Added predictive value of prehospital measurement of point-of-care lactate in an adult general EMS population in Sweden: a multi-centre observational study.

BACKGROUND: Emergency medical services (EMS) personnel must rapidly assess and transport patients with time-sensitive conditions to optimise patient outcomes. Serum lactate, a valuable in-hospital biomarker, has become more accessible in EMS settings through point-of-care (POC) testing. Although POC lactate levels are valuable in specific patient groups, its broader application in EMS remains unclear. This study assessed the additional predictive value of POC lactate levels in a general adult EMS population. METHODS: This prospective observational study (March 2018 to September 2019) involved two EMS organisations in Västra Götaland, Sweden. Patients were triaged using the Rapid Triage and Treatment System (RETTS). POC lactate levels were measured using StatStrip Xpress devices. Non-consecutive patients who received EMS and were aged 18 years and above were available for inclusion if triaged into RETTS levels: red, orange, yellow, or green if respiratory rate of ≥ 22 breaths/min. Outcomes were adverse outcomes, including a time-sensitive diagnosis, sequential organ failure assessment (SOFA) score ≥ 2, and 30-day mortality. Statistical analyses included descriptive statistics, imputation, and regression models to assess the impact of the addition of POC lactate levels to a base model (comprising patient age, sex, presence of past medical conditions, vital signs, pain, EMS response time, assessed triage condition, and triage level) and a RETTS triage model. RESULTS: Of 4,546 patients (median age 75 [57, 84] years; 49% male), 32.4% had time-sensitive conditions, 12.5% met the SOFA criteria, and 7.4% experienced 30-day mortality. The median POC lactate level was 1.7 (1.2, 2.5) mmol/L. Patients with time-sensitive conditions had higher lactate levels (1.9 mmol/L) than those with non-time-sensitive conditions (1.6 mmol/L). The probability of a time-sensitive condition increased with increasing lactate level. The addition of POC lactate marginally enhanced the predictive models, with a 1.5% and 4% increase for the base and RETTS triage models, respectively. POC lactate level as a sole predictor showed chance-only level predictive performance. CONCLUSIONS: Prehospital POC lactate assessment provided limited additional predictive value in a general adult EMS population. However, it may be beneficial in specific patient subgroups, emphasizing the need for its judicious use in prehospital settings.

Prehospital lactate levels in blood as a seizure biomarker: A multi-center observational study.

OBJECTIVE: The objective of this study was to assess the value of prehospital measurement of lactate level in blood for diagnosis of seizures in cases of transient loss of consciousness. METHODS: Between March 2018 and September 2019, prehospital lactate was measured with a point-of-care device by the emergency medical services in an area serving a population of 900 000. A total of 383 cases of transient loss of consciousness were identified and categorized as tonic-clonic seizure (TCS), other seizure, syncope, or other cause, according to the final diagnosis in the electronic medical records system. Receiver operating characteristic curve analyses were used to identify the optimal lactate cut-off. RESULTS: A total of 383 cases were included (135 TCS, 42 other seizure, 163 syncope, and 43 other causes). The median lactate level in TCS was 7.0 mmol/L, compared to a median of 2.0 mmol/L in all other cases (P < .001). The area under the curve (AUC) of TCS vs nonepileptic causes was 0.87 (95% confidence interval [CI] 0.83-0.91). The optimal cut-off (Youden index, 67.8%) was 4.75 mmol/L, with 79% sensitivity (95% CI 71-85) and 89% specificity (95% CI 85-93) for TCS. SIGNIFICANCE: Prehospital lactate can be a valuable tool for identifying seizures in transient loss of consciousness. For acceptable specificity, a higher cut-off than that previously demonstrated for hospital-based measurements must be used when values obtained close to the time of the event are interpreted.