The effect of early coronary angiography (CAG) in out-of-hospital cardiac arrest (OHCA) patients without ST-elevation (STE) is still controversial. It is not known which subgroups of patients without STE are the most likely to benefit. The objective of this study was to evaluate the association between emergency CAG and neurologic outcomes and identify subgroups with improved outcomes when emergency CAG was performed. This prospective, multicenter, observational cohort study was based on data from the Korean Hypothermia Network prospective registry (KORHN-PRO) 1.0. Adult OHCA patients who were treated with targeted temperature management (TTM) without any obvious extracardiac cause were included. Patients were dichotomized into early CAG (≤24 h) and no early CAG (>24 h or not performed) groups. High-risk patients were defined as having the Global Registry of Acute Coronary Events (GRACE) score > 140, time from collapse to return of spontaneous circulation (ROSC) > 30 min, lactate level > 7.0 mmol/L, arterial pH < 7.2, cardiac enzyme elevation and ST deviation. The primary outcome was good neurologic outcome at 6 months after OHCA. Of the 1373 patients from the KORHN-PRO 1.0 database, 678 patients met the inclusion criteria. The early CAG group showed better neurologic outcomes at 6 months after cardiac arrest (CA) (adjusted odds ratio: 2.21 (1.27–3.87), p = 0.005). This was maintained even after propensity score matching (adjusted odds ratio: 2.23 (1.39–3.58), p < 0.001). In the subgroup analysis, high-risk patients showed a greater benefit from early CAG. In contrast, no significant association was found in low-risk patients. Early CAG was associated with good neurologic outcome at 6 months after CA and should be considered in high-risk patients.

Evolution toward brain death (BD) in out-of-hospital cardiac arrest patients with targeted temperature management (TTM) provides opportunities for organ donation. However, knowledge regarding BD in these patients is limited. We retrospectively analyzed the TTM registry of one hospital where life-sustaining therapy was not withdrawn. In-hospital death patients were categorized into BD and non-BD groups. We explored the process of evolution toward BD and its predictors by comparing the serial measurements of clinical variables and the results of various prognostic tests between the two groups. Of the 121 patients who died before hospital discharge, 19 patients (15.7%) developed BD at a median of 6 (interquartile range, 5.0–7.0) days after cardiac arrest. Four patients with pupillary light reflexes at 48 h eventually developed BD. The area under the curves of the gray-to-white matter ratio (GWR) on early brain computed tomography images and the level of S100 calcium-binding protein B (S100B) at 72 h were 0.67 (95% CI, 0.55–0.77) and 0.70 (95% CI, 0.55–0.83), respectively. In conclusion, approximately one-sixth of all in-hospital deaths were diagnosed with BD at a median of 6 days after cardiac arrest. The use of GWR and serial S100B measurements may help to screen potential BD.

This study aimed to develop a machine learning (ML)-based model for identifying patients who had a significant coronary artery disease among out-of-hospital cardiac arrest (OHCA) survivors without ST-segment elevation (STE). This multicenter observational study used data from the Korean Hypothermia Network prospective registry (KORHN-PRO) gathered between October 2015 and December 2018. We used information available before targeted temperature management (TTM) as predictor variables, and the primary outcome was a significant coronary artery lesion in coronary angiography (CAG). Among 1373 OHCA patients treated with TTM, 331 patients without STE who underwent CAG were enrolled. Among them, 127 patients (38.4%) had a significant coronary artery lesion. Four ML algorithms, namely regularized logistic regression (RLR), random forest classifier (RF), CatBoost classifier (CBC), and voting classifier (VC), were used with data collected before CAG. The VC model showed the highest accuracy for predicting significant lesions (area under the curve of 0.751). Eight variables (older age, male, initial shockable rhythm, shorter total collapse duration, higher glucose and creatinine, and lower pH and lactate) were significant to ML models. These results showed that ML models may be useful in developing early predictive tools for identifying high-risk patients with a significant stenosis in CAG.

Spectrin breakdown products 145 kDa (SBDP145) and neurofilament heavy chain (Nf-H) have been identified as potential biomarkers of neuronal injury. However, their ability to predict hypoxic-ischemic brain injury following cardiac arrest in humans is not well understood. This study aimed to investigate whether SBDP145 and Nf-H could be used as biomarkers to predict neurological outcomes after cardiac arrest. This prospective study was conducted at two academic hospitals and included adults who survived after cardiac arrest. Blood samples were collected at 0, 24, and 48 h after the return of spontaneous circulation, and biomarker analyses were performed to measure SBDP145 and Nf-H. Poor neurological outcome was defined as a modified Rankin Score of 4–6, and diagnostic performance was determined by receiver-operating characteristics analysis. A total of 56 patients were included in this study. There were no significant differences in levels of SBDP145 or Nf-H between the poor and good outcome groups at any time point. Areas under the receiver-operating characteristics curve of SBDP145 and Nf-H were small, ranging from 0.51 to 0.7. At 0, 24, and 48 h, SBDP145 showed very low sensitivity (18.61 %, 13.89 %, and 13.79 %, respectively) and accuracy (33.93 %, 36.74 %, and 39.02 %, respectively) at a cut-off value for 100 % specificity. Nf-H also showed very low sensitivity (9.30 %, 16.67 %, and 0 %, respectively) and accuracy (29.09 %, 36.74 %, and 30.95 %, respectively). SBDP145 and Nf-H were found to be poor predictors of poor neurological outcomes six months after cardiac arrest.