Development and Implementation of a Pediatric Cardiac Anesthesia/Intensive Care Database

Background Risk factors of delayed extubation, prolonged intensive care unit (ICU) length of stay (LOS), and mortality have not been studied for patients administered fast-track cardiac anesthesia (FTCA). The authors' goals were to determine risk factors of outcomes and cardiac risk scores (CRS) for CABG patients undergoing FTCA. Methods Consecutive CABG patients undergoing FTCA were prospectively studied. Outcome variables were delayed extubation > 10 h, prolonged ICU LOS > 48 h, and mortality. Univariate analyses were performed followed by multiple logistic regression to derive risk factors of the three outcomes. Simplified integer-based CRS were derived from logistic models. Bootstrap validation was performed to assess and compare the predictive abilities of CRS and logistic models for the three outcomes. Results The authors studied 885 patients. Twenty-five percent had delayed extubation, 17% had prolonged ICU LOS, and 2.6% died. Risk factors of delayed extubation were increased age, female gender, postoperative use of intraaortic balloon pump, inotropes, bleeding, and atrial arrhythmia. Risk factors of prolonged ICU LOS were those of delayed extubation plus preoperative myocardial infarction and postoperative renal insufficiency. Risk factors of mortality were female gender, emergency surgery, and poor left ventricular function. CRSs were modeled for the three outcomes. The area under the receiver operating characteristic curve for the CRS-logistic models was not significantly different: 0.707/0.702 for delayed extubation, 0.851/0.855 for prolonged ICU LOS, and 0.657/0.699 for mortality. Conclusion In CABG patients undergoing FTCA, the authors derived and validated risk factors of delayed extubation, prolonged ICU LOS, and mortality. Furthermore, they developed a simplified CRS system with similar predictive abilities as the logistic models.

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Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach

BMJ Open Gastroenterology ◽

10.1136/bmjgast-2021-000761 ◽

2021 ◽

Vol 8 (1) ◽

pp. e000761

Author(s):

Hao Du ◽

Kewin Tien Ho Siah ◽

Valencia Zhang Ru-Yan ◽

Readon Teh ◽

Christopher Yu En Tan ◽

...

Keyword(s):

Machine Learning ◽

Critical Care ◽

Intensive Care ◽

Hospital Mortality ◽

Receiver Operating Curve ◽

Study Cohort ◽

Risk Of Death ◽

Severity Scores ◽

Mimic Iii ◽

Care Database

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.MethodologyThe demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of resultsFrom 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.ConclusionOur machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

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Magic Bullets in Cardiac Anesthesia and Intensive Care

Journal of Cardiothoracic and Vascular Anesthesia ◽

10.1053/j.jvca.2012.01.040 ◽

2012 ◽

Vol 26 (3) ◽

pp. 455-458 ◽

Cited By ~ 1

Author(s):

Giovanni Landoni ◽

Laura Ruggeri ◽

Alberto Zangrillo

Keyword(s):

Intensive Care ◽

Cardiac Anesthesia

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Mortality and intensive care volume in ventilated patients from 1995 to 2009 in the Australian and New Zealand binational adult patient intensive care database*

Critical Care Medicine ◽

10.1097/ccm.0b013e318236f2af ◽

2012 ◽

Vol 40 (3) ◽

pp. 800-812 ◽

Cited By ~ 23

Author(s):

John L. Moran ◽

Patricia J. Solomon

Keyword(s):

Intensive Care ◽

New Zealand ◽

Adult Patient ◽

Care Database ◽

Ventilated Patients

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Mortality reduction in cardiac anesthesia and intensive care: results of the first International Consensus Conference

Acta Anaesthesiologica Scandinavica ◽

10.1111/j.1399-6576.2010.02381.x ◽

2011 ◽

Vol 55 (3) ◽

pp. 259-266 ◽

Cited By ~ 50

Author(s):

G. LANDONI ◽

J. G. AUGOUSTIDES ◽

F. GUARRACINO ◽

F. SANTINI ◽

M. PONSCHAB ◽

...

Keyword(s):

Intensive Care ◽

Consensus Conference ◽

Mortality Reduction ◽

Cardiac Anesthesia ◽

International Consensus

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Conventional risk prediction models fail to accurately predict mortality risk among patients with coronavirus disease 2019 in intensive care units: a difficult time to assess clinical severity and quality of care

Journal of Intensive Care ◽

10.1186/s40560-021-00557-5 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Hideki Endo ◽

Hiroyuki Ohbe ◽

Junji Kumasawa ◽

Shigehiko Uchino ◽

Satoru Hashimoto ◽

...

Keyword(s):

Intensive Care ◽

Intensive Care Units ◽

Risk Prediction ◽

Prediction Models ◽

Clinical Severity ◽

Risk Prediction Models ◽

Prediction Tools ◽

Difficult Time ◽

Care Database

AbstractSince the start of the coronavirus disease 2019 (COVID-19) pandemic, it has remained unknown whether conventional risk prediction tools used in intensive care units are applicable to patients with COVID-19. Therefore, we assessed the performance of established risk prediction models using the Japanese Intensive Care database. Discrimination and calibration of the models were poor. Revised risk prediction models are needed to assess the clinical severity of COVID-19 patients and monitor healthcare quality in ICUs overwhelmed by patients with COVID-19.

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