While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87–99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.

Purpose Physicians not infrequently encounter very sick sepsis patients with normal serum lactate. This phenomenon of lactate discordance, where the serum lactate does not match illness severity, is poorly understood. Methods Using three intensive care unit (ICU) datasets from the U.S. and Spain, we identified adult sepsis patients in the highest quartile of severity of illness scores with at least 1 serum lactate measurement available within 24 hours of ICU admission. Separate logistic regression, random forests and partial least square models were built for each data set to identify characteristics differentiating patients with normal versus high serum lactate and cross-validated in the other datasets. Results Of the 4,861 sickest patients identified, approx. 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotype. While performance varied between datasets, logistic regression consistently performed best (Area under the receiver operator curve 87-99%, accuracy 79-97%).The variables most strongly associated with normal serum lactate were serum chloride, bicarbonate and blood urea nitrogen, while liver disease, coagulopathy and serum sodium were associated with high serum lactate. Conclusion These analyses provide initial insights on patient characteristics associated with normal serum lactate levels among patients with high illness severity. Additional studies should be performed to confirm these findings and establish the underlying pathophysiological mechanisms. The next step is to disentangle which associations represents an etiology, consequence or an epiphenomenon of high versus normal serum lactate among the sickest patients in the ICU.

Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (<65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

Background: The steroids are currently used as standard treatment for severe COVID-19. However, the evidence is weak. Our aim is to determine if the use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes.Methods: A secondary analysis derived from multicenter, observational study of adult critically ill patients with confirmed COVID-19 disease admitted to 63 ICUs in Spain. Three phenotypes were derived by non-supervised clustering analysis from whole population and classified as (A: severe, B: critical and C: life-threatening). The primary outcome was ICU mortality. We performed a Multivariate analysis after propensity score full matching (PS), Cox proportional hazards (CPH), Cox covariate time interaction (TIR), Weighted Cox Regression (WCR) and Fine-Gray analysis(sHR) to assess the impact of corticosteroids on ICU mortality according to the whole population and distinctive patient clinical phenotypes. Results: A total of 2,017 patients were analyzed, 1171(58%) with corticosteroids. After PS, corticosteroids were shown not to be associated with ICU mortality (OR:1.0,95%CI:0.98-1.15). Corticosteroids were administered in 298/537(55.5%) patients of “A” phenotype and their use was not associated with ICU mortality (HR=0.85[0.55-1.33]). A total of 338/623(54.2%) patients in “B” phenotype received corticosteroids. The CPH (HR =0.65 [0.46-0.91]) and TIR regression (1- 25 day tHR=0.56[0.39-0.82] and >25 days tHR=1.53[1.03-7.12]) showed a biphasic effect of corticosteroids due to proportional assumption violation. No effect of corticosteroids on ICU mortality was observed when WCR was performed (wHR=0.72[0.49-1.05]). Finally, 535/857(62.4%) patients in “C” phenotype received corticosteroids. The CPH (HR=0.73[0.63-0.98]) and TIR regression (1- 25 day tHR=0.69[ 0.53-0.89] and >25 days tHR=1.30[ 1.14-3.25]) showed a biphasic effect of corticosteroids and proportional assumption violation. However, wHR (0.75[0.58-0.98]) and sHR (0.79[0.63-0.98]) suggest a protective effect of corticosteroids on ICU mortality. Conclusion: Our finding warns against the widespread use of corticosteroids in all critically ill patients with COVID-19 at moderate-high dose. Only patients with the highest severity could benefit from steroid treatment although this effect on clinical outcome was minimized during ICU stay.