Lung Transplantation in Patients With Systemic Scleroderma-Description of the First Consecutive Cases in Poland: Case Series Report and a Short Literature Review.

Pulmonary complications of systemic scleroderma (SSc), such as interstitial lung disease and pulmonary hypertension (PH), are responsible for up to 60% of deaths among patients. For many years, most centers considered SSc a contraindication to lung transplantation (LTx); however, recent publications show that appropriately selected SSc candidates for LTx give results comparable to patients with idiopathic PH or idiopathic pulmonary fibrosis. This paper presents the cases of a 60-year-old male patient (patient 1) and a 42-year-old female patient (patient 2) diagnosed with SSc in 2019 and 2013, respectively. In both patients, interstitial-fibrotic changes in the lungs leading to respiratory failure were confirmed by high-resolution computed tomography as well as pulmonary hypertension (WHO group 3), which was also diagnosed during right heart catheterization. In both cases, despite pharmacotherapy, pulmonary fibrosis progressed, leading to severe respiratory failure. The patients were referred for LTx qualification. LTx was possible to consider in patients due to the lack of significant changes in other internal organs. Double LTx was successfully performed in both patients (patient 1-July 19, 2022; patient 2-September 14, 2022). They were discharged from the hospital in good condition on the 22nd and 20th postoperative day, respectively. LTx is a last-chance therapy that saves lives among patients with extreme respiratory failure in the course of SSc. It prolongs and improves the quality of life. The selection of appropriate patients is key to the success of the procedure.

Lung Transplantation in Patients With Pulmonary Hypertension With Extracorporeal Membrane Oxygenation (ECMO) Support: 5-Year Experience.

Lung transplantation (LTx) is the only treatment option of patients (pts) with pulmo-nary hypertension (PH) when pharmacologic treatment is unsatisfactory. ECMO is essential during LTx in every patient with pulmonary arterial hypertension and in most patients with sec-ondary PH. This is a retrospective, single-center study comparing LTx outcomes in patients with and without PH covering a 5-year experience. In the years 2018-2023, 219 LTx were performed, of which 56 (25.6%) with ECMO support, among which PH was diagnosed in 34pts (60.7%) in WHO groups 1,3,4: 19pts, 14pts. and 1pt respectively. The veno-arterial type of ECMO was used in patients with PH as intraoperative support (n = 34; 100%). The early (30-day) and long-term survival (1 year) of patients with and without PH did not differ statistically: 91.2% (95% CI: 82.1%-100%) vs. 77.3% (95% CI: 82.1%-100%)(P = .48) and 53.0% (95% CI: 36.6%-76.7 %) vs. 41.3% (95%CI: 23.1-74.0) (P = .48) respectively and the median hospitalization time from ECMO weaning to dis-charge was also comparable: 31 days (Q1-Q3: 21-40; IQR 20) vs. 28 days (Q1-Q3: 24-42; IQR :18) (P = .99). Patients with or without PH undergoing LTx with ECMO have comparable survival and hospital stay outcomes despite being the most challenging of all lung diseases treated with lung transplantation.

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS: We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). RESULTS: Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). CONCLUSION: Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project.

AIMS: As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach. METHODS AND RESULTS: We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration. CONCLUSIONS: Using a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD.