Factors of Prolonged Intensive Care Unit Stay After Surgery in Patients with Type A Acute Aortic Dissection

Abstract Background Blood glucose variability is associated with poor prognosis after cardiac surgery, but the relationship between glucose variability and postoperative delirium in patients with acute aortic dissection is unclear. The study aims to investigate the association of blood glucose variability with postoperative delirium in acute aortic dissection patients. Methods We prospectively analyzed 257 patients including 103 patients with delirium. The patients were divided into two groups according to whether delirium was present. The outcome measures were postoperative delirium, the length of the Intensive Care Unit stay, and the duration of hospital stay. Multivariable Cox competing risk survival models was used to assess. Results A total of 257 subjects were enrolled, including 103 patients with delirium. There were statistically significant differences between the two groups in body mass index, history of cardiac surgery, first admission blood glucose, white blood cell counts, Acute Physiology and Chronic Health Evaluation II score, hypoxemia, mechanical ventilation duration, and the length of Intensive Care Unit stay(P < 0.05). The delirium group exhibited significantly higher values of the mean of blood glucose (MBG) and the standard deviation of blood glucose (SDBG) than in the non-delirium group(P < 0.05). In model 1, the adjusted hazard ratio (AHR) of the standard deviation of blood glucose was 1.436(P < 0.05). In Model 2, the standard deviation of blood glucose (AHR = 1.418, 95%CI = 1.195–1.681, P < 0.05) remained significant after adjusting for confounders. The area under the curve of the SDBG was 0.763(95%CI = 0.704–0.821, P < 0.01). The sensitivity was 81.6%, and the specificity was 57.8%. Conclusions Glucose variability is associated with the risk of delirium in patients after aortic dissection surgery, and high glycemic variability increases the risk of postoperative delirium.

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Factors influencing intensive care unit length of stay after surgery for acute aortic dissection type A

The Annals of Thoracic Surgery ◽

10.1016/s0003-4975(01)03572-x ◽

2002 ◽

Vol 73 (3) ◽

pp. 714-718 ◽

Cited By ~ 7

Author(s):

Daniel Hoefer ◽

Elfriede Ruttmann ◽

Markus Riha ◽

Wolfgang Schobersberger ◽

Andreas Mayr ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Length Of Stay ◽

Aortic Dissection ◽

Acute Aortic Dissection ◽

Unit Length ◽

Type A ◽

Intensive Care Unit Length ◽

Factors Influencing ◽

Aortic Dissection Type A

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Association Between Glucose Variability And Postoperative Delirium In Acute Aortic Dissection Patients: An Observational Study

10.21203/rs.3.rs-41883/v1 ◽

2020 ◽

Author(s):

Yan-Juan Lin ◽

Ling-Yu Lin ◽

Yan-Chun Peng ◽

Hao-Ruo Zhang ◽

Liang-wan Chen ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Blood Glucose ◽

Standard Deviation ◽

Aortic Dissection ◽

Intensive Care Unit Stay ◽

Postoperative Delirium ◽

Acute Aortic Dissection ◽

Glucose Variability ◽

Blood Glucose Variability

Abstract Background: Blood glucose variability is associated with poor prognosis after cardiac surgery, but the relationship between glucose variability and postoperative delirium in patients with acute aortic dissection is unclear. The study aims to investigate the association of blood glucose variability with postoperative delirium in acute aortic dissection patients.Methods: We prospectively analyzed 257 patients including 103 patients with delirium. The patients were divided into two groups according to whether delirium was present. The outcome measures were postoperative delirium, the length of the Intensive Care Unit stay, and the duration of hospital stay. Multivariable Cox competing risk survival models was used to assess.Results: A total of 257 subjects were enrolled, including 103 patients with delirium. There were statistically significant differences between the two groups in age, body mass index, first admission blood glucose, white blood cell counts, Acute Physiology and Chronic Health Evaluation II score, hypoxemia, mechanical ventilation duration, and the length of Intensive Care Unit stay (P<0.05). The median of the mean of blood glucose and the standard deviation of blood glucose were higher in the delirium group than in the non-delirium group, and the difference was statistically significant (P<0.05). In model 1, the adjusted hazard ratio of the standard deviation of blood glucose was 1.436 (P<0.05). In Model 2, the standard deviation of blood glucose (AHR=1.418, 95% CI=1.195-1.681, P<0.05) remained significant after adjusting for confounders (P<0.05). The area under the curve of the standard deviation of blood glucose was 0.763 (95% CI=0.704-0.821, P<0.01). The sensitivity was 81.6%, and the specificity was 57.8%. Conclusions: Glucose variability is associated with the risk of delirium in patients after aortic dissection surgery, and high glycemic variability increases the risk of postoperative delirium.

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Association of glucose variability with postoperative delirium in acute aortic dissection patients: an observational study

10.21203/rs.3.rs-18296/v1 ◽

2020 ◽

Author(s):

Yanjuan Lin ◽

Ling-Yu Lin ◽

Yan-Chun Peng ◽

Hao-Ruo Zhang ◽

Liang-wan Chen ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Blood Glucose ◽

Standard Deviation ◽

Aortic Dissection ◽

Intensive Care Unit Stay ◽

Postoperative Delirium ◽

Acute Aortic Dissection ◽

Glucose Variability ◽

Blood Glucose Variability

Abstract Background Blood glucose variability is associated with poor prognosis after cardiac surgery, but the relationship between glucose variability and postoperative delirium in patients with acute aortic dissection is unclear. The aim of this study is to investigate the association of blood glucose variability with postoperative delirium in acute aortic dissection patients. Methods We prospectively analyzed 257 patients including 103 patients with delirium. The patients was categorized into two groups according to whether delirium was present. The outcome measures were postoperative delirium, the length of Intensive Care Unit stay and the duration of hospital stay. Multivariable Cox competing risk survival models was used to assess. Results A total of 257 subjects were enrolled, including 103 patients with delirium. There were statistically significant differences between the two groups in age, body mass index, first admission blood glucose, white blood cell counts, Acute Physiology and Chronic Health Evaluation II score, hypoxemia, mechanical ventilation duration and the length of Intensive Care Unit stay (P < 0.05). The median of mean of blood glucose and standard deviation of blood glucose were higher in the delirium group than in the non-delirium group, and the difference was statistically significant (P < 0.05). In model 1, the adjusted hazard ratio of standard deviation of blood glucose was 1.436 (P < 0.05). In Model 2, the SDBG (AHR = 1.418, 95% CI = 1.195–1.681, P < 0.05) remained significant after adjusting for confounders (P < 0.05). The area under curve of the SDBG ROC was 0.763 (95% CI = 0.704–0.821, P < 0.01). The sensitivity was 81.6%, and the specificity was 57.8%. Conclusions Glucose variability is associated with the risk of delirium in patients after aortic dissection surgery, and high glycemic variability increases the risk of postoperative delirium.

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Admission Systolic Blood Pressure Predicts Post-Operative Delirium of Acute Aortic Dissection Patients in the Intensive Care Unit

International Journal of General Medicine ◽

10.2147/ijgm.s329689 ◽

2021 ◽

Vol Volume 14 ◽

pp. 5939-5948

Author(s):

Zuli Fu ◽

Qian Xu ◽

Chiyuan Zhang ◽

Hui Bai ◽

Xuliang Chen ◽

...

Keyword(s):

Blood Pressure ◽

Intensive Care Unit ◽

Intensive Care ◽

Aortic Dissection ◽

Systolic Blood Pressure ◽

Acute Aortic Dissection

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Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.675431 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qiuying Chen ◽

Bin Zhang ◽

Jue Yang ◽

Xiaokai Mo ◽

Lu Zhang ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Intensive Care ◽

Random Forest ◽

Length Of Stay ◽

Aortic Dissection ◽

Decision Tree ◽

Type A ◽

Acute Type ◽

Type A Aortic Dissection

Background: Patients with acute type A aortic dissection are usually transferred to the intensive care unit (ICU) after surgery. Prolonged ICU length of stay (ICU-LOS) is associated with higher level of care and higher mortality. We aimed to develop and validate machine learning models for predicting ICU-LOS after acute type A aortic dissection surgery.Methods: A total of 353 patients with acute type A aortic dissection transferred to ICU after surgery from September 2016 to August 2019 were included. The patients were randomly divided into the training dataset (70%) and the validation dataset (30%). Eighty-four preoperative and intraoperative factors were collected for each patient. ICU-LOS was divided into four intervals (<4, 4–7, 7–10, and >10 days) according to interquartile range. Kendall correlation coefficient was used to identify factors associated with ICU-LOS. Five classic classifiers, Naive Bayes, Linear Regression, Decision Tree, Random Forest, and Gradient Boosting Decision Tree, were developed to predict ICU-LOS. Area under the curve (AUC) was used to evaluate the models' performance.Results: The mean age of patients was 51.0 ± 10.9 years and 307 (87.0%) were males. Twelve predictors were identified for ICU-LOS, namely, D-dimer, serum creatinine, lactate dehydrogenase, cardiopulmonary bypass time, fasting blood glucose, white blood cell count, surgical time, aortic cross-clamping time, with Marfan's syndrome, without Marfan's syndrome, without aortic aneurysm, and platelet count. Random Forest yielded the highest performance, with an AUC of 0.991 (95% confidence interval [CI]: 0.978–1.000) and 0.837 (95% CI: 0.766–0.908) in the training and validation datasets, respectively.Conclusions: Machine learning has the potential to predict ICU-LOS for acute type A aortic dissection. This tool could improve the management of ICU resources and patient-throughput planning, and allow better communication with patients and their families.

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