Prognostic Impact of Systemic Coagulation-Inflammation Index in Acute Type A Aortic Dissection Surgery

A novel nomogram model to predict in-hospital mortality in patients with acute type A aortic dissection after surgery

BACKGROUND

Acute type A aortic dissection is a dangerous disease that threatens public health. In recent years, with the progress of medical technology, the mortality rate of patients after surgery has been gradually reduced, leading that previous prediction models may not be suitable for nowadays. Therefore, the present study aims to find new independent risk factors for predicting in-hospital mortality and construct a nomogram prediction model.

METHODS

The clinical data of 341 consecutive patients in our center from 2019 to 2023 were collected, and they were divided into two groups according to the death during hospitalization. The independent risk factors were analyzed by univariate and multivariate logistic regression, and the nomogram was constructed and verified based on these factors.

RESULTS

age, preoperative lower limb ischemia, preoperative activated partial thromboplastin time (APTT), preoperative platelet count, Cardiopulmonary bypass (CPB) time and postoperative acute kidney injury (AKI) independently predicted in-hospital mortality of patients with acute type A aortic dissection after surgery. The area under the receiver operating characteristic curve (AUC) for the nomogram was 0.844. The calibration curve and decision curve analysis verified that the model had good quality.

CONCLUSION

The new nomogram model has a good ability to predict the in-hospital mortality of patients with acute type A aortic dissection after surgery.

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.

Short- and Long-term survival prediction in patients with acute type A aortic dissection undergoing open surgery

BACKGROUND

Acute Type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease associated with high mortality rates, where surgical intervention remains the primary life-saving treatment. However, the mortality rate for ATAAD operations continues to be alarmingly high. To address this critical issue, our study aimed to assess the correlation between preoperative laboratory examination, clinical imaging data, and postoperative mortality in ATAAD patients. Additionally, we sought to establish a reliable prediction model for evaluating the risk of postoperative death.

METHODS

In this study, a total of 384 patients with acute type A aortic dissection (ATAAD) who were admitted to the emergency department for surgical treatment were included. Based on preoperative laboratory examination and clinical imaging data of ATAAD patients, logistic analysis was used to obtain independent risk factors for postoperative in-hospital death. The survival prediction model was based on cox regression analysis and displayed as a nomogram.

RESULTS

Logistic analysis identified several independent risk factors for postoperative in-hospital death, including Marfan syndrome, previous cardiac surgery history, previous renal dialysis history, direct bilirubin, serum phosphorus, D-dimer, white blood cell, multiple aortic ruptures and age. A survival prediction model based on cox regression analysis was established and presented as a nomogram. The model exhibited good discrimination and significantly improved the prediction of death risk in ATAAD patients.

CONCLUSIONS

In this study, we developed a novel survival prediction model for acute type A aortic dissection based on preoperative clinical features. The model demonstrated good discriminatory power and improved accuracy in predicting the risk of death in ATAAD patients undergoing open surgery.

Operative Mortality After Type A Aortic Dissection Surgery: Differences Based on Sex and Age

Background

There is controversy regarding sex differences in short-term mortality in acute type A aortic dissection (ATAAD).

Objectives

This study aimed to investigate the impact of sex differences on 30-day operative mortality after ATAAD surgery and to determine if other covariates modify the association.

Methods

Consecutive patients (N = 5670) with surgically repaired ATAAD were identified from the multicenter China 5A study. The primary outcome was operative mortality. The age dependency was modeled using a cubic spline curve.

Results

There were 1,503 females (26.5%) and 4,167 males (73.5%). Females were older and had a lower percentage of comorbidities compared with males. Females had higher mortality compared to males (10.2% vs 8.2%, P = 0.019); however, there was no difference after propensity analyses (adjusted OR: 1.334 [95% CI: 0.918-1.938]). There was an interaction with sex and age (P interaction = 0.035): older age was associated with higher odds of operative mortality among females (OR: 1.045 [95% CI: 1.029-1.061]) compared with males (OR: 1.025 [95% CI: 1.016-1.035]). The risk of mortality for males and females appears to diverge at 55 years of age (P interaction = 0.019): females under 55 years of age had similar odds to males (OR: 0.852 [95% CI: 0.603-1.205]) but higher odds when over 55 years (OR: 1.420 [95% CI: 1.096-1.839]) compared to males.

Conclusions

Under the age of 55 years, females have similar odds of operative mortality compared with males; however, over the age of 55 years females have higher odds than males. Understanding differences in risk allows for individualized treatment strategies. (Additive Anti-inflammatory Action for Aortopathy & Arteriopathy; NCT04398992).

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 %) (β -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 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.

Systemic Coagulation Inflammation Index Associated With Bleeding in Acute Coronary Syndrome

AIM

Assessment of the inflammatory component of acute coronary syndrome (ACS) and the degree of activation of the coagulation cascade may provide prognostic information. The systemic coagulation-inflammation index (SCI) assesses both inflammation and the coagulation system, and it has also been found to be associated with clinical outcomes. We investigated the relationship between SCI and in-hospital clinical events (acute kidney injury, cardiogenic shock, life-threatening arrhythmia, bleeding) and mortality.

MATERIAL AND METHODS

The study included 396 patients aged ≥18 yrs who were hospitalized with a diagnosis of ACS. The SCI was calculated using the formula: platelet count (103 / µl) X fibrinogen (g / l) / white blood cell (WBC) count (103 / µl). Patients were divided into two groups according to whether their SCI score was >100 or <100, and the relationship between clinical and laboratory characteristics was analyzed accordingly.

RESULTS

The mean age of the patients was 61.4±12.2 years and 78.3 % (n=310) were male. The type of ACS was NSTEMI in 56.1 % (n=222). The responsible vessel was the left anterior descending artery (LAD) in 42.4 % of the patients (n=168). The mean SCI score was 97.5±47.1. WBC, neutrophil, and lymphocyte counts were higher in the SCI <100 group, whereas fibrinogen, C-reactive protein, and platelet count were higher in the SCI >100 group. Bleeding from any cause as an in-hospital complication was significantly higher in patients with SCI >100 (p<0.05). Other in-hospital events were not significantly associated with SCI (p>0.05).

CONCLUSIONS

Bleeding in ACS patients was significantly more common in the group with SCI >100. Thus, SCI may be a useful parameter for predicting in-hospital bleeding complications in ACS. On the other hand, SCI was not associated with mortality and other in-hospital clinical events.

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.