Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs, interpret and learn from such feedbacks, and display related and flexible independent actions that help the entity reach a specific aim over a period of time. By extending its health-care applications continuously, the ultimate AI target is to use machine simulation of human intelligence processes such as learning, reasoning, and self-correction, to mimic human behaviour. AI is extensively used in diverse sectors of medicine, including clinical trials, drug discovery and development, understanding of target-disease associations, disease prediction, imaging, and precision medicine. In this review, we firstly describe the limitations and challenges of the AI tools and techniques utilized in medicine, followed by current uses and applications of AI in the translational field, highlighting the cardio-renal preclinical models with potential to contribute to future clinical research.
Introduction In chronic kidney disease (CKD) patients, the case fatality rate caused by the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is higher than in the general population [1]. In patients hospitalised with COVID 19, right heart dysfunction is present in 20% of the cases, associated with an increased risk of all-cause death [2]. Purpose CARDIO-SCARS in CKD is a currently ongoing multi-center observational match controlled trial that aims to assess the cardiovascular (CV) risk in a CKD (stages 3 to 5), dialysis and kidney transplant population, following SARS-CoV-2 infection, by using clinical evaluation, various techniques and novel biomarkers (ClinicalTrials.gov Identifier NCT05125913). We hereby report the main baseline echocardiographic parameters that assess the right ventricular (RV) function. Methods We conducted a cross-sectional study that included 263 patients with CKD (dialysis, transplantation or eGFR <60 ml/min/1.73 m2). For assessing RV function, 5 parameters were measured: fractional area change (FAC, %), tricuspid annular plane systolic excursion (TAPSE, mm), tricuspid S' wave (S tric, cm/s), Tei index and right ventricular free wall longitudinal strain (RVFWLS, %). Patients in atrial fibrillation, with a permanent pacemaker or with a poor acoustic window were excluded. Results Our study included 263 patients with CKD, divided in two groups: 168 patients post COVID-19 (94 in dialysis, 38 post kidney transplantation and 36 with CKD) and 95 patients in the control group (57 in dialysis, 30 post kidney transplantation and 8 with CKD).The mean age was 57.3±15.4 years (median 60 years old), 55.2% of the patients were males, 24% were diabetic and 9.5% were smokers. The mean duration of dialysis in the COVID-19 group was 63.8 months vs. 62.6 months in the control group. In the COVID group, the echocardiography was performed at a mean distance of 2.2±2.1 months after testing positive for SARS-CoV-2.All the parameters of RV function were better in the control group (FAC (%): 43.6±11 vs. 41.3±11; TAPSE (mm): 23.2±6.9 vs. 21.4±4.9; S tric (cm/s): 13.4±3.4 vs. 13±3.1; Tei index: 0.5±0.2 vs. 0.6±0.2; RVFWLS (%): −20.1±3.8 vs. −18.6±5.1. After performing a two-sample t-Test, statistical significance was reached only for TAPSE, Tei index and RVFWLS (0.008, 0.0001 and 0.006, respectively). Conclusions Our study is the first to describe echocardiographic alterations post-COVID in a CKD population. All CKD patients had lower values of RV parameters than those reported in the general population. Still, the CKD COVID group had lower values than CKD control group, with the same magnitude as the changes reported in the general population [3,4]. The evolution of these parameters and their prognostic significance is of interest, regarding long-term CV sequelae of COVID-19. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The Ministry of Research and Innovation, CNCS-UEFISCDI
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