Introduction: Accumulation of plasma mitochondrial DNA (mtDNA) following severe trauma has been shown to correlate with the development of systemic inflammatory response syndrome (SIRS) and may predict mortality. Our objective was to investigate the relationship between levels of circulatory mtDNA following pancreaticoduodenectomy (PD) and the postoperative course. Methods: Levels of plasma mtDNA were assessed by real-time PCR of the mitochondrial genes ND1 and COX3 in 23 consecutive patients who underwent PD 1 day prior to surgery, within 8 h after surgery, and on postoperative day (POD)1 and POD5. The abundance of mtDNA was assessed relative to preoperative levels and in relation to parameters reflecting the postoperative clinical course. Results: When pooled for all patients, the circulating mtDNA levels were significantly increased after surgery. However, while a significant (at least >2-fold and up to >20-fold) rise was noted in 11 patients, no change in mtDNA levels was noted in the other 12 following surgery. Postoperative rise in circulating mtDNA was associated with an increased rate of postoperative fever until day 5, decreased hemoglobin and albumin levels, and increased white blood cell counts. These patients also suffered from increased rates of delayed gastric emptying. No significant differences were demonstrated in other postoperative parameters. Conclusion: Circulating mtDNA surge is associated with an inflammatory response following PD and may potentially be used as an early marker for postoperative course. Studies of larger patient cohorts are warranted.

Background.: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. Methods.: We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms’ outputs based on a single preoperative ECG were correlated with patient mortality data. Results.: Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00–9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). Conclusions.: The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.