A preoperative mortality risk assessment model for Stanford type A acute aortic dissection

Kuang, Juntao; Yang, Jing; Wang, Qiuji; Yu, Changjiang; Liu, Ying; Fan, Ruixin

doi:10.1186/s12872-020-01802-9

Cited by 26 publications

(18 citation statements)

References 28 publications

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“…Preoperative hyperlactatemia was recognized to predict in-hospital mortality in patients with Stanford type A acute aortic dissection in some studies [ 7 , 8 ]. Nevertheless, some other studies suggested that both preoperative hyperlactatemia and POHL had poor predictive performance for mortality [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Risk factors and in-hospital mortality of postoperative hyperlactatemia in patients after acute type A aortic dissection surgery

Wang

Huang

et al. 2021

BMC Cardiovasc Disord

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Background Hyperlactatemia may be caused by increased production due to tissue hypoxia or non-hypoxia. The aim of this study was first to identify risk factors for postoperative hyperlactatemia (POHL) after Stanford type A acute aortic dissection surgery (AADS) and construct a predictive model, and second to evaluate the impact of POHL on prognosis. Methods This retrospective study involved patients undergoing AADS from January 2016 to December 2019 in Wuhan Union Hospital. Multivariate logistic regression analysis was performed to identify independent risk factors for POHL. A nomogram predicting POHL was established based on these factors and was validated in the original dataset. The receiver operating characteristic curve was drawn to assess the ability of postoperative lactate levels to predict the in-hospital mortality. Results A total of 188 patients developed POHL after AADS (38.6%). Male gender, surgery history, red blood cell transfusion and cardiopulmonary bypass time were identified as independent predictors. The C-index of the prediction model for POHL was 0.72, indicating reasonable discrimination. The model was well calibrated by visual inspection and goodness-of-fit test (Hosmer–Lemeshow χ2 = 10.25, P = 0.25). Decision and clinical impact curves of the model showed good clinical utility. The overall in-hospital mortality rate was 10.1%. Postoperative lactate levels showed a moderate predictive power for postoperative in-hospital mortality (C-index: 0.72). Conclusion We developed and validated a prediction model for POHL in patients undergoing AADS, which may have clinical utility in personal risk evaluation and preventive interventions. The POHL could be a good predictor for in-hospital mortality.

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Section: Introductionmentioning

confidence: 99%

Risk factors and in-hospital mortality of postoperative hyperlactatemia in patients after acute type A aortic dissection surgery

Wang

Huang

et al. 2021

BMC Cardiovasc Disord

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“…We retrospectively reviewed the medical records of the patients according to ID from our electronic database by two doctors ( Yansong Xu, Zheng Liang). Based on the current literature (19,20), we selected the following variables: Baseline characteristics, clinical features, laboratory results, imaging procedures, and outcomes of the patients. The baseline DLR was measured by dividing the D-dimr to the lymphocyte counts.…”

Section: Data Extraction and Variable Screeningmentioning

confidence: 99%

Admission D-dimer to Lymphocyte counts ratio as a novel biomarker for predicting the in-hospital mortality in patients with acute aortic dissection

liang

Liu

et al. 2022

Preprint

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Background Inflammatory factor is a well-established indictor for vascular disease but D-dimer to Lymphocyte count ratio (DLR) is not measured in routine clinical care. Screening of DLR in individuals may identify individuals at in-hopital mortality of acute aortic dissection(AD).Methods A retrospective analysis of clinical data from 2013 to 2020 was conducted to identify which factors were related to in-hospital mortality risk. Baseline clinical features, cardiovascular risk factors, and laboratory parameters were obtained from the hospital database. The end point was in-hospital mortality. Forward conditional logistic regression was performed to identify independent risk factors for in-hospital death. Cut-off value of DLR should be ideally calculated by Receiver operating characteristic (ROC) analysis. Results The in-hospital mortality rate was 15% (48 of 320 patients). Patients with in-hospital mortality had higher admission mean DLR level than the alive group (1740 vs. 1010, P < .05). The cutoff point of DLR was 907. The in-hospital mortality rate in high level DLR group was significantly higher than that in low level DLR group (P < .05). Univariate analysis showed that 8 of 38 factors were associated with in-hospital mortality (P<.05), including admission WBC, Neutrophil, lymphocyte, neutrophil/lymphocyte（NLR）, Prothrombin time（PT）, Heart rate(HR), D - dimer, DLR. In multivariate analysis, DLR (odds ratio [OR] 2.127, 95% CI 1.034-4.373, P=0.040), HR (odds ratio [OR] 1.016, 95% CI 1.002-1.030, P=0.029) and PT (odds ratio [OR] 1.231, 95% CI 1.018-1.189, P=0.032) were determined as independent predictors of in-hospital mortality (P < .05). Conclusions: Serum DLR level on admission is an independent predictor of in-hospital mortality in patients with acute AD.

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“… 1–3 Emergency physicians need a simple and accessible tool to identify patients suffering from AAS who are at high risk of in-hospital mortality, thereby helping them make medical decisions and allocate resources for high-risk patients as soon as possible. 4 Making use of such a tool would also help patients’ families understand the severity of the disease so they can make quicker and better decisions for their family member.…”

Section: Introductionmentioning

confidence: 99%

“…Second, a large proportion of existing models are based on surgery, which is less useful in the emergency department (ED). 9 , 13 Third, some of the identified predictors need a manual assessment process, such as false lumen/true lumen of ascending aorta ≥0.75, 4 which can be too time consuming for emergency physicians. Our objective was to develop and validate a practical risk stratification approach that could be used to identify AAS patients’ in-hospital mortality in the emergency department.…”

Section: Introductionmentioning

confidence: 99%

Early Prediction Model of Acute Aortic Syndrome Mortality in Emergency Departments

Wang¹,

Zhang²,

Du³

et al. 2022

IJGM

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Purpose Acute aortic syndrome is a constellation of life-threatening medical conditions for which rapid assessment and targeted intervention are important for the prognosis of patients who are at high risk of in-hospital death. The current study aims to develop and externally validate an early prediction mortality model that can be used to identify high-risk patients with acute aortic syndrome in the emergency department. Patients and Methods This retrospective multi-center observational study enrolled 1088 patients with acute aortic syndrome admitted to the emergency departments of two hospitals in China between January 2017 and March 2021 for model development. A total of 210 patients with acute aortic syndrome admitted to the emergency departments of Peking University Third Hospital between January 2007 and December 2021 was enrolled for model validation. Demographics and clinical factors were collected at the time of emergency department admission. The predictive variables were determined by referring to the results of previous studies and the baseline analysis of this study. The study’s endpoint was in-hospital death. To assess internal validity, we used a fivefold cross-validation method. Model performance was validated internally and externally by evaluating model discrimination using the area under the receiver-operating characteristic curve (AUC). A nomogram was developed based on the binary regression results. Results In the development cohort, 1088 patients with acute aortic syndromes were included, and 88 (8.1%) patients died during hospitalization. In the validation cohort, 210 patients were included, and 20 (9.5%) patients died during hospitalization. The final model included the following variables: digestive system symptoms (OR=2.25; P=0.024), any pulse deficit (OR=7.78; P<0.001), creatinine (µmol/L)(OR=1.00; P=0.018), lesion extension to iliac vessels (OR=4.49; P<0.001), pericardial effusion (OR=2.67; P=0.008), and Stanford type A (OR=10.46; P<0.001). The model’s AUC was 0.838 (95% CI 0.784–0.892) in the development cohort and 0.821 (95% CI 0.750–0.891) in the validation cohort, and the Hosmer–Lemeshow test showed p=0.597. The fivefold cross-validation demonstrated a mean accuracy of 0.94, a mean precision of 0.67, and a mean recall of 0.13. Conclusion This risk prediction tool uses simple variables to provide robust prediction of the risk of in-hospital death from acute aortic syndrome and validated well in an independent cohort. The tool can help emergency clinicians quickly identify high-risk acute aortic syndrome patients, although further studies are needed for verifying the prospective data and the results of our study.

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A preoperative mortality risk assessment model for Stanford type A acute aortic dissection

Cited by 26 publications

References 28 publications

Risk factors and in-hospital mortality of postoperative hyperlactatemia in patients after acute type A aortic dissection surgery

Risk factors and in-hospital mortality of postoperative hyperlactatemia in patients after acute type A aortic dissection surgery

Admission D-dimer to Lymphocyte counts ratio as a novel biomarker for predicting the in-hospital mortality in patients with acute aortic dissection

Early Prediction Model of Acute Aortic Syndrome Mortality in Emergency Departments

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