The use of a new automatic device for patients' assessment at Triage in Emergency Department

Objectives: To assess time saving in an Emergency Department arising out of the introduction of automatic devices (Carescape™ V100) to measure vital signs compared to the manual devices. Methods: We performed a prospective, observational study of eligible patients referring to Sant'Andrea Hospital Emergency Department during the entire month of October 2009, randomly assigned into two groups. In the first group of 476 patients vital signs measurements were detected with manual devices, while in the second group of 477 patients with automatic device Carescape™ V100. Results: Data indicated that the comparison of the total time between the two groups gave a significant difference (1993 vs 1518 min, p < 0.001). No differences were found with respect to age, sex and priority codes. Significant differences were also found when comparing the subgroups of the same acuity categories: white codes 4.33 vs 2.27 (min), p < 0.05; green codes 4.28 vs 3.37 (min), p < 0.001; yellow codes 3.92 vs 2.72 (min), p < 0.001. Conclusions: Our data demonstrated a statistical significance between the two groups with a difference of 475 minutes spent in Triage procedures including vital signs measurements. In conclusion time saved by vital signs automatic device could allow ED physicians to make a qualified approach with an earlier diagnosis and a more rapid and effective therapy, possibly improving patients' outcomes. ABSTRACT of data concerning vital signs quality assessment, because we did not compare the two methods in the same patient and we did not correlate Triage priority evaluation with patients' outcomes. In the future further studies should be specifically aimed to address this issue. In conclusion time saved by vital signs automatic device could allow ED physicians to make a qualified approach to patient with an earlier diagnosis and a more rapid and effective therapy, possibly improving patients' outcomes

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient&rsquo;s quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited

Temporal Patterns of Glucagon and Its Relationships with Glucose and Insulin following Ingestion of Different Classes of Macronutrients

Background: glucagon secretion and inhibition should be mainly determined by glucose and insulin levels, but the relative relevance of each factor is not clarified, especially following ingestion of different macronutrients. We aimed to investigate the associations between plasma glucagon, glucose, and insulin after ingestion of single macronutrients or mixed-meal. Methods: thirty-six participants underwent four metabolic tests, based on administration of glucose, protein, fat, or mixed-meal. Glucagon, glucose, insulin, and C-peptide were measured at fasting and for 300 min following food ingestion. We analyzed relationships between time samples of glucagon, glucose, and insulin in each individual, as well as between suprabasal area-under-the-curve of the same variables (∆AUCGLUCA, ∆AUCGLU, ∆AUCINS ) over the whole participants’ cohort. Results: in individuals, time samples of glucagon and glucose were related in only 26 cases (18 direct, 8 inverse relationships), whereas relationship with insulin was more frequent (60 and 5, p < 0.0001). The frequency of significant relationships was different among tests, especially for direct relationships (p ≤ 0.006). In the whole cohort, ∆AUCGLUCA was weakly related to ∆AUCGLU (p ≤ 0.02), but not to ∆AUCINS, though basal insulin secretion emerged as possible covariate. Conclusions: glucose and insulin are not general and exclusive determinants of glucagon secretion/inhibition after mixed-meal or macronutrients ingestion

TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy

The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy

The use of a new automatic device for patients' assessment at Triage in Emergency Department.

Obiettivo: Valutare in Pronto Soccorso il risparmio di tempo ottenuto grazie all’impiego di un dispositivo automatico (Carescape™ V100) per misurare i parametri vitali rispetto agli attuali dispositivi manuali. Metodi: Abbiamo condotto uno studio prospettico osservazionale su pazienti eleggibili afferenti al Pronto Soccorso dell’Ospedale Sant’Andrea durante tutto il mese di ottobre 2009, suddivisi in modo casuale in due gruppi. Nel primo gruppo, di 476 pazienti, la rilevazione dei parametri vitali è stata effettuata con dispositivi manuali, mentre nel secondo gruppo, di 477 pazienti, con dispositivo automatico Carescape™ V100. Risultati: I dati hanno evidenziato che la differenza dei tempi totali tra i due gruppi è risultata statisticamente significativa (1993 vs 1518 min, p < 0,001). I due gruppi di studio erano omogenei per età, sesso e numerosità del campione nell’ambito dello stesso codice-colore. Differenze significative sono state invece riscontrate confrontando i tempi nei sottogruppi divisi per codice di priorità: codici bianchi 4,33 vs 2,27 (min), p < 0,05; codici verdi 4,28 vs 3,37 (min), p < 0,001; codici gialli 3,92 vs 2,72 (min), p < 0,001. Conclusioni: I risultati di questo studio hanno dimostrato tra i due gruppi una differenza statisticamente significativa di 475 minuti totali impiegati nelle procedure di triage, compresa la misurazione dei parametri vitali. In conclusione, il tempo risparmiato nella rilevazione di tali parametri mediante un dispositivo automatico potrebbe consentire ai medici d’emergenza-urgenza di avere un approccio qualificato con una diagnosi precoce e una terapia più rapida ed efficace, migliorando possibilmente l’outcome dei pazienti

Glucometabolism in Kidney Transplant Recipients with and without Posttransplant Diabetes: Focus on Beta-Cell Function

Posttransplant diabetes mellitus (PTDM) is a common complication after kidney transplantation. Pathophysiologically, whether beta-cell dysfunction rather than insulin resistance may be the predominant defect in PTDM has been a matter of debate. The aim of the present analysis was to compare glucometabolism in kidney transplant recipients with and without PTDM. To this aim, we included 191 patients from a randomized controlled trial who underwent oral glucose tolerance tests (OGTTs) 6 months after transplantation. We derived several basic indices of beta-cell function and insulin resistance as well as variables from mathematical modeling for a more robust beta-cell function assessment. Mean ± standard deviation of the insulin sensitivity parameter PREDIM was 3.65 ± 1.68 in PTDM versus 5.46 ± 2.57 in NON-PTDM. Model-based glucose sensitivity (indicator of beta-cell function) was 68.44 ± 57.82 pmol∙min−1∙m−2∙mM−1 in PTDM versus 143.73 ± 112.91 pmol∙min−1∙m−2∙mM−1 in NON-PTDM, respectively. Both basic indices and model-based parameters of beta-cell function were more than 50% lower in patients with PTDM, indicating severe beta-cell impairment. Nonetheless, some defects in insulin sensitivity were also present, although less marked. We conclude that in PTDM, the prominent defect appears to be beta-cell dysfunction. From a pathophysiological point of view, patients at high risk for developing PTDM may benefit from intensive treatment of hyperglycemia over the insulin secretion axis

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services