Introduction This study assessed the efficacy and safety of insulin glargine 300 U/mL (Gla-300) during hospitalization and therapy intensification at discharge in insufficiently controlled people with type 2 diabetes. Research design and methods COBALTA (for its acronym in Spanish, COntrol Basal durante la hospitalizacion y al ALTA) was a multicenter, open-label, single-arm, phase IV trial including 112 evaluable inpatients with type 2 diabetes insufficiently controlled (glycosylated hemoglobin (HbA1c) 8%–10%) with basal insulin and/or non-insulin antidiabetic drugs. Patients were treated with a basal–bolus–correction insulin regimen with Gla-300 during the hospitalization and with Gla-300 and/or non-insulin antidiabetics for 6 months after discharge. The primary endpoint was the HbA1c change from baseline to month 6 postdischarge. Results HbA1c levels decreased from 8.8%±0.6% at baseline to 7.2%±1.1% at month 6 postdischarge (p<0.001, mean change 1.6%±1.1%). All 7-point blood glucose levels decreased from baseline to 24 hours predischarge (p≤0.001, mean changes from 25.1±66.6 to 63.0±85.4 mg/dL). Fasting plasma glucose also decreased from baseline to 24 hours predischarge (p<0.001), month 3 (p<0.001) and month 6 (p<0.001) postdischarge (mean changes 51.5±90.9, 68.2±96.0 and 77.6±86.4 mg/dL, respectively). Satisfaction was high and hyperglycemia/hypoglycemia perception was low according to the Diabetes Treatment Satisfaction Questionnaire at month 6 postdischarge. The incidence of confirmed (glucose<70 mg/dL)/severe hypoglycemia was 25.0% during hospitalization and 59.1% 6 months after discharge. No safety concerns were reported. Conclusions Inpatient and intensification therapy at discharge with Gla-300 improved significantly glycemic control of patients with type 2 diabetes insufficiently controlled with other basal insulin and/or non-insulin antidiabetic medication, with high treatment satisfaction. Gla-300 could therefore be a treatment choice for hospital and postdischarge diabetes management.

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.

Background: There is a dearth of evidence regarding Health-Related Quality of Life (HRQoL) in nonvalvular atrial fibrillation (NVAF) patients undergoing oral anticoagulation therapy. Our objective was to describe HRQoL in NVAF patients on oral anticoagulation, focusing on uncontrolled patients on vitamin K antagonists (VKAs) versus controlled patients on VKAs or non-vitamin K antagonist oral anticoagulants (NOACs), in a real-world setting. Additionally, we assessed the clinical characteristics of patients with uncontrolled anticoagulation. Methods: An observational, multicentre, and cross-sectional study, enrolling 38 Spanish Hospitals' Internal Medicine Departments. HRQoL was assessed using the validated Spanish version of the Sawicki questionnaire. High self-perceived HRQoL was indicated by high scores in the general treatment satisfaction and self-efficacy dimensions, and by low scores in the strained social network, daily hassles and distress dimensions. Results: Five hundred and one patients were included for assessment. Mean scores ± SD were closer to a high perceived HRQoL in controlled than uncontrolled patients for the five dimensions of the questionnaire: 4.9 ± 1.0 versus 3.6 ± 1.3 for general treatment satisfaction; 4.3 ± 1.0 versus 3.6 ± 1.0 for self-efficacy, 3.1 ± 0.9 versus 3.9 ± 1.1 for strained social network, 2.1 ± 0.8 versus 3.0 ± 1.0 for daily hassles and 1.8 ± 0.9 versus 2.6 ± 1.2 for distress. Conclusions: HRQoL in patients with controlled anticoagulant status treated with NOACs or VKAs was better than in patients with uncontrolled anticoagulant status. This seems to indicate that anticoagulation control status influences perception of HRQoL, highlighting the importance of its evaluation when assessing HRQoL in NVAF patients.

Purpose: This study aims to address the pressing need for accurate forecasting of healthcare resource demands during the COVID-19 pandemic. It presents a novel approach that combines a stochastic Markov model and a discrete event simulation model to dynamically predict hospital admissions and daily occupancy of hospital and ICU beds. Design/methodology/approach: The research builds upon existing work related to predicting COVID-19 spread and patient influx to hospital emergency departments. The proposed model was developed and validated at San Juan de Alicante University Hospital from July 10, 2020, to January 10, 2022, and externally validated at Hospital Vega Baja. The model involves an admissions generator based on a stochastic Markov model, feeding data into a discrete event simulation model in the R programming language. The probabilities of hospital admission were calculated based on age-stratified positive SARS-COV-2 results from the health department's catchment population. The discrete event simulation model simulates distinct patient pathways within the hospital to estimate bed occupancy for the upcoming week. The performance of the model was measured using the median absolute difference (MAD) between predicted and actual demand. Findings: When applied to data from San Juan hospital, the admissions generator demonstrated a MAD of 6 admissions/week (interquartile range [IQR] 2-11). The MAD between the model's predictions and actual bed occupancy was 20 beds/day (IQR 5-43), equivalent to 5% of total hospital beds. For ICU occupancy, the MAD was 4 beds/day (IQR 2-7), constituting 25% of ICU beds. Evaluation with data from Hospital Vega Baja showcased an admissions generator MAD of 2.42 admissions/week (IQR 1.02-7.41). The MAD between the model's predictions and actual bed occupancy was 18 beds/day (IQR 19.57-38.89), approximately 5.1% of hospital beds. The ICU occupancy MAD was 3 beds/day (IQR 1-5), making up 21.4% of ICU beds. Practical implications: The dynamic predictions of hospital admissions, ward beds, and ICU occupancy for COVID-19 patients proved highly valuable to hospital managers, facilitating early and informed planning of resource allocation. Additionally, this study underscores the importance of utilizing simulation techniques to predict and manage hospital occupancy levels, thereby enhancing decision-making in hospital bed management, not only during pandemics but also during regular periods. Originality/value: This study introduces a novel hybrid approach that combines stochastic modeling and discrete event simulation to forecast healthcare resource demands during the COVID-19 pandemic. The methodology's effectiveness in predicting admissions and bed occupancy contributes to improved resource planning and situational awareness.

Mesenteric panniculitis is a chronic inflammation of the mesenteric adipose tissue, of uncertain clinical significance and with a possible link to malignancy. This study evaluated the clinical, radiological and prognostic characteristics of primary and secondary forms in an internal medicine cohort. A retrospective cohort of adults with a radiological diagnosis of mesenteric panniculitis (2013-2024), analyzing demographic data, comorbidities, clinical presentation, radiological findings and clinical course. Cases were classified as primary or secondary when associated malignancy, autoimmune disease, or infection was present. One hundred eleven patients included (50% women; mean age 72.2±13.1 years). 61.3% were primary and 38.7% secondary. 31.5% were asymptomatic; abdominal pain was the most common symptom. Mesenteric thickening was the predominant radiological finding. During follow-up, 85.6% remained clinically stable. Overall mortality was 8.1%, slightly higher in secondary forms, mainly those associated with neoplasms, with no significant differences compared to primary cases. Mesenteric panniculitis mainly affected older adults, with a generally indolent course in primary forms, whilst secondary forms showed greater comorbidity.