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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 ( 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 parameters , 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-mortality. 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 concentration 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).
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Brunoetal. Ann. Intensive Care (2021) 11:128
Lactate isassociated withmortality
invery old intensive care patients suering
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
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 ( 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).
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Open Access
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
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Brunoetal. Ann. Intensive Care (2021) 11:128
e disease caused by Sars-CoV-2, COVID-19, has domi-
nated daily life in numerous intensive care units (ICU)
worldwide, since the beginning of 2020. Respiratory fail-
ure with or without shock led to high mortality. In ICU
admitted patients, up to 30–50% of the patients did not
survive the first month [13]. us, early and reliable
identification of complex disease courses is of pivotal
importance in COVID-19 (Additional file1).
In emergency and critical care medicine, serum lac-
tate and its kinetics are useful parameters for critically ill
patients as a marker of severity of illness [46]. A signifi-
cant advantage is that the determination of serum lactate
is widely and rapidly available as a point-of-care measure-
ment [7]. Hyperlactatemia is an indicator of physiological
stress, and anaerobic metabolism, and a “powerful pre-
dictor of mortality” [6]. Basically, lactate can be used for
two purposes. It can be used both for risk stratification
and to monitor the response to therapy. Elevated lactate
is a diagnostic criterion for septic shock following the
sepsis-3 consensus. Lactate “clearance” is a target param-
eter for volume substitution in the absence of major liver
dysfunction [8]. Although serum lactate and its kinetics
have been applied as an essential diagnostic and target
parameter in septic patients for more than 20years, the
evidence remains scarce for patients suffering from pneu-
monia and ARDS.
Despite this lack of evidence, current guidelines recom-
mend the use of lactate and lactate kinetics in COVID-19
[9]. Until now, the value of serum lactate and its kinet-
ics in predicting a severe course in COVID-19 is unclear.
is lack of evidence is especially true in the particularly
vulnerable population of very old intensive care unit
patients. Yet, this subgroup has been disproportionally
affected by the need for ICU admissions and a high mor-
tality [3, 10, 11].
is multicenter study addresses this lack of evidence
and investigates the value of serum lactate at admission
and its kinetics for outcome prediction in a large pro-
spectively enrolled population of older ICU patients.
Design andsettings
is multicenter study is part of the Very old Intensive
care Patients (VIP) project and has been endorsed by the
European Society of Intensive Care Medicine (ESICM)
(https:// www. vipst udy. org). e study was registered at (ID: NCT04321265) and adhered to
the European Union General Data Privacy Regulation
(GDPR) directive. is investigation aimed to under-
stand which factors can predict mortality in elderly
COVID-19 patients to help detect these patients early
(the COVIP study, COVID-19 in very old intensive care
patients). As in the previous VIP studies [3, 12, 13],
national coordinators recruited the intensive care units
(ICUs), coordinated national and local ethical permis-
sions, and supervised patient recruitment at the national
level. Ethical approval was mandatory for study participa-
tion. In most countries informed consent was obligatory
for inclusion. is study extracted patient data from 151
ICUs from 26 independent countries, including Euro-
pean ICUs, and the Asian, African, and Americas.
Study population
e COVIP study recruited consecutieve patients with
proven COVID-19 aged 70 years or older who were
admitted to an ICU. e data set was extracted from the
COVIP study database on 4th February and contained
patients from 19th March 2020 to 4th February 2021.
Data collection started at ICU admission. Data about
pre-ICU triage were not available. e admission day was
defined as day 1, and all consecutive days were numbered
sequentially from that date.
Data collection
All centers used a uniform online electronic case
report form (eCRF). Only patients with a documented
highest serum lactate value on days 1 and 2 were
included for this subgroup analysis (above 2mmol/L).
Reporting was possible both in [mg/dL] or [mmol/L],
depending on local routine. For ease of comparison,
all laboratory values were converted to [mmol/] (1mg/
dL = 0.111mmol/L). The first arterial blood gas (ABG)
analysis, including pO2 [mmHg] and the FiO2 [%], was
recorded to calculate the pO2/FiO2-ratio on admission.
For the sequential organ failure assessment (SOFA)
score on admission, each element was entered and
the eCRF calculated the total score. Furthermore, we
assessed the need for non-invasive or invasive venti-
lation with its duration, prone positioning, tracheos-
tomy, vasopressor use and renal replacement therapy.
The eCRF also documented any limitation of life-sus-
taining therapy during the ICU-stay. The frailty level
prior to the acute illness and hospital admission was
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.
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Brunoetal. Ann. Intensive Care (2021) 11:128
assessed using the Clinical Frailty Scale (CFS) [3, 12,
13]. In addition, the eCRF recorded information about
gender, age, length of ICU stay symptom onset, and
duration of symptoms before ICU-and hospital admis-
sion. Furthermore, the eCRF asked about the presence
of preexisting comorbidities.
Lactate and∆Lactate
Patients were clustered according to their lac-
tate concentration on ICU admission. The arbi-
trary cutoff value was an initial lactate concentration
2.0mmol/L. A lactate value below 2 mmol/L was
defined as within the normal range. ∆ Lactate in the
first 24h was defined as maximum serum lactate at
admission minus maximum serum lactate on day 2,
divided by lactate at admission multiplied by 100 [4].
A positive value indicates a fall in serum lactate and a
negative value signifies rising serum lactate. This has
been confirmed in larger cohorts as a valuable and
simple tool for outcome prediction [4].
Data storage
The eCRF and database were hosted on a secure server
in Aarhus University, Denmark.
Lactate =100×
Maximum serum lactate on admission
Maximum serum lactate on day2
Maximum serum lactate on admission
Statistical analysis
e primary outcome was ICU mortality, secondary out-
comes were 30-day and 3-month mortality. Continuous
data points were expressed as median and interquartile
range. Differences between independent groups were cal-
culated using the MannWhitney U test. Categorical data
are expressed as numbers (percentage). e Chi-square
test was applied to calculate differences between groups.
Univariate und multivariable logistic regression analy-
ses were performed to assess associations with baseline
serum lactate and mortality. We chose the co-variables
for the multivariable model (age, SOFA score, CFS and
gender) based on clinical experience and previous lit-
erature [4, 5]. Marginal predictive means with respective
95% confidence intervals (CI) were calculated. All tests
were two-sided, and a p value of < 0.05 was considered
statistically significant. Stata 16 was used for all statistical
analyses (StataCorp LLC, 4905 Lakeway Drive, College
Station, Brownsville, Texas, USA).
Fig. 1 CONSORT diagram
Study population
In total, 2860 patients with an available baseline serum
lactate value were included (Fig.1). Overall, 68% (1940
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Brunoetal. Ann. Intensive Care (2021) 11:128
patients) patients had no abnormal elevation of serum
lactate on the day of admission to the ICU, while 32%
(920 patients) evidenced an elevated serum lactate
(Fig.2) (Table1).
Baseline serum lactate
Patients with a lactate greater than/equal to 2mmol/L
were older [76 (72–79) vs. 75 (72–78) years, p = 0.007],
but not more frail [CFS 3 (2–4) vs. 3 (2–4), p = 0.47].
Patients with elevated serum lactate had a higher SOFA
score on admission (Table1). ere were no differences
in symptom duration or time in the hospital before ICU
admission [Symptoms prior to hospitalization 2 days
(1–5) vs. 2 days (1–5), p = 0.96; symptoms prior to
ICU-admission 6days (3–9) vs. 7days (3–10), p = 0.38].
Both groups evidenced similar incidences of pre-exist-
ing comorbidities (diabetes, coronary vascular disease,
chronic renal failure, arterial hypertension, pulmonary
disease) with the exception for heart failure, which was
significantly more often in patients with an elevated base-
line serum lactate (18% vs. 14%, p = 0.004).
On day 2, the maximum serum lactate was lower in
the group of patients with initially elevated serum lactate
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). e 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]. ere was no difference regarding non-inva-
sive ventilation [25% (230) vs. 26% (501), p = 0.61] or tra-
cheostomy [19% (173) vs. 19% (364), p = 0.95]. Patients
with elevated baseline lactate had a significantly higher
Fig. 2 Distribution of maximum serum lactate values on day 1 (= day
of ICU-admission), [mmol/L]
Table 1 Baseline characteristics
CFS clinical frailty scale, SOFA score sequential organ failure Assessment for the rst 24h, IQR interquartile range
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
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Brunoetal. Ann. Intensive Care (2021) 11:128
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
(p = 0.12).
In a univariate regression analysis, the baseline lactate
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
1.16 95% CI 1.09–1.23; p < 0.001).
In the multivariable analysis, the maximum lactate con-
centration 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, SOFA, CFS and sex
∆ Serum lactate in24h
In 826 patients (29%), there was a baseline lactate
2mmol/L and sufficient data to calculate the ∆ serum
lactate over 24h. In both sub-groups, patients were pre-
dominantly male (p = 0.37, Table 3). ere were no dif-
ferences regarding age, SOFA score on admission, prior
hospitalizations, or the duration of symptoms before
ICU admission. e median CFS did not differ (Table3).
For pre-existing comorbidities, there was no difference
between groups except for pulmonary diseases, which
were significantly more common in those patients with
rising lactate [27% (n = 58) vs. 19% (n = 117), p = 0.017].
Intensive care treatment, especially regarding non-
invasive ventilation [27% (57) vs. 24% (146), p = 0.45],
vasoactive drugs [70% (151) vs. 74% (444), p = 0.34],
and renal replacement therapy [21% (45) vs. 18% (112),
p = 0.42] did not differ between both groups. In patients
with ∆ serum lactate over 24 h less than 0, intubation
occurred in 71% (152) compared to 76% (464) (p = 0.12),
prone positioning was used in 48% (72) compared to
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
from the group of patients with a ∆ serum lactate 0%
(18%, p = 0.79). erapy limitations or de-escalations did
also not differ between groups. erapy was withheld
in 29% (62) of the patients with a negative ∆ serum lac-
tate, and in 30% (185) of the other group (p = 0.68), while
treatment was withdrawn in 19% (40) and 22% (134) of
the patients, respectively (p = 0.32). Length of stay was
longer in patients with a ∆ Lactate 24h > 0% [285 (439)
h vs. 348h (400), p = 0.029]. After exclusion of non-sur-
vivors, there was no significant difference between both
groups [475 (526) h vs. 456h (552), p = 4.77]. e dura-
tion of mechanical ventilation was significantly longer in
Table 2 Primary and secondary outcomes
ICU intensive care unit
Variables Baseline Lactate 2mmol/L Baseline Lac tate < 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
Fig. 3 Kaplan–Meier for patients with a baseline lactate 2 mmol/L
(red line) compared to patients with a baseline lactate < 2 mmol/L
(blue line) (3-month mortality, ± standard deviation)
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Brunoetal. Ann. Intensive Care (2021) 11:128
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).
is 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
with elevated baseline lactate and patients with rising
lactate suffered from significantly higher ICU-mortality.
ese parameters were independently associated with
ICU-mortality. To our knowledge, this study represents
the first prospective observational study in critically ill
Table 3 Baseline characteristics of the subgroup analysis according to the Δ Lactate 24 h [%]
CFS clinical frailty scale, SOFA score sequential organ failure assessment for the rst 24h, IQR interquartile range
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
Table 4 Primary and secondary outcomes of the subgroup analysis according to the Δ Lactate 24 h [%]
ICU intensive care unit
Variables ∆ Lactate 24h 0% ∆ Lac tate 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
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)
Page 7 of 9
Brunoetal. Ann. Intensive Care (2021) 11:128
old patients with COVID-19 on the value of lactate and
lactate kinetics for outcome prediction.
ese findings are of pivotal importance as lactate and
its kinetics are already amongst the most important labo-
ratory parameters for diagnosing septic shock and guiding
treatment. e current sepsis guidelines advocate meas-
urement of lactate at ICU admission and during ICU stay
as one of the best variables to assess the response to treat-
ment [14]. Indeed, a meta-analysis by Pan etal. included
seven randomized controlled trials with 1301 patients to
compare the early lactate clearance-directed therapy to
central venous oxygen saturation (ScvO2)-guided ther-
apy as a potentially more effective resuscitation target.
ey concluded that the use of an early lactate clearance-
directed therapy resulted in a decreased in-hospital mor-
tality, shorter ICU stay, shorter mechanical ventilation
time, and lower APACHE II scores [15].
COVID-19 might be considered as a very unique type
of sepsis. us, the “First Update on the Surviving Sep-
sis Campaign Guidelines on the Management of Adults
with Coronavirus Disease 2019 (COVID-19) in the
ICU” were developed [9]. In this statement, the authors
suggest using dynamic parameters, such as serum lac-
tate measurement, over static parameters to assess fluid
responsiveness in adults with COVID-19 and shock,
though with a weak level of evidence [9].
Severe COVID-19 is usually characterized by a fast-
developing pneumonitis and respiratory failure. Currently,
there are only very limited data about serum lactate values
in this special type of pneumonitis and in pneumonia in
general: Gwak etal. collected consecutive data from 397
patients who were hospitalized with community acquired
pneumonia (CAP); 18% of these patients were admitted to
the ICU. ey found an independent association between
the initial serum lactate concentration and in-hospital
mortality (aOR1.24; 95% CI 1.01–1.53) [16]. A subgroup
analysis of the INFAUCI-Study by Pereira etal. investi-
gated prognostic markers in patients suffering from severe
community (in most cases bacterial) acquired pneumonia.
In their analysis, the mean serum lactate on admission was
higher than in our study (3.0 ± 3.1mmol/L) and indepen-
dently associated with intra-hospital mortality (OR 1.13;
95% CI 1.00–1.32, in a sub-group analysis of patients with
septic shock: OR 1.11; 95% CI 1.00–1.37, respectively).
Interestingly, they found no association with mortality at
6months [17]. In another retrospective analysis involving
553 patients, Jo etal. showed the non-inferiority of a base-
line serum lactate combined with an early warning score
compared to established pneumonia scores such as CURB-
65 in predicting outcomes of patients with CAP [18].
Regarding serum lactate levels in COVID-19, the pre-
sent study is in line with Goodall et al., who found a
relationship between higher lactate levels (aHR 2.67)
and an increased mortality in 981 patients [19]. Kayina
etal. examined 235 patients and demonstrated that non-
survivors had a higher baseline serum lactate (p < 0.01,
n = 122) [20]. In a very small cohort of 45 ICU-patients
suffering from COVID-19, Vassiliou et al. found that
maximum lactate on admission was independently
related to 28-day ICU-mortality. Lactate’s area under
the curve for detecting 28-day ICU mortality was 0.77
(p = 0.008). Mixed model analysis showed that mean daily
lactate levels were higher in non-survivors (p < 0.0001).
Interestingly, when lactate levels were compared to the
SOFA scores they showed a similar time pattern [21]. A
retrospective cohort study by Gregoriano etal. included
99 patients with severe COVID-19. In this cohort, lactate
on admission was amongst the highest prognostic factors
for severe COVID-19 progression (lactate on ambient air
AUC 0.67; lactate with O2 supply AUC 0.70) [22].
However, the present study also reveals another very
relevant insight into old ICU patients with COVID-
19: almost 70% of patients had no elevated lactate at all
on admission. is contradicts previous studies, such
as by Li et al. [23]. ese found, in 204 older patients
( 60years) diagnosed with COVID-19, an elevated lac-
tate (median 2.3mmol/L) in 84% of the patients [23].
Our study has some methodological limitations. We lack a
control group of younger COVID-19 patients for compari-
son or a comparable age cohort of patients who were not or
could not be admitted to the ICU. In addition, the COVIP
database does not capture information on time from symp-
toms onset to ICU admission, from pre-ICU care and triage
or from and level of care, while in the ICU (e.g., nurse-to-
patient ratio). ese treatment limitations may affect the
care of older ICU patients [24]. Participating countries
varied widely in their care structure. is results in a large
heterogeneity of treatments. e study also cannot answer
whether lactate kinetics-guided therapy prospectively
gives a mortality benefit in critically ill septic patients with
COVID-19. When lactate values were documented, only
the first 48h were recorded, with the highest value per 24h
in each case. However, these parameters have been used as
a benchmark in most studies to date, so the determination
seems more than adequate to answer the hypothesis. e
∆ Lactate subgroup analysis lacks patients who died in the
first 24h. However, this accounted only for 32 patients of
the study cohort. Pre-existing liver disease might influence
lactate and its kinetics. Nevertheless, in the multivariate
analysis, results have been adjusted to SOFA score which
includes liver function. Possibly, patients with pulmonary
artery embolism are more frequently in shock leading to
elevated lactate. ough, this association is speculative as
the study did not investigate the occurrence of pulmonary
Page 8 of 9
Brunoetal. Ann. Intensive Care (2021) 11:128
artery embolism as there were no reports of clustered pul-
monary artery emboli when establishing the COVIP study
design in February 2020.
In critically ill old intensive care patients suffering from
COVID-19, most critically ill old COVID-19 patients had
normal serum lactate on admission. However, in those
who had an elevated lactate, lactate and lactate kinetics
are valuable tools for outcome prediction.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s13613- 021- 00911-8.
Additional le1. List of Collaborators: COVIP-Study.
The COVIP study group consists of the authors and the following persons:
Philipp Eller, Michael Joannidis, Dieter Mesotten, Pascal Reper, Sandra Oeyen,
Walter Swinnen, Nicolas Serck, Elisabeth Dewaele, Edwin Chapeta, Helene Brix,
Jens Brushoej, Pritpal Kumar, Helene Korvenius Nedergaard, Tim Koch John-
sen, Camilla Bundesen, Maria Aagaard Hansen, Stine Uhrenholt, Helle Bund-
gaard, Jesper Fjølner, Richard Innes, James Gooch, Lenka Cagova, Elizabeth
Potter, Michael Reay, Miriam Davey, Mohammed Abdelshafy Abusayed, Sally
Humphreys, Amy Collins, Avinash Aujayeb, Susannah Leaver, Waqas Khaliq,
Ayman Abdelmawgoad Habib, Mohammed A Azab, Kyrillos Wassim, Yumna A.
Elgazzar, Rehab Salah, Hazem Maarouf Abosheaishaa, Aliae AR Mohamed Hus-
sein, Ahmed Y. Azzam, Samar Tharwat, Yasmin Khair y Nasreldin Mohamed Ali,
Omar Elmandouh, Islam Galal, Ahmed Abu-Elfatth, Karam Motawea, Moham-
mad Elbahnasawy, Mostafa Shehata, Mohamed Elbahnasawy, Mostafa Tayeb,
Nermin Osman, Wafaa Abdel-Elsalam, Aliae Mohamed Hussein, Amer Aldhalia,
Arnaud Galbois, Bertrand Guidet, Cyril Charron, Caroline Hauw Berlemont,
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
Jurcisin, Buno Megarbane, Benjamin Glenn Chousterman, François Dépret,
Marc Garnier, Sebastien Besset, Johanna Oziel, Alexis Ferre, Stéphane Dauger,
Guillaume Dumas, Bruno Goncalves, Lucie Vettoretti, Didier Thevenin, Stefan
Schaller, Muhammed Kurt, Andreas Faltlhauser, Christian Meyer, Milena
Milovanovic, Matthias Lutz, Gonxhe Shala, Hendrik Haake, Winfried Randerath,
Anselm Kunstein, Patrick Meybohm, Stephan Steiner, Eberhard Barth, Tudor
Poerner, Philipp Simon, Marco Lorenz, Zouhir Dindane, Karl Friedrich Kuhn,
Martin Welte, Ingo Voigt, Hans-Joachim Kabitz, Jakob Wollborn, Ulrich Goebel,
Sandra Emily Stoll, Detlef Kindgen-Milles, Simon Dubler, Christian Jung, Kristina
Fuest, Michael Schuster, Stephan Steiner, Antonios Papadogoulas, Francesk
Mulita, Nikoletta Rovina, Zoi Aidoni, Evangelia Chrisanthopoulou, Eumorfia
Kondili, Ioannis Andrianopoulos, Mohan Gurjar, Ata Mahmoodpoor, Rand Hus-
sein, Maytham Aqeel Al-Juaifari, Abdullah Khudhur Ahmed Karantenachy, Sigal
Sviri, Ahmed Elsaka, Brian Marsh, Vittoria Comellini, Farah Al-Ali, Sari Almani,
Almu´Atasim Khamees, Khayry Al-Shami, Ibrahim Salah El Din, Taha Abubaker,
Hazem Ahmed, Ahmed Rabha, Abdulmueti Alhadi, Marwa Emhamed,
Saedah Abdeewi, Abdurraouf Abusalama, Abdulmueti Alhadi, Mohammed
Huwaysh, Esraa Abdalqader Alghati, Abdelilah Ghannam, Silvio A Namendys-
Sylva, Martijn Groenendijk, Mirjam Evers, Lenneke Van Lelyveld-Haas, Iwan
Meynaar, Alexander Daniel Cornet, Marieke Zegers, Willem Dieperink, Dylan
De Lange, Tom Dormans, Michael Hahn, Britt Sjøbøe, Hans Frank Strietzel,
Theresa Olasveengen, Luis Romundstad, Finn H. Andersen, John George
Grace Massoud, Aamir Ghafoor Khan, Shahd Al-Qasrawi, Sarah Amro, Anna
Kluzik, Paweł Zatorski, Tomasz Drygalski, Wojciech Szczeklik, Jakub Klimkiewicz,
Joanna Solek-Pastuszka, Dariusz Onichimowski, Miroslaw Czuczwar, Ryszard
Gawda, Jan Stefaniak, Karina Stefanska-Wronka, Ewa Zabul, Ana Isabel Pinho
Oliveira, Rui Assis, Maria De Lurdes Campos Santos, Henrique Santos, Filipe
Sousa Cardoso, André Gordinho, Ioana Marina Grintescu, Dana Tomescu,
Mohamed Raafat Badawy, M José Arche Banzo, Begoña Zalba-Etayo, Patricia
Jimeno Cubero, Jesús Priego, Gemma Gomà, Teresa Maria Tomasa-Irriguible,
Susana Sancho, Aida Fernández Ferreira, Eric Mayor Vázquez, Ángela Prado
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,
Mahmoud Saleh, Momin Majed Yousuf Hilles, Enas M. Y Abualqumboz, Nawfel
Ben-Hamouda, Andrea Roberti, Yvan Fleury, Nour Abidi, Joerg C. Schefold,
Ivan Chau, Alexander Dullenkopf, Mohammad Karam Chaaban, Mohammed
Mouaz Shebani, Ahmad Hmaideh, Aymen Shaher, Ayca Sultan Sahin, Kemal
Tolga Saracoglu, Mohammed Al-Sadawi, Richard Pugh, Sara Smuts and Rafat
Ameen Mohammed Al-Saban.
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
paper. SB and PHB and JM and MK and AB and AM and FA and AA and BG and
MC and SC and LF and JF and ML and BM and RM and SO and CÖ and BBP
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.
Open Access funding enabled and organized by Projekt DEAL. This study was
endorsed by the ESICM. Free support for running the electronic database and
was granted from the dep. of Epidemiology, University of Aarhus, Denmark.
The support of the study in France by a Grant from Fondation Assistance Pub-
lique-Hôpitaux de Paris pour la recherche is greatly appreciated. In Norway,
the study was supported by a grant from the Health Region West. In addition,
the study was supported by a Grant from the European Open Science Cloud
(EOSC). has received funding from the European Union’s
Horizon Programme call H2020-INFRAEOSC-05-2018-2019, Grant Agreement
Number 831644. This work was supported by the Forschungskommission of
the Medical Faculty of the Heinrich-Heine-University Düsseldorf, and No. 2020-
21 to RRB for a Clinician Scientist Track.
Availability of data and materials
Individual participant data that underlie the results reported in this article are
available to investigators whose proposed use of the data has been approved
by the COVIP steering committee. The anonymized data can be requested
from the authors if required.
Ethics approval and consent to participate
The primary competent ethics committee was the Ethics Committee of the
University of Duesseldorf, Germany. Institutional research ethic board approval
was obtained from each study site.
Consent for publication
The manuscript does not contain any individual person’s data in any form.
Competing interests
The authors declare that they have no competing interests. JCS reports
grants (full departmental disclosure) from Orion Pharma, Abbott Nutrition
International, B. Braun Medical AG, CSEM AG, Edwards Lifesciences Services
GmbH, Kenta Biotech Ltd, Maquet Critical Care AB, Omnicare Clinical Research
AG, Nestle, Pierre Fabre Pharma AG, Pfizer, Bard Medica S.A., Abbott AG,
Anandic Medical Systems, Pan Gas AG Healthcare, Bracco, Hamilton Medical
AG, Fresenius Kabi, Getinge Group Maquet AG, Dräger AG, Teleflex Medical
GmbH, Glaxo Smith Kline, Merck Sharp and Dohme AG, Eli Lilly and Company,
Baxter, Astellas, Astra Zeneca, CSL Behring, Novartis, Covidien, Philips Medical,
Phagenesis Ltd, Prolong Pharmaceuticals and Nycomed outside the submit-
ted work. The money went into departmental funds. No personal financial
gain applied.
Author details
1 Department of Cardiology, Pulmonology and Vascular Medicine, Medical
Faculty, Heinrich-Heine-University Duesseldorf, Moorenstraße 5, 40225 Dues-
seldorf, Germany. 2 Department of Anaesthesiology, Perioperative Medicine
and Intensive Care Medicine, Paracelsus Medical University, Salzburg, Austria.
Page 9 of 9
Brunoetal. Ann. Intensive Care (2021) 11:128
3 Department of Clinical Medicine, University of Bergen, Bergen, Norway.
4 Department of Anaestesia and Intensive Care, Haukeland University
Hospital, Bergen, Norway. 5 Department of Intensive Care, Aarhus University
Hospital, Aarhus, Denmark. 6 Department of Intensive Care Medicine, CIBER
Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli,
Autonomous University of Barcelona, Sabadell, Spain. 7 Department of Acute
Medicine, Geneva University Hospitals, Geneva, Switzerland. 8 Department
of Intensive Care Medicine, Inselspital, Universitätsspital, University of Bern,
Bern, Switzerland. 9 Deptartment of Medical Intensive Care, Hadassah Medical
Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem,
Israel. 10 General Intensive Care Unit, Deptartment of Anesthesiology, Critical
Care and Pain Medicine, Hadassah Medical Center and Faculty of Medicine,
Hebrew University of Jerusalem, Jerusalem, Israel. 11 Center for Intensive Care
and Perioperative Medicine, Jagiellonian University Medical College, Krakow,
Poland. 12 Faculty of Medicine, University of Tripoli, Tripoli, Libya. 13 Division
of Intensive Care and Emergency Medicine, Department of Internal Medicine,
Medical University Innsbruck, Innsbruck, Austria. 14 Department of Intensive
Care 1K12IC, Ghent University Hospital, Ghent, Belgium. 15 Intensive Care Unit
General Hospital of Larissa, Larissa, Greece. 16 Department of Anesthesiolgy,
Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard
Medical School, Boston, USA. 17 Hospital Clínico Universitario Lozano Blesa,
Zaragoza, Spain. 18 Department of Anesthesiology and Intensive Care, Klinikum
Rechts Der Isar, Technical University of Munich, Munich, Germany. 19 Mater Mis-
ericordiae University Hospital, Dublin, Ireland. 20 Department of Anaesthesia
and Intensive Care, Ålesund Hospital, Ålesund, Norway. 21 Department of Cir-
culation and Medical Imaging, Norwegian University of Science and Technol-
ogy, Trondheim, Norway. 22 Unidade de Cuidados Intensivos Neurocríticos
E Trauma, Hospital de São José, Centro Hospitalar Universitário de Lisboa
Central, Faculdade de Ciências Médicas de Lisboa, Nova Medical School,
Lisbon, Portugal. 23 General Intensive Care, St George´S University Hospitals
NHS Foundation Trust, London, UK. 24 Institut Pierre Louis D’Epidémiologie Et
de Santé Publique, Equipe: épidémiologie hospitalière qualité et organisa-
tion des soins, Sorbonne Universités, UPMC Univ Paris 06, INSERM, UMR_S
1136, 75012 Paris, France. 25 Assistance Publique–Hôpitaux de Paris, service de
réanimation médicale, Hôpital Saint-Antoine, 75012 Paris, France. 26 Depart-
ment of Intensive Care Medicine, University Medical Center, University Utrecht,
Utrecht, The Netherlands.
Received: 31 May 2021 Accepted: 25 July 2021
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Full-text available
Background The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19 ICU patients. Methods A prospective multicentre study of COVID-19 patients ≥ 70 years admitted to intensive care in 138 ICUs from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the clinical frailty scale. Additionally, comorbidities, management strategies and treatment limitations were recorded. Results The study included 1346 patients (28% female) with a median age of 75 years (IQR 72–78, range 70–96), 16.3% were older than 80 years, and 21% of the patients were frail. The overall survival at 30 days was 59% (95% CI 56–62), with 66% (63–69) in fit, 53% (47–61) in vulnerable and 41% (35–47) in frail patients ( p < 0.001). In frail patients, there was no difference in 30-day survival between different age categories. Frailty was linked to an increased use of treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treatment limitations and comorbidities, frailty was independently associated with lower survival. Conclusion Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and comorbidities. Trial registration NCT04321265 , registered 19 March 2020.
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Importance: Among COVID-19 cases, especially the (frail) elderly show a high number of severe infections, hospital admissions, complications, and death. The highest mortality is found between 80 and 89 years old. Why do these patients have a higher risk of severe COVID-19? In this narrative review we address potential mechanisms regarding viral transmission, physical reserve and the immune system, increasing the severity of this infection in elderly patients. Observations: First, the spread of COVID-19 may be enhanced in elderly patients. Viral shedding may be increased, and early identification may be complicated due to atypical disease presentation and limited testing capacity. Applying hygiene and quarantine measures, especially in patients with cognitive disorders including dementia, can be challenging. Additionally, elderly patients have a decreased cardiorespiratory reserve and are more likely to have co-morbidity including atherosclerosis, rendering them more susceptible to complications. The aging innate and adaptive immune system is weakened, while there is a pro-inflammatory tendency. The effects of SARS-CoV-2 on the immune system on cytokine production and T-cells, further seem to aggravate this pro-inflammatory tendency, especially in patients with cardiovascular comorbidity, increasing disease severity. Conclusions and relevance: The combination of all factors mentioned above contribute to the disease severity of COVID-19 in the older patient. While larger studies of COVID-19 in elderly patients are needed, understanding the factors increasing disease severity may improve care and preventative measures to protect the elderly patient at risk for (severe) COVID-19 in the future.
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COVID-19 has caused a major global pandemic and necessitated unprecedented public health restrictions in almost every country. Understanding risk factors for severe disease in hospitalised patients is critical as the pandemic progresses. This observational cohort study aimed to characterise the independent associations between the clinical outcomes of hospitalised patients and their demographics, comorbidities, blood tests and bedside observations. All patients admitted to Northwick Park Hospital, London, UK between 12 March and 15 April 2020 with COVID-19 were retrospectively identified. The primary outcome was death. Associations were explored using Cox proportional hazards modelling. The study included 981 patients. The mortality rate was 36.0%. Age (adjusted hazard ratio (aHR) 1.53), respiratory disease (aHR 1.37), immunosuppression (aHR 2.23), respiratory rate (aHR 1.28), hypoxia (aHR 1.36), Glasgow Coma Scale <15 (aHR 1.92), urea (aHR 2.67), alkaline phosphatase (aHR 2.53), C-reactive protein (aHR 1.15), lactate (aHR 2.67), platelet count (aHR 0.77) and infiltrates on chest radiograph (aHR 1.89) were all associated with mortality. These important data will aid clinical risk stratification and provide direction for further research.
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Coronavirus disease-19 (COVID-19) continues to be a health threat worldwide. Increased blood lactate is common in intensive care unit (ICU) patients; however, its association with outcomes in ICU COVID-19 patients remains currently unexplored. In this retrospective, observational study we assessed whether lactate is associated with outcomes in COVID-19 patients. Blood lactate was measured on ICU admission and thereafter daily up to day 14 in 45 patients with confirmed COVID-19 pneumonia. Acute physiology and chronic health evaluation (APACHE II) was calculated on ICU admission, and sequential organ failure assessment (SOFA) score was assessed on admission and every second day. The cohort was divided into survivors and non-survivors based on 28-day ICU mortality (24.4%). Cox regression analysis revealed that maximum lactate on admission was independently related to 28-day ICU mortality with time in the presence of APACHE II (RR = 2.45, p = 0.008). Lactate’s area under the curve for detecting 28-day ICU mortality was 0.77 (p = 0.008). Mixed model analysis showed that mean daily lactate levels were higher in non-survivors (p < 0.0001); the model applied on SOFA scores showed a similar time pattern. Thus, initial blood lactate was an independent outcome predictor in COVID-19 ICU patients. The time course of lactate mirrors organ dysfunction and is associated with poor clinical outcomes.
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Background The outbreak of Coronavirus Disease 2019 (COVID-19) has become a global public health emergency. Methods 204 elderly patients (≥60 years old) diagnosed with COVID-19 in Renmin Hospital of Wuhan University from January 31st to February 20th, 2020 were included in this study. Clinical endpoint was in-hospital death. Results Of the 204 patients, hypertension, diabetes, cardiovascular disease, and chronic obstructive pulmonary disease (COPD) were the most common coexisting conditions. 76 patients died in the hospital. Multivariate analysis showed that dyspnea (hazards ratio (HR) 2.2, 95% confidence interval (CI) 1.414 to 3.517;p < 0.001), older age (HR 1.1, 95% CI 1.070 to 1.123; p < 0.001), neutrophilia (HR 4.4, 95% CI 1.310 to 15.061; p = 0.017) and elevated ultrasensitive cardiac troponin I (HR 3.9, 95% CI 1.471 to 10.433; p = 0.006) were independently associated with death. Conclusion Although so far the overall mortality of COVID-19 is relatively low, the mortality of elderly patients is much higher. Early diagnosis and supportive care are of great importance for the elderly patients of COVID-19.
Purpose: Early lactate clearance is an important parameter for prognosis assessment and therapy control in sepsis. Patients with a lactate clearance >0% might differ from patients with an inferior clearance in terms of intensive care management and outcomes. This study analyzes a large collective with regards to baseline risk distribution and outcomes. Methods: In total, 3299 patients were included in this analysis, consisting of 1528 (46%) ≤0% and 1771 (54%) >0% patients. The primary endpoint was intensive care unit (ICU) mortality. Multilevel logistic regression analyses were used to compare both groups: A baseline model (model 1) with lactate clearance as a fixed effect and ICU as a random effect was installed. For model 2, patient characteristics (model 2) were included. For model 3, intensive care treatment (mechanical ventilation and vasopressors) was added to the model. Models 1 and 2 were used to evaluate the primary and secondary outcomes, respectively. Model 3 was only used to evaluate the primary outcomes. Adjusted odds ratios (aORs) with respective 95% confidence intervals (CI) were calculated. Results: The cohorts had no relevant differences regarding the gender, BMI, age, heart rate, body temperature, and baseline lactate. Neither the primary infection focuses nor the ethnic background differed between both groups. In both groups, the most common infection sites were of pulmonary origin, the urinary tract, and the gastrointestinal tract. Patients with lactate clearance >0% evidenced lower sepsis-related organ failure assessment (SOFA) scores (7 ± 6 versus 9 ± 6; p < 0.001) and creatinine (1.53 ± 1.49 versus 1.80 ± 1.67; p < 0.001). The ICU mortality differed significantly (14% versus 32%), and remained this way after multivariable adjustment for patient characteristics and intensive care treatment (aOR 0.43 95% CI 0.36-0.53; p < 0.001). In the additional sensitivity analysis, the lack of lactate clearance was associated with a worse prognosis in each subgroup. Conclusion: In this large collective of septic patients, the 6 h lactate clearance is an independent method for outcome prediction.
The emergence of coronavirus disease 2019 (COVID‐19) has led to high demand for intensive care services worldwide. However, the mortality of patients admitted to the Intensive Care Unit (ICU) with COVID‐19 is unclear. Here, we perform a systematic review and meta‐analysis, in line with PRISMA guidelines, to assess the reported ICU mortality for patients with confirmed COVID‐19. We searched MEDLINE, EMBASE, PubMed and Cochrane databases up to 31 May 2020 for studies reporting ICU mortality for adult patients admitted with COVID‐19. The primary outcome measure was death in intensive care as a proportion of completed intensive care unit admissions, either through discharge from the ICU or death. The definition thus excluded patients still alive on ICU. Twenty‐four observational studies including 10,150 patients were identified from centres across Asia, Europe, and North America. In‐ICU mortality in reported studies ranged from 0–84.6%. Seven studies reported outcome data for all patients. In the remaining studies, the proportion of patients discharged from ICU at the point of reporting varied from 24.5–97.2%. In patients with completed ICU admissions with COVID‐19 infection, combined ICU mortality was 41.6% (95%CI 34.0–49.7%, I ² = 93.2%). Subgroup analysis by continent showed that mortality is broadly consistent across the globe. As the pandemic has progressed the reported mortality rates have reduced from above 50% to close to 40%. The in‐ICU mortality from COVID‐19 is higher than usually seen in ICU admissions with other viral pneumonias. Importantly, the mortality from completed episodes of ICU differs considerably from the crude mortality rates in some early reports.