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Item Hospital readmission and mortality associations to frailty in hospitalized patients with coronary heart disease(Elsevier, 2021) Davis-Ajami, Mary L.; Chang, Pei-Shuin; Wu, JunBackground: Frailty is associated with poor quality outcomes. Objective: To examine associations between frailty and hospital readmission or mortality in Coronary Heart Disease (CHD). Methods: Retrospectively assessed the 2016 US Nationwide Readmissions Database (NRD) including adults ≥ 65 years with pre-existing CHD. A validated Hospital Frailty Risk Score (HFRS) using ICD-10-CM codes identified frailty risk. Outcomes included: Readmission (30-day and subsequent readmission after index event) and in- hospital morality (during index event, readmission, and at 30-day readmission). Results: Among 1.1 million eligible patients, low, intermediate, and high frailty risk accounted for 48.9%, 46.7%, and 4.4% of the sample. Compared to low frailty risk, intermediate and high frailty risk showed significantly higher overall readmission rates (40.9% vs. 31.4%, 41.7% vs. 31.4%) and 30-day readmission rates (21.9% vs. 15.7%, 23.5% vs. 15.7%), respectively. After adjustment, higher in-hospital mortality and readmission rates were associated with higher frailty risk. The associations between in-hospital mortality and frailty depended on the presence of acute coronary syndrome. Conclusions: Readmission and mortality rates increased proportionally to the level of frailty risk in older adults with CHD. CHD, frailty risk, and older age profoundly negatively impact health outcomes and increases risk of death and readmission.Item On shared gamma‐frailty conditional Markov model for semicompeting risks data(Wiley, 2020-10) Li, Jing; Zhang, Ying; Bakoyannis, Giorgos; Gao, Sujuan; Biostatistics, School of Public HealthSemicompeting risks data are a mixture of competing risks data and progressive state data. This type of data occurs when a nonterminal event is subject to truncation by a well-defined terminal event, but not vice versa. The shared gamma-frailty conditional Markov model (GFCMM) has been used to analyze semicompeting risks data because of its flexibility. There are two versions of this model: the restricted and the unrestricted model. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model sometimes yields nonparametrically biased estimation. In this article, we provide a practical guideline for using the GFCMM in the analysis of semicompeting risk data that includes: (a) a score test to assess if the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (b) a graphical illustration to justify whether the unrestricted model yields nonparametric estimation with substantial bias for cases where the test provides a statistical significant result against the restricted model. This guideline was applied to the Indianapolis-Ibadan Dementia Project data as an illustration to explore how dementia occurrence changes mortality risk.Item Sarcopenia, frailty and cachexia patients detected in a multisystem electronic health record database(BMC, 2020-07-31) Moorthi, Ranjani N.; Liu, Ziyue; El-Azab, Sarah A.; Lembcke, Lauren R.; Miller, Matthew R.; Broyles, Andrea A.; Imel, Erik A.; Medicine, School of MedicineBackground: Sarcopenia, cachexia and frailty have overlapping features and clinical consequences, but often go unrecognized. The objective was to detect patients described by clinicians as having sarcopenia, cachexia or frailty within electronic health records (EHR) and compare clinical variables between cases and matched controls. Methods: We conducted a case-control study using retrospective data from the Indiana Network for Patient Care multi-health system database from 2016 to 2017. The computable phenotype combined ICD codes for sarcopenia, cachexia and frailty, with clinical note text terms for sarcopenia, cachexia and frailty detected using natural language processing. Cases with these codes or text terms were matched to controls without these codes or text terms matched on birth year, sex and race. Two physicians reviewed EHR for all cases and a subset of controls. Comorbidity codes, laboratory values, and other coded clinical variables were compared between groups using Wilcoxon matched-pair sign-rank test for continuous variables and conditional logistic regression for binary variables. Results: Cohorts of 9594 cases and 9594 matched controls were generated. Cases were 59% female, 69% white, and a median (1st, 3rd quartiles) age 74.9 (62.2, 84.8) years. Most cases were detected by text terms without ICD codes n = 8285 (86.4%). All cases detected by ICD codes (total n = 1309) also had supportive text terms. Overall 1496 (15.6%) had concurrent terms or codes for two or more of the three conditions (sarcopenia, cachexia or frailty). Of text term occurrence, 97% were used positively for sarcopenia, 90% for cachexia, and 95% for frailty. The remaining occurrences were negative uses of the terms or applied to someone other than the patient. Cases had lower body mass index, albumin and prealbumin, and significantly higher odds ratios for diabetes, hypertension, cardiovascular and peripheral vascular diseases, chronic kidney disease, liver disease, malignancy, osteoporosis and fractures (all p < 0.05). Cases were more likely to be prescribed appetite stimulants and caloric supplements. Conclusions: Patients detected with a computable phenotype for sarcopenia, cachexia and frailty differed from controls in several important clinical variables. Potential uses include detection among clinical cohorts for targeting recruitment for research and interventions.