Abstract
Background
Prehospital lactate measurement is increasingly used for early risk stratification in emergency medical services. However, whether baseline metabolic derangement modifies the association between prehospital transfer time and mortality across different emergency conditions remains unclear. We evaluated the interaction between prehospital lactate levels and transfer time in relation to early mortality.
Methods
We conducted a prospective multicenter cohort study including 10,111 consecutive adults attended by advanced life support emergency medical services in Spain between February 2019 and January 2026. All patients underwent point-of-care blood gas analysis with lactate determination during the initial prehospital assessment. The primary outcome was all-cause 48-hour mortality. Multivariable logistic regression models adjusted for demographic characteristics, comorbidity burden, physiological variables, Glasgow Coma Scale score, and diagnostic category were used to assess associations between lactate, prehospital transfer time, and mortality. Lactate was modeled using restricted cubic splines, and interaction terms between lactate and transfer time were evaluated.
Results
The median age was 68 years (interquartile range [IQR], 52–80), 61.5% of participants were male, and 48-hour mortality was 9.5%. Median prehospital lactate was 2.65 mmol/L (IQR, 1.78–4.19). Lactate concentrations and prehospital transfer times differed significantly across diagnostic categories (both p < .001). In adjusted analyses, higher lactate levels and longer prehospital transfer times were independently associated with increased mortality. A significant interaction between lactate and time was observed (p < .001), indicating that the prognostic impact of prehospital delay varied according to baseline metabolic status. Trauma and endocrine-metabolic emergencies showed the strongest lactate-dependent time sensitivity, with progressively higher mortality associated with longer prehospital transfer times at elevated lactate levels. In contrast, infectious diseases demonstrated a strong monotonic association between lactate and mortality without significant interaction with prehospital transfer time. The interaction was predominantly observed among patients with intermediate neurological impairment.
Conclusions
The association between prehospital transfer time and early mortality varied substantially across emergency conditions and depended on baseline metabolic status as reflected by prehospital lactate levels. Integration of point-of-care lactate assessment into early prehospital evaluation may help identify patients in whom the association between prehospital transfer time and mortality is strongest and may support future individualized transport prioritization strategies.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13054-026-06175-7.
Key Points
Question: Does prehospital lactate modify the association between prehospital transfer time and early mortality across emergency diagnostic categories?
Findings: In this multicenter cohort study including 10,111 patients attended by emergency medical services, the association between prehospital transfer time and 48-hour mortality varied according to baseline lactate levels and diagnostic category. Significant lactate-dependent time sensitivity was observed particularly in trauma and endocrine-metabolic emergencies.
Meaning: These findings suggest that integration of prehospital lactate assessment with early clinical evaluation may improve risk stratification and support more individualized prehospital prioritization strategies.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13054-026-06175-7.
Introduction
Prehospital care represents a critical phase in the management of patients with potentially life-threatening acute illnesses [1]. Across multiple clinical scenarios, early identification of patients at high risk of deterioration is essential to optimize prehospital decision-making, including transport prioritization, activation of advanced resources, and selection of the destination hospital [2]. However, accurately identifying patients who are particularly vulnerable to delays in care remains a major challenge in emergency medicine [3].
Traditionally, decision-making in the prehospital setting has relied primarily on vital signs and clinical severity scales. Nevertheless, these tools may underestimate early physiologic derangements, particularly in patients with compensated hypoperfusion or early metabolic stress. In this context, serum lactate has emerged as a sensitive biomarker of microcirculatory dysfunction and systemic metabolic impairment and has been consistently associated with illness severity and mortality in sepsis, trauma, shock, and other acute conditions [4, 5].
The implementation of point-of-care devices has enabled lactate measurement to be incorporated into prehospital emergency care, allowing rapid metabolic assessment even before hospital arrival. Several studies have demonstrated that prehospital lactate measurement improves early risk stratification and may complement conventional clinical assessment [6–9]. In addition, studies performed in emergency department settings have suggested that early lactate assessment may help guide clinical decision-making and prioritization strategies in critically ill patients [10, 11]. However, these approaches have not yet been adequately translated to the prehospital environment, where decisions regarding hospital destination, transport prioritization, and activation of advanced resources must be made with limited information and under substantial time constraints. Consequently, the operational implications of prehospital lactate measurement for emergency decision-making remain poorly understood.
In particular, it remains uncertain whether baseline lactate levels identify patients in whom prehospital transfer time is more strongly associated with short-term mortality, and whether this relationship differs according to the underlying diagnostic category. This question is especially relevant from an organizational perspective, as the benefit of minimizing prehospital transfer time may not be uniform across disease categories or among patients with different degrees of metabolic derangement. Identifying subgroups in whom delays in prehospital care have a particularly relevant prognostic impact could contribute to the development of more individualized strategies for triage and hospital destination planning.
Therefore, the aim of this study was to evaluate the association between prehospital transfer time, baseline lactate levels, and 48-hour mortality in a multicenter cohort of patients attended by emergency medical services. We also explored whether prehospital lactate modifies the prognostic impact of prehospital transfer time and whether this interaction differs across diagnostic categories.
Methods
Study design and setting
A prospective multicenter cohort study was conducted including consecutive adult patients attended by prehospital emergency medical services (EMS) and transported with high priority to hospital emergency departments. This study was designed according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for observational studies [12].
The study was carried out in the Castilla y León region (Spain), including the provinces of Burgos, Salamanca, Segovia, and Valladolid, with participation of both advanced life support (ALS) and basic life support (BLS) units integrated within the regional EMS system. ALS teams were composed of a physician, an emergency-registered nurse, and emergency medical technicians, operating continuously under standardized regional advanced life support protocols. As part of the standardized initial assessment, point-of-care blood gas analysis including lactate determination was routinely performed after vascular access was obtained and before therapeutic interventions whenever feasible.
Participants
Adult patients (≥ 18 years) attended by ALS teams between February 2019 and January 2026 and subsequently transferred to hospital emergency departments were eligible for inclusion. Patients were included consecutively regardless of the underlying acute pathology if prehospital blood gas analysis including lactate determination was performed during the initial assessment. Lactate measurements were obtained during the first patient contact as part of routine prehospital evaluation. Although the exact sampling time was not recorded, according to the operational protocol blood sampling was generally performed within the first minutes following EMS arrival and usually within 10 min of patient assessment. Patients with cardiac arrest without return of spontaneous circulation at scene, those receiving palliative care, pregnant women, cases with major safety concerns for EMS personnel, and subjects with incomplete information regarding mortality, diagnosis, prehospital lactate levels, or prehospital transfer time were excluded.
Diagnostic categories
Patients were classified into predefined diagnostic categories according to the main clinical diagnosis established during emergency care: infectious diseases, endocrine diseases, central nervous system diseases, cardiovascular diseases, respiratory diseases, digestive diseases, trauma, intoxications, and other conditions. The “other” category was created because of the low number of patients in certain predefined categories, including ophthalmologic, otorhinolaryngologic, anaphylactic, psychiatric, hematologic, and urologic emergencies.
Variables and outcome definitions
The primary outcome was all-cause mortality within 48 h after prehospital assessment.
Prehospital transfer time was defined as the interval between the initial EMS patient assessment (arrival on scene) and hospital arrival at the receiving emergency department.
In addition, the interval between EMS activation and EMS arrival at the scene (activation-to-arrival time) was calculated to characterize the prehospital response interval preceding patient assessment. Exploratory analyses were performed to evaluate differences across diagnostic categories and their association with 48-hour mortality.
Baseline demographic characteristics, comorbidities, physiological variables, prehospital interventions, and blood gas parameters were recorded during the initial prehospital evaluation. Comorbidity burden was assessed using the age-adjusted Charlson Comorbidity Index (ACCI). Prehospital lactate and blood gas parameters were measured using the Epoc® Blood Analysis System (Siemens Healthcare GmbH, Erlangen, Germany). Additional collected variables included systolic blood pressure, respiratory rate, heart rate, oxygen saturation, Glasgow Coma Scale (GCS), vasoactive support, and need for mechanical ventilation.
Statistical analysis
Continuous variables are presented as median and interquartile range (IQR; 25th–75th percentile), whereas categorical variables are expressed as frequencies and percentages. Comparisons between diagnostic groups were performed using the Kruskal–Wallis test for continuous variables and the χ² test for categorical variables.
Missing data for mortality, diagnostic category, lactate levels, and prehospital transfer time were below 1% (0.13%); therefore, no imputation procedures were performed. To explore the relationship between baseline lactate levels, prehospital transfer time, and 48-hour mortality, mortality rates were graphically represented according to deciles of lactate and deciles of prehospital transfer time for each diagnostic category.
Multivariable logistic regression models were constructed to evaluate the association between prehospital transfer time, lactate levels, and 48-hour mortality. Given the non-linear association between lactate and mortality, lactate was modeled using restricted cubic splines with predefined knots at 2, 4, and 6 mmol/L. Interaction terms between prehospital transfer time and lactate spline components were subsequently evaluated. The primary multivariable models were adjusted for age, sex, ACCI, systolic blood pressure, respiratory rate, heart rate, diagnostic category, and Glasgow Coma Scale.
In diagnostic groups showing significant time–lactate interaction, additional models adjusted for Glasgow Coma Scale were performed, followed by stratified sensitivity analyses according to predefined neurological status categories (GCS ≤ 8, GCS 9–12, and GCS ≥ 13). Adjusted predicted probabilities derived from spline regression models were graphically represented both in the overall cohort and across diagnostic categories.
All analyses were performed using Stata statistical software version 17 (StataCorp LLC, College Station, TX, USA). A two-sided p < .05 was considered statistically significant.
Use of large language models
Large Language Models (LLMs) were used to assist with English language editing of the manuscript. LLMs were also used to support the conceptual design and drafting of the graphical abstract. All scientific content, study design, data analysis, interpretation of results, and final editorial decisions were performed and verified by the authors. The authors take full responsibility for the content of the manuscript.
Results
General characteristics of the cohort
A total of 10,111 patients attended by prehospital emergency medical services were included. Median age was 68 years (IQR, 52–80), 61.5% were male, and the median age-adjusted Charlson Comorbidity Index score was 4 (IQR, 2–7), reflecting a cohort with a substantial burden of comorbidity. Median Glasgow Coma Scale score was 15 (IQR, 13–15), whereas 12.8% of patients required mechanical ventilation and 4.3% vasoactive support during the initial prehospital assessment.
From a metabolic and blood gas perspective, median prehospital lactate level was 2.65 mmol/L (IQR, 1.78–4.19), with a median pH of 7.36 (IQR, 7.29–7.41). Overall 48-hour mortality was 9.5%. Additional baseline characteristics are summarized in Table 1.
Table 1.
Characteristics of the sample
| Variable | Overall Cohort (n = 10,111) | Survivors at 48 h (n = 9,146) | 48 h Mortality (n = 965) | p-value |
|---|---|---|---|---|
| Age, years | 68 (52–80) | 67 (52–79) | 76 (61–85) | < 0.001 |
| Male sex, n (%) | 6,214 (61.5) | 5,642 (61.7) | 572 (59.3) | 0.143 |
| ACCI | 4 (2–7) | 4 (2–7) | 6 (4–9) | < 0.001 |
| Respiratory rate | 19 (15–25) | 19 (15–24) | 21 (15–30) | < 0.001 |
| Central venous oxygen saturation (cSO₂), % | 47.9 (34.5–67.6) | 49.4 (36.2–68.7) | 33.6 (23–53.2) | < 0.001 |
| Systolic blood pressure, mmHg | 133 (112–153) | 134 (114–153) | 120 (85–150) | < 0.001 |
| Diastolic blood pressure, mmHg | 77 (63–90) | 77 (65–90) | 65 (46–87) | < 0.001 |
| Heart rate, bpm | 88 (72–110) | 88 (72–108) | 95 (70–118) | < 0.001 |
| Temperature, °C | 36.0 (35.7–36.6) | 36.0 (35.8–36.6) | 36.0 (35.3–36.5) | < 0.001 |
| Glasgow Coma Scale | 15 (13–15) | 15 (14–15) | 7 (3–13) | < 0.001 |
| pH | 7.36 (7.29–7.41) | 7.37 (7.30–7.42) | 7.19 (7.01–7.32) | < 0.001 |
| pCO₂, mmHg | 42.5 (34.5–50.1) | 41.6 (34.2–49.2) | 48.2 (39–67.4) | < 0.001 |
| HCO₃⁻, mmol/L | 23 (20–26.4) | 23.1 (20.5–26.6) | 18.8 (14.6–23.2) | < 0.001 |
| Base excess (BEecf) | -1 (-4.2–1.7) | -1 (-3.3–1.9) | -6 (-12.1–-1.3) | < 0.001 |
| Sodium, mmol/L | 140 (136–141) | 140 (137–141) | 140 (136–142) | 0.025 |
| Potassium, mmol/L | 4.1 (3.8–4.5) | 4.1 (3.7–4.5) | 4.4 (3.8–5.5) | < 0.001 |
| Glucose, mg/dL | 133 (108–180) | 131 (106–171) | 188 (135–255) | < 0.001 |
| Lactate, mmol/L | 2.65 (1.78–4.19) | 2.45 (1.7–3.65) | 6.78 (4.59–9.43) | < 0.001 |
| Urea, mmol/L | 6.7 (4.7–9.8) | 6.5 (4.6–9.4) | 10.7 (7.7–15.6) | < 0.001 |
| Creatinine, mg/dL | 0.93 (0.76–1.24) | 0.91 (0.76–1.18) | 1.43 (0.97–2.09) | < 0.001 |
| Mechanical ventilation, n (%) | 1,289 (12.8) | 808 (8.8) | 481 (49.8) | < 0.001 |
| Vasoactive support, n (%) | 434 (4.3) | 198 (2.2) | 236 (24.5) | < 0.001 |
The most frequent diagnostic categories were cardiovascular diseases (31.5%) and central nervous system diseases (19.2%). 48-h–hour mortality differed substantially across diagnostic categories, with the highest rates observed in trauma (16.9%) and infectious diseases (14.7%), whereas intoxications (5.1%) and other conditions (1.4%) showed the lowest mortality rates.
Prehospital transfer time, baseline lactate levels, and 48-hour mortality differed significantly across diagnostic groups (P<.001 for all analyses). Trauma patients showed the longest prehospital transfer times and the highest mortality, whereas endocrine diseases and central nervous system disorders presented the highest baseline lactate concentrations (Table 2). To further characterize prehospital response intervals, the median EMS activation-to-arrival time was 10 min (IQR, 8–16) in the overall cohort. This interval was relatively consistent across diagnostic categories, although trauma patients showed longer activation-to-arrival times (13 min [IQR, 9–24]; P<.001 across groups). Detailed results according to diagnostic category are presented in Supplementary Table S1.
Table 2.
Prehospital time, baseline lactate levels, and 48-hour mortality according to diagnostic category
| Diagnostic Group | Total N | Deaths at 48 h, n (%) | Prehospital Time, Median, min | Baseline Lactate, Median, mmol/L |
|---|---|---|---|---|
| Infectious diseases | 913 | 134 (14.7) | 49 (41–60) | 2.84 (1.88–4.32) |
| Other conditions | 142 | 2 (1.4) | 41 (34–54) | 2.67 (2.10–3.86) |
| Endocrine diseases | 286 | 35 (12.2) | 49 (40–59) | 3.50 (2.34–6.22) |
| Central nervous system diseases | 1,937 | 167 (8.6) | 44 (34–56) | 3.34 (1.94–8.09) |
| Cardiovascular diseases | 3,189 | 211 (6.6) | 44 (36–56) | 2.28 (1.56–3.28) |
| Respiratory diseases | 846 | 72 (8.5) | 46 (38–56) | 2.28 (1.56–3.61) |
| Digestive diseases | 411 | 28 (6.8) | 45 (37–56) | 2.74 (1.86–3.81) |
| Trauma | 1,656 | 279 (16.9) | 54 (41.7–72) | 3.12 (2.19–4.84) |
| Poisoning / Intoxications | 731 | 37 (5.1) | 45 (35–55) | 2.11 (1.33–3.33) |
Distribution of major diagnostic categories, prehospital transfer time, baseline lactate levels, and 48-hour mortality in the overall cohort. Continuous variables are presented as median (interquartile range [IQR]) and categorical variables as number (percentage). Significant differences were observed across diagnostic groups for both prehospital time and baseline lactate levels (Kruskal–Wallis p < .001 for both comparisons)
Association between prehospital time, lactate, and early mortality
Multivariable analysis showed that older age, male sex, higher comorbidity burden, increased respiratory rate, and lower Glasgow Coma Scale score were independently associated with higher 48-hour mortality. In addition, both prehospital transfer time and baseline lactate levels were significantly associated with early mortality risk (Supplementary Table S2).
Nonlinear associations between lactate and mortality were explored using restricted cubic spline transformations with predefined knots at 2, 4, and 6 mmol/L (Fig. 1). Significant interaction between prehospital transfer time and lactate spline terms was observed, suggesting that the prognostic impact of prehospital transfer time may depend on the patient’s baseline metabolic state (Supplementary Table S2).
Fig. 1.
Predicted probability of 48-hour mortality according to baseline lactate levels and prehospital time. Adjusted predicted probabilities of 48-hour mortality according to baseline prehospital lactate concentrations for different prehospital transfer times (30, 60, and 90 min), derived from multivariable logistic regression models including restricted cubic spline lactate terms and interaction analyses between lactate and prehospital transfer time. Lactate was modeled using restricted cubic splines with knots at 2, 4, and 6 mmol/L. Increasing lactate levels were associated with progressively higher predicted mortality, particularly in patients with longer prehospital transfer times, supporting a significant interaction between metabolic severity and prehospital delay. Dashed lines represent 95% confidence intervals
Because of the strong association between Glasgow Coma Scale score and early mortality, stratified analyses were performed according to predefined Glasgow categories (≥ 13, 9–12, and ≤ 8). 48–hour mortality increased progressively from 3.5% in patients with Glasgow ≥ 13 to 16.5% in those with Glasgow 9–12 and 37.0% among patients with Glasgow ≤ 8 (P<.001).
In stratified analyses, the effect of prehospital transfer time showed marked heterogeneity. Among patients with intermediate Glasgow scores (9–12), both prehospital transfer time and the time–lactate interaction were significantly associated with higher 48-hour mortality, whereas these associations disappeared in patients with preserved or severely impaired neurological status (Supplementary Table S3).
Activation-to-arrival time was not associated with 48-hour mortality in univariable analyses or after adjustment for age, sex, diagnostic category, baseline lactate levels, and prehospital transfer time (Supplementary Table S4).
Heterogeneity of the association between prehospital time, lactate, and mortality across diagnostic categories
Stratified analyses according to diagnostic category demonstrated marked heterogeneity in the association between prehospital transfer time, baseline lactate levels, and 48-hour mortality. Graphical representations of these associations are shown in Figs. 2.
Fig. 2.
Association between prehospital time, baseline lactate levels, and 48-hour mortality across diagnostic categories. Observed and predicted associations between prehospital time, baseline lactate levels, and 48-hour mortality across major diagnostic groups. For each diagnostic category, the left panel shows observed 48-hour mortality according to deciles of prehospital transfer time, whereas the middle panel displays observed mortality according to baseline lactate deciles. Error bars represent 95% confidence intervals. The right panels show adjusted predicted probabilities of 48-hour mortality according to baseline prehospital lactate levels for different prehospital transfer times (30, 60, and 90 min), derived from multivariable logistic regression models including cubic spline lactate terms and interaction analyses between lactate and prehospital time. Marked heterogeneity was observed across diagnostic categories. Significant time–lactate interactions were predominantly identified in endocrine diseases and trauma, where increasing prehospital time was associated with disproportionately higher mortality at elevated lactate levels. In contrast, infectious, cardiovascular, respiratory, and intoxication-related conditions showed strong lactate-associated mortality gradients without significant interaction with prehospital transfer time. Dashed lines represent 95% confidence intervals
In infectious diseases, baseline lactate showed a strong and progressive association with early mortality, whereas prehospital transfer time was not significantly associated with mortality after multivariable adjustment (OR, 1.06; 95% CI, 0.94–1.18; P=.335), without evidence of significant time–lactate interaction. In central nervous system diseases, prehospital transfer time showed an independent association with 48-hour mortality (OR, 1.04; 95% CI, 1.00–1.08; P=.046). The relationship between lactate and mortality was nonlinear, with a more pronounced increase at intermediate lactate levels, although no significant interaction between time and lactate was identified.
Cardiovascular and respiratory diseases showed a mixed pattern, with significant associations between baseline lactate, physiologic variables, and early mortality, whereas prehospital transfer time lost statistical significance after multivariable adjustment and no significant time–lactate interactions were observed. In digestive diseases, no consistent independent associations were identified between prehospital transfer time, baseline lactate, and early mortality. These findings should be interpreted cautiously because of the low number of mortality events (n = 28).
The most relevant time–lactate interaction patterns were observed in endocrine-metabolic diseases and trauma. In both groups, the association between prehospital transfer time and mortality became progressively stronger at higher lactate levels, suggesting that the clinical impact of prehospital transfer time depends on the patient’s baseline metabolic state. In endocrine diseases, time–lactate interaction terms reached statistical significance (P=.010 for both spline interaction terms), and the association persisted after additional adjustment for Glasgow Coma Scale score in sensitivity analyses. Owing to the limited number of patients (n = 286), further Glasgow-stratified analyses were not feasible. In trauma patients, prehospital transfer time was independently associated with higher 48-hour mortality (OR, 1.07; 95% CI, 1.02–1.12; P =.006), and significant interaction between transfer time and baseline lactate was also observed (P=.035 and P=.017 for spline interaction terms). Stratified analyses demonstrated that this interaction was predominantly present among patients with intermediate Glasgow scores (9–12), whereas it was not observed in patients with Glasgow ≥ 13 or ≤ 8.
In intoxications, although lactate showed a trend toward association with mortality, no consistent effects of prehospital transfer time or significant time–lactate interactions were identified. Complete multivariable stratified models are provided in Supplementary Tables S5–S7.
Discussion
In this multicenter cohort of patients attended by prehospital emergency medical services, we found that the prognostic relevance of prehospital transfer time was not uniform across diagnostic categories and appeared to depend on the patient’s baseline metabolic state as reflected by initial lactate levels. Although prehospital lactate was consistently associated with early mortality, heterogeneous patterns emerged across disease groups, including predominantly lactate-driven conditions, diseases with greater sensitivity to prehospital transfer time, and, most notably, clinical scenarios characterized by significant interaction between hyperlactatemia and prehospital transfer time (Table 3).
Table 3.
Summary of the associations between prehospital time, baseline lactate levels, and 48-hour mortality across diagnostic categories
| Diagnostic group | Dominant prognostic pattern | Time effect | Lactate effect | Time–lactate interaction |
|---|---|---|---|---|
| Infectious diseases | Lactate-driven | Limited | Strong linear | No |
| CNS diseases | Time-sensitive | Significant | Non-linear | No |
| Cardiovascular diseases | Mixed | Moderate | Moderate–strong | No |
| Respiratory diseases | Mixed | Moderate | Moderate–strong | No |
| Digestive diseases | Uncertain | Limited | Limited | No clear interaction |
| Endocrine diseases | Lactate-dependent time effect | Strong at high lactate | Strong | Significant |
| Intoxications | Modest | Limited | Moderate | No |
| Trauma | Lactate-dependent time effect | Strong at high lactate | Strong | Significant |
This table summarizes the predominant prognostic patterns identified across major diagnostic groups regarding the association between prehospital transfer time, baseline lactate levels, and 48-hour mortality. Associations were derived from multivariable logistic regression models including cubic spline lactate terms and interaction analyses between lactate and prehospital time. Three major prognostic phenotypes emerged: (1) predominantly lactate-driven conditions, characterized by strong associations between lactate and mortality with limited contribution of prehospital time; (2) primarily time-sensitive conditions, in which prehospital delay showed an independent association with mortality; and (3) lactate-dependent time-sensitive conditions, where the prognostic impact of prehospital time increased progressively at higher lactate levels. Full adjusted models are provided in the Supplementary Appendix
These findings expand previous evidence identifying prehospital lactate as an early marker of disease severity and adverse outcomes in critically ill patients [3, 7, 8, 13]. While prior studies have consistently demonstrated the prognostic value of lactate in sepsis, trauma, and acute cardiovascular disease, most have focused primarily on the isolated predictive role of the biomarker [11, 14–16]. Our results suggest that lactate may identify patients in whom the association between prehospital transfer time and mortality is stronger, identifying patient subgroups particularly vulnerable to prolonged prehospital transfer times.
From a pathophysiological perspective, the interaction observed in trauma patients appears highly plausible. Elevated lactate levels in trauma likely reflect tissue hypoperfusion, oxygen debt, and evolving hemorrhagic shock [4]. In this setting, even modest increases in prehospital transfer time may be associated with ongoing progression of microcirculatory dysfunction and secondary organ injury. Our findings support the hypothesis that the clinical impact of delayed care depends not only on the absolute duration of prehospital time but also on the patient’s physiologic reserve and baseline metabolic derangement, for which lactate may represent a particularly informative biomarker.
Similarly, endocrine-metabolic diseases demonstrated a significant interaction between prehospital transfer time and lactate, even after additional adjustment for Glasgow Coma Scale score. Although the limited number of events warrants cautious interpretation, this pattern may reflect the progressive and potentially reversible nature of severe metabolic emergencies, particularly hyperglycemic crises. In these patients, ongoing physiologic deterioration during the prehospital phase may be amplified in the presence of advanced metabolic stress or tissue hypoperfusion [17].
In contrast, infectious diseases showed a strong monotonic association between lactate and mortality, consistent with extensive prior evidence identifying hyperlactatemia as one of the main prognostic markers in sepsis and septic shock [11, 14, 18–20]. Previous studies have also demonstrated that delays in treatment initiation are associated with increased mortality in sepsis [21–23]. However, in our cohort, prehospital transfer time showed a relatively limited effect after multivariable adjustment. One possible explanation is that median prehospital transfer time was relatively short (approximately 50 min), likely remaining below the temporal thresholds usually associated with clinically meaningful deterioration in sepsis. These observations may indicate that, in advanced sepsis, lactate primarily reflects an already established state of systemic physiologic derangement at the time of initial assessment, such that relatively small differences in prehospital transfer time exert a comparatively limited impact on immediate prognosis.
In central nervous system diseases, prehospital transfer time maintained an independent association with mortality, although without significant interaction with lactate. Particularly noteworthy was the nonlinear relationship observed between lactate and mortality in this group. Moderate lactate elevations may reflect sustained cerebral hypoperfusion, whereas extremely elevated lactate concentrations may be influenced by transient phenomena such as generalized seizures or acute catecholaminergic discharge, mechanisms previously described in neurologic emergencies [24, 25].
Another important finding was that the interaction between prehospital transfer time and lactate was concentrated primarily among patients with intermediate neurologic impairment (Glasgow Coma Scale score 9–12), whereas it disappeared in patients with preserved or profoundly depressed neurologic status. This pattern may indicate the existence of a potentially modifiable physiologic window in which prehospital transfer time may still substantially influence prognosis. In low-risk patients, the absolute impact of prehospital transfer time is likely limited, whereas in patients with severe neurologic injury, prognosis may be determined predominantly by the magnitude of the initial insult.
Overall, our findings partially challenge the traditional concept of “time-sensitive disease” as a uniform and universal phenomenon, in line with previous studies reporting heterogeneous associations between prehospital time intervals and survival outcomes [26]. Rather, they suggest that the clinical relevance of delayed care depends on both the underlying pathophysiology and the degree of baseline metabolic derangement. In this context, point-of-care lactate assessment represents a rapid and widely accessible tool capable of identifying patients with significant hypoperfusion and high mortality risk even before hospital arrival [27]. Integration of baseline lactate with the initial clinical diagnosis and neurologic assessment may improve prognostic stratification and could potentially support critical prehospital decision-making, including transport prioritization, early activation of advanced resources, and destination hospital selection. Our findings suggest that selected subgroups, particularly patients with marked hyperlactatemia and intermediate neurologic impairment, may benefit from strategies aimed at minimizing delays and facilitating early transfer to higher-capability centers. Although these findings require prospective validation, they support the future development of more individualized prehospital decision-making models based on early metabolic biomarkers.
An additional strength of the present study is the near-complete availability of prehospital lactate measurements. Unlike many previous prehospital lactate studies, in which biomarker availability depended on selective blood sampling, convenience recruitment, or post hoc linkage of laboratory data, prehospital lactate, diagnostic category, and 48-hour mortality were available for all included patients. Only 13 observations (0.13%) had missing information, exclusively related to prehospital time variables. Previous cohorts have reported substantially higher proportions of missing or excluded cases due to unavailable lactate measurements or inability to reliably link blood gas analyses to individual patients [13, 28, 29]. The systematic implementation of point-of-care blood gas analysis as part of the standardized initial assessment performed by physician- and nurse-staffed advanced life support teams likely reduced selection bias and enhanced the internal validity and representativeness of our findings.
This study has several limitations. First, its observational design precludes causal inference and does not eliminate residual confounding. Second, prehospital transfer time may reflect not only avoidable delays in care but also greater clinical complexity, the need for advanced stabilization procedures, or geographic and logistical differences between EMS systems. In addition, lactate measurements were obtained using point-of-care devices in the prehospital setting. Although this reflects real-world clinical practice and constitutes an operational strength of the study, it may introduce analytical variability compared with centralized laboratory measurements. In addition, the exact time elapsed between EMS arrival and lactate measurement was not recorded. Although lactate determination was routinely performed during the initial patient assessment and generally within the first minutes of contact according to the operational protocol, we cannot exclude variability in sampling timing across clinical scenarios. This may have been particularly relevant in conditions requiring complex extrication, advanced stabilization procedures, or other urgent interventions, potentially introducing some degree of measurement heterogeneity between diagnostic categories. Furthermore, only a single baseline lactate determination was available, without information regarding lactate clearance or subsequent metabolic evolution. Another limitation is the absence of detailed information regarding pharmacological treatments administered during prehospital care, limiting mechanistic interpretation of some observed associations. Finally, some diagnostic subgroups included relatively few mortality events, particularly digestive and endocrine diseases, potentially resulting in imprecise estimates in complex multivariable models. Consequently, findings related to time–lactate interactions should be considered exploratory and require external validation in independent cohorts and prospective studies specifically designed to evaluate biomarker-guided prehospital prioritization strategies.
In summary, the prognostic impact of prehospital transfer time showed marked heterogeneity across diagnostic categories and depended on the patient’s baseline metabolic state as reflected by initial lactate levels. Although prehospital lactate was associated with higher early mortality, the interaction between hyperlactatemia and prehospital transfer time was particularly relevant in trauma and endocrine-metabolic emergencies. These findings suggest that early point-of-care lactate assessment, integrated with the initial clinical and neurologic evaluation, may improve risk stratification and could potentially inform future prehospital prioritization strategies. However, these observations should be considered exploratory and require prospective validation before influencing clinical decision-making.
Supplementary Information
Acknowledgements
The authors acknowledge the Spanish Society of Diabetes (Sociedad Española de Diabetes, SED) for its decisive support of this project through the “XV Ayudas SED a Proyectos de Investigación en Diabetes dirigidos por Jóvenes Investigadores.
Author contributions
F.S.-V. conceived and designed the study, developed the research hypothesis, performed the statistical analyses, prepared the figures, interpreted the data, and drafted the original manuscript. R.L.-I., C.P.V., J.D.-R., A.S.-G., and F.M.-R. contributed to data collection, data validation, and critical revision of the manuscript. A.S.-G. and F.M.-R. additionally contributed to the development of the research project, interpretation of the findings, and writing and revision of the manuscript. M.A.S.-N., J.A.A.-M., V.N.-M., and M.M. critically reviewed the manuscript and contributed to the interpretation of the results. All authors reviewed and approved the final version of the manuscript.
Funding
This work was supported by the Institute of Health Carlos III (Spain) and co-financed by the European Union [grant number DTS23/00010] for FM-R, and by the MCIN/AEI/10.13039/501100011033 and the European Union [grant number PID2024-160665OA-I00] for AS-G. The funding bodies had no role in the design of the study, data collection, analysis, interpretation of data, writing of the manuscript, or the decision to submit the manuscript for publication.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to institutional and privacy restrictions but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This is an analysis of data obtained from three consecutive studies conducted between February 2019 and January 2026, following the same methodological criteria (ISRCTN17676798, ISRCTN48326533, and ISRCTN49321933). The study protocol was approved by the ethics committees of all participating institutions (reference numbers: PI-049-19 and PI-GR-19-1258). All procedures were conducted in accordance with the principles of the Declaration of Helsinki and followed the STROBE guidelines for observational studies. Informed consent was obtained from all participants or their legally authorized representatives according to local regulations and the requirements of the participating institutions.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Francisco Martín-Rodríguez and Ancor Sanz-García joint senior authors.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to institutional and privacy restrictions but are available from the corresponding author on reasonable request.