A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients

Impact of D-dimer on in-hospital mortality following aortic dissection: A systematic review and meta-analysis

BACKGROUND

The utility of D-dimer (DD) as a biomarker for acute aortic dissection (AD) is recognized. Yet, its predictive value for in-hospital mortality remains uncertain and subject to conflicting evidence.

AIM

To conduct a meta-analysis of AD-related in-hospital mortality (ADIM) with elevated DD levels.

METHODS

We searched PubMed, Scopus, Embase, and Google Scholar for AD and ADIM literature through May 2022. Heterogeneity was assessed using I 2 statistics and effect size (hazard or odds ratio) analysis with random-effects models. Sample size, study type, and patients' mean age were used for subgroup analysis. The significance threshold was P < 0.05.

RESULTS

Thirteen studies (3628 patients) were included in our study. The pooled prevalence of ADIM was 20% (95%CI: 15%-25%). Despite comparable demographic characteristics and comorbidities, elevated DD values were associated with higher ADIM risk (unadjusted effect size: 1.94, 95%CI: 1.34-2.8; adjusted effect size: 1.12, 95%CI: 1.05-1.19, P < 0.01). Studies involving patients with a mean age of < 60 years exhibited an increased mortality risk (effect size: 1.43, 95%CI: 1.23-1.67, P < 0.01), whereas no significant difference was observed in studies with a mean age > 60 years. Prospective and larger sample size studies (n > 250) demonstrated a heightened likelihood of ADIM associated with elevated DD levels (effect size: 2.57, 95%CI: 1.30-5.08, P < 0.01 vs effect size: 1.05, 95%CI: 1.00-1.11, P = 0.05, respectively).

CONCLUSION

Our meta-analysis shows elevated DD increases in-hospital mortality risk in AD patients, highlighting the need for larger, prospective studies to improve risk prediction models.

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.

A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning

Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner.

Proximal vs Extensive Repair in Acute Type A Aortic Dissection Surgery

BACKGROUND

This purpose of this study was to evaluate the impact of proximal vs extensive repair on mortality and how this impact is influenced by patient characteristics.

METHODS

Of 5510 patients with acute type A aortic dissection from 13 Chinese hospitals (2016-2021) categorized by proximal vs extensive repair, 4038 patients were used for for model derivation using eXtreme gradient boosting and 1472 patients for model validation.

RESULTS

Operative mortality of extensive repair was higher than proximal repair (10.4% vs 2.9%; odd ratio [OR], 3.833; 95% CI, 2.810-5.229; P < .001) with a number needed to harm of 15 (95% CI, 13-19). Seven top features of importance were selected to develop an alphabet risk model (age, body mass index, platelet-to-leucocyte ratio, albumin, hemoglobin, serum creatinine, and preoperative malperfusion), with an area under the curve of 0.767 (95% CI, 0.733-0.800) and 0.727 (95% CI, 0.689-0.764) in the derivation and validation cohorts, respectively. The absolute rate differences in mortality between the 2 repair strategies increased progressively as predicted risk rose; however it did not become statistically significant until the predicted risk exceeded 4.5%. Extensive repair was associated with similar risk of mortality (OR, 2.540; 95% CI, 0.944-6.831) for patients with a risk probability < 4.5% but higher risk (OR, 2.164; 95% CI, 1.679-2.788) for patients with a risk probability > 4.5% compared with proximal repair.

CONCLUSIONS

Extensive repair is associated with higher mortality than proximal repair; however it did not carry a significantly higher risk of mortality until the predicted probability exceeded a certain threshold. Choosing the right surgery should be based on individualized risk prediction and treatment effect. (ClinicalTrials.gov no. NCT04918108.).

Prediction of in-hospital death following acute type A aortic dissection

Background

Our goal was to create a prediction model for in-hospital death in Chinese patients with acute type A aortic dissection (ATAAD).

Methods

A retrospective derivation cohort was made up of 340 patients with ATAAD from Tianjin, and the retrospective validation cohort was made up of 153 patients with ATAAD from Nanjing. For variable selection, we used least absolute shrinkage and selection operator analysis, and for risk scoring, we used logistic regression coefficients. We categorized the patients into low-, middle-, and high-risk groups and looked into the correlation with in-hospital fatalities. We established a risk classifier based on independent baseline data using a multivariable logistic model. The prediction performance was determined based on the receiver operating characteristic curve (ROC). Individualized clinical decision-making was conducted by weighing the net benefit in each patient by decision curve analysis (DCA).

Results

We created a risk prediction model using risk scores weighted by five preoperatively chosen variables [AUC: 0.7039 (95% CI, 0.643–0.765)]: serum creatinine (Scr), D-dimer, white blood cell (WBC) count, coronary heart disease (CHD), and blood urea nitrogen (BUN). Following that, we categorized the cohort's patients as low-, intermediate-, and high-risk groups. The intermediate- and high-risk groups significantly increased hospital death rates compared to the low-risk group [adjusted OR: 3.973 (95% CI, 1.496–10.552), P &lt; 0.01; 8.280 (95% CI, 3.054–22.448), P &lt; 0.01, respectively). The risk score classifier exhibited better prediction ability than the triple-risk categories classifier [AUC: 0.7039 (95% CI, 0.6425–0.7652) vs. 0.6605 (95% CI, 0.6013–0.7197); P = 0.0022]. The DCA showed relatively good performance for the model in terms of clinical application if the threshold probability in the clinical decision was more than 10%.

Conclusion

A risk classifier is an effective strategy for predicting in-hospital death in patients with ATAAD, but it might be affected by the small number of participants.

Preoperative Predictors of Adverse Clinical Outcome in Emergent Repair of Acute Type A Aortic Dissection in 15 Year Follow Up

Background: Acute type A aortic dissection (AAAD) has high mortality. Improvements in surgical technique have lowered mortality but postoperative functional status and decreased quality of life due to debilitating deficits remain of concern. Our study aims to identify preoperative conditions predictive of undesirable outcome to help guide perioperative management. Methods: We performed retrospective analysis of 394 cases of AAAD who underwent repair in our institution between 2001 and 2018. A combined endpoint of parameters was defined as (1) 30-day versus hospital mortality, (2) new neurological deficit, (3) new acute renal insufficiency requiring postoperative renal replacement, and (4) prolonged mechanical ventilation with need for tracheostomy. Results: Total survival/ follow-up time averaged 3.2 years with follow-up completeness of 94%. Endpoint was reached by 52.8%. Those had higher EuroSCORE II (7.5 versus 5.5), higher incidence of coronary artery disease (CAD) (9.2% versus 3.2%), neurological deficit (ND) upon presentation (26.4% versus 11.8%), cardiopulmonary resuscitation (CPR) (14.4% versus 1.6%) and intubation (RF) before surgery (16.9% versus 4.8%). 7-day mortality was 21.6% versus 0%. Hospital mortality 30.8% versus 0%. Conclusions: This 15-year follow up shows, that unfavorable postoperative clinical outcome is related to ND, CAD, CPR and RF on arrival.

Risk factors for 30-day mortality in patients who received DeBakey type I aortic dissection repair surgery

Objective

This study aimed to identify risk factors for 30-day mortality in patients who received DeBakey type I aortic dissection (AD) repair surgery.

Methods

A total of 830 consecutive patients who received acute DeBakey type I AD surgery between 2014 and 2019 were included in the study. The associations between 30-day mortality and perioperative parameters were examined in order to identify risk factors.

Results

Our data suggested that the overall 30-day mortality rate of all enrolled patients was 11.7%. Unsurprisingly, non-survivors were older and more frequently accompanied with histories of cardiovascular diseases. For intraoperative parameters, the prevalence of coronary artery bypass grafting and cardiopulmonary bypass times were increased in non-survivors. In addition, acute kidney injury (AKI), dialysis, stroke, and deep sternal wound infection were more commonly seen among non-survivors. The multivariate logistic regression analysis suggested that cardiovascular disease history, preoperative D-dimer level, drainage volume 24 h after surgery, and postoperative AKI were independent risk factors for 30-day mortality after DeBakey type I aortic dissection repair surgery.

Conclusions

Our study demonstrated that cardiovascular disease history, preoperative D-dimer level, drainage volume 24 h after surgery as well as postoperative AKI were risk factors for 30-day mortality after DeBakey type I aortic dissection repair surgery.

Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients

Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction

Purpose

Acute myocardial infarction (AMI) is a common cardiovascular disease with a poor prognosis. The aim of this study was to construct a nomogram for predicting the long-term survival of critically ill patients with AMI. This nomogram will help in assessing disease severity, guiding treatment, and improving prognosis.

Patients and Methods

The clinical data of patients with AMI were extracted from the MIMIC-III v1.4 database. Cox proportional hazards models were adopted to identify independent prognostic factors. A nomogram for predicting the long-term survival of these patients was developed on the basis of the results of multifactor analysis. The discriminative ability and accuracy of the multifactor analysis were evaluated according to concordance index (C-index) and calibration curves.

Results

A total of 1202 patients were included in the analysis. The patients were randomly divided into a training set (n = 841) and a validation set (n = 361). Multivariate analysis revealed that age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation, all of which were incorporated into the nomogram, were independent predictive factors of AMI. Moreover, the nomogram exhibited favorable performance in predicting the 4-year survival of patients with AMI. The training set and the validation set had a C-index of 0.789 (95% confidence interval [CI]: 0.765–0.813) and 0.762 (95% CI: 0.725–0.799), respectively.

Conclusion

The nomogram constructed herein can accurately predict the long-term survival of critically ill patients with AMI.

Association Between the Admission Serum Bicarbonate and Short-Term and Long-Term Mortality in Acute Aortic Dissection Patients Admitted to the Intensive Care Unit

Objective

Serum bicarbonate (HCO3^-) level is strongly related to multiple cardiovascular complications. Currently, there is no study evaluating the prognostic ability of serum HCO3^- level in intensive care unit (ICU) patients with acute aortic dissection (AAD). Hence, this study was to assess the relationship between admission serum HCO3^- level and clinical outcomes in patients with AAD.

Design Settings and Participants

Clinical data were extracted from the MIMIC-III database. Cox proportional hazards models and Kaplan-Meier (KM) survival curve were used to evaluate the association between serum HCO3^- levels and short- and long-term mortality in ICU patients with AAD. The subgroup analysis and the receiver operating characteristic (ROC) curve analysis and further KM survival curve based on best cut-off value were applied to assessment of the performance of HCO3^- in predicting the mortality in each period (30 days, 90 days, 1 year and 5 years).

Main Results

Firstly, 336 eligible patients were trisected to low-HCO3^- level group (<22 mmol/L), mid-HCO3^- level group (22-24 mmol/L) and high-HCO3^- level group (>24 mmol/L). Then, in multivariate analysis, the serum HCO3^- of low levels (<22 mmol/L) was a significant risk predictor of all-cause mortality in 30 days, 90 days, 1 year and 5 years. Subgroup analyses indicated that there is no interaction in most strata. Finally, areas under ROC curve ranged from 0.60 to 0.69.

Conclusion

The low HCO3^- serum level measured at ICU admission significantly predicts short-term and long-term mortality in AAD patients.

Characteristics and prognosis of acute type A aortic dissection with negative D-dimer result

BACKGROUND

Evidence regarding the characteristics and prognosis in acute type A aortic dissection (AAD) patients with negative D-dimer result is limited. We aimed to investigate the characteristics and prognosis in AAD patients with negative D-dimer result.

METHODS AND RESULTS

370 AAD patients within 24 h of symptom onset were enrolled in a hospital in China from January 2014 to December 2018. Nine (2.43%) and 361 (97.57%) exhibited negative and positive D-dimer results, respectively. The average age of nine negative D-dimer result participants was 47.67 ± 10.95 years old, and about seven (77.78%) of them were male. The negative group showed a significantly lower blood pressure, white blood cell, hemoglobin, activated partial thromboplastin, ejection fraction and symptom with pain than the positive group. Multivariate analysis showed white blood cell (×10⁹/L) (P = 0.008; odds ratio, 0.566) and symptom with pain (P < 0.001; odds ratio, 0.013) were significantly related to a negative result. The result of the fully-adjusted model showed negative D-dimer result was negatively associated with in-hospital mortality compared with positive group in AAD patients after adjusting confounders (OR = 0.34, 95%CI 0.01 to 10.82).

CONCLUSIONS

Negative D-dimer result is strongly influenced by white blood cell and symptom with pain. Negative D-dimer result was negatively associated with in-hospital mortality compared with positive group in AAD patients.