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Infrared pupillometry to help predict neurological outcome for patients achieving return of spontaneous circulation following cardiac arrest: a systematic review protocol.

ABSTRACT: BACKGROUND:Despite advances in resuscitation care, mortality rates following cardiac arrest (CA) remain high. Between one-quarter (in-hospital CA) and two-thirds (out of hospital CA) of patients admitted comatose to intensive care die of neurological injury. Neuroprognostication determines an informed and timely withdrawal of life sustaining treatment (WLST), sparing the patient unnecessary suffering, alleviating family distress and allowing a more utilitarian use of resources. The latest Resuscitation Council UK (2015) guidance on post-resuscitation care provides the current multi-modal neuroprognostication strategy to predict neurological outcome. Its modalities include neurological examination, neurophysiological tests, biomarkers and radiology. Despite each of the current strategy's predictive modalities exhibiting limitations, meta-analyses show that three, namely PLR (pupillary light reflex), CR (corneal reflex) and N20 SSEP (somatosensory-evoked potential), accurately predict poor neurological outcome with low false positive rates. However, the quality of evidence is low, reducing confidence in the strategy's results. While infrared pupillometry (IRP) is not currently used as a prognostication modality, it can provide a quantitative and objective measure of pupillary size and PLR, giving a definitive view of the second and third cranial nerve activity, a predictor of neurological outcome. METHODS:The proposed study will test the hypothesis, "in those patients who remain comatose following return of spontaneous circulation (ROSC) after CA, IRP can be used early to help predict poor neurological outcome". A comprehensive review of the evidence using a PRISMA-P (2015) compliant methodology will be underpinned by systematic searching of electronic databases and the two authors selecting and screening eligible studies using the Cochrane data extraction and assessment template. Randomised controlled trials and retrospective and prospective studies will be included, and the quality and strength of evidence will be assessed using the Grading of Recommendation, Assessment and Evaluation (GRADE) approach. DISCUSSION:IRP requires rudimentary skill and is objective and repeatable. As a clinical prognostication modality, it may be utilised early, when the strategy's other modalities are not recommended. Corroboration in the evidence would promote early use of IRP and a reduction in ICU bed days. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42018118180.

SUBMITTER: Monk A

PROVIDER: S-EPMC6876106 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Infrared pupillometry to help predict neurological outcome for patients achieving return of spontaneous circulation following cardiac arrest: a systematic review protocol.

Monk Alex A Patil Shashank S

Systematic reviews 20191125 1

<h4>Background</h4>Despite advances in resuscitation care, mortality rates following cardiac arrest (CA) remain high. Between one-quarter (in-hospital CA) and two-thirds (out of hospital CA) of patients admitted comatose to intensive care die of neurological injury. Neuroprognostication determines an informed and timely withdrawal of life sustaining treatment (WLST), sparing the patient unnecessary suffering, alleviating family distress and allowing a more utilitarian use of resources. The lates ...[more]

PMID: 31767023

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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.

Project description:BACKGROUND:Continuous electroencephalography (cEEG), interpreted by an experienced neurologist, has been reported to be useful in predicting neurological outcome in adult patients post cardiac arrest. Amplitude-integrated electroencephalography (aEEG) is a type of quantitative EEG and is easily interpreted by a non-neurologist. A few studies have shown the effectiveness of aEEG in prognostication among adult patients post cardiac arrest. In this study, we hypothesized that the pattern of aEEG after return of spontaneous circulation (ROSC) could successfully categorize patients post cardiac arrest according to their expected neurological outcome. METHODS:We assessed the comatose survivors of out-of-hospital cardiac arrest who received targeted temperature management with midazolam-based sedation and were monitored with aEEG at our tertiary emergency care center from January 2013 to June 2017. We categorized the patients into categories 1 (C1) to 4 (C4). C1 included patients who regained continuous normal voltage (CNV) within 12 h post ROSC, C2 included those who recovered CNV 12-36 h post ROSC, C3 included those who did not recover CNV before 36 h post ROSC, and C4 included those who had burst suppression at any time post ROSC. We evaluated the outcomes of neurological function for each category at hospital discharge. A good outcome was defined as a cerebral performance category of 1 or 2. RESULTS:A total of 61 patients were assessed (median age, 60 years), among whom 42 (70%) had an initial shockable rhythm, and 52 (85%) had cardiac etiology. Of all 61 patients, 40 (66%) survived to hospital discharge and 27 (44%) had a good neurological outcome. Of 20 patients in C1, 19 (95%) had a good outcome, while the percentage dropped to 57% among C2 patients. No patients in C3 or C4 had a good outcome. Three patients could not be classified into any category. CONCLUSIONS:The pattern of aEEG during the early post-cardiac-arrest period can successfully categorize patients according to their neurological prognoses and could be used as a potential guide to customize post-cardiac-arrest care for each patient.

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Infrared pupillometry to help predict neurological outcome for patients achieving return of spontaneous circulation following cardiac arrest: a systematic review protocol.

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Infrared pupillometry to help predict neurological outcome for patients achieving return of spontaneous circulation following cardiac arrest: a systematic review protocol.

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