Dataset Information

Early lactate and glucose kinetics following return to spontaneous circulation after out-of-hospital cardiac arrest.

ABSTRACT:

Objective

Lactate has been shown to be preferentially metabolized in comparison to glucose after physiological stress, such as strenuous exercise. Derangements of lactate and glucose are common after out-of-hospital cardiac arrest (OHCA). Therefore, we hypothesized that lactate decreases faster than glucose after return-to-spontaneous-circulation (ROSC) after OHCA.

Results

We included 155 OHCA patients in our analysis. Within the first 8 h of presentation to the emergency department, 843 lactates and 1019 glucoses were available, respectively. Lactate decreased to 50% of its initial value within 1.5 h (95% CI [0.2-3.6 h]), while glucose halved within 5.6 h (95% CI [5.4-5.7 h]). Also, in the first 8 h after presentation lactate decreases more than glucose in relation to their initial values (lactate 72.6% vs glucose 52.1%). In patients with marked hyperlactatemia after OHCA, lactate decreased expediently while glucose recovered more slowly, whereas arterial pH recovered at a similar rapid rate as lactate. Hospital non-survivors (N = 82) had a slower recovery of lactate (P = 0.002) than survivors (N = 82). The preferential clearance of lactate underscores its role as a prime energy substrate, when available, during recovery from extreme stress.

SUBMITTER: Freire Jorge P

PROVIDER: S-EPMC8120923 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This study aimed to identify neurological and pathophysiological factors that predicted return of spontaneous circulation (ROSC) among patients with out-of-hospital cardiac arrest (OHCA). This prospective 1-year observational study evaluated patients with cardiogenic OHCA who were admitted to a tertiary medical center, Nippon Medical School Hospital. Physiological and neurological examinations were performed at admission for quantitative infrared pupillometry (measured with NPi-200, NeurOptics, CA, USA), arterial blood gas, and blood chemistry. Simultaneous blood samples were also collected to determine levels of neuron-specific enolase (NSE), S-100b, phosphorylated neurofilament heavy subunit, and interleukin-6. In-hospital standard advanced cardiac life support was performed for 30?minutes.The ROSC (n?=?26) and non-ROSC (n?=?26) groups were compared, which a revealed significantly higher pupillary light reflex ratio, which was defined as the percent change between maximum pupil diameter before light stimuli and minimum pupil diameter after light stimuli, in the ROSC group (median: 1.3% [interquartile range (IQR): 0.0-2.0%] vs. non-ROSC: (median: 0%), (Cut-off: 0.63%). Furthermore, NSE provided the great sensitivity and specificity for predicting ROSC, with an area under the receiver operating characteristic curve of 0.86, which was created by plotting sensitivity and 1-specificity. Multivariable logistic regression analyses revealed that the independent predictors of ROSC were maximum pupillary diameter (odds ratio: 0.25, 95% confidence interval: 0.07-0.94, P?=?0.04) and NSE at admission (odds ratio: 0.96, 95% confidence interval: 0.93-0.99, P?=?0.04). Pupillary diameter was also significantly correlated with NSE concentrations (r?=?0.31, P?=?0.027). Conclusively, the strongest predictors of ROSC among patients with OHCA were accurate pupillary diameter and a neuronal biomarker, NSE. Quantitative pupillometry may help guide the decision to terminate resuscitation in emergency departments using a neuropathological rationale. Further large-scale studies are needed.

Project description:BackgroundWe performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods.MethodsWe performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome.ResultsA total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352-0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612-0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860-1.0000); sensitivity, 0.9594 (95% CI, 0.9245-0.9943); specificity, 0.9714 (95% CI, 0.9162-1.0000); PPV, 0.9916 (95% CI, 0.9752-1.0000); NPV, 0.8718 (95% CI, 0.7669-0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825-0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845-0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087-0.9867); sensitivity, 0.9595 (95% CI, 0.9145-1.0000); specificity, 0.6500 (95% CI, 0.5022-0.7978); PPV, 0.8353 (95% CI, 0.7564-0.9142); NPV, 0.8966 (95% CI, 0.7857-1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets.ConclusionWe established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Dataset Information

Early lactate and glucose kinetics following return to spontaneous circulation after out-of-hospital cardiac arrest.

Objective

Results

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