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

Inflammatory Trajectory and Anti-Inflammatory Pharmacotherapy in Frozen Elephant Trunk-Treated Acute Type I Aortic Dissection

Background

Acute DeBakey type I aortic dissection is associated with high morbidity and mortality. Little is known regarding the role of leukocyte trajectory in prognosis.

Methods

We included adult acute DeBakey type I aortic dissection patients with emergency frozen elephant trunk and total arch replacement in 2 cardiovascular centers (2020-2022). We used latent class mixed model to group patients according to their leukocyte patterns from hospital admission to the first 5 days after surgery. We investigated the association of leukocyte trajectory and 30-day and latest follow-up mortality (October 31, 2023), exploratorily analyzing the effects of ulinastatin treatment on outcome.

Results

Of 255 patients included, 3 distinct leukocyte trajectories were identified: 196 in group I (decreasing trajectory), 34 in group II (stable trajectory), and 25 in group III (rising trajectory). Overall, 30-day mortality was 25 (9.8%), ranging from 8.2% (16/196) in group I, 8.8% (3/34) in group II, to 24.0% (6/25) in group III (P for trend = .036). Group III was associated with higher mortality both at 30 days (adjusted hazard ratio, 3.260; 95% CI, 1.071-9.919; P = .037) and at the last follow-up (adjusted hazard ratio, 2.840; 95% CI, 1.098-7.345; P = .031) compared with group I.

Conclusions

Distinct and clinically relevant groups can be identified by analyzing leukocyte trajectories, and a rising trajectory was associated with higher short-term and midterm mortality.

Prognostic implication of residual inflammatory trajectories in acute type I aortic dissection: dual-center prospective cohort study

BACKGROUND

Peripheral platelet-white blood cell ratio (PWR) integrating systemic inflammatory and coagulopathic pathways is a key residual inflammatory measurement in the management of acute DeBakey type I aortic dissection (AAD); however, trajectories of PWR in AAD is poorly defined.

METHODS

Two AAD cohorts were included in two cardiovascular centers (2020-2022) if patients underwent emergency total arch replacement with frozen elephant trunk implantation. PWR data were collected over time at baseline and five consecutive days after surgery. Trajectory patterns of PWR were determined using the latent class mixed modelling (LCMM). Cox regression was used to determine independent risk factors. By adding PWR Trajectory, a user-friendly nomogram was developed for predicting mortality after surgery.

RESULTS

Two hundred forty-six patients with AAD were included with a median follow-up of 26 (IRQ 20-37) months. Three trajectories of PWR were identified [cluster α 45(18.3%), β105 (42.7%), and γ 96 (39.0%)]. Cluster γ was associated with higher risk of mortality at follow-up (crude HR, 3.763; 95% CI: 1.126-12.574; P =0.031) than cluster α. By the addition of PWR trajectories, an inflammatory nomogram, composed of age, hemoglobin, estimated glomerular filtration rate, and cardiopulmonary time was developed and internally validated, with adequate discrimination [the area under the receiver-operating characteristic curve 0.765, 95% CI: 0.660-0.869)], calibration, and clinical utility.

CONCLUSION

Based on PWR trajectories, three distinct clusters were identified with short-term outcomes, and longitudinal residual inflammatory shed some light to individualize treatment strategies for AAD.

Inflammatory profiles define phenotypes with clinical relevance in acute type A aortic dissection

Association of distinct inflammatory profiles with short-term mortality is little known in type A aortic dissection (TAAD). Latent class analysis was used to identify distinct inflammatory profiles based on leukocyte, neutrophils, monocyte, lymphocytes, platelet, fibrinogen, D-dimer, neutrophils-lymphocyte ratio, platelet-lymphocyte ratio, and lymphocyte-monocyte ratio. We identified 193 patients with median age of 56 (IQR 47-63) years and 146 males. Patients were divided as hyper-inflammatory profiles (84 [43.5%]) and hypo-inflammatory profiles (109 [56.5%]). Although baseline characteristics were not different, hyper-inflammatory patients had higher 6-month mortality (20 [23.8%] vs. 11 [10.1%]; P = 0.014) and 30-day mortality (18 [21.4%] vs. 9 [8.3%], P = 0.009) than hypo-inflammatory patients. After adjustment for potential confounders, hyper-inflammatory profiles remain associated with higher risk of 6-month mortality than hypo-inflammatory profiles (adjusted OR 2.427 [95%CI 1.154, 5.105], P = 0.019). Assessment of preoperative inflammatory profiles adds clarity regarding the extent of inflammatory response to TAAD aetiopathologies, highlighting individual anti-inflammatory pharmacotherapy for TAAD. ClinicalTrials.gov Identifier: NCT04398992.

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.