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

Abstract

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.

Keywords: Emergency medical service; Lactate; Patient assessment; Point-of-care; Prehospital.

Fig. 1

A: distribution, median (Q2) and quartiles (Q1, Q3) of point-of-care lactate in non-time-sensitive and time-sensitive conditions. B: probability of a time-sensitive condition with 95% confidence intervals for continuous point-of-care lactate mmol/L

Fig. 2

Back-to-back histogram compares the distribution (n = 4,546 on each side) of predicted probabilities from two models: the base model (left) and the base model with POC lactate (right). If POC lactate adds important information the histogram widens. If the histograms to the left and and to the right have similar distributions (i.e., similar shapes and variances), it suggests that POC lactate might not have substantially changed the predicted probabilities. It implies that the other variables in the base model may already capture most of the variability in the outcome

Fig. 3

Receiver Operating Characteristic (ROC) curve illustrating the performance of the five models in classifying individuals with and without a time-sensitive condition. The curve shows the trade-off between the true positive rate (sensitivity) and the false positive rate (1—specificity) across different classification thresholds