Anti-inflammatory response-based risk assessment in acute type A aortic dissection: A national multicenter cohort study.

Background: Early identification of patients at high risk of operative mortality is important for acute type A aortic dissection (TAAD). We aimed to investigate whether patients with distinct risk stratifications respond differently to anti-inflammatory pharmacotherapy. Methods: From 13 cardiovascular hospitals, 3110 surgically repaired TAAD patients were randomly divided into a training set (70%) and a test set (30%) to develop and validate a risk model to predict operative mortality using extreme gradient boosting. Performance was measured by the area under the receiver operating characteristic curve (AUC). Subgroup analyses were performed by risk stratifications (low versus middle-high risk) and anti-inflammatory pharmacotherapy (absence versus presence of ulinastatin use). Results: A simplified risk model was developed for predicting operative mortality, consisting of the top ten features of importance: platelet-leukocyte ratio, D-dimer, activated partial thromboplastin time, urea nitrogen, glucose, lactate, base excess, hemoglobin, albumin, and creatine kinase-MB, which displayed a superior discrimination ability (AUC: 0.943, 95 % CI 0.928-0.958 and 0.884, 95 % CI 0.836-0.932) in the derivation and validation cohorts, respectively. Ulinastatin use was not associated with decreased risk of operative mortality among each risk stratification, however, ulinastatin use was associated with a shorter mechanical ventilation duration among patients with middle-high risk (defined as risk probability >5.0 %) (ß -1.6 h, 95 % CI [-3.1, -0.1] hours; P = 0.048). Conclusion: This risk model reflecting inflammatory, coagulation, and metabolic pathways achieved acceptable predictive performances of operative mortality following TAAD surgery, which will contribute to individualized anti-inflammatory pharmacotherapy.

Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection.

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

A Novel Inflammation-Based Risk Score Predicts Mortality in Acute Type A Aortic Dissection Surgery: The Additive Anti-inflammatory Action for Aortopathy and Arteriopathy Score.

Objective: To develop an inflammation-based risk stratification tool for operative mortality in patients with acute type A aortic dissection. Methods: Between January 1, 2016 and December 31, 2021, 3124 patients from Beijing Anzhen Hospital were included for derivation, 571 patients from the same hospital were included for internal validation, and 1319 patients from other 12 hospitals were included for external validation. The primary outcome was operative mortality according to the Society of Thoracic Surgeons criteria. Least absolute shrinkage and selection operator regression were used to identify clinical risk factors. A model was developed using different machine learning algorithms. The performance of the model was determined using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves, and Brier score for calibration. The final model (5A score) was tested with respect to the existing clinical scores. Results: Extreme gradient boosting was selected for model training (5A score) using 12 variables for prediction-the ratio of platelet to leukocyte count, creatinine level, age, hemoglobin level, prior cardiac surgery, extent of dissection extension, cerebral perfusion, aortic regurgitation, sex, pericardial effusion, shock, and coronary perfusion-which yields the highest AUC (0.873 [95% confidence interval (CI) 0.845-0.901]). The AUC of 5A score was 0.875 (95% CI 0.814-0.936), 0.845 (95% CI 0.811-0.878), and 0.852 (95% CI 0.821-0.883) in the internal, external, and total cohort, respectively, which outperformed the best existing risk score (German Registry for Acute Type A Aortic Dissection score AUC 0.709 [95% CI 0.669-0.749]). Conclusion: The 5A score is a novel, internally and externally validated inflammation-based tool for risk stratification of patients before surgical repair, potentially advancing individualized treatment. Trial Registration: clinicaltrials.gov Identifier: NCT04918108.

Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm.

Aims: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications. Methods and results: A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); P = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); P < 0.0001]. Conclusion: In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.

Effect of cardiopulmonary bypass reoxygenation on myocardial dysfunction following pediatric tetralogy of Fallot repair.

BACKGROUND: Little is known regarding the effect of cardiopulmonary bypass (CPB) reoxygenation on cardiac function following tetralogy of Fallot repair. We hypothesized that hyperoxic reoxygenation would be more strongly associated with myocardial dysfunction in children with tetralogy of Fallot. METHODS: We investigated the association of perfusate oxygenation (PpO2) associated with myocardial dysfunction among children aged 6-72 months who underwent complete repair of tetralogy of Fallot in 2012-2018. Patients were divided into two groups: lower PpO2 group (≤ 250 mmHg) and higher PpO2 (> 250 mmHg) group based on the highest value of PpO2 during aortic occlusion. The odd ratio (ORs) and 95% confidence intervals (CIs) were estimated by logistic regression models. RESULTS: This study included 163 patients perfused with lower PpO2 and 213 with higher PpO2, with median age at surgery 23.3 (interquartile range [IQR] 12.5-39.4) months, 164 female (43.6%), and median body mass index 15.59 (IQR 14.3-16.9) kg/m2. After adjustment for baseline, clinical and procedural variables, patients with higher PpO2 were associated with higher risk of myocardial dysfunction than those with lower PpO2 (OR 1.770; 95% CI 1.040-3.012, P = 0.035). Higher PpO2, lower SpO2, lower pulmonary annular Z-score, and longer CPB time were independent risk factors for myocardial dysfunction. CONCLUSIONS: Association exists between higher PpO2 and myocardial dysfunction risk in patients with tetralogy of Fallot, highlighting the modulation of reoxygenation during aortic occlusion to reduce cardiovascular damage following tetralogy of Fallot repair. TRIAL REGISTRATION: Clinical Trials. gov number NCT03568357. June 26, 2018.