Bruno et al. Ann. Intensive Care (2021) 11:128 https://doi.org/10.1186/s13613-021-00911-8 RESEARCH Open Access Lactate is associated with mortality in very old intensive care patients suffering from COVID‑19: results from an international observational study of 2860 patients Raphael Romano Bruno1†, Bernhard Wernly2†, Hans Flaatten3,4, Jesper Fjølner5, Antonio Artigas6, Bernardo Bollen Pinto7, Joerg C. Schefold8, Stephan Binnebössel1, Philipp Heinrich Baldia1, Malte Kelm1, Michael Beil9, Sivri Sigal9, Peter Vernon van Heerden10, Wojciech Szczeklik11, Muhammed Elhadi12, Michael Joannidis13, Sandra Oeyen14, Tilemachos Zafeiridis15, Jakob Wollborn16, Maria José Arche Banzo17, Kristina Fuest18, Brian Marsh19, Finn H. Andersen20,21, Rui Moreno22, Susannah Leaver23, Ariane Boumendil24,25, Dylan W. De Lange26, Bertrand Guidet24,25 and Christian Jung1* on behalf of the COVIP Study Group Abstract Purpose: Lactate is an established prognosticator in critical care. However, there still is insufficient evidence about its role in predicting outcome in COVID-19. This is of particular concern in older patients who have been mostly affected during the initial surge in 2020. Methods: This prospective international observation study (The COVIP study) recruited patients aged 70 years or older (ClinicalTrials.gov ID: NCT04321265) admitted to an intensive care unit (ICU) with COVID-19 disease from March 2020 to February 2021. In addition to serial lactate values (arterial blood gas analysis), we recorded several param- eters, including SOFA score, ICU procedures, limitation of care, ICU- and 3-month mortality. A lactate concentration ≥ 2.0 mmol/L on the day of ICU admission (baseline) was defined as abnormal. The primary outcome was ICU-mortal- ity. The secondary outcomes 30-day and 3-month mortality. Results: In total, data from 2860 patients were analyzed. In most patients (68%), serum lactate was lower than 2 mmol/L. Elevated baseline serum lactate was associated with significantly higher ICU- and 3-month mortality (53% vs. 43%, and 71% vs. 57%, respectively, p < 0.001). In the multivariable analysis, the maximum lactate concentration on day 1 was independently associated with ICU mortality (aOR 1.06 95% CI 1.02–1.11; p = 0.007), 30-day mortality (aOR 1.07 95% CI 1.02–1.13; p = 0.005) and 3-month mortality (aOR 1.15 95% CI 1.08–1.24; p < 0.001) after adjustment for age, gender, SOFA score, and frailty. In 826 patients with baseline lactate ≥ 2 mmol/L sufficient data to calculate the difference between maximal levels on days 1 and 2 (∆ serum lactate) were available. A decreasing lactate concentra- tion over time was inversely associated with ICU mortality after multivariate adjustment for SOFA score, age, Clinical Frailty Scale, and gender (aOR 0.60 95% CI 0.42–0.85; p = 0.004). *Correspondence: christian.jung@med.uni-duesseldorf.de † Raphael Romano Bruno and Bernhard Wernly have contributed equally 1 Department of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Duesseldorf, Moorenstraße 5, 40225 Duesseldorf, Germany Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. Bruno et al. Ann. Intensive Care (2021) 11:128 Page 2 of 9 Conclusion: In critically ill old intensive care patients suffering from COVID-19, lactate and its kinetics are valuable tools for outcome prediction. Trial registration number: NCT04321265. Introduction ClinicalTrials.gov (ID: NCT04321265) and adhered to The disease caused by Sars-CoV-2, COVID-19, has domi- the European Union General Data Privacy Regulation nated daily life in numerous intensive care units (ICU) (GDPR) directive. This investigation aimed to under- worldwide, since the beginning of 2020. Respiratory fail- stand which factors can predict mortality in elderly ure with or without shock led to high mortality. In ICU COVID-19 patients to help detect these patients early admitted patients, up to 30–50% of the patients did not (the COVIP study, COVID-19 in very old intensive care survive the first month [1–3]. Thus, early and reliable patients). As in the previous VIP studies [3, 12, 13], identification of complex disease courses is of pivotal national coordinators recruited the intensive care units importance in COVID-19 (Additional file 1). (ICUs), coordinated national and local ethical permis- In emergency and critical care medicine, serum lac- sions, and supervised patient recruitment at the national tate and its kinetics are useful parameters for critically ill level. Ethical approval was mandatory for study participa- patients as a marker of severity of illness [4–6]. A signifi- tion. In most countries informed consent was obligatory cant advantage is that the determination of serum lactate for inclusion. This study extracted patient data from 151 is widely and rapidly available as a point-of-care measure- ICUs from 26 independent countries, including Euro- ment [7]. Hyperlactatemia is an indicator of physiological pean ICUs, and the Asian, African, and Americas. stress, and anaerobic metabolism, and a “powerful pre- dictor of mortality” [6]. Basically, lactate can be used for Study population two purposes. It can be used both for risk stratification The COVIP study recruited consecutieve patients with and to monitor the response to therapy. Elevated lactate proven COVID-19 aged 70 years or older who were is a diagnostic criterion for septic shock following the admitted to an ICU. The data set was extracted from the sepsis-3 consensus. Lactate “clearance” is a target param- COVIP study database on 4th February and contained eter for volume substitution in the absence of major liver patients from 19th March 2020 to 4th February 2021. dysfunction [8]. Although serum lactate and its kinetics Data collection started at ICU admission. Data about have been applied as an essential diagnostic and target pre-ICU triage were not available. The admission day was parameter in septic patients for more than 20 years, the defined as day 1, and all consecutive days were numbered evidence remains scarce for patients suffering from pneu- sequentially from that date. monia and ARDS. Despite this lack of evidence, current guidelines recom- Data collection mend the use of lactate and lactate kinetics in COVID-19 All centers used a uniform online electronic case [9]. Until now, the value of serum lactate and its kinet- report form (eCRF). Only patients with a documented ics in predicting a severe course in COVID-19 is unclear. highest serum lactate value on days 1 and 2 were This lack of evidence is especially true in the particularly included for this subgroup analysis (above 2 mmol/L). vulnerable population of very old intensive care unit Reporting was possible both in [mg/dL] or [mmol/L], patients. Yet, this subgroup has been disproportionally depending on local routine. For ease of comparison, affected by the need for ICU admissions and a high mor- all laboratory values were converted to [mmol/] (1 mg/ tality [3, 10, 11]. dL = 0.111 mmol/L). The first arterial blood gas (ABG) This multicenter study addresses this lack of evidence analysis, including ­pO2 [mmHg] and the F ­ iO2 [%], was and investigates the value of serum lactate at admission recorded to calculate the p ­ O2/FiO2-ratio on admission. and its kinetics for outcome prediction in a large pro- For the sequential organ failure assessment (SOFA) spectively enrolled population of older ICU patients. score on admission, each element was entered and the eCRF calculated the total score. Furthermore, we Methods assessed the need for non-invasive or invasive venti- Design and settings lation with its duration, prone positioning, tracheos- This multicenter study is part of the Very old Intensive tomy, vasopressor use and renal replacement therapy. care Patients (VIP) project and has been endorsed by the The eCRF also documented any limitation of life-sus- European Society of Intensive Care Medicine (ESICM) taining therapy during the ICU-stay. The frailty level (https://​www.​vipst​udy.​org). The study was registered at prior to the acute illness and hospital admission was Bruno et al. Ann. Intensive Care (2021) 11:128 Page 3 of 9 assessed using the Clinical Frailty Scale (CFS) [3, 12, Statistical analysis 13]. In addition, the eCRF recorded information about The primary outcome was ICU mortality, secondary out- gender, age, length of ICU stay symptom onset, and comes were 30-day and 3-month mortality. Continuous duration of symptoms before ICU-and hospital admis- data points were expressed as median and interquartile sion. Furthermore, the eCRF asked about the presence range. Differences between independent groups were cal- of preexisting comorbidities. culated using the MannWhitney U test. Categorical data are expressed as numbers (percentage). The Chi-square test was applied to calculate differences between groups. Lactate and ∆ Lactate Univariate und multivariable logistic regression analy- Patients were clustered according to their lac- ses were performed to assess associations with baseline tate concentration on ICU admission. The arbi- serum lactate and mortality. We chose the co-variables trary cutoff value was an initial lactate concentration for the multivariable model (age, SOFA score, CFS and ≥ 2.0 mmol/L. A lactate value below 2 mmol/L was gender) based on clinical experience and previous lit- defined as within the normal range. ∆ Lactate in the erature [4, 5]. Marginal predictive means with respective first 24 h was defined as maximum serum lactate at 95% confidence intervals (CI) were calculated. All tests admission minus maximum serum lactate on day 2, were two-sided, and a p value of < 0.05 was considered divided by lactate at admission multiplied by 100 [4]. statistically significant. Stata 16 was used for all statistical A positive value indicates a fall in serum lactate and a analyses (StataCorp LLC, 4905 Lakeway Drive, College negative value signifies rising serum lactate. This has Station, Brownsville, Texas, USA). been confirmed in larger cohorts as a valuable and simple tool for outcome prediction [4].     mmol mmol Maximum serum lactate on admission − Maximum serum lactate on day2 L L Lactate = 100×   Maximum serum lactate on admission mmol L Data storage Results The eCRF and database were hosted on a secure server Study population in Aarhus University, Denmark. In total, 2860 patients with an available baseline serum lactate value were included (Fig. 1). Overall, 68% (1940 Fig. 1 CONSORT diagram Bruno et al. Ann. Intensive Care (2021) 11:128 Page 4 of 9 patients) patients had no abnormal elevation of serum admission [Symptoms prior to hospitalization 2 days lactate on the day of admission to the ICU, while 32% (1–5) vs. 2 days (1–5), p = 0.96; symptoms prior to (920 patients) evidenced an elevated serum lactate ICU-admission 6 days (3–9) vs. 7 days (3–10), p = 0.38]. (Fig. 2) (Table 1). Both groups evidenced similar incidences of pre-exist- ing comorbidities (diabetes, coronary vascular disease, Baseline serum lactate chronic renal failure, arterial hypertension, pulmonary Patients with a lactate greater than/equal to 2 mmol/L disease) with the exception for heart failure, which was were older [76 (72–79) vs. 75 (72–78) years, p = 0.007], significantly more often in patients with an elevated base- but not more frail [CFS 3 (2–4) vs. 3 (2–4), p = 0.47]. line serum lactate (18% vs. 14%, p = 0.004). Patients with elevated serum lactate had a higher SOFA On day 2, the maximum serum lactate was lower in score on admission (Table 1). There were no differences the group of patients with initially elevated serum lactate in symptom duration or time in the hospital before ICU than on the day of admission, but still significantly higher than in the group of patients with non-elevated lac- tate on admission [2.2 mmol/L (1.7–3.0) vs. 1.4 mmol/L (1.1–1.8), p < 0.001]. Patients with an elevated baseline serum lactate had significantly higher ∆ lactate in 24 h than patients with a normal baseline lactate [25% (0–45) vs. − 7% (− 33–12), p < 0.001] (Table 1). The two groups demonstrated differences in intensive care therapy. Patients with an elevated baseline lactate were signifi- cantly more likely to receive mechanical ventilation [74% (677) vs. 70% (1359), p = 0.042], renal replacement ther- apy [19% (171) vs. 15% (288), p = 0.013], and vasoactive drugs [72% (654) vs. 68% (1311), p = 0.038], but prone positioning occurred significantly less often in patients with an elevated serum lactate [49% (329) vs. 57% (764), p < 0.001]. There was no difference regarding non-inva- sive ventilation [25% (230) vs. 26% (501), p = 0.61] or tra- Fig. 2 Distribution of maximum serum lactate values on day 1 (= day cheostomy [19% (173) vs. 19% (364), p = 0.95]. Patients of ICU-admission), [mmol/L] with elevated baseline lactate had a significantly higher Table 1 Baseline characteristics Variables Baseline lactate ≥ 2 mmol/L Baseline lactate < 2 mmol/L p value n = 920 (32%) n = 1940 (68%) Male gender [n] (%) 678 (74%) 1356 (70%) 0.033 Age [years] (IQR) 72–79 (76) 72–78 (75) 0.007 Age 70–79 [n] (%) 715 (78%) 1554 (80%) 0.15 Age 80–89 [n] (%) 194 (21%) 373 (19%) 0.24 Age > 90 [n] (%) 10 (1%) 12 (1%) 0.18 CFS (IQR) 2–4 (3) 2–4 (3) 0.47 Diabetes [n] (%) 334 (37%) 690 (36%) 0.68 Coronary vascular disease [n] (%) 226 (25%) 441 (23%) 0.26 Chronic renal failure [n] (%) 163 (18%) 331 (17%) 0.65 Arterial hypertension [n] (%) 599 (65%) 1325 (69%) 0.086 Pulmonary disease [n] (%) 200 (22%) 456 (24%) 0.30 Heart failure [n] (%) 164 (18%) 264 (14%) 0.004 Lactate on day 1 [mmol/L] (IQR) 2.3–3.8 (2.8) 1.1–1.7 (1.4) < 0.001 SOFA score (IQR) 4–8 (6) 3–8 (5) < 0.001 Symptoms prior to hospitalization (days) 2 (1–5) 2 (1–5) 0.96 Symptoms prior to ICU-admission (days) 6 (3–9) 7 (3–10) 0.38 CFS clinical frailty scale, SOFA score sequential organ failure Assessment for the first 24 h, IQR interquartile range Bruno et al. Ann. Intensive Care (2021) 11:128 Page 5 of 9 ICU-, 30-day and 3-month mortality (Table 2). Length of ICU was lower in patients with an elevated base- line serum lactate [336 h (408) vs. 377 (433), p = 0.037]. After exclusion of non-survivors, there was no differ- ence between both groups [475 h (526) vs. 570 h (552), p = 0.48]. Accordingly, the duration of mechanical ven- tilation was significantly longer in patients with normal baseline [343 h (360) vs. 367 h (360), p = 0.026]. Again, after exclusion of non-survivors and patients without mechanical ventilation, there remained no significant dif- ference between both groups [323 h (414) vs. 311 (445) h, p = 0.41]. Treatment was withheld in 30% (271) of the patients with an elevated and in 30% (565) of the patients with a normal baseline lactate (p = 0.85). Treatment was withdrawn in 21% (188) and 18% (348), respectively Fig. 3 Kaplan–Meier for patients with a baseline lactate ≥ 2 mmol/L (p = 0.12). (red line) compared to patients with a baseline lactate < 2 mmol/L In a univariate regression analysis, the baseline lactate (blue line) (3-month mortality, ± standard deviation) was significantly associated with ICU mortality (OR 1.12 95% CI 1.07–1.17; p < 0.001), 30-day mortality (OR 1.11 95% CI 1.06–1.16; p < 0.001) and 3-month mortality (OR Intensive care treatment, especially regarding non- 1.16 95% CI 1.09–1.23; p < 0.001). invasive ventilation [27% (57) vs. 24% (146), p = 0.45], In the multivariable analysis, the maximum lactate con- vasoactive drugs [70% (151) vs. 74% (444), p = 0.34], centration on day 1 was independently associated with and renal replacement therapy [21% (45) vs. 18% (112), ICU mortality (aOR 1.06 95% CI 1.02–1.11; p = 0.007), p = 0.42] did not differ between both groups. In patients 30-day mortality (aOR 1.07 95% CI 1.02–1.13; p = 0.005) with ∆ serum lactate over 24 h less than 0, intubation and 3-month mortality (aOR 1.15 95% CI 1.08–1.24; occurred in 71% (152) compared to 76% (464) (p = 0.12), p < 0.001) after adjustment for age, SOFA, CFS and sex prone positioning was used in 48% (72) compared to (Fig. 3). 49% (226) (p = 0.76). During the ICU stay, 40 patients with a ∆ serum lactate over 24 h less than 0 received a tracheostomy (19%), while this was true for 109 patients ∆ Serum lactate in 24 h from the group of patients with a ∆ serum lactate ≥ 0% In 826 patients (29%), there was a baseline lactate (18%, p = 0.79). Therapy limitations or de-escalations did ≥ 2 mmol/L and sufficient data to calculate the ∆ serum also not differ between groups. Therapy was withheld lactate over 24 h. In both sub-groups, patients were pre- in 29% (62) of the patients with a negative ∆ serum lac- dominantly male (p = 0.37, Table 3). There were no dif- tate, and in 30% (185) of the other group (p = 0.68), while ferences regarding age, SOFA score on admission, prior treatment was withdrawn in 19% (40) and 22% (134) of hospitalizations, or the duration of symptoms before the patients, respectively (p = 0.32). Length of stay was ICU admission. The median CFS did not differ (Table 3). longer in patients with a ∆ Lactate 24 h > 0% [285 (439) For pre-existing comorbidities, there was no difference h vs. 348 h (400), p = 0.029]. After exclusion of non-sur- between groups except for pulmonary diseases, which vivors, there was no significant difference between both were significantly more common in those patients with groups [475 (526) h vs. 456 h (552), p = 4.77]. The dura- rising lactate [27% (n = 58) vs. 19% (n = 117), p = 0.017]. tion of mechanical ventilation was significantly longer in Table 2 Primary and secondary outcomes Variables Baseline Lactate ≥ 2 mmol/L Baseline Lactate < 2 mmol/L p value n = 920 (32%) n = 1940 (68%) ICU mortality 465 (53%) 817 (43%) < 0.001 30-day mortality 496 (56%) 854 (46%) < 0.001 3-month mortality 533 (71%) 924 (57%) < 0.001 ICU length of stay (hours, IQR) 336 (408) 377 (433) 0.037 Duration of mechanical ventilation (hours, IQR) 343 (360) 367 (360) 0.026 ICU intensive care unit Bruno et al. Ann. Intensive Care (2021) 11:128 Page 6 of 9 Table 3 Baseline characteristics of the subgroup analysis according to the Δ Lactate 24 h [%] Variables ∆ Lactate 24 h ≤ 0% ∆ Lactate 24 h > 0% p value n = 215 (26%) n = 611 (74%) Male gender [n] (%) 152 (71%) 447 (73%) 0.37 Age [years] (IQR) 73–79 (75) 72–79 (76) 0.60 Age 70–79 [n] (%) 180 (80%) 484 (76%) 0.28 Age 80–89 [n] (%) 42 (19%) 143 (23%) 0.22 Age > 90 [n] (%) 3 (1%) 6 (1%) 0.63 CFS (IQR) 2–4 (3) 2–4 (3) 0.13 Diabetes [n] (%) 75 (35%) 231 (38%) 0.43 Coronary vascular disease [n] (%) 46 (22%) 154 (26%) 0.23 Chronic renal failure [n] (%) 43 (20%) 102 (17%) 0.27 Arterial hypertension [n] (%) 140 (65%) 403 (66%) 0.84 Pulmonary disease [n] (%) 58 (27%) 117 (19%) 0.017 Heart failure [n] (%) 42 (20%) 108 (18%) 0.85 SOFA score (IQR) 4–9 (6) 4–8 (6) 0.63 CFS clinical frailty scale, SOFA score sequential organ failure assessment for the first 24 h, IQR interquartile range Table 4 Primary and secondary outcomes of the subgroup analysis according to the Δ Lactate 24 h [%] Variables ∆ Lactate 24 h ≤ 0% ∆ Lactate 24 h > 0% p value n = 225 (26%) n = 634 (74%) ICU mortality 131 (61%) 303 (50%) 0.004 30-day mortality 128 (62%) 331 (55%) 0.077 3-month mortality 139 (79%) 353 (68%) 0.007 ICU length of stay (hours) 285 (439) 348 (400) 0.029 Duration of mechanical ventilation (hours) 210 (390) 277 (340) 0.106 ICU intensive care unit patients with a positive ∆ Lactate 24 h [210 h (390) vs. 277 (340), p = 0.106]. However, after exclusion of non- survivors, the duration of mechanical ventilation was comparable [323 h (414) vs. 307 h (436) p = 0.364]. ICU mortality was significantly higher for patients with ris- ing lactate [61% (n = 131) vs. 50% (n = 303), p = 0.004, Table 4, Fig. 4]. Although not statistically significant, ∆ lactate was associated with ICU mortality after multivariable adjust- ment for age, SOFA score, and gender (aOR 0.997 95% CI 0.993–1.000; p = 0.05). A decreasing lactate (∆ lactate > 0%) was inversely associated with ICU mortality (OR 0.63 95% CI 0.46–0.87; p = 0.004) and remained so after multivariable adjustment for SOFA score, age, CFS and gender (aOR 0.60 95% CI 0.42–0.85; p = 0.004). Fig. 4 Kaplan–Meier for patients with a ∆ lactate 24 h > 0% (red line) compared to patients with a ∆ lactate 24 h ≤ 0% (blue line) (3-month mortality, ± standard deviation) Discussion This sub-group analysis in very old ICU patients with COVID-19 examined the value of baseline lactate and lactate kinetics as predictors of outcome. Both patients These parameters were independently associated with with elevated baseline lactate and patients with rising ICU-mortality. To our knowledge, this study represents lactate suffered from significantly higher ICU-mortality. the first prospective observational study in critically ill Bruno et al. Ann. Intensive Care (2021) 11:128 Page 7 of 9 old patients with COVID-19 on the value of lactate and and an increased mortality in 981 patients [19]. Kayina lactate kinetics for outcome prediction. et al. examined 235 patients and demonstrated that non- These findings are of pivotal importance as lactate and survivors had a higher baseline serum lactate (p < 0.01, its kinetics are already amongst the most important labo- n = 122) [20]. In a very small cohort of 45 ICU-patients ratory parameters for diagnosing septic shock and guiding suffering from COVID-19, Vassiliou et al. found that treatment. The current sepsis guidelines advocate meas- maximum lactate on admission was independently urement of lactate at ICU admission and during ICU stay related to 28-day ICU-mortality. Lactate’s area under as one of the best variables to assess the response to treat- the curve for detecting 28-day ICU mortality was 0.77 ment [14]. Indeed, a meta-analysis by Pan et al. included (p = 0.008). Mixed model analysis showed that mean daily seven randomized controlled trials with 1301 patients to lactate levels were higher in non-survivors (p < 0.0001). compare the early lactate clearance-directed therapy to Interestingly, when lactate levels were compared to the central venous oxygen saturation (­ScvO2)-guided ther- SOFA scores they showed a similar time pattern [21]. A apy as a potentially more effective resuscitation target. retrospective cohort study by Gregoriano et al. included They concluded that the use of an early lactate clearance- 99 patients with severe COVID-19. In this cohort, lactate directed therapy resulted in a decreased in-hospital mor- on admission was amongst the highest prognostic factors tality, shorter ICU stay, shorter mechanical ventilation for severe COVID-19 progression (lactate on ambient air time, and lower APACHE II scores [15]. AUC 0.67; lactate with O2 supply AUC 0.70) [22]. COVID-19 might be considered as a very unique type However, the present study also reveals another very of sepsis. Thus, the “First Update on the Surviving Sep- relevant insight into old ICU patients with COVID- sis Campaign Guidelines on the Management of Adults 19: almost 70% of patients had no elevated lactate at all with Coronavirus Disease 2019 (COVID-19) in the on admission. This contradicts previous studies, such ICU” were developed [9]. In this statement, the authors as by Li et al. [23]. These found, in 204 older patients suggest using dynamic parameters, such as serum lac- (≥ 60 years) diagnosed with COVID-19, an elevated lac- tate measurement, over static parameters to assess fluid tate (median 2.3 mmol/L) in 84% of the patients [23]. responsiveness in adults with COVID-19 and shock, though with a weak level of evidence [9]. Limitations Severe COVID-19 is usually characterized by a fast- Our study has some methodological limitations. We lack a developing pneumonitis and respiratory failure. Currently, control group of younger COVID-19 patients for compari- there are only very limited data about serum lactate values son or a comparable age cohort of patients who were not or in this special type of pneumonitis and in pneumonia in could not be admitted to the ICU. In addition, the COVIP general: Gwak et al. collected consecutive data from 397 database does not capture information on time from symp- patients who were hospitalized with community acquired toms onset to ICU admission, from pre-ICU care and triage pneumonia (CAP); 18% of these patients were admitted to or from and level of care, while in the ICU (e.g., nurse-to- the ICU. They found an independent association between patient ratio). These treatment limitations may affect the the initial serum lactate concentration and in-hospital care of older ICU patients [24]. Participating countries mortality (aOR1.24; 95% CI 1.01–1.53) [16]. A subgroup varied widely in their care structure. This results in a large analysis of the INFAUCI-Study by Pereira et al. investi- heterogeneity of treatments. The study also cannot answer gated prognostic markers in patients suffering from severe whether lactate kinetics-guided therapy prospectively community (in most cases bacterial) acquired pneumonia. gives a mortality benefit in critically ill septic patients with In their analysis, the mean serum lactate on admission was COVID-19. When lactate values were documented, only higher than in our study (3.0 ± 3.1 mmol/L) and indepen- the first 48 h were recorded, with the highest value per 24 h dently associated with intra-hospital mortality (OR 1.13; in each case. However, these parameters have been used as 95% CI 1.00–1.32, in a sub-group analysis of patients with a benchmark in most studies to date, so the determination septic shock: OR 1.11; 95% CI 1.00–1.37, respectively). seems more than adequate to answer the hypothesis. The Interestingly, they found no association with mortality at ∆ Lactate subgroup analysis lacks patients who died in the 6 months [17]. In another retrospective analysis involving first 24 h. However, this accounted only for 32 patients of 553 patients, Jo et al. showed the non-inferiority of a base- the study cohort. Pre-existing liver disease might influence line serum lactate combined with an early warning score lactate and its kinetics. Nevertheless, in the multivariate compared to established pneumonia scores such as CURB- analysis, results have been adjusted to SOFA score which 65 in predicting outcomes of patients with CAP [18]. includes liver function. Possibly, patients with pulmonary Regarding serum lactate levels in COVID-19, the pre- artery embolism are more frequently in shock leading to sent study is in line with Goodall et al., who found a elevated lactate. Though, this association is speculative as relationship between higher lactate levels (aHR 2.67) the study did not investigate the occurrence of pulmonary Bruno et al. Ann. Intensive Care (2021) 11:128 Page 8 of 9 artery embolism as there were no reports of clustered pul- Mohamed Raafat Badawy, M José Arche Banzo, Begoña Zalba-Etayo, Patricia Jimeno Cubero, Jesús Priego, Gemma Gomà, Teresa Maria Tomasa-Irriguible, monary artery emboli when establishing the COVIP study Susana Sancho, Aida Fernández Ferreira, Eric Mayor Vázquez, Ángela Prado design in February 2020. Mira, Mercedes Ibarz, David Iglesias, Susana Arias-Rivera, Fernando Frutos- Vivar, Sonia Lopez-Cuenca, Cesar Aldecoa, David Perez-Torres, Isabel Canas- Perez, Luis Tamayo-Lomas, Cristina Diaz-Rodriguez, Pablo Ruiz De Gopegui, Conclusion Mahmoud Saleh, Momin Majed Yousuf Hilles, Enas M. Y Abualqumboz, Nawfel Ben-Hamouda, Andrea Roberti, Yvan Fleury, Nour Abidi, Joerg C. Schefold, In critically ill old intensive care patients suffering from Ivan Chau, Alexander Dullenkopf, Mohammad Karam Chaaban, Mohammed COVID-19, most critically ill old COVID-19 patients had Mouaz Shebani, Ahmad Hmaideh, Aymen Shaher, Ayca Sultan Sahin, Kemal normal serum lactate on admission. However, in those Tolga Saracoglu, Mohammed Al-Sadawi, Richard Pugh, Sara Smuts and Rafat Ameen Mohammed Al-Saban. who had an elevated lactate, lactate and lactate kinetics are valuable tools for outcome prediction. Authors’ contributions BW, RRB and CJ analyzed the data and wrote the first draft of the manuscript. HF and BG and DL and IS contributed to statistical analysis and improved the Supplementary Information paper. SB and PHB and JM and MK and AB and AM and FA and AA and BG and The online version contains supplementary material available at https://​doi.​ MC and SC and LF and JF and ML and BM and RM and SO and CÖ and BBP org/​10.​1186/​s13613-​021-​00911-8. and MAB and WS and AV and CW and TZ and JCS and JW gave guidance and improved the paper. All authors read and approved the final manuscript. Additional file 1. List of Collaborators: COVIP-Study. Funding Open Access funding enabled and organized by Projekt DEAL. This study was endorsed by the ESICM. Free support for running the electronic database and Acknowledgements was granted from the dep. of Epidemiology, University of Aarhus, Denmark. The COVIP study group consists of the authors and the following persons: The support of the study in France by a Grant from Fondation Assistance Pub- Philipp Eller, Michael Joannidis, Dieter Mesotten, Pascal Reper, Sandra Oeyen, lique-Hôpitaux de Paris pour la recherche is greatly appreciated. In Norway, Walter Swinnen, Nicolas Serck, Elisabeth Dewaele, Edwin Chapeta, Helene Brix, the study was supported by a grant from the Health Region West. In addition, Jens Brushoej, Pritpal Kumar, Helene Korvenius Nedergaard, Tim Koch John- the study was supported by a Grant from the European Open Science Cloud sen, Camilla Bundesen, Maria Aagaard Hansen, Stine Uhrenholt, Helle Bund- (EOSC). EOSCsecretariat.eu has received funding from the European Union’s gaard, Jesper Fjølner, Richard Innes, James Gooch, Lenka Cagova, Elizabeth Horizon Programme call H2020-INFRAEOSC-05-2018-2019, Grant Agreement Potter, Michael Reay, Miriam Davey, Mohammed Abdelshafy Abusayed, Sally Number 831644. This work was supported by the Forschungskommission of Humphreys, Amy Collins, Avinash Aujayeb, Susannah Leaver, Waqas Khaliq, the Medical Faculty of the Heinrich-Heine-University Düsseldorf, and No. 2020- Ayman Abdelmawgoad Habib, Mohammed A Azab, Kyrillos Wassim, Yumna A. 21 to RRB for a Clinician Scientist Track. Elgazzar, Rehab Salah, Hazem Maarouf Abosheaishaa, Aliae AR Mohamed Hus- sein, Ahmed Y. Azzam, Samar Tharwat, Yasmin Khairy Nasreldin Mohamed Ali, Availability of data and materials Omar Elmandouh, Islam Galal, Ahmed Abu-Elfatth, Karam Motawea, Moham- Individual participant data that underlie the results reported in this article are mad Elbahnasawy, Mostafa Shehata, Mohamed Elbahnasawy, Mostafa Tayeb, available to investigators whose proposed use of the data has been approved Nermin Osman, Wafaa Abdel-Elsalam, Aliae Mohamed Hussein, Amer Aldhalia, by the COVIP steering committee. The anonymized data can be requested Arnaud Galbois, Bertrand Guidet, Cyril Charron, Caroline Hauw Berlemont, from the authors if required. Guillaume Besch, Jean-Philippe Rigaud, Julien Maizel, Michel Djibré, Philippe Burtin, Pierre Garcon, Saad Nseir, Xavier Valette, Nica Alexandru, Nathalie Marin, Marie Vaissiere, Gaëtan Plantefeve, Hervé Mentec, Thierry Vanderlinden, Igor Declarations Jurcisin, Buno Megarbane, Benjamin Glenn Chousterman, François Dépret, Marc Garnier, Sebastien Besset, Johanna Oziel, Alexis Ferre, Stéphane Dauger, Ethics approval and consent to participate Guillaume Dumas, Bruno Goncalves, Lucie Vettoretti, Didier Thevenin, Stefan The primary competent ethics committee was the Ethics Committee of the Schaller, Muhammed Kurt, Andreas Faltlhauser, Christian Meyer, Milena University of Duesseldorf, Germany. Institutional research ethic board approval Milovanovic, Matthias Lutz, Gonxhe Shala, Hendrik Haake, Winfried Randerath, was obtained from each study site. Anselm Kunstein, Patrick Meybohm, Stephan Steiner, Eberhard Barth, Tudor Poerner, Philipp Simon, Marco Lorenz, Zouhir Dindane, Karl Friedrich Kuhn, Consent for publication Martin Welte, Ingo Voigt, Hans-Joachim Kabitz, Jakob Wollborn, Ulrich Goebel, The manuscript does not contain any individual person’s data in any form. Sandra Emily Stoll, Detlef Kindgen-Milles, Simon Dubler, Christian Jung, Kristina Fuest, Michael Schuster, Stephan Steiner, Antonios Papadogoulas, Francesk Competing interests Mulita, Nikoletta Rovina, Zoi Aidoni, Evangelia Chrisanthopoulou, Eumorfia The authors declare that they have no competing interests. JCS reports Kondili, Ioannis Andrianopoulos, Mohan Gurjar, Ata Mahmoodpoor, Rand Hus- grants (full departmental disclosure) from Orion Pharma, Abbott Nutrition sein, Maytham Aqeel Al-Juaifari, Abdullah Khudhur Ahmed Karantenachy, Sigal International, B. Braun Medical AG, CSEM AG, Edwards Lifesciences Services Sviri, Ahmed Elsaka, Brian Marsh, Vittoria Comellini, Farah Al-Ali, Sari Almani, GmbH, Kenta Biotech Ltd, Maquet Critical Care AB, Omnicare Clinical Research Almu´Atasim Khamees, Khayry Al-Shami, Ibrahim Salah El Din, Taha Abubaker, AG, Nestle, Pierre Fabre Pharma AG, Pfizer, Bard Medica S.A., Abbott AG, Hazem Ahmed, Ahmed Rabha, Abdulmueti Alhadi, Marwa Emhamed, Anandic Medical Systems, Pan Gas AG Healthcare, Bracco, Hamilton Medical Saedah Abdeewi, Abdurraouf Abusalama, Abdulmueti Alhadi, Mohammed AG, Fresenius Kabi, Getinge Group Maquet AG, Dräger AG, Teleflex Medical Huwaysh, Esraa Abdalqader Alghati, Abdelilah Ghannam, Silvio A Namendys- GmbH, Glaxo Smith Kline, Merck Sharp and Dohme AG, Eli Lilly and Company, Sylva, Martijn Groenendijk, Mirjam Evers, Lenneke Van Lelyveld-Haas, Iwan Baxter, Astellas, Astra Zeneca, CSL Behring, Novartis, Covidien, Philips Medical, Meynaar, Alexander Daniel Cornet, Marieke Zegers, Willem Dieperink, Dylan Phagenesis Ltd, Prolong Pharmaceuticals and Nycomed outside the submit- De Lange, Tom Dormans, Michael Hahn, Britt Sjøbøe, Hans Frank Strietzel, ted work. The money went into departmental funds. No personal financial Theresa Olasveengen, Luis Romundstad, Finn H. Andersen, John George gain applied. Grace Massoud, Aamir Ghafoor Khan, Shahd Al-Qasrawi, Sarah Amro, Anna Kluzik, Paweł Zatorski, Tomasz Drygalski, Wojciech Szczeklik, Jakub Klimkiewicz, Author details 1 Joanna Solek-Pastuszka, Dariusz Onichimowski, Miroslaw Czuczwar, Ryszard Department of Cardiology, Pulmonology and Vascular Medicine, Medical Gawda, Jan Stefaniak, Karina Stefanska-Wronka, Ewa Zabul, Ana Isabel Pinho Faculty, Heinrich-Heine-University Duesseldorf, Moorenstraße 5, 40225 Dues- Oliveira, Rui Assis, Maria De Lurdes Campos Santos, Henrique Santos, Filipe seldorf, Germany. 2 Department of Anaesthesiology, Perioperative Medicine Sousa Cardoso, André Gordinho, Ioana Marina Grintescu, Dana Tomescu, and Intensive Care Medicine, Paracelsus Medical University, Salzburg, Austria. Bruno et al. Ann. Intensive Care (2021) 11:128 Page 9 of 9 3 Department of Clinical Medicine, University of Bergen, Bergen, Norway. 8. Singer M, Deutschman CS, Seymour CW, et al. The third international 4 Department of Anaestesia and Intensive Care, Haukeland University consensus definitions for sepsis and septic shock (sepsis-3). JAMA. Hospital, Bergen, Norway. 5 Department of Intensive Care, Aarhus University 2016;315(8):801–10. Hospital, Aarhus, Denmark. 6 Department of Intensive Care Medicine, CIBER 9. Alhazzani W, Evans L, Alshamsi F, et al. Surviving sepsis campaign Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli, guidelines on the management of adults with coronavirus disease 2019 Autonomous University of Barcelona, Sabadell, Spain. 7 Department of Acute (COVID-19) in the ICU: 55. Crit Care Med. 2021;49(3):e219-34. Medicine, Geneva University Hospitals, Geneva, Switzerland. 8 Department 10. Phua J, Weng L, Ling L, et al. 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