scholarly journals Association between anion gap and mortality of aortic aneurysm in intensive care unit after open surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yijing Gao ◽  
Zilin Hong ◽  
Runnan Shen ◽  
Shiran Zhang ◽  
Guochang You ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Aortic Aneurysm ◽  
Clinical Outcome ◽  
Open Surgery ◽  
Operating Characteristic ◽  
Univariate Analysis ◽  
Simplified Acute Physiology Score ◽  
Anion Gap ◽  
Prognosis Factor

Abstract Background There has not been a well-accepted prognostic model to predict the mortality of aortic aneurysm patients in intensive care unit after open surgery repair. Otherwise, our previous study found that anion gap was a prognosis factor for aortic aneurysm patients. Therefore, we wanted to investigate the relationship between anion gap and mortality of aortic aneurysm patients in intensive care unit after open surgery repair. Methods From Medical Information Mart for Intensive Care III, data of aortic aneurysm patients in intensive care unit after open surgery were enrolled. The primary clinical outcome was defined as death in intensive care unit. Univariate analysis was conducted to compare the baseline data in different groups stratified by clinical outcome or by anion gap level. Restricted cubic spline was drawn to find out the association between anion gap level and mortality. Subgroup analysis was then conducted to show the association in different level and was presented as frost plot. Multivariate regression models were built based on anion gap and were adjusted by admission information, severity score, complication, operation and laboratory indicators. Receiver operating characteristic curves were drawn to compare the prognosis ability of anion gap and simplified acute physiology score II. Decision curve analysis was finally conducted to indicate the net benefit of the models. Results A total of 405 aortic aneurysm patients were enrolled in this study and the in-intensive-care-unit (in-ICU) mortality was 6.9%. Univariate analysis showed that elevated anion gap was associated with high mortality (P value < 0.001), and restricted cubic spline analysis showed the positive correlation between anion gap and mortality. Receiver operating characteristic curve showed that the mortality predictive ability of anion gap approached that of simplified acute physiology score II and even performed better in predicting in-hospital mortality (P value < 0.05). Moreover, models based on anion gap showed that 1 mEq/L increase of anion gap improved up to 42.3% (95% confidence interval 28.5–59.8%) risk of death. Conclusions The level of serum anion gap was an important prognosis factor for aortic aneurysm mortality in intensive care unit after open surgery.

scholarly journals Postoperative Haematocrit and Outcome in Critically Ill Surgical Patients

2017 ◽  
Vol 30 (7-8) ◽  
pp. 555 ◽  
Author(s):  
Ana Martins Lopes ◽  
Diana Silva ◽  
Gabriela Sousa ◽  
Joana Silva ◽  
Alice Santos ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Intensive Care Unit Admission ◽  
Hospital Stay ◽  
Univariate Analysis ◽  
Simplified Acute Physiology Score ◽  
Surgical Intensive Care Unit ◽  
Haematocrit Level ◽  
Cardiac Events ◽  
Surgical Intensive Care

Introduction: Haematocrit has been studied as an outcome predictor. The aim of this study was to evaluate the correlation between low haematocrit at surgical intensive care unit admission and high disease scoring system score and early outcomes.Material and Methods: This retrospective study included 4398 patients admitted to the surgical intensive care unit between January 2006 and July 2013. Acute physiology and chronic health evaluation and simplified acute physiology score II values were calculated and all variables entered as parameters were evaluated independently. Patients were classified as haematocrit if they had a haematocrit < 30% at surgical intensive care unit admission. The correlation between admission haematocrit and outcome was evaluated by univariate analysis and linear regression.Results: A total of 1126 (25.6%) patients had haematocrit. These patients had higher rates of major cardiac events (4% vs 1.9%, p < 0.001), acute renal failure (11.5% vs 4.7%, p < 0.001), and mortality during surgical intensive care unit stay (3% vs 0.8%, p < 0.001) and hospital stay (12% vs 5.9%, p < 0.001).Discussion: A haematocrit level < 30% at surgical intensive care unit admission was frequent and appears to be a predictor for poorer outcome in critical surgical patients.Conclusion: Patients with haematocrit had longer surgical intensive care unit and hospital stay lengths, more postoperative complications, and higher surgical intensive care unit and hospital mortality rates.

Code Blue Events in the Neonatal and Pediatric Intensive Care Units at a Tertiary Care Children's Hospital

2020 ◽  
Author(s):  
Catherine M. Groden ◽  
Erwin T. Cabacungan ◽  
Ruby Gupta
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Life Support ◽  
Univariate Analysis ◽  
Tertiary Care ◽  
Neonatal Resuscitation ◽  
Advanced Life Support ◽  
Pediatric Intensive Care ◽  
Code Blue ◽  
Neonatal Resuscitation Program

Objective The authors aim to compare all code blue events, regardless of the need for chest compressions, in the neonatal intensive care unit (NICU) versus the pediatric intensive care unit (PICU). We hypothesize that code events in the two units differ, reflecting different disease processes. Study Design This is a retrospective analysis of 107 code events using the code narrator, which is an electronic medical record of real-time code documentation, from April 2018 to March 2019. Events were divided into two groups, NICU and PICU. Neonatal resuscitation program algorithm was used for NICU events and a pediatric advanced life-support algorithm was used for PICU events. Events and outcomes were compared using univariate analysis. The Mann–Whitney test and linear regressions were done to compare the total code duration, time from the start of code to airway insertion, and time from airway insertion to end of code event. Results In the PICU, there were almost four times more code blue events per month and more likely to involve patients with seizures and no chronic condition. NICU events more often involved ventilated patients and those under 2 months of age. The median code duration for NICU events was 2.5 times shorter than for PICU events (11.5 vs. 29 minutes), even when adjusted for patient characteristics. Survival to discharge was not different in the two groups. Conclusion Our study suggests that NICU code events as compared with PICU code events are more likely to be driven by airway problems, involve patients <2 months of age, and resolve quickly once airway is taken care of. This supports the use of a ventilation-focused neonatal resuscitation program for patients in the NICU. Key Points

scholarly journals 61. Using Machine Learning for Prediction of Poor Clinical Outcomes in Adult Patients Hospitalized with COVID-19

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S162-S163
Author(s):  
Guillermo Rodriguez-Nava ◽  
Daniela Patricia Trelles-Garcia ◽  
Maria Adriana Yanez-Bello ◽  
Chul Won Chung ◽  
Sana Chaudry ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Operating Characteristic ◽  
Decision Rules ◽  
Training Model ◽  
Inpatient Mortality ◽  
Prediction Rules ◽  
Testing Model ◽  
Measures Of Performance

Abstract Background As the ongoing COVID-19 pandemic develops, there is a need for prediction rules to guide clinical decisions. Previous reports have identified risk factors using statistical inference model. The primary goal of these models is to characterize the relationship between variables and outcomes, not to make predictions. In contrast, the primary purpose of machine learning is obtaining a model that can make repeatable predictions. The objective of this study is to develop decision rules tailored to our patient population to predict ICU admissions and death in patients with COVID-19. Methods We used a de-identified dataset of hospitalized adults with COVID-19 admitted to our community hospital between March 2020 and June 2020. We used a Random Forest algorithm to build the prediction models for ICU admissions and death. Random Forest is one of the most powerful machine learning algorithms; it leverages the power of multiple decision trees, randomly created, for making decisions. Results 313 patients were included; 237 patients were used to train each model, 26 were used for testing, and 50 for validation. A total of 16 variables, selected according to their availability in the Emergency Department, were fit into the models. For the survival model, the combination of age &gt;57 years, the presence of altered mental status, procalcitonin ≥3.0 ng/mL, a respiratory rate &gt;22, and a blood urea nitrogen &gt;32 mg/dL resulted in a decision rule with an accuracy of 98.7% in the training model, 73.1% in the testing model, and 70% in the validation model (Table 1, Figure 1). For the ICU admission model, the combination of age &lt; 82 years, a systolic blood pressure of ≤94 mm Hg, oxygen saturation of ≤93%, a lactate dehydrogenase &gt;591 IU/L, and a lactic acid &gt;1.5 mmol/L resulted in a decision rule with an accuracy of 99.6% in the training model, 80.8% in the testing model, and 82% in the validation model (Table 2, Figure 2). Table 1. Measures of Performance in Predicting Inpatient Mortality Conclusion We created decision rules using machine learning to predict ICU admission or death in patients with COVID-19. Although there are variables previously described with statistical inference, these decision rules are customized to our patient population; furthermore, we can continue to train the models fitting more data with new patients to create even more accurate prediction rules. Figure 1. Receiver Operating Characteristic (ROC) Curve for Inpatient Mortality Table 2. Measures of Performance in Predicting Intensive Care Unit Admission Figure 2. Receiver Operating Characteristic (ROC) Curve for Intensive Care Unit Admission Disclosures All Authors: No reported disclosures

scholarly journals Validation of the Simplified Acute Physiology Score 3 Scoring System in a Korean Intensive Care Unit

2011 ◽  
Vol 52 (1) ◽  
pp. 59 ◽  
Author(s):  
So Yeon Lim ◽  
Cho Rom Ham ◽  
So Young Park ◽  
Suhyun Kim ◽  
Maeng Real Park ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Scoring System ◽  
Simplified Acute Physiology Score ◽  
Acute Physiology Score

scholarly journals Effect of Sleep Disorder on Delirium in Post-Cardiac Surgery Patients

2020 ◽  
Vol 47 (5) ◽  
pp. 627-633 ◽  
Author(s):  
Hongbai Wang ◽  
Liang Zhang ◽  
Qipeng Luo ◽  
Yinan Li ◽  
Fuxia Yan
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Sleep Quality ◽  
Sleep Disorder ◽  
Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Post Cardiac Surgery

ABSTRACT:Background:Post-cardiac surgery patients exhibit a higher incidence of postoperative delirium (PD) compared to non-cardiac surgery patients. Patients with various cardiac diseases suffer from preoperative sleep disorder (SPD) induced by anxiety, depression, breathing disorder, or other factors.Objective:To examine the effect of sleep disorder on delirium in post-cardiac surgery patients.Methods:We prospectively selected 186 patients undergoing selective cardiac valve surgery. Preoperative sleep quality and cognitive function of all eligible participants were assessed through the Pittsburgh Sleep Quality Index (PSQI) and the Montreal Cognitive Assessment, respectively. The Confusion Assessment Method for Intensive Care Unit was used to assess PD from the first to seventh day postoperatively. Patients were divided into two groups according to the PD diagnosis: (1) No PD group and (2) the PD group.Results:Of 186 eligible patients, 29 (15.6%) were diagnosed with PD. A univariate analysis showed that gender (p = 0.040), age (p = 0.009), SPD (p = 0.008), intraoperative infusion volume (p = 0.034), postoperative intubation time (p = 0.001), and intensive care unit stay time (p = 0.009) were associated with PD. A multivariate logistic regression analysis demonstrated that age (odds ratio (OR): 1.106; p = 0.001) and SPD (OR: 3.223; p = 0.047) were independently associated with PD. A receiver operating characteristic curve demonstrated that preoperative PSQI was predictive of PD (area under curve: 0.706; 95% confidence interval: 0.595–0.816). A binomial logistic regression analysis showed that there was a significant association between preoperative 6 and 21 PSQI scores and PD incidence (p = 0.009).Conclusions:Preoperative SPD was significantly associated with PD and a main predictor of PD.

Sign in / Sign up

Export Citation Format

Share Document