Renal Vessel Extension With Cryopreserved Vascular Grafts: Overcoming Surgical Pitfalls in Living Donor Kidney Transplant.

In LDKT, right kidneys and kidneys with anomalous vascularization are often deferred because of concerns on complications and vascular reconstructions. To date, only few reports have examined renal vessel extension with cryopreserved vascular grafts in LDKT. The aim of this study is to investigate the effect of renal vessel extension on short-term outcomes and ischemia times in LDKT. From 2012 to 2020, recipients of LDKT with renal vessels extension were compared with standard LDKT recipients. Subset analysis of rights grafts and grafts with anomalous vascularization, with or without renal vessel extension, was performed. Recipients of LDKT with (n = 54) and without (n = 91) vascular extension experienced similar hospital stays, surgical complications and DGF rates. For grafts with multiple vessels, renal vessel extension granted a faster implantation time (44±5 vs. 72±14 min), which resulted comparable to that of standard anatomy grafts. Right kidney grafts with vascular extension had a faster implantation time compared to right kidney grafts without vascular lengthening (43±5 vs. 58±9 min), and a comparable implantation time to left kidney grafts. Renal vessel extension with cryopreserved vascular grafts allows faster implantation time in right kidney grafts or grafts with anomalous vascularization, maintaining similar surgical and functional outcomes.

Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in Type 2 Diabetes: A Machine-learning Projection Using Data from Clinical Practice.

PURPOSE: Recently, the 2022 American Diabetes Association and European Association for the Study of Diabetes (ADA-EASD) consensus report stressed the importance of weight control in the management of patients with type 2 diabetes; weight control should be a primary target of therapy. This retrospective analysis evaluated, through an artificial-intelligence (AI) projection of data from the AMD Annals database-a huge collection of most Italian diabetology medical records covering 15 years (2005-2019)-the potential effects of the extended use of sodium-glucose co-transporter 2 inhibitors (SGLT-2is) and of glucose-like peptide 1 receptor antagonists (GLP-1-RAs) on HbA1c and weight. METHODS: Data from 4,927,548 visits in 558,097 patients were retrospectively extracted using these exclusion criteria: type 1 diabetes, pregnancy, age >75 years, dialysis, and lack of data on HbA1c or weight. The analysis revealed late prescribing of SGLT-2is and GLP-1-RAs (innovative drugs), and considering a time frame of 4 years (2014-2017), a paradoxic greater percentage of combined-goal (HbA1c <7% and weight gain <2%) achievement was found with older drugs than with innovative drugs, demonstrating aspects of therapeutic inertia. Through a machine-learning AI technique, a "what-if" analysis was performed, using query models of two outcomes: (1) achievement of the combined goal at the visit subsequent to a hypothetical initial prescribing of an SGLT-2i or a GLP-1-RA, with and without insulin, selected according to the 2018 ADA-EASD diabetes recommendations; and (2) persistence of the combined goal for 18 months. The precision values of the two models were, respectively, sensitivity, 71.1 % and 69.8%, and specificity, 67% and 76%. FINDINGS: The first query of the AI analysis showed a great improvement in achievement of the combined goal: 38.8% with prescribing in clinical practice versus 66.5% with prescribing in the "what-if" simulation. Addressing persistence at 18 months after the initial achievement of the combined goal, the simulation showed a potential better performance of SGLT-2is and GLP-1-RAs with respect to each antidiabetic pharmacologic class or combination considered. IMPLICATIONS: AI appears potentially useful in the analysis of a great amount of data, such as that derived from the AMD Annals. In the present study, an LLM analysis revealed a great potential improvement in achieving metabolic targets with SGLT-2i and GLP-1-RA utilization. These results underscore the importance of early, timely, and extended use of these new drugs.

Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis.

INTRODUCTION: The aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes. RESEARCH DESIGN AND METHODS: Overall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005-2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain. RESULTS: The combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control. CONCLUSIONS: Treating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention.