Glycemic control and prescription profiles in internal medicine inpatients: The role of frailty

AIM

This work aims to characterize the clinical profile of individuals with frailty syndrome, diabetes mellitus (DM), and hyperglycemia during hospitalization in regard to glycemic control and treatment regimen.

METHODS

This cross-sectional multicentric study included patients with DM or hyperglycemia at admission. Demographic data, blood glucose values, treatment administered during hospitalization, and treatment indicated at discharge were analyzed. The sample was divided into three groups according to score on a frailty questionnaire. Generalized additive models were used to describe the relationship between either glycemic variability (GV) or minimum capillary blood glucose and hypoglycemia. Models were adjusted for age, comorbidity, and sarcopenia.

RESULTS

A total of 1,137 patients were analyzed. Patients with frailty syndrome had more comorbidity and sarcopenia, worse renal function, and lower albumin and lymphocyte levels. A GV between 21% and 60% was related to a higher probability of hypoglycemia, especially in patients with frailty. Regarding minimum capillary blood glucose, patients with frailty had the highest probability of hypoglycemia. This probability remained significant even in the group with frailty in which, with a reference value of 200 mg/dl, the adjusted odds ratio of a minimum capillary blood glucose of 151 mg/dL was 1.08 (95% confidence interval (1.12-1.05)). Baseline treatments showed a significant predominance of insulin use in the frailest groups.

CONCLUSIONS

Patients with frailty had more sarcopenia and undernourishment. These patients were managed in a similar manner during hospitalization to patients without frailty, despite their higher risk of hypoglycemia according to GV or minimum capillary blood glucose levels.

Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.

High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study

Background: Describe the profile of patients with obesity in internal medicine to determine the role of adiposity and related inflammation on the metabolic risk profile and, identify various “high-risk obesity” phenotypes by means of a cluster analysis. This study aimed to identify different profiles of patients with high-risk obesity based on a cluster analysis. Methods: Cross-sectional, multicenter project that included outpatients attended to in internal medicine. A total of 536 patients were studied. The mean age was 62 years, 51% were women. Patients were recruited from internal medicine departments over two weeks in November and December 2021 and classified into four risk groups according to body mass index (BMI) and waist circumference (WC). High-risk obesity was defined as BMI > 35 Kg/m2 or BMI 30−34.9 Kg/m2 and a high WC (>102 cm for men and >88 cm for women). Hierarchical and partitioning clustering approaches were performed to identify profiles. Results: A total of 462 (86%) subjects were classified into the high-risk obesity group. After excluding 19 patients missing critical data, two profiles emerged: cluster 1 (n = 396) and cluster 2 (n = 47). Compared to cluster 1, cluster 2 had a worse profile, characterized by older age (77 ± 16 vs. 61 ± 21 years, p < 0.01), a Charlson Comorbidity Index > 3 (53% vs. 5%, p < 0.001), depression (36% vs. 19%, p = 0.008), severe disability (64% vs. 3%, p < 0.001), and a sarcopenia score ≥ 4 (79% vs. 16%, p < 0.01). In addition, cluster 2 had greater inflammation than cluster 1 (hsCRP: 5.8 ± 4.1 vs. 2.1 ± 4.5 mg/dL, p = 0.008). Conclusions: Two profiles of subjects with high-risk obesity were identified. Based on that, older subjects with obesity require measures that target sarcopenia, disability, psychological health, and significant comorbidities to prevent further health deterioration. Longitudinal studies should be performed to identify potential risk factors of subjects who progress from cluster 1 to cluster 2.