Preoperative Nomogram and Risk Calculator for Postoperative Hypoxemia and Related Clinical Outcomes Following Stanford Type A Acute Aortic Dissection Surgery

Prediction Model of in Hospital Death for Stanford Type A Aortic Dissection Based on a Meta-Analysis of 24 Cohorts

Patients with Stanford type A aortic dissection (TAAD) have high postoperative mortality. This study aimed to develop a prediction model for in-hospital death after surgery in patients with TAAD. The derivation cohort came from a meta-analysis. Major risk factors were counted. The corresponding hazard ratio was reported to establish a prediction model for in-hospital death in patients with TAAD. Validation cohorts from 2 centres were used to evaluate the prediction model. The meta-analysis included 24 cohort studies with a total of 11,404 patients and 1,554 patients died early after surgery. Risk factors for the prediction model included age, body mass index, smoking, coronary heart disease, preoperative stroke, shock, preoperative cardiopulmonary resuscitation, pericardial tamponade and malperfusion. Patients with TAAD admitted to the First and the Fourth Hospital of Hebei Medical University between January 2020 and June 2024 were retrospectively collected. Patients from the 2 hospitals constituted validation cohorts A (n = 262) and B (n = 138). Risk scores were calculated for model validation and the prediction model demonstrated better differentiation for validation cohort A, with an area under the curve of 0.886 (95% confidence interval 0.842 to 0.931). This study established a simple risk prediction model, including 13 risk factors, to predict in-hospital death in patients with TAAD. However, multicenter data is still needed to evaluate the prediction accuracy of the model.

Technological integration in predicting hypoxemia risk for improved surgical outcomes in Type A aortic dissection

BackgroundPostoperative hypoxemia is a severe complication in patients undergoing surgery for acute Type A aortic dissection (AAD), with significant impacts on recovery and clinical outcomes. Technological advancements in risk assessment models offer opportunities for early intervention and optimized care.ObjectiveTo develop and validate a technology-driven predictive model for hypoxemia based on clinical and intraoperative risk factors, enhancing postoperative management strategies.MethodsA retrospective cohort of 242 patients was analyzed, including 77 with hypoxemia (PaO2/FiO2 ≤ 200 mmHg) and 165 without. Key clinical variables, intraoperative factors, and postoperative outcomes were examined. Spearman correlation analysis and receiver operating characteristic (ROC) curve analysis were conducted to identify and validate predictive markers.ResultsProlonged time from symptom onset to surgery (>48 h), aortic cross-clamp time, and deep hypothermic circulatory arrest time (DHCA) emerged as the most significant predictors (all p < 0.001). DHCA time demonstrated the highest sensitivity (0.961) and area under the curve (AUC = 0.891). Additional significant predictors included intraoperative blood product use and prolonged mechanical ventilation, with cumulative predictive value for hypoxemia risk.ConclusionThe integration of clinical variables into a technology-enhanced prediction model provides robust early warnings of postoperative hypoxemia risk. Implementing timely surgical interventions and refined intraoperative management can minimize adverse respiratory outcomes, improving recovery in AAD patients.

A novel nomogram for predicting prolonged mechanical ventilation after acute type A aortic dissection surgery: a retrospective study investigating the impact of ventilation duration on postoperative outcomes

OBJECTIVE

Acute type A aortic dissection (ATAAD) is a devastating cardiovascular disease with extraordinary morbidity and mortality. Prolonged mechanical ventilation (PMV) is a common complication following ATAAD surgery, leading to adverse outcomes. This study aimed to investigate the correlation between mechanical ventilation time (MVT) and prognosis and to devise a nomogram for predicting PMV after ATAAD surgery.

METHODS

This retrospective study enrolled 1049 ATAAD patients from 2011 to 2019. Subgroups were divided into < 12 h, 12 h to < 24 h, 24 h to < 48 h, 48 h to < 72 h, and ≥ 72 h according to MVT. Clinical characteristics and outcomes were compared among the groups. Using multivariable logistic regression analyses, we investigated the relationship between each stratification of MVT and mortality. A nomogram was constructed based on the refined multivariable logistic regression model for predicting PMV.

RESULTS

The total mortality was 11.8% (124/1049). The results showed that the groups with MVT 48 h to < 72 h and ≥ 72 h had significantly higher operative mortality compared to other MVT categories. Multivariate logistic regression analysis showed that MVT ≥72 h was significantly associated with higher short-term mortality. Thus, a nomogram was presented to elucidate the association between PMV (MVT ≥72 h) and risk factors including advanced age, preoperative cerebral ischemia, ascending aorta replacement, concomitant coronary artery bypass grafting (CABG), longer cardiopulmonary bypass (CPB), and large-volume intraoperative fresh frozen plasma (FFP) transfusion. The nomogram exhibited strong predictive performance upon validation.

CONCLUSIONS

Safely extubating patients within 72 h after ATAAD surgery is crucial for achieving favorable outcomes. The developed and validated nomogram provides a valuable tool for predicting PMV and optimizing postoperative care to improve patient prognosis. This novel nomogram has the potential to guide clinical decision-making and resource allocation in the management of ATAAD patients.

Study on risk factors and treatment strategies of hypoxemia after acute type a aortic dissection surgery

Acute type A aortic dissection is a life-threatening cardiovascular disease characterized by rapid onset and high mortality. Emergency surgery is the preferred and reliable treatment option. However, postoperative complications significantly impact patient prognosis. Hypoxemia, a common complication, poses challenges in clinical treatment, negatively affecting patient outcomes and increasing the risk of mortality. Therefore, it is crucial to study and comprehend the risk factors and treatment strategies for hypoxemia following acute type A aortic dissection to facilitate early intervention.

Postoperative nomogram and risk calculator of acute renal failure for Stanford type A aortic dissection surgery

BACKGROUND

This study aimed to explore the risk factors of acute renal failure (ARF) after Stanford type A aortic dissection (AAD) surgery, establish a nomogram prediction model and calculate the risk of ARF.

MATERIAL AND METHODS

241 AAD patients who received aortic surgery in the department of cardiovascular surgery, Zhongnan Hospital of Wuhan University were enrolled in this study. All enrolled patients were divided into the ARF group and non-ARF group. The clinical data of the two groups were collected and compared. The independent risk factors of ARF after aortic surgery were analyzed by univariate and multivariate logistic regression analyses. Moreover, a nomogram prediction model was generated. The calibration curve, ROC curve and independent external validation were performed to evaluate the nomogram prediction model.

RESULTS

67 patients were diagnosed with ARF within 48 h after the operation. Univariate and multivariate logistic regression analyses showed that hypertension, preoperative renal artery involvement, CPB time extension and postoperative decreased platelet lymphocyte ratio were the independent risk factors of ARF after AAD surgery. The nomogram model could predict the risk of ARF with a sensitivity of 81.3% and a specificity of 78.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.839. External data validation was performed with a sensitivity of 79.2% and a specificity of 79.8%.

CONCLUSIONS

Hypertension, preoperative renal artery involvement, CPB time extension and postoperative decreased platelet lymphocyte ratio could predict the risk of ARF after AAD surgery.

Postoperative pulmonary complications in patients undergoing aortic surgery: A single-center retrospective study

Postoperative pulmonary complications (PPCs) are among the most common complications after cardiovascular surgery. This study aimed to explore the real incidence of and risk factors for PPC in patients with acute type A aortic dissection (ATAAD) who underwent total aortic arch replacement combined with the frozen elephant trunk (TAR + FET). In total, 305 ATAAD patients undergoing TAR + FET from January 2021 to August 2022 in a single-center were divided into PPCs or non-PPCs group. The incidence of PPCs was calculated, risk factors of PPCs were analyzed, and postoperative outcomes were compared between these 2 groups. The incidence of any PPC was 29.2%. And the incidence of respiratory infection, respiratory failure, pleural effusion, atelectasis, pneumothorax, acute respiratory distress syndrome, aspiration pneumonitis, pulmonary edema and bronchospasm was 23.0%, 12.5%, 10.5%, 1.0%, 0.7%, 1.0%, 0%, 0.7%, 0%, respectively. The logistic regression analysis revealed that the history of diabetes, history of renal dysfunction, preoperative SpO2 <90%, cardiopulmonary bypass duration, fresh frozen plasma volume and platelet concentrates volume were independent risk factors for PPCs. Among 2 groups, postoperative ventilation duration, postoperative length of stay in intensive care unit and hospital were (73.5 ± 79.0 vs 24.8 ± 35.2 hours; P < .001), (228.3 ± 151.2 vs 95.2 ± 72.0 hours; P < .001) and (17.9 ± 8.8 vs 11.5 ± 6.2 days; P < .001). There was no difference between 2 groups of in-hospital mortality rate. Additionally, other short-term outcomes were also significantly poorer in patients with PPCs. PPCs are common in ATAAD patients undergoing TAR + FET, and could be multifactorial. PPCs occurrence are associated with poor patient outcomes postoperatively and worth further investigation.

The risk factors of postoperative hypoxemia in patients with Stanford type A acute aortic dissection

Hypoxemia is one of the most common complications in patients after Stanford type A acute aortic dissection surgery. The aim of this study was to investigate the association of circulating ANG II level with postoperative hypoxemia and to identify the risk factors for postoperative hypoxemia in Stanford type A acute aortic dissection patients. In this study, 88 patients who underwent Stanford type A acute aortic dissection surgery were enrolled. Postoperative hypoxemia is defined by the oxygenation index (OI). Perioperative clinical data were collected and the serum ANG II and sACE2 levels were measured. The differences in the basic characteristics, intraoperative details, biochemical parameters, laboratory test data and clinical outcomes were compared between the hypoxemia group and the non-hypoxemia group by univariate analysis. Multivariate logistic regression analysis was performed on the variables with P < .1 in univariate analysis or that were considered clinically important to identify risk factors for postoperative hypoxemia. Twenty-five patients (28.4%) were considered to have postoperative hypoxemia (OI ≤ 200 mm Hg). The ANG II concentration remained a risk factor associated with postoperative hypoxemia [OR = 1.018, 95% CI (1.003-1.034), P = .022]. The other risk factors remaining in the logistic regression model were BMI [OR = 1.417, 95% CI (1.159-1.733), P = .001] and cTnI [OR = 1.003, 95% CI (1.000-1.005), P = .032]. Elevated levels of ANG II, BMI and cTnI are risk factors for postoperative hypoxemia in patients with Stanford type A acute aortic dissection.

The predictive values of monocyte-lymphocyte ratio in postoperative acute kidney injury and prognosis of patients with Stanford type A aortic dissection

Objectives

Postoperative acute kidney injury (pAKI) is a serious complication of Stanford type A aortic dissection (TAAD) surgery, which is significantly associated with the inflammatory response. This study aimed to explore the relationship between blood count-derived inflammatory markers (BCDIMs) and pAKI and to construct a predictive model for pAKI.

Methods

Patients who underwent TAAD surgery were obtained from our center and the Medical Information Mart for Intensive Care (MIMIC)-IV database. The differences in preoperative BCDIMs and clinical outcomes of patients with and without pAKI were analyzed. Logistic regression was used to construct predictive models based on preoperative BCDIMs or white cell counts (WCCs). The performance of the BCDIMs and WCCs models was evaluated and compared using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), Hosmer-Lemeshow test, calibration plot, net reclassification index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA). The Kaplan-Meier curves were applied to compare the survival rate between different groups.

Results

The overall incidence of pAKI in patients who underwent TAAD surgery from our center was 48.63% (124/255). The presence of pAKI was associated with longer ventilation time, higher incidence of cerebral complications and postoperative hepatic dysfunction, and higher in-hospital mortality. The results of the logistic regression indicated that the monocyte-lymphocyte ratio (MLR) was an independent risk factor for pAKI. The BCDIMs model had good discriminating ability, predictive ability, and clinical utility. In addition, the performance of the BCDIMs model was significantly better than that of the WCCs model. Analysis of data from the MIMIC-IV database validated that MLR was an independent risk factor for pAKI and had predictive value for pAKI. Finally, data from the MIMIC-IV database demonstrated that patients with a high MLR had a significantly poor 28-day survival rate when compared to patients with a low MLR.

Conclusion

Our study suggested that the MLR is an independent risk factor for pAKI. A predictive model based on BCDIMs had good discriminating ability, predictive ability, and clinical utility. Moreover, the performance of the BCDIMs model was significantly better than that of the WCCs model. Finally, a high MLR was significantly associated with poor short-term survival of patients who underwent TAAD surgery.

The prognosis of patients with postoperative hyperglycemia after Stanford type A aortic dissection surgery and construction of prediction model for postoperative hyperglycemia

Objective

The mortality of type A aortic dissection (TAAD) is extremely high. The effect of postoperative hyperglycemia (PHG) on the prognosis of TAAD surgery is unclear. This study aims to investigate the prognosis of patients with PHG after TAAD surgery and construct prediction model for PHG.

Methods

Patients underwent TAAD surgery from January 2016 to December 2020 in Xiangya Hospital were collected. A total of 203 patients were included and patients were divided into non PHG group and PHG group. The occurrence of postoperative delirium, cardiac complications, spinal cord complication, cerebral complications, acute kidney injury (AKI), hepatic dysfunction, hypoxemia, and in-hospital mortality were compared between two groups. Data from MIMIC-IV database were further applied to validate the relationship between PHG and clinical outcomes. The prediction model for PHG was then constructed using Extreme Gradient Boosting (XGBoost) analysis. The predictive value of selected features was further validated using patient data from MIMIC-IV database. Finally, the 28-days survival rate of patient with PHG was analyzed using data from MIMIC-IV database.

Results

There were 86 patients developed PHG. The incidences of postoperative AKI, hepatic dysfunction, and in-hospital mortality were significant higher in PHG group. The ventilation time after surgery was significant longer in PHG group. Data from MIMIC-IV database validated these results. Neutrophil, platelet, lactic acid, weight, and lymphocyte were selected as features for prediction model. The values of AUC in training and testing set were 0.8697 and 0.8286 respectively. Then, five features were applied to construct another prediction model using data from MIMIC-IV database and the value of AUC in the new model was 0.8185. Finally, 28-days survival rate of patients with PHG was significantly lower and PHG was an independent risk factor for 28-days mortality after TAAD surgery.

Conclusion

PHG was significantly associated with the occurrence of AKI, hepatic dysfunction, increased ventilation time, and in-hospital mortality after TAAD surgery. The feature combination of neutrophil, platelet, lactic acid, weight, and lymphocyte could effectively predict PHG. The 28-days survival rate of patients with PHG was significantly lower. Moreover, PHG was an independent risk factor for 28-days mortality after TAAD surgery.

Postoperative hypoxemia for patients undergoing Stanford type A aortic dissection

Clinically, it is widely recognized that surgical treatment is the preferred and reliable option for Stanford type A aortic dissection. Stanford type A aortic dissection is an emergent and serious cardiovascular disease characterized with an acute onset, poor prognosis, and high mortality. However, the incidences of postoperative complications are relatively higher due to the complexity of the disease and its intricate procedure. It has been considered that hypoxemia, one of the most common postoperative complications, plays an important role in having a worse clinical prognosis. Therefore, the effective intervention of postoperative hypoxemia is significant for the improved prognosis of patients with Stanford type A aortic dissection.

High preoperative bradykinin level is a risk factor for severe postoperative hypoxaemia in acute aortic dissection surgery

NEW FINDINGS

What is the central question of this study? Hypoxaemia can lead to increased postoperative mortality in patients: what are the independent risk factors for severe hypoxaemia after acute Stanford type A aortic dissection? What is the main finding and its importance? Severe postoperative hypoxaemia was found in 36.4% of patients, and it was determined that high preoperative bradykinin levels and increased BMI were independent predictors of severe postoperative hypoxaemia in patients with acute Stanford type A aortic dissection. For obese patients with high preoperative bradykinin levels, more attention should be paid to preventing severe postoperative hypoxaemia.

ABSTRACT

Severe hypoxaemia after cardiac surgery is associated with serious complications and a high risk of mortality. The purpose of this study is to investigate the independent risk factors of severe postoperative hypoxaemia in patients with acute Stanford type A aortic dissection. We collected 77 patients with acute Stanford type A aortic dissection who underwent surgical treatment. The primary outcome was severe postoperative hypoxaemia (PaO2 /FiO2  ≤ 100 mmHg), and a multivariate logistic regression analysis was performed to assess the independent predictors of risk for this. A mixed-effects analysis of variance model and a receiver operating characteristic (ROC) curve were generated to evaluate the predictive probabilities of risk factors for severe postoperative hypoxaemia. A total of 36.4% of patients developed severe postoperative hypoxaemia. The multivariate logistic regression analysis identified high preoperative bradykinin level (odds ratio (OR) = 55.918, P < 0.001) and increased body mass index (BMI; OR = 1.292, P = 0.032) as independent predictors of severe postoperative hypoxaemia in patients with acute Stanford type A aortic dissection. The mixed-effect analysis of variance model and ROC curve indicated that high preoperative bradykinin level and BMI were significant predictors of severe postoperative hypoxaemia (area under the ROC curve = 0.834 and 0.764, respectively). High preoperative bradykinin levels and obesity were independent risk factors for severe postoperative hypoxaemia in patients with acute Stanford type A aortic dissection. For obese patients with high levels of bradykinin before surgery, clinicians should actively take measures to block bradykinin-mediated inflammatory reactions.

Hotspots and development frontiers of postoperative complications of AD: Bibliometric analysis - a review

The research on the postoperative complications of aortic dissection (AD) has received great attention from scholars all over the world, and the number of research articles in this field has consistently increased year after year. However, no bibliometric reports have been published yet to analyze the scientific output and the current situation in this field. The Bibliometrix R-package, VOSviewer, and CiteSpace software were used to conduct a bibliometric analysis of the hotspots and development frontiers of AD. A total of 1242 articles were retrieved. The USA, China, and Japan had the highest number of publications. The five keywords with the highest frequency were "analysis," "incidence," "acute type," "graft," and "risk factor." The results also indicated that the research in related fields had shifted from surgical treatment and utilizing experience to the evidence-based exploration of risk factors and the construction of prediction models to help better manage postoperative complications of AD. This is the first bibliometric analysis of global publications on the postoperative complications of AD. The current research hotspots focus on three areas: common postoperative complications of AD, exploration of the related risk factors, and management of complications. Future research could focus on identifying risk factors through meta-analysis and using a multicenter database for AD as well as building relevant models to predict the development of complications to better facilitate the clinical management of AD patients.

Nomogram and risk calculator for severe hypoxemia after heart valve surgery

Background

Hypoxemia is a very common issue in patients undergoing heart valve surgery (HVS), related to poor clinical outcomes. However, studies on severe hypoxemia (SH) after HVS have not been reported. The aims of this study were to identify predictors for SH in patients undergoing HVS and to develop and validate a risk prediction model.

Methods

Patients undergoing HVS between 2016 and 2019 in a cardiovascular center were enrolled and were assigned to training and validation sets by a 7:3 ratio. Based on whether patients developed SH, they were divided into two groups. By univariate and multivariate analysis, predictors for SH were identified. Based on the predictors and logistic rule, a nomogram and a risk calculator were generated. The model was evaluated using calibration, discrimination and clinical utility.

Results

The incidence rates of SH, moderate hypoxemia and mild hypoxemia were respectively 2.4, 23.9, and 58.2%. By multivariate analysis, seven independent risk factors for SH after HVS were identified, including body mass index, chronic obstructive pulmonary disease, renal insufficiency, white blood cell count, serum globulin, cardiopulmonary bypass time, and surgical types. The logistic model demonstrated satisfactory discrimination, calibration and clinical utility in both the training and validation sets. A nomogram and a risk calculator based on the logistic model were generated for easy application. Risk stratification was performed and three risk intervals were defined according to the nomogram and clinical practice. In addition, compared to patients without SH, patients with SH had significantly poorer clinical outcomes.

Conclusions

Postoperative hypoxemia was prevalent after HVS, related to poor clinical outcomes. A logistic model including seven independent predictors for SH after HVS were established and validated, which demonstrated satisfactory discrimination, calibration and clinical utility. The results of this study may provide help to individualized risk assessment, early prevention and perioperative management.

Incidence, Risk Factors, and Outcomes of Severe Hypoxemia After Cardiac Surgery

Background

Hypoxemia is common in patients undergoing cardiac surgery, however, few studies about severe hypoxemia (SH) after cardiac surgery exist. The objectives of this study were to clarify the incidence, risk factors, and outcomes of SH after cardiac surgery.

Methods

Patients undergoing cardiac surgery from 2016 to 2019 in a single center were enrolled and were divided into two groups based on whether postoperative SH developed. Independent risk factors for SH were identified by univariate and multivariate analysis. Model selection statistics were applied to help determine the most parsimonious final model.

Results

Severe hypoxemia developed in 222 of the 5,323 included patients (4.2%), was associated with poorer clinical outcomes. Six independent risk factors for SH after cardiac surgery were identified by multivariate analysis, such as surgical types, white blood cell (WBC) count, body mass index (BMI), serum albumin, cardiopulmonary bypass (CPB) time, and intraoperative transfusion of red blood cells (RBCs). After comprehensively considering the discrimination, calibration, and simplicity, the most appropriate and parsimonious model was finally established using four predictors, such as WBC count, BMI, CPB time, and intraoperative transfusion of RBCs. A nomogram and a web-based risk calculator based on the final model were constructed to facilitate clinical practice. Patients were stratified into three risk groups based on the nomogram and clinical practice.

Conclusion

Severe hypoxemia was common after cardiac surgery and was associated with poorer clinical outcomes. A parsimonious final model with good discrimination, calibration, and clinical utility was constructed, which may be helpful for personalized risk assessment and targeted intervention.