Characteristics and outcomes of out-of-hospital cardiac arrest in a hilly area: Utstein Registry data from the Nagasaki Medical Region, Japan.

Aim: To analyze characteristics and investigate prognostic indicators of out-of-hospital cardiac arrest (OHCA) in a hilly area in Japan. Methods: A retrospective population-based study was conducted using the Utstein Registry for 4280 OHCA patients in the Nagasaki Medical Region (NMR) registered over the 10-year period from 2011 to 2020. The main outcome measure was a favorable cerebral performance category (CPC 1-2). Sites at which OHCA occurred were classified into "sloped places (SPs)" (not easily accessible by emergency medical services [EMS] personnel due to slopes) and "accessible places (APs)" (EMS personnel could park an ambulance close to the site). The characteristics and prognosis based on CPC were compared between SPs and APs, and multivariable analysis was performed. Results: No significant improvement in prognosis occurred in the NMR from 2011 to 2020. Prognosis in SPs was significantly worse than that in APs. However, multivariable analysis did not identify SP as a prognostic indicator. The following factors were associated with survival and CPC 1-2: age group, witness status, first documented rhythm, bystander-initiated cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use, use of mechanical CPR (m-CPR) device or esophageal obturator airway (EOA), and year. Both m-CPR and EOA use were associated with a poor prognosis. Conclusion: In a hilly area, OHCA patients in SPs had a worse prognosis than those in APs, but SPs was not significantly associated with prognosis by multivariable analysis. Interventions to increase bystander-initiated CPR and AED use could potentially improve outcomes of OHCA in the NMR.

Effectiveness of a doctor dispatch system activated by an advanced automatic collision notification from a single-vehicle accident: A case report.

A 79-year-old woman collided with a cliff in a passenger automobile. The fire department acknowledged an automated collision notification from the D-Call Net (DCN) at 1 min after the accident and called for doctors by helicopter ("Doctor-Heli" [DH] in Japan) 9 min after the injury. The DH reached the victim 28 min after the injury, and examinations revealed pain in the right side of her chest, tachypnea, and a weak radial artery pulse (indicating shock). The DH arrived at the hospital 49 min after the injury. A thoracic drainage was performed for right-sided tension pneumothorax. She recovered from the shock, but was diagnosed with flail chest and placed on a respirator. She was extubated on postoperative day 6 and transferred to a rehabilitation hospital on postoperative day 57. Due to the DCN, the patient received treatment 15 min earlier than the time taken by the conventional system. Emergency response task forces must develop strategies for connecting DCN warnings to a rapid medical response.

Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study.

BACKGROUND: Ventilator weaning protocols are commonly implemented for patients receiving mechanical ventilation. However, despite such protocols, the rate of extubation failure remains high. This study analyzed the usefulness and accuracy of machine learning in predicting extubation success. METHODS: We retrospectively evaluated data from patients who underwent intubation for respiratory failure and received mechanical ventilation in an intensive care unit (ICU). Information on 57 features, including patient demographics, vital signs, laboratory data, and ventilator data, were extracted. Extubation failure was defined as re-intubation within 72 hours of extubation. For supervised learning, data were labeled as intubation-required or not. We used three learning algorithms (Random Forest, XGBoost, and LightGBM) to predict successful extubation. We also analyzed important features and evaluated the area under curve (AUC) and prediction metrics. RESULTS: Overall, 13 of the 117 included patients required re-intubation. LightGBM had the highest AUC (0.950), followed by XGBoost (0.946) and Random Forest (0.930). The accuracy, precision, and recall performance were 0.897, 0.910, and 0.909 for Random Forest; 0.910, 0.912, and 0.931 for XGBoost; and 0.927, 0.915, and 0.960 for LightGBM, respectively. The most important feature was duration of mechanical ventilation, followed by fraction of inspired oxygen, positive end-expiratory pressure, maximum and mean airway pressures, and Glasgow Coma Scale. CONCLUSIONS: Machine learning predicted successful extubation of ICU patients on mechanical ventilation. LightGBM had the best overall performance. Duration of mechanical ventilation was the most important feature in all models.

Feasibility and Safety of Intravascular Temperature Management for Severe Heat Stroke: A Prospective Multicenter Pilot Study.

OBJECTIVES: Heat stroke is a life-threatening condition with high mortality and morbidity. Although several cooling methods have been reported, the feasibility and safety of treating heat stroke using intravascular temperature management are unclear. This study evaluated the efficacies of conventional treatment with or without intravascular temperature management for severe heat stroke. DESIGN: Prospective multicenter study. SETTING: Critical care and emergency medical centers at 10 tertiary hospitals. PATIENTS: Patients with severe heat stroke hospitalized during two summers. INTERVENTIONS: Conventional cooling with or without intravascular temperature management. MEASUREMENTS AND MAIN RESULTS: Cooling efficacy, Sequential Organ Failure Assessment score, occurrence rate of serious adverse events, and prognosis based on the modified Rankin Scale and Cerebral Performance Category. Patient outcomes were compared between five centers that were prospectively assigned to perform conventional cooling (control group: eight patients) and five centers that were assigned to perform conventional cooling plus intravascular temperature management (intravascular temperature management group: 13 patients), based on equipment availability. Despite their higher initial temperatures, all patients in the intravascular temperature management group reached the target temperature of 37°C within 24 hours, although only 50% of the patients in the control group reached 37°C (p < 0.01). The intravascular temperature management group also had a significant decrease in the Sequential Organ Failure Assessment score during the first 24 hours after admission (4.0 vs 1.5; p = 0.04). Furthermore, the intravascular temperature management group experienced fewer serious adverse events during their hospitalization, compared with the control group. The percentages of favorable outcomes at discharge and 30 days after admission were not statistically significant. CONCLUSIONS: The combination of intravascular temperature management and conventional cooling was safe and feasible for treating severe heat stroke. The results indicate that better temperature management may help prevent organ failure. A large randomized controlled trial is needed to validate our findings.