Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature. METHODS: In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population. RESULTS: Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels. CONCLUSIONS: Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.

Absence of negativization of nasal swab test and frailty as risk factors for mortality in elderly COVID-19 patients admitted in long-term care facilities.

METHODS: A limited amount of data is now available on prognostic factors and mortality among elderly people resident in Long-Term Care facilities and in post-acute units. These populations (in particular those with underlying chronic medical conditions) seem to have higher risk of morbidity and mortality related to COVID-19 disease, but further evidence is needed. The aim of our study is to investigate the impact of some well-known prognostic factors in elderly patients (≥ 65 years) with COVID-19 admitted in the Long-Term Care setting in AUSL Ferrara, Italy. We performed binary regression logistic analysis for some variables (demographic data, clinical data including nasal swab test (NST) at discharge and frailty assessments) to find potential predictors of mortality. We subsequently tested statistically significant variables using Kaplan-Meier curves and Cox-regression models to find survival outcomes and related hazard ratio. RESULTS: Risk factors associated with increased mortality resulted NST at discharge, infection, age and frailty. At a further secondary analysis carried out between NST at discharge, age and clinical frailty scale (CFS) < 5, we found a positive correlation between NST at discharge and CFS < 5. Kaplan-Meier curves showed a statistically significant difference regarding frailty and NST at discharge but not for age. CONCLUSION: Our study showed that absence of negativization of NST at discharge and frailty are strong predictors for mortality in elderly COVID-19 patients admitted in Long-Term Care facilities, while age and the comorbidity burden are less important.

The prevention of adverse drug reactions in older subjects.

Adverse drug reactions (ADRs) are a public health problem in older subjects, being responsible for a significant morbidity, disability and mortality. Older subjects are more susceptible to develop ADRs mainly due to polypharmacy, multimorbidity and inappropriate prescribing. The prevention of these drug related negative events represents an important aim for physicians treating older patients. Several strategies could potentially be employed, including state of the art education of medical students and physicians concerning principles of geriatric medicine and appropriate prescription in older subjects, reduction of inappropriate drug use by means of computerized decision support systems, pharmacist involvement and comprehensive geriatric assessment, and finally the identification of at risk older patients. However, there is currently a lack of scientific evidence demonstrating that these strategies can achieve a reduction in ADRs and therefore future intervention studies should be performed to evaluate the best intervention to decrease the burden of drug related problems in the older population.