scholarly journals Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit

2021 ◽  
pp. 1-1
Author(s):  
Tingyi Wanyan ◽  
Akhil Vaid ◽  
Jessica K De Freitas ◽  
Sulaiman Somani ◽  
Riccardo Miotto ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Relational Learning ◽  
Prediction Of Mortality

scholarly journals Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anil K. Palepu ◽  
Aditya Murali ◽  
Jenna L. Ballard ◽  
Robert Li ◽  
Samiksha Ramesh ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Traumatic Brain Injury ◽  
Intensive Care ◽  
Brain Injury ◽  
Neurological Function ◽  
Series Data ◽  
Admission Diagnosis ◽  
Electronic Health Record Data ◽  
Prediction Of Mortality ◽  
Icu Discharge

AbstractTraumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.

PREDICTION OF MORTALITY ASSOCIATED TO PULMONARY TUBERCULOSIS IN THE INTENSIVE CARE UNIT

1999 ◽  
Vol 27 (Supplement) ◽  
pp. A93
Author(s):  
Jorge Pedroza ◽  
Patricia Nuche ◽  
Julian Armendariz ◽  
Patricia Cornejo ◽  
Julio C Robledo ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pulmonary Tuberculosis ◽  
Prediction Of Mortality

scholarly journals Combination of APACHE Scoring Systems with Adductor Pollicis Muscle Thickness for the Prediction of Mortality in Patients Who Spend More Than One Day in the Intensive Care Unit

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Elahe Nematifard ◽  
Seyed Hossein Ardehali ◽  
Shaahin Shahbazi ◽  
Hassan Eini-Zinab ◽  
Zahra Vahdat Shariatpanahi
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Scoring Systems ◽  
Roc Curves ◽  
Adductor Pollicis ◽  
Interquartile Range ◽  
Apache Ii ◽  
Adductor Pollicis Muscle ◽  
Prediction Of Mortality ◽  
Apache Iii

Background. The objective of the present study was to compare the ability of Acute Physiology and Chronic Health Evaluation (APACHE) scoring systems with the combination of an anthropometric variable score “adductor pollicis muscle (APM) thickness” to the APACHE systems in predicting mortality in the intensive care unit. Methods. A prospective observational study was conducted with the APM thickness in the dominant hand, and APACHE II and III scores were measured for each patient upon admission. Given scores for the APM thickness were added to APACHE score systems to make two composite scores of APACHE II-APM and APACHE III-APM. The accuracy of the two composite models and APACHE II and III systems in predicting mortality of patients was compared using the area under the ROC curve. Results. Three hundred and four patients with the mean age of 54.75 ± 18.28 years were studied, of which 96 (31.57%) patients died. Median (interquartile range) of APACHE II and III scores was 15 (12–20) and 47 (33–66), respectively. Median (interquartile range) of APM thickness was 15 (12–17) mm, respectively. The area under the ROC curves for the prediction of mortality was 0.771 (95% CI: 0.715–0.827), 0.802 (95% CI: 0.751–0.854), 0.851 (95% CI: 0.807–0.896), and 0.865 (95% CI: 0.822–0.908) for APACHE II, APACHE III, APACHE II-APM, and APACHE III-APM, respectively. Conclusion. Although improvements in the area under ROC curves were not statistically significant when the APM thickness added to the APACHE systems, but the numerical value added to AUCs are considerable.

Thyroid hormone levels improve the prediction of mortality among patients admitted to the intensive care unit

2006 ◽  
Vol 32 (4) ◽  
pp. 616-616
Author(s):  
Ioanna Dimopoulou ◽  
Konstantinos Stamoulis ◽  
Panagiotis Lyberopoulos ◽  
Petros Kopterides
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Thyroid Hormone ◽  
Hormone Levels ◽  
Prediction Of Mortality ◽  
Thyroid Hormone Levels
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