Project description:Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF. Methods: We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort. Results: A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort. Conclusions: We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Project description:BackgroundCirrhosis always goes with profound immunity compromise, and makes those patients easily be the target of pneumonia. Cirrhotic patients with pneumonia have a dramatically increased mortality. To recognize the risk factors of mortality and to optimize stratification are critical for improving survival rate.MethodsTwo hundred and three cirrhotic patients with pneumonia at a tertiary care hospital were included in this retrospective study. Demographical, clinical and laboratory parameters, severity models and prognosis were recorded. Multivariate Cox regression analysis was used to identify independent predictors of 30-day and 90-day mortality. Area under receiver operating characteristics curves (AUROC) was used to compare the predictive value of different prognostic scoring systems.ResultsPatients with nosocomial acquired or community acquired pneumonia indicated similar prognosis after 30- and 90-day follow-up. However, patients triggered acute-on-chronic liver failure (ACLF) highly increased mortality (46.4% vs 4.5% for 30-day, 69.6% vs 11.2% for 90-day). Age, inappropriate empirical antibiotic therapy (HR: 2.326 p?=?0.018 for 30-day and HR: 3.126 p?<?0.001 for 90-day), bacteremia (HR: 3.037 p?=?0.002 for 30-day and HR: 2.651 p?=?0.001 for 90-day), white blood cell count (WBC) (HR: 1.452 p?<?0.001 for 30-day and HR: 1.551 p?<?0.001 for 90-day) and total bilirubin (HR: 1.059 p?=?0.002 for 90-day) were independent factors for mortality in current study. Chronic liver failure-sequential organ failure assessment (CLIF-SOFA) displayed highest AUROC (0.89 and 0.90, 95% CI: 0.83-0.95 and 0.85-0.95 for 30-day and 90-day respectively) in current study.ConclusionsThis study found age, bacteremia, WBC, total bilirubin and inappropriate empirical antibiotic therapy were independently associated with increased mortality. Pneumonia triggered ACLF remarkably increased mortality. CLIF-SOFA was more accurate in predicting mortality than other five prognostic models (model for end-stage liver disease (MELD), MELD-Na, quick sequential organ failure assessment (qSOFA), pneumonia severity index (PSI), Child-Turcotte-Pugh (CTP) score).

Project description:Background and aimsPatients with cirrhosis have an increased risk of postoperative mortality for a range of surgeries; however, they are also at risk of postoperative complications such as infection and cirrhosis decompensation. To date, there are no prediction scores that specifically risk stratify patients for these morbidities.MethodsThis was a retrospective study using data of patients with cirrhosis who underwent diverse surgeries in the Veterans Health Administration. Validated algorithms and/or manual adjudication were used to ascertain postoperative decompensation and postoperative infection through 90 days. Multivariable logistic regression was used to evaluate prediction models in derivation and validation sets using variables from the recently-published Veterans Outcomes and Costs Associated with Liver Disease (VOCAL)-Penn cirrhosis surgical risk scores for postoperative mortality. Models were compared with the Mayo risk score, Model for End-stage Liver Disease (MELD)-sodium, and Child-Turcotte-Pugh (CTP) scores.ResultsA total 4712 surgeries were included; patients were predominantly male (97.2 %), white (63.3 %), and with alcohol-related liver disease (35.3 %). Through 90 postoperative days, 8.7 % of patients experienced interval decompensation, and 4.5 % infection. Novel VOCAL-Penn prediction models for decompensation demonstrated good discrimination for interval decompensation (C-statistic 0.762 vs 0.663 Mayo vs 0.603 MELD-sodium vs 0.560 CTP; P < .001); however, discrimination was only fair for postoperative infection (C-statistic 0.666 vs 0.592 Mayo [P = .13] vs 0.502 MELD-sodium [P < .001] vs 0.503 CTP [P < .001]). The model for interval decompensation had excellent calibration in both derivation and validation sets.ConclusionWe report the derivation and internal validation of a novel, parsimonious prediction model for postoperative decompensation in patients with cirrhosis. This score demonstrated superior discrimination and calibration as compared with existing clinical standards, and will be available at www.vocalpennscore.com.

Project description:BackgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF.MethodsWe enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort.ResultsA total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort.ConclusionsWe have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Dataset Information

Risk Prediction Models for Post-Operative Mortality in Patients With Cirrhosis.

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