scholarly journals Determining association between blood glucose variability and postoperative delirium in acute aortic dissection patients: methodological issues

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Cheng-Wen Li ◽  
Fu-Shan Xue ◽  
Bin Hu
Keyword(s):  
Blood Glucose ◽  
Aortic Dissection ◽  
Postoperative Delirium ◽  
Operating Characteristic ◽  
Acute Aortic Dissection ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Glucose Variability ◽  
Methodological Issues ◽  
Blood Glucose Variability

AbstractThe letter to the editor made several comments regarding possible methodological issues in the recent article by Lin et al. determining the association between blood glucose variability and postoperative delirium in patients undergoing acute aortic dissection surgery with cardiopulmonary bypass, which is published in Journal of Cardiothoracic Surgery. 2021; 16(1):82. Our concerns included the lack of some important perioperative factors associated with postoperative delirium, the process of establishing multivariate model and the method of using the receiver operating characteristic curve analysis to assess the predictive performance of the standard deviation of blood glucose for the development of POD. We would like to invite the authors to comment on these and believe that clarifying these issues would improve the transparency of this study and interpretation of findings.

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

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.

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

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.

scholarly journals Association between glucose variability and postoperative delirium in acute aortic dissection patients: an observational study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yan-Juan Lin ◽  
Ling-Yu Lin ◽  
Yan-Chun Peng ◽  
Hao-Ruo Zhang ◽  
Liang-wan Chen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Blood Glucose ◽  
Standard Deviation ◽  
Aortic Dissection ◽  
Intensive Care Unit Stay ◽  
Postoperative Delirium ◽  
Acute Aortic Dissection ◽  
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 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.

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

2021 ◽  
Vol 8 ◽  
Author(s):  
Tuo Guo ◽  
Zhuo Fang ◽  
Guifang Yang ◽  
Yang Zhou ◽  
Ning Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Dissection ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Acute Aortic Dissection ◽  
Characteristic Curve ◽  
Survivor Group ◽  
Treatment Type ◽  
Operating Characteristic Curve

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.

scholarly journals PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data

2019 ◽  
Vol 33 ◽  
pp. 1118-1125 ◽  
Author(s):  
Patrick Schwab ◽  
Walter Karlen
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Term Data

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.

Novel radiomics signature predicts lymph node metastasis in T1 colorectal carcinoma.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 506-506
Author(s):  
Yanlei Ma ◽  
Sheng Zhang ◽  
Xinxiang Li ◽  
Tianye Niu
Keyword(s):  
Lymph Node ◽  
Colorectal Carcinoma ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cancer Center ◽  
T1 Colorectal Carcinoma ◽  
Auc Value ◽  
Receiver Operating

506 Background: This study evaluates the predictive performance of radiomic features in metastasis of T1 colorectal carcinoma (CRC) to lymph nodes. Methods: A total of 10 200 CRC patients from our clinical cancer center included in this analysis. 225 eligible cases diagnosed with T1 CRC were included and divided into two groups: computed tomography (CT) image group (n = 82) and magnetic resonance image (MRI) group (n = 143) based on the preoperative image data available. A total of 548 radiomic features were extracted from each case and analyzed, and then a panel of radiomic features associated with lymph node metastases (LNM) were selected using Mann-Whitney U test. Combining these selected radiomic features and clinical data, the predictive performance for LNM was calculated using receiver operating characteristic (ROC) curves. Results: The prediction accuracy for LNM of T1 CRC could be improved to 0.88 by area under the receiver operating characteristic curve (AUC) through integration of one radiomic feature and three clinical indicators in CT group. In the group of contrast enhanced T1-weighted MRI (T1w-MRI), combination of two radiomic features and three clinical parameters present an AUC value of 0.85. In the group of T2-weighted MRI (T2w-MRI), combination of four radiomic features and five clinical characteristics identified T1 tumors with LNM with an AUC value of 0.87. Conclusions: The current study present a good predictive performance of combination of radiomic features with clinic characteristic in identifying T1 CRC with LNM, which may provide an important opportunity for us to make clinical treatment decision-making for T1 CRC patients.

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