorithms did not consider the new HIV-1/HIV-2 antibody differentiation immunoassay tests that MIMIC-IV contains. Our work provided insight into the structure and data elements in MIMIC-IV and proposed a new HIV phenotyping algorithm to fill in these gaps. The results included MIMIC-IV's data tables and elements used, 1,781 and 1,843 HIV cases from MIMIC-IV's versions 0.4 and 2.1, respectively, and summary statistics of these two HIV case cohorts. They could be used for the development of statistical and machine learning models in future studies about the disease.
Background: Older cancer survivors likely experience physical function limitations due to cancer and its treatments, leading to disability and early mortality. Existing studies have focused on factors associated with surgical complications and mortality risk rather than factors associated with the development of poor disability status (DS), a proxy measure of poor performance status, in cancer survivors. We aimed to identify factors associated with the development of poor DS among older survivors of colorectal cancer (CRC) and compare poor DS rates to an age-sex-matched, non-cancer cohort. Methods: This retrospective cohort study utilized administrative data from the Texas Cancer Registry Medicare-linked database. The study cohort consisted of 13,229 survivors of CRC diagnosed between 2005 and 2013 and an age-sex-matched, non-cancer cohort of 13,225 beneficiaries. The primary outcome was poor DS, determined by Davidoff's method, using predictors from 12 months of Medicare claims after cancer diagnosis. Multivariable Cox proportional hazards regression was used to identify risk factors associated with the development of poor DS. Results: Among the survivors of CRC, 97% were 65 years or older. After a 9-year follow-up, 54% of survivors of CRC developed poor DS. Significant factors associated with future poor DS included: age at diagnosis (hazard ratio [HR] = 3.50 for >80 years old), female sex (HR = 1.50), race/ethnicity (HR = 1.34 for Hispanic and 1.21 for Black), stage at diagnosis (HR = 2.26 for distant metastasis), comorbidity index (HR = 2.18 for >1), and radiation therapy (HR = 1.21). Having cancer (HR = 1.07) was significantly associated with developing poor DS in the pooled cohorts; age and race/ethnicity were also significant factors. Conclusions: Our findings suggest that a CRC diagnosis is independently associated with a small increase in the risk of developing poor DS after accounting for other known factors. The study identified risk factors for developing poor DS in CRC survivors, including Hispanic and Black race/ethnicity, age, sex, histologic stage, and comorbidities. These findings underscore the importance of consistent physical function assessments, particularly among subsets of older survivors of CRC who are at higher risk of disability, to prevent developing poor DS.
Introduction: Since February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States. Methods: A retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022. Results and discussion: In this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90 days (n = 19,679), and 13% were readmitted within 30 days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90 days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7-0.9, and aOR: 0.8, 95% CI: 0.8-0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0-1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose-response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0-1.2), 60 (aOR: 1.1, 95% CI: 1.2-1.2), and 90 days (aOR: 1.2, 95% CI: 1.1-1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.
COVID-19 , Health Information Exchange , Adult , Humans , United States , COVID-19/epidemiology , Patient Readmission , Retrospective Studies , Social Determinants of Health , SARS-CoV-2 , Hospitalization
Phenotyping for Type 2 Diabetes (T2DM) is needed due to the increasing demand for T2DM research on electronic health records (EHRs). eMERGE is a reliable and interpretable rule-based algorithm for the identification of T2DM cases and controls in EHRs. MIMIC-IV, an extension of MIMIC-III, contains more than 520,000 hospital admissions and has become a valuable EHR database for secondary medical research. However, there was no prior work to extract T2DM cases and controls from MIMIC-IV, which requires a comprehensive knowledge of the database. Our work provided insight into the structure and data elements in MIMIC-IV and adapted eMERGE to accomplish the task. The results included MIMIC-IV's data tables and elements used, 12,735 cases and 9,828 controls of T2DM, and summary statistics of the cohorts in comparison with those on other EHR databases. They could be used for the development of statistical and machine learning models in future studies about the disease.
