Evaluation of Liver Graft Recipient Workup in Predicting of Early Cardiovascular Events During Liver Transplantation: A Single-Center Experience

Renaissance of Modified Charlson Comorbidity Index in Prediction of Short- and Long-Term Survival After Liver Transplantation?

BACKGROUND Orthotopic liver transplantation (OLT) is the standard of care for end-stage liver disease. The Charlson Comorbidity Index (CCI) was originally created to assess the survival rate of patients with chronic diseases, although it was modified and adopted in OLT recipients as CCI-OLT. MATERIAL AND METHODS In total of 248 consecutive liver transplant recipients with viral cirrhosis in 98 (39.5%) patients were included. CCI-OLT was calculated assigning a weight of 3 to chronic obstructive pulmonary disease; weight of 2 to coronary artery disease, connective tissue disease, and renal insufficiency; and a weight of 1 to diabetes mellitus. RESULTS CCI-OLT was significantly correlated with recipient age (p<0.001; R=0.333) and was a significant risk factor for early post-transplant mortality (p=0.004). The presence of diabetes mellitus significantly increased the odds of early mortality (p=0.010). The optimal cut-off for CCI-OLT in prediction of mortality during the first 90 days after transplantation was ≥1, with an AUROC of 0.780 (95% CI: 0.670-0.891; p<0.001). Increasing CCI-OLT was a significant risk factor for worse 5-year post-transplant survival (p=0.001), along with coronary artery disease (p=0.008) and diabetes mellitus (p=0.021). The optimal cut-off for prediction of 5-year mortality for CCI-OLT was ≥1, with the AUROC of 0.638 (95% CI: 0.544-0.733; p=0.004). CONCLUSIONS CCI-OLT is a useful tool for measuring the effect of pretransplant comorbidities and to stratify the effect of risk on both short- and long-term outcomes after OLT. Recipient age and diabetes strongly affected short-term survival after OLT, and metabolic and vascular complications were the leading causes of death at 5 years after OLT.

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.