Design and setting
This was an observational study using data from the Japanese Association for Acute Medicine out-of-hospital cardiac arrest (JAAM-OHCA) registry, a nationwide hospital-based prospective observational data registry established in 2014 by the Registry Organizing Committee to improve treatment strategies, emergency care systems, and patient outcomes, between June 2014 and December 2017. Currently, 87 institutions are included in the registry; 66 of which are university hospitals and critical care centers. These critical care centers are accredited by the Ministry of Health, Labor, and Welfare and are equipped to provide highly specialized treatments, such as extracorporeal cardiopulmonary resuscitation, percutaneous coronary intervention, and TTM 24 h a day. The remaining 21 hospitals were not accredited as critical care centers but provided local emergency medical services. From June 2014 to December 2017, a total of 34,754 OHCA patients were enrolled in the JAAM-OHCA registry. This registry collected both pre- and post-hospitalization data, integrated by the JAAM-OHCA registry committee. Pre-hospitalization data were obtained from the All-Japan Utstein Registry of the Fire and Disaster Management Agency. In-hospital data were collected by physicians or medical staff at each institution using an Internet-based system. The design and data collection methods for the JAAM-OHCA registry have been described previously10. The registry was approved by the Ethics Committee of Kyoto University Graduate School of Medicine (R-1045) and by each participating hospital. The use of data from the registry and retrospective analysis of anonymized data were approved by the Ethics Committee of Sapporo Medical University (312-3032).
We included patients with PCAS admitted to the hospitals after an OHCA caused by hanging who were registered in the JAAM-OHCA registry between June 2014 and December 2017. Patients with missing pre- or in-hospital data were excluded.
We collected and described the following clinical data from the JAAM-OHCA registry: sex, age, bystander performed cardiopulmonary resuscitation (CPR), an initial cardiac rhythm at the scene, prehospital intravenous line, prehospital epinephrine administration, prehospital airway management, prehospital return of spontaneous circulation (ROSC), time from call to ROSC, cardiac rhythm on arrival, Glasgow coma scale (GCS) on arrival, body temperature on arrival, epinephrine administration after arrival, and initial blood gas analysis after arrival.
The outcome was favorable neurological status at 1-month after OHCA. The neurological outcome was assessed based on the Glasgow–Pittsburgh cerebral performance category (CPC) scale. The CPC scale is a five-category scale with the following categories: 1, good cerebral performance; 2, moderate cerebral disability; 3, severe cerebral disability; 4, coma or vegetative state; and 5, death or brain death. A favorable neurologic status was defined as a CPC scale of 1 or 2.
Continuous variables were compared using the Mann–Whitney U test, and categorical variables were compared using the chi-squared test or Fischer’s exact test. Propensity score analysis was performed to compare patients with PCAS by hanging who received TTM (TTM group) and those who did not (non-TTM group) using. Logistic regression analysis was performed to estimate propensity score and to predict whether patients with PCAS by hanging to receive TTM. Variables included in the model were age, sex, prehospital variables (bystander performed CPR, an initial cardiac rhythm at the scene, prehospital epinephrine administration, prehospital ROSC, and time from call to ROSC), in-hospital variables (cardiac rhythm on arrival, GCS on arrival, body temperature on arrival, and epinephrine administration after arrival), and arterial blood gas analysis (pH, partial pressure of carbon dioxide, base excess, and lactate level). In the unadjusted cohort, the individual propensity score was incorporated into the model as covariates, and propensity score-adjusted odds ratio (OR) was calculated using logistic regression analysis. Each patient in the TTM group patient was matched to a TTM group using nearest-neighbor matching without replacement. A caliper width equal to 0.25 of the standard deviation of the logit of the propensity score was used. The standardized mean difference was used to evaluate the covariate balance and a standardized difference of <0.1 represents a significant balance. Inverse probability of treatment weighting (IPTW) analysis was performed using propensity score-based weights, with trimming of the non-overlap regions to ensure that patients had a non-zero probability of receiving either treatment. Patients who received TTM were weighted using the inverse of the propensity score, whereas those who did not were weighted using the inverse of 1 minus the propensity score. Weights were stabilized to reduce the influence of extreme weights. ORs for the outcomes were estimated in the propensity score-matched and IPTW-matched cohorts. A two-sided P-value <0.05 was considered statistically significant for all tests. All analyses were performed using IBM SPSS software (version 24.0; IBM Corp., Armonk, NY, USA) and EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan).