The European Resuscitation Council (ERC) establishes guidelines for cardiopulmonary resuscitation (CPR) under standard conditions and special circumstances but without specific instructions for nighttime situations with reduced visibility. The aim of this study was to evaluate the feasibility of performing CPR at night under two different conditions, in darkness with ambient light and with the additional illumination of a headlamp, as well as to determine the quality of the maneuver.

A crossover, randomized pilot study involving nineteen lifeguards was conducted, with each participant performing two five-minute CPR tests: complete darkness with headlamp and natural night environment at the beach without additional lighting. Both tests were conducted with a 30:2 ratio of chest compression (CC) to ventilations using mouth-to-pocket mask technique in the darkness of the night with a 30-minute break between them. Outcome measures included quality of CPR, number of CCs, mean depth of CCs, mean rate of CCs, and number of effective ventilations. Results were reported as the mean or median difference (MD) between the two groups with 95% confidence interval (CI) using techniques for paired data.

There were no statistically significant differences between the two lighting conditions for the outcomes of CPR quality, mean depth of CCs, or number of effective ventilations. The number of CCs was lower when performed without the headlamp (MD: -8; 95%CI, -15 to 0). In addition, the mean rate of CCs was lower when performed without the headlamp (MD: -3; 95%CI, -5 to -1).

The rescuers performed CPR at night with good quality, both in darkness and with the illumination of a headlamp. The use of additional lighting with a headlamp does not appear to be essential for conducting resuscitation.

Vital signs are an essential component of the prehospital assessment of patients encountered in an emergency response system and during mass-casualty disaster events. Limited data exist to define meaningful vital sign ranges to predict need for advanced care.

The aim of this study was to identify vital sign ranges that were maximally predictive of requiring a life-saving intervention (LSI) among adults cared for by Emergency Medical Services (EMS).

A retrospective study of adult prehospital encounters that resulted in hospital transport by an Advanced Life Support (ALS) provider in the 2022 National EMS Information System (NEMSIS) dataset was performed. The outcome was performance of an LSI, a composite measure incorporating critical airway, medication, and procedural interventions, categorized into eleven groups: tachydysrhythmia, cardiac arrest, airway, seizure/sedation, toxicologic, bradycardia, airway foreign body removal, vasoactive medication, hemorrhage control, needle decompression, and hypoglycemia. Cut point selection was performed in a training partition (75%) to identify ranges for heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), oxygen saturation, and Glasgow Coma Scale (GCS) by using an approach intended to prioritize specificity, keeping sensitivity constrained to at least 25%.

Of 18,259,766 included encounters (median age 63 years; 51.8% male), 6.3% had at least one LSI, with the most common being airway interventions (2.2%). Optimal ranges for vital signs included 47-129 beats/minute for HR, 8-30 breaths/minute for RR, 96-180mmHg for SBP, >93% for oxygen saturation, and >13 for GCS. In the test partition, an abnormal vital sign had a sensitivity of 75.1%, specificity of 66.6%, and positive predictive value (PPV) of 12.5%. A multivariable model encompassing all vital signs demonstrated an area under the receiver operator characteristic curve (AUROC) of 0.78 (95% confidence interval [CI], 0.78-0.78). Vital signs were of greater accuracy (AUROC) in identifying encounters needing airway management (0.85), needle decompression (0.84), and tachydysrhythmia (0.84) and were lower for hemorrhage control (0.52), hypoglycemia management (0.68), and foreign body removal (0.69).

Optimal ranges for adult vital signs in the prehospital setting were statistically derived. These may be useful in prehospital protocols and medical alert systems or may be incorporated within prediction models to identify those with critical illness and/or injury for patients with out-of-hospital emergencies.