Mean arterial pressure of 65 mm Hg versus 85-100 mm Hg in comatose survivors after cardiac arrest: Rationale and study design of the Neuroprotect post–cardiac arrest trial

Ameloot, Koen; Deyne, C. De; Ferdinande, Bert; Dupont, Matthias; Palmers, Pieter-Jan; Petit, Thibault; Eertmans, Ward; Moonen, C.; Belmans, Ann; Lemmens, Robin; Dens, Joseph; Janssens, Stefan

doi:10.1016/j.ahj.2017.06.010

Cited by 30 publications

(23 citation statements)

References 19 publications

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“…Higher MAP did not influence cerebral saturation (or any other outcome), perhaps because of the adequate cerebral perfusion pressure obtained already at the low normal level of MAP. In contrast, a recent randomised trial, presented at American Heart Association 2018, did report that a higher target MAP (85-100 mmHg) improved cerebral perfusion and oxygenation [11]. Signs of anoxic brain damage on magnetic resonance imaging and functional outcome were, however, neutral.…”

mentioning

confidence: 91%

Physiological interventions in cardiac arrest: passing the pilot phase

2018

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confidence: 91%

Physiological interventions in cardiac arrest: passing the pilot phase

2018

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“…Secondly, Yu et al's study included vital signs, namely, HR, SBP and temperature, while ours included RR, HR, DBP, MBP, SBP and temperature. Nowadays MBP and DBP are widely accepted as important predictors for ICU patients (29)(30)(31). So, it may be insu cient to predict the mortality risk without MBP and DBP.…”

Section: Discussionmentioning

confidence: 99%

Learning to Predict in-hospital mortality risk in intensive care unit with attention-based Temporal convolution network

Chen

Yang

et al. 2020

Preprint

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Background: Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods: 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results: Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48h after ICU admission were 0.837(0.824 -0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (<50%). Conclusions: Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.

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“…Secondly, Yu et's study included vital signs HR, SBP and temperature, while ours included RR, HR, DBP, MBP, SBP and temperature. Nowadays MBP and DBP are widely accepted as important predictors for ICU patients (29)(30)(31). So, it may be insu cient to predict the mortality risk without MBP and DBP.…”

Section: Discussionmentioning

confidence: 99%

Dynamic predict in-hospital mortality risk in intensive care unit with a new deep learning of artificial intelligence

CHEN¹,

Y²,

Yang³

et al. 2020

Preprint

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Background Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods Finally, 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results The Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837(0.824–0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (< 50%). Conclusions Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.

show abstract

Mean arterial pressure of 65 mm Hg versus 85-100 mm Hg in comatose survivors after cardiac arrest: Rationale and study design of the Neuroprotect post–cardiac arrest trial

Cited by 30 publications

References 19 publications

Physiological interventions in cardiac arrest: passing the pilot phase

Physiological interventions in cardiac arrest: passing the pilot phase

Learning to Predict in-hospital mortality risk in intensive care unit with attention-based Temporal convolution network

Dynamic predict in-hospital mortality risk in intensive care unit with a new deep learning of artificial intelligence

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