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

BACKGROUND: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers. METHODS: We performed a post hoc analysis of 932 patients from the Target Temperature Management trial. We focused on comatose patients at 24, 48, and 72 h post-cardiac arrest and excluded patients who were awake or deceased at these time points. 80% of the patients were allocated for model development (training set) and 20% for internal validation (test set). To investigate the prognostic potential of different levels of biomarkers (clinically available and research-grade), patients' background information, and intensive care observation and treatment, we created three models for each time point: (1) clinical variables, (2) adding clinically accessible biomarkers, e.g., neuron-specific enolase (NSE) and (3) adding research-grade biomarkers, e.g., neurofilament light (NFL). Patient outcome was the dichotomised Cerebral Performance Category (CPC) at six months; a good outcome was defined as CPC 1-2 whilst a poor outcome was defined as CPC 3-5. The area under the receiver operating characteristic curve (AUROC) was calculated for all test sets. RESULTS: AUROC remained below 90% when using only clinical variables throughout the first three days in the ICU. Adding clinically accessible biomarkers such as NSE, AUROC increased from 82 to 94% (p < 0.01). The prognostic accuracy remained excellent from day 1 to day 3 with an AUROC at approximately 95% when adding research-grade biomarkers. The models which included NSE after 72 h and NFL on any of the three days had a low risk of false-positive predictions while retaining a low number of false-negative predictions. CONCLUSIONS: In this exploratory study, ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance

Alzheimer Disease Blood Biomarkers in Patients With Out-of-Hospital Cardiac Arrest

Importance: Blood phosphorylated tau (p-tau) and amyloid-β peptides (Aβ) are promising peripheral biomarkers of Alzheimer disease (AD) pathology. However, their potential alterations due to alternative mechanisms, such as hypoxia in patients resuscitated from cardiac arrest, are not known. Objective: To evaluate whether the levels and trajectories of blood p-tau, Aβ42, and Aβ40 following cardiac arrest, in comparison with neural injury markers neurofilament light (NfL) and total tau (t-tau), can be used for neurological prognostication following cardiac arrest. Design, Setting, and Participants: This prospective clinical biobank study used data from the randomized Target Temperature Management After Out-of-Hospital Cardiac Arrest (TTM) trial. Unconscious patients with cardiac arrest of presumed cardiac origin were included between November 11, 2010, and January 10, 2013, from 29 international sites. Serum analysis for serum NfL and t-tau were performed between August 1 and August 23, 2017. Serum p-tau, Aβ42, and Aβ40 were analyzed between July 1 and July 15, 2021, and between May 13 and May 25, 2022. A total of 717 participants from the TTM cohort were examined: an initial discovery subset (n = 80) and a validation subset. Both subsets were evenly distributed for good and poor neurological outcome after cardiac arrest. Exposures: Serum p-tau, Aβ42, and Aβ40 concentrations using single molecule array technology. Serum levels of NfL and t-tau were included as comparators. Main Outcomes and Measures: Blood biomarker levels at 24 hours, 48 hours, and 72 hours after cardiac arrest. Poor neurologic outcome at 6-month follow-up, defined according to the cerebral performance category scale as category 3 (severe cerebral disability), 4 (coma), or 5 (brain death). Results: This study included 717 participants (137 [19.1%] female and 580 male [80.9%]; mean [SD] age, 63.9 [13.5] years) who experienced out-of-hospital cardiac arrest. Significantly elevated serum p-tau levels were observed at 24 hours, 48 hours, and 72 hours in cardiac arrest patients with poor neurological outcome. The magnitude and prognostication of the change was greater at 24 hours (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI, 0.95-0.97), which was similar to NfL (AUC, 0.94; 95% CI, 0.92-0.96). However, at later time points, p-tau levels decreased and were weakly associated with neurological outcome. In contrast, NfL and t-tau maintained high diagnostic accuracies, even 72 hours after cardiac arrest. Serum Aβ42 and Aβ40 concentrations increased over time in most patients but were only weakly associated with neurological outcome. Conclusions and Relevance: In this case-control study, blood biomarkers indicative of AD pathology demonstrated different dynamics of change after cardiac arrest. The increase of p-tau at 24 hours after cardiac arrest suggests a rapid secretion from the interstitial fluid following hypoxic-ischemic brain injury rather than ongoing neuronal injury like NfL or t-tau. In contrast, delayed increases of Aβ peptides after cardiac arrest indicate activation of amyloidogenic processing in response to ischemia

Standardized EEG interpretation accurately predicts prognosis after cardiac arrest.

OBJECTIVE: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. METHODS: In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3-5 until 180 days. RESULTS: Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p &lt; 0.001). Specificity and sensitivity were not significantly affected by targeted temperature or sedation. A benign EEG was found in 1% of the patients with a poor outcome. CONCLUSIONS: Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome

Quantitative versus standard pupillary light reflex for early prognostication in comatose cardiac arrest patients: an international prospective multicenter double-blinded study.

To assess the ability of quantitative pupillometry [using the Neurological Pupil index (NPi)] to predict an unfavorable neurological outcome after cardiac arrest (CA). We performed a prospective international multicenter study (10 centers) in adult comatose CA patients. Quantitative NPi and standard manual pupillary light reflex (sPLR)-blinded to clinicians and outcome assessors-were recorded in parallel from day 1 to 3 after CA. Primary study endpoint was to compare the value of NPi versus sPLR to predict 3-month Cerebral Performance Category (CPC), dichotomized as favorable (CPC 1-2: full recovery or moderate disability) versus unfavorable outcome (CPC 3-5: severe disability, vegetative state, or death). At any time between day 1 and 3, an NPi ≤ 2 (n = 456 patients) had a 51% (95% CI 49-53) negative predictive value and a 100% positive predictive value [PPV; 0% (0-2) false-positive rate], with a 100% (98-100) specificity and 32% (27-38) sensitivity for the prediction of unfavorable outcome. Compared with NPi, sPLR had significantly lower PPV and significantly lower specificity (p  &lt; 0.001 at day 1 and 2; p  = 0.06 at day 3). The combination of NPi ≤ 2 with bilaterally absent somatosensory evoked potentials (SSEP; n = 188 patients) provided higher sensitivity [58% (49-67) vs. 48% (39-57) for SSEP alone], with comparable specificity [100% (94-100)]. Quantitative NPi had excellent ability to predict an unfavorable outcome from day 1 after CA, with no false positives, and significantly higher specificity than standard manual pupillary examination. The addition of NPi to SSEP increased sensitivity of outcome prediction, while maintaining 100% specificity

Single versus Serial Measurements of Neuron-Specific Enolase and Prediction of Poor Neurological Outcome in Persistently Unconscious Patients after Out-Of-Hospital Cardiac Arrest - A TTM-Trial Substudy

Background: Prediction of neurological outcome is a crucial part of post cardiac arrest care and prediction in patients remaining unconscious and/or sedated after rewarming from targeted temperature management (TTM) remains difficult. Current guidelines suggest the use of serial measurements of the biomarker neuron-specific enolase (NSE) in combination with other predictors of outcome in patients admitted after out-of-hospital cardiac arrest (OHCA). This study sought to investigate the ability of NSE to predict poor outcome in patients remaining unconscious at day three after OHCA. In addition, this study sought to investigate if serial NSE measurements add incremental prognostic information compared to a single NSE measurement at 48 hours in this population. Methods: This study is a post-hoc sub-study of the TTM trial, randomizing OHCA patients to a course of TTM at either 33°C or 36°C. Patients were included from sites participating in the TTMPLOS trial biobank sub study. NSE was measured at 24, 48 and 72 hours after ROSC and followup was concluded after 180 days. The primary end point was poor neurological function or death defined by a cerebral performance category score (CPC-score) of 3 to 5. Results: A total of 685 (73%) patients participated in the study. At day three after OHCA 63 (9%) patients had died and 473 (69%) patients were not awake. In these patients, a single NSE measurement at 48 hours predicted poor outcome with an area under the receiver operating characteristics curve (AUC) of 0.83. A combination of all three NSE measurements yielded the highest discovered AUC (0.88, p = .0002). Easily applicable combinations of serial NSE measurements did not significantly improve prediction over a single measurement at 48 hours (AUC 0.58-0.84 versus 0.83). Conclusion: NSE is a strong predictor of poor outcome after OHCA in persistently unconscious patients undergoing TTM, and NSE is a promising surrogate marker of outcome in clinical trials. While combinations of serial NSE measurements may provide an increase in overall prognostic information, it is unclear whether actual clinical prognostication with low false-positive rates is improved by application of serial measurements in persistently unconscious patients. The findings of this study should be confirmed in another prospective cohort