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Background: As advanced life support (ALS) provided by emergency medical services (EMS) on scene becomes more common, the scene time interval (STI) for which EMS providers stay on scene tends to lengthen. We investigated the relationship between the STI and neurological outcome of patients at hospital discharge when ALS was provided by EMS on scene.Methods: We conducted a retrospective analysis of prospectively collected out-of-hospital cardiac arrest (OHCA) data between August 2015 and December 2018. A restricted cubic spline curve was used to investigate the relationship between the STI and neurologic outcome, and patients were divided into two groups based on the cut-off value obtained through receiver operating characteristic (ROC) analysis. Comparisons of outcomes between the two groups were performed before and after propensity score matching.Results: 4548 patients were included in the analysis. In ROC analysis, the optimal cut-off value for STI was 19 min. For the group with an STI <19 min, survival admission, survival discharge, and good neurologic outcome at hospital discharge were all higher than for the group with STI ≥19 min before and after propensity score matching. The multivariable model also showed that the STI ≥19 min was significantly associated with poor neurologic outcome at hospital discharge compared with the STI <19 min (adjusted odds ratio, 2.00; 95% CI, 1.40-2.88).Conclusions: A duration of on-scene ALS more than 19 min was associated with a poor neurologic outcome of patients at hospital discharge in OHCA. [ABSTRACT FROM AUTHOR]

Several studies have previously reported that a prolonged emergency department length of stay (EDLOS) is associated with poor outcomes in critically ill patients. This study was performed to investigate the relationship between the EDLOS and the neurologic outcome at 28 days in out-of-hospital cardiac arrest (OHCA) patients. We conducted a retrospective analysis of prospectively collected data from OHCA patients who achieved the return of spontaneous circulation (ROSC) in the EDs of three urban tertiary teaching hospitals from December 2013 to October 2020. Patients were divided into four groups according to the EDLOS, according to the quartile distribution: EDLOS <107 min, EDLOS 107–176 min, EDLOS 176–275 min, and EDLOS ≥275 min. Comparisons of outcomes among the groups and multivariable logistic regression analysis were performed. A total of 807 patients were included in the analysis. The proportions of patients with a good neurologic outcome at 28 days in the groups with EDLOS <107 min, EDLOS 107–176 min, EDLOS 176–275 min, and EDLOS ≥275 min were 37.0%, 29.8%, 26.9, and 20.4%, respectively (p < 0.001). In the multivariable analysis, the odds ratios for a poor neurologic outcome at 28 days in the groups with EDLOS 107–176 min, EDLOS 176–275 min, and EDLOS ≥275 min compared with the group with EDLOS <107 min were 1.19 (95% CI, 0.67–2.13), 1.73 (95% CI, 0.95–3.21), and 1.91 (95% CI, 1.03–3.57), respectively. An EDLOS longer than 275 min after the ROSC was independently associated with a poor neurologic outcome at 28 days. [ABSTRACT FROM AUTHOR]

Objectives: An index combining respiratory rate and oxygenation (ROX) has been introduced, and the ROX index is defined as the ratio of oxygen saturation by pulse oximetry/fraction of inspired oxygen to respiratory rate. In sepsis, hypoxemia and tachypnea are commonly observed. We performed this study to investigate the association between the ROX index and 28-day mortality in patients with sepsis or septic shock.Methods: This retrospective study included 2862 patients. The patients were divided into three groups according to the ROX index: Group I (ROX index >20), Group II (ROX index >10 and ≤ 20), and Group III (ROX index ≤10).Results: The median ROX index was significantly lower in the nonsurvivors than in the survivors (12.8 and 18.2, respectively) (p < 0.001). The 28-day mortality rates in Groups I, II and III were 14.5%, 21.3% and 34.4%, respectively (p < 0.001). In the multivariable Cox regression analysis, Group III had an approximately 40% higher risk of death than Group I during the 28-day period (hazard ratio = 1.41, 95% confidence interval 1.13-1.76). The area under the curve of the ROX index was significantly higher than that of the quick Sequential Organ Failure Assessment score (p < 0.001).Conclusions: The ROX index was lower in nonsurvivors than in survivors, and a ROX index less than or equal to 10 was an independent prognostic factor for 28-day mortality in patients with sepsis or septic shock. Therefore, the ROX index could be used as a prognostic marker in sepsis. [ABSTRACT FROM AUTHOR]

Background: Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardiac arrest in emergency departments.Methods: This is a single-center electronic health record (EHR)-based study. The primary outcome was the development of cardiac arrest within 24 h after prediction. Three ANN models were trained: multilayer perceptron (MLP), long-short-term memory (LSTM), and hybrid. These were compared to other classifiers including the modified early warning score (MEWS), logistic regression, and random forest. We used AUROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the comparison.Results: During the study period, there were a total of 374,605 ED visits and 2,910,321 patient status updates. The ANN models (MLP, LSTM, and hybrid) achieved higher AUROC (AUROC: 0.929, 0.933, and 0.936; 95% confidential interval: 0.926-0.932, 0.930-0.936, and 0.933-0.939, respectively) compared to the non-ANN models, and the hybrid model exhibited the best performance. The ANN classifiers displayed higher performance in most of the test characteristics when the threshold levels of the classifiers were fixed to display the same positive result as those at the three MEWS thresholds (score ≥ 3, ≥4, and ≥5), and when compared with each other.Conclusions: The ANN improves upon MEWS and conventional machine learning algorithms for the prediction of cardiac arrests in emergency departments. The hybrid ANN model utilizing both baseline and sequence information achieved the best performance. [ABSTRACT FROM AUTHOR]