Short- and Long-Term Mortality Prediction in Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU) Using Machine Learning

2021 ◽  
Author(s):  
Ryoung-Eun Ko ◽  
Min-Kyue Shin ◽  
Sung Woo Oh ◽  
Yeonchan Seong ◽  
Kyeongman Jeon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Critically Ill ◽  
Mortality Prediction ◽  
Short And Long Term ◽  
Long Term Mortality

Short, and long-term mortality among cardiac intensive care unit patients started on continuous renal replacement therapy

2020 ◽  
Vol 55 ◽  
pp. 64-72 ◽  
Author(s):  
Vasken Keleshian ◽  
Kianoush B. Kashani ◽  
Panagiotis Kompotiatis ◽  
Gregory W. Barsness ◽  
Jacob C. Jentzer
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Renal Replacement Therapy ◽  
Continuous Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Cardiac Intensive Care Unit ◽  
Short And Long Term ◽  
Cardiac Intensive Care ◽  
Long Term Mortality

scholarly journals Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254894
Author(s):  
Firdaus Aziz ◽  
Sorayya Malek ◽  
Khairul Shafiq Ibrahim ◽  
Raja Ezman Raja Shariff ◽  
Wan Azman Wan Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Risk Score ◽  
Fasting Blood Glucose ◽  
Mortality Prediction ◽  
Machine Learning Algorithms ◽  
Timi Score ◽  
Short And Long Term ◽  
Long Term Mortality

Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Factors Affecting the Short and Long Term Mortality of Infected Patients in Intensive Care Unit

2015 ◽  
Vol 4 (2) ◽  
pp. 117-124
Author(s):  
Ali Kutlucan ◽  
Murat Erdoğan ◽  
Leyla Kutlucan ◽  
Handan Ankaralı ◽  
Fatih Ermiş ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Factors Affecting ◽  
Short And Long Term ◽  
Long Term Mortality

scholarly journals Lower short- and long-term mortality associated with overweight and obesity in a large cohort study of adult intensive care unit patients

Critical Care ◽  
2012 ◽  
Vol 16 (6) ◽  
pp. R235 ◽  
Author(s):  
Swapna Abhyankar ◽  
Kira Leishear ◽  
Fiona M Callaghan ◽  
Dina Demner-Fushman ◽  
Clement J McDonald
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Overweight And Obesity ◽  
Large Cohort ◽  
Adult Intensive Care Unit ◽  
Large Cohort Study ◽  
Short And Long Term ◽  
Long Term Mortality

scholarly journals Effect of Systematic Intensive Care Unit Triage on Long-term Mortality Among Critically Ill Elderly Patients in France

JAMA ◽  
2017 ◽  
Vol 318 (15) ◽  
pp. 1450 ◽  
Author(s):  
Bertrand Guidet ◽  
Guillaume Leblanc ◽  
Tabassome Simon ◽  
Maguy Woimant ◽  
Jean-Pierre Quenot ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Elderly Patients ◽  
Critically Ill ◽  
Long Term Mortality

Elevated cardiac troponin is an independent risk factor for short- and long-term mortality in medical intensive care unit patients

2008 ◽  
Vol 36 (3) ◽  
pp. 759-765 ◽  
Author(s):  
Luciano Babuin ◽  
Vlad C. Vasile ◽  
Jose A. Rio Perez ◽  
Jorge R. Alegria ◽  
High-Seng Chai ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Risk Factor ◽  
Intensive Care ◽  
Cardiac Troponin ◽  
Independent Risk Factor ◽  
Medical Intensive Care Unit ◽  
Short And Long Term ◽  
Medical Intensive Care ◽  
Long Term Mortality

scholarly journals No impact of weather conditions on the outcome of intensive care unit patients

2021 ◽  
Author(s):  
Raphael Romano Bruno ◽  
Bernhard Wernly ◽  
Maryna Masyuk ◽  
Johanna M. Muessig ◽  
Rene Schiffner ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Weather Conditions ◽  
Air Pressure ◽  
Different Seasons ◽  
Weather Changes ◽  
Long Term Mortality

SummaryGlobal warming leads to increased exposure of humankind to meteorological variation, including short-term weather changes. Weather conditions involve changes in temperature, heat and cold, in air pressure and in air humidity. Every single condition influences the incidence and mortality of different diseases such as myocardial infarction and stroke. This study investigated the impact of weather conditions on short- and long-term mortality of 4321 critically ill patients (66 ± 14 years, 2638 men) admitted to an intensive care unit (ICU) over a period of 5 years. Meteorological information (air temperature, air pressure and humidity) for the same period was retrieved. The influence of absolute weather parameters, different seasons, sudden weather changes including “warm” and “cold” spells on ICU and long-term mortality was analyzed. After correction for Simplified Acute Physiology Score (SAPS-2), no impact of meteorological conditions on mortality was found. Different seasons, sudden weather changes, “warm spells” or “cold spells” did not affect the outcome of critically ill patients.

Association Between Albumin Level and Mortality Among Cardiac Intensive Care Unit Patients

2020 ◽  
pp. 088506662096387
Author(s):  
Mitchell Padkins ◽  
Thomas Breen ◽  
Nandan Anavekar ◽  
Gregory Barsness ◽  
Kianoush Kashani ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Albumin Level ◽  
Post Discharge ◽  
Cardiac Intensive Care Unit ◽  
Short And Long Term ◽  
Cardiac Intensive Care ◽  
Long Term Mortality

Purpose: To study the effect of hypoalbuminemia on short- and long-term mortality in Cardiac Intensive Care Unit (CICU) patients. Methods: We reviewed 12,418 unique CICU patients from 2007 to 2018. Hypoalbuminemia was defined as an admission albumin level <3.5 g/dL. Predictors of hospital mortality were identified using multivariable logistic regression. Results: We included 2,680 patients (22%) with a measured admission albumin level. The median age was 68 (39% females). Admission diagnoses included acute coronary syndrome, heart failure, cardiac arrest, and cardiogenic shock. The median albumin level was 3.4 g/dL and 55% of patients had hypoalbuminemia. Hospital mortality occurred in 16%, and patients with hypoalbuminemia had higher hospital mortality (21% vs. 9%, adjusted OR 2.64, 95% CI 2.09-3.34, p < 0.001). Albumin level was inversely associated with hospital mortality (adjusted OR 0.60 per 1 g/dL higher albumin level, 95% CI 0.47-0.75, p <0.001), with a stepwise increase in the hospital mortality at lower albumin levels. Post-discharge mortality was higher in hospital survivors with hypoalbuminemia, and increased as a function of lower albumin levels. Conclusion: Hypoalbuminemia is common in CICU patients and associated with higher short- and long-term mortality. Progressively lower serum albumin was incrementally associated with higher hospital and post-discharge mortality.

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