[HTML][HTML] Use of artificial intelligence in critical care: opportunities and obstacles

MR Pinsky, A Bedoya, A Bihorac, L Celi, M Churpek… - Critical Care, 2024 - Springer
Background Perhaps nowhere else in the healthcare system than in the intensive care unit
environment are the challenges to create useful models with direct time-critical clinical …

Data mining techniques for predicting acute kidney injury after elective cardiac surgery

J Van Eyck, J Ramon, F Guiza, G Meyfroidt… - Critical Care, 2012 - Springer
Methods A total of 810 adult (> 18 years) elective cardiac surgery patients, admitted to the
surgical ICU of the University Hospital of Leuven between 18 January 2007 and 8 January …

[HTML][HTML] The Academic Medical Center Linear Disability Score for evaluation of physical reserve on admission to the ICU: can we query the relatives?

JGM Hofhuis, MGW Dijkgraaf, A Hovingh, RL Braam… - Critical Care, 2011 - Springer
Introduction Evaluating the pre-morbid functional status in critically ill patients is important
and frequently done using the physical component score (PCS) of the Short Form 36 …

[HTML][HTML] Creating the ICU of the future: patient-centred design to optimise recovery

O Tronstad, D Flaws, S Patterson, R Holdsworth… - Critical Care, 2023 - Springer
Abstract Background Intensive Care survival continues to improve, and the number of ICU
services is increasing globally. However, there is a growing awareness of the detrimental …

[HTML][HTML] External validation of a machine learning model to predict hemodynamic instability in intensive care unit

C Dung-Hung, T Cong, J Zeyu, OY Yu-Shan… - Critical Care, 2022 - Springer
Background Early prediction model of hemodynamic instability has the potential to improve
the critical care, whereas limited external validation on the generalizability. We aimed to …

[HTML][HTML] 'Safety by DEFAULT': introduction and impact of a paediatric ward round checklist

S Sharma, MJ Peters - Critical Care, 2013 - Springer
Introduction Poor communication is a source of risk. This can be particularly significant in
areas of high clinical acuity such as intensive care. Ward rounds are points where large …

[HTML][HTML] A multivariate Bayesian model for assessing morbidity after coronary artery surgery

B Biagioli, S Scolletta, G Cevenini, E Barbini… - Critical care, 2006 - Springer
Introduction Although most risk-stratification scores are derived from preoperative patient
variables, there are several intraoperative and postoperative variables that can influence …

[HTML][HTML] Effect of an automated notification system for deteriorating ward patients on clinical outcomes

CP Subbe, B Duller, R Bellomo - Critical Care, 2017 - Springer
Background Delayed response to clinical deterioration of ward patients is common. Methods
We performed a prospective before-and-after study in all patients admitted to two clinical …

[HTML][HTML] Whole blood transcriptomics identifies subclasses of pediatric septic shock

JO Yang, MS Zinter, M Pellegrini, MY Wong, K Gala… - Critical Care, 2023 - Springer
Background Sepsis is a highly heterogeneous syndrome, which has hindered the
development of effective therapies. This has prompted investigators to develop a precision …

Developing a screening instrument for predicting psychological morbidity after critical illness

A Schandl, M Bottai, E Hellgren, Ö Sundin, P Sackey - Critical care, 2013 - Springer
Methods Potential risk factors for psychological problems were prospectively collected at
ICU discharge. Two months after ICU discharge 252 ICU survivors received the …