The “Diabetes Comorbidome”: A Different Way for Health Professionals to Approach the Comorbidity Burden of Diabetes

(1) Background: The disease burden related to diabetes is increasing greatly, particularly in older subjects. A more comprehensive approach towards the assessment and management of diabetes’ comorbidities is necessary. The aim of this study was to implement our previous data identifying and representing the prevalence of the comorbidities, their association with mortality, and the strength of their relationship in hospitalized elderly patients with diabetes, developing, at the same time, a new graphic representation model of the comorbidome called “Diabetes Comorbidome”. (2) Methods: Data were collected from the RePoSi register. Comorbidities, socio-demographic data, severity and comorbidity indexes (Cumulative Illness rating Scale CIRS-SI and CIRS-CI), and functional status (Barthel Index), were recorded. Mortality rates were assessed in hospital and 3 and 12 months after discharge. (3) Results: Of the 4714 hospitalized elderly patients, 1378 had diabetes. The comorbidities distribution showed that arterial hypertension (57.1%), ischemic heart disease (31.4%), chronic renal failure (28.8%), atrial fibrillation (25.6%), and COPD (22.7%), were the more frequent in subjects with diabetes. The graphic comorbidome showed that the strongest predictors of death at in hospital and at the 3-month follow-up were dementia and cancer. At the 1-year follow-up, cancer was the first comorbidity independently associated with mortality. (4) Conclusions: The “Diabetes Comorbidome” represents the perfect instrument for determining the prevalence of comorbidities and the strength of their relationship with risk of death, as well as the need for an effective treatment for improving clinical outcomes

Antidiabetic Drug Prescription Pattern in Hospitalized Older Patients with Diabetes

Objective: To describe the prescription pattern of antidiabetic and cardiovascular drugs in a cohort of hospitalized older patients with diabetes. Methods: Patients with diabetes aged 65 years or older hospitalized in internal medicine and/or geriatric wards throughout Italy and enrolled in the REPOSI (REgistro POliterapuie SIMI—Società Italiana di Medicina Interna) registry from 2010 to 2019 and discharged alive were included. Results: Among 1703 patients with diabetes, 1433 (84.2%) were on treatment with at least one antidiabetic drug at hospital admission, mainly prescribed as monotherapy with insulin (28.3%) or metformin (19.2%). The proportion of treated patients decreased at discharge (N = 1309, 76.9%), with a significant reduction over time. Among those prescribed, the proportion of those with insulin alone increased over time (p = 0.0066), while the proportion of those prescribed sulfonylureas decreased (p < 0.0001). Among patients receiving antidiabetic therapy at discharge, 1063 (81.2%) were also prescribed cardiovascular drugs, mainly with an antihypertensive drug alone or in combination (N = 777, 73.1%). Conclusion: The management of older patients with diabetes in a hospital setting is often sub-optimal, as shown by the increasing trend in insulin at discharge, even if an overall improvement has been highlighted by the prevalent decrease in sulfonylureas prescription

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population

Machine Learning and Antibiotic Management

Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers

Real Evidence and Misconceptions about Malignant Hyperthermia in Children: A Narrative Review

Malignant hyperthermia is a rare but life-threatening pharmacogenetic disorder triggered by exposure to specific anesthetic agents. Although this occurrence could affect virtually any patient during the perioperative time, the pediatric population is particularly vulnerable, and it has a five-fold higher incidence in children compared to adults. In the last few decades, synergistic efforts among leading anesthesiology, pediatrics, and neurology associations have produced new evidence concerning the diagnostic pathway, avoiding unnecessary testing and limiting false diagnoses. However, a personalized approach and an effective prevention policy focused on clearly recognizing the high-risk population, defining perioperative trigger-free hospitalization, and rapid activation of supportive therapy should be improved. Based on epidemiological data, many national scientific societies have produced consistent guidelines, but many misconceptions are common among physicians and healthcare workers. This review shall consider all these aspects and summarize the most recent updates

Back Analysis of the 2014 San Leo Landslide Using Combined Terrestrial Laser Scanning and 3D Distinct Element Modelling

Landslides of the lateral spreading type, involving brittle geological units overlying ductile terrains, are a common occurrence in the sandstone and limestone plateaux of the northern Apennines of Italy. The edges of these plateaux are often the location of rapid landslide phenomena, such as rock slides, rock falls and topples. In this paper, we present a back analysis of a recent landslide (February 2014), involving the north-eastern sector of the San Leo rock slab (northern Apennines, Emilia-Romagna Region) which is a representative example of this type of phenomena. The aquifer hosted in the fractured slab, due to its relatively higher secondary permeability in comparison to the lower clayey units leads to the development of perennial and ephemeral springs at the contact between the two units. The related piping erosion phenomena, together with slope processes in the clay-shales have led to the progressive undermining of the slab, eventually predisposing large-scale landslides. Stability analyses were conducted coupling terrestrial laser scanning (TLS) and distinct element methods (DEMs). TLS point clouds were analysed to determine the pre- and post-failure geometry, the extension of the detachment area and the joint network

Counterfeit drugs detection by measurement of tablets and secondary packaging colour

The growth of pharmaceutical counterfeiting is a major public health problem. This growth is resulting in a proportional increase in the number of samples that medicines control laboratories have to test. Thus the need for simple and affordable preliminary screening methods to be used by inspectors to decide in the field whether to collect a sample for further laboratory analysis or not. This paper intends to evaluate the possibility to employ for preliminary examinations of suspicious samples an optical spectrophotometer (colorimeter) used in the graphic industry, capable of measuring the reflectance visible spectrum of solid materials. The colorimeter was tested on original and counterfeited Viagra, Cialis and Levitra by measuring the colour of tablets' surface and of a specific spot of the packages. Various batches of the original drugs were employed both to investigate precision and robustness of the technique and to build spectral libraries. These libraries were used to compare suspicious samples to the corresponding original by means of a wavelength distance pattern recognition method. The method was eventually tested on suspicious samples sized by police authorities in order to evaluate its effectiveness. The device resulted precise and robust toward ambient conditions changes, although some limits emerged: the libraries of original samples need a frequent update and a lower precision is to be expected for tablets which surface is extremely convex. (C) 2010 Elsevier B.V. All rights reserved

La franosità storica e attuale nella città fortificata di San Leo (Rimini)

Il lavoro illustra la franosità storica e attuale nella città fortificata di San Leo (Rimini) e le dinamiche attuali tramite monitoraggio satellitare e misure da terra

Machine Learning and Antibiotic Management

Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from "very low" to "very high"). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers