Advanced searches left 3/3

Cardiac Arrest Algorithm - Europe PMC

Summarized by Plex Scholar
Last Updated: 12 October 2021

* If you want to update the article please login/register

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm.

History Prognostication of neurological result in patients that remain comatose after heart attack resuscitation is complex. We hypothesised that cumulative details obtained throughout the first 3 days of extensive care can generate a trustworthy model for predicting neurological end result complying with out-of-hospital heart attack using artificial neural network with and without biomarkers. Approaches We did a message hoc evaluation of 932 patients from the Target Temperature Management test. Person end result was the dichotomised Cerebral Performance Category at six months; a good result was specified as CPC 1-2 whilst an inadequate end result was specified as CPC 3-5. The versions which consisted of NSE after 72 h and NFL on any one of the 3 days had a low threat of false-positive predictions while keeping a low number of false-negative forecasts.

Source link: https://europepmc.org/article/MED/33632280


Does a combination of ≥2 abnormal tests vs. the ERC-ESICM stepwise algorithm improve prediction of poor neurological outcome after cardiac arrest? A post-hoc analysis of the ProNeCA multicentre study.

History Bilaterally missing pupillary light reflexes or N20 waves of short-latency stimulated potentials are recommended by the 2015 ERC-ESICM standards as robust, first-line predictors of inadequate neurological end result after heart attack. We compared the prognostic precision of the ERC-ESICM prognostication method vs. that of a new strategy incorporating ≥ 2 uncommon arise from any one of PLR, SSEPs, EEG, CT and SM. Outcomes We evaluated 210 grown-up comatose resuscitated patients of whom 164 had inadequate neurological end result at six months. FPRs and level of sensitivities of the ≥ 2 abnormal examination strategy vs. the ERC-ESICM algorithm were 0 [0-8] % vs. 7 [1-18] % and 49 [41-57] % vs. 63 [56-71] %, respectively. Making use of different SSEP/EEG meanings increased the variety of patients with ≥ 2 concordant examination outcomes and the level of sensitivity of both methods, without any loss of specificity.

Source link: https://europepmc.org/article/MED/33338571

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions