Abstract
Introduction
In this study, we aimed to investigate the relationship between blood lactate levels and lactate kinetics (lactate clearance and Δ lactate) for predicting mortality in patients with COVID-19 admitted to the emergency department.
Methods
This study was performed as a retrospective study that included patients admitted to the emergency department between March 1st, 2020, and January 1st, 2022. Lactate levels were recorded at the first admission (0 h lactate) and the highest blood lactate levels in the first 24 h of follow-up (2nd highest lactate). Lactate kinetics were calculated. Clinical severity was determined according to the quick COVID Severity Index (qCSI).
Results
300 patients were included in the study. Lactate levels at admission were similar in groups with or without mortality, but 2nd highest lactate levels were found to be significantly higher in the group with mortality (p < 0.001). Lactate clearance and ∆ lactate levels were also found to be lower in the mortality group (p < 0.001). Lactate kinetics in patients in the clinically low severity group were lower in the mortality group (p = 0.02 and p = 0.039, respectively). In the low-intermediate and high-intermediate groups, 0-h lactate and 2nd highest lactate levels were found to be higher in the mortality group, and lactate kinetics were similar in the groups with and without mortality. In the group with high clinical severity, 2nd highest lactate levels were found to be higher in the group with mortality (p = 0.010). Lactate kinetics were also found to be significantly lower in the mortality group (p < 0.001).
In the high qCSI group, based on ROC analysis, the AUC for 2nd highest lactate levels predicting mortality was 0.642 (95% CI: 0.548–0.728). The optimal cut-off value for mortality was greater than >2.4 mmol/L (60.6% sensitivity, 67.4% specificity). The AUC for lactate clearance was 0.748 (95% CI: 0.659–0.824). The lactate clearance cut-off value was ≤ −177.78% (49.3% sensitivity, 100% specificity). The AUC for ∆ lactate was 0.707 (95% CI: 0.616–0.787). The optimal ∆ lactate cut-off was ≤ −2 mmol/L (45.1% sensitivity, 93.5% specificity).
Conclusion
In COVID-19, 2nd highest blood lactate and lactate kinetics were found to be prognostic indicators of the disease. High 2nd highest lactate levels and low lactate kinetics in patients with high clinical severity were guiding physicians regarding the outcome of the disease.
Keywords: COVID-19, Lactate, Lactate kinetics, qCSI, Mortality
1. Introduction
Coronavirus disease 2019 (COVID-19) occurs in a wide clinical spectrum, from asymptomatic cases to severe pneumonia. A study conducted before vaccination stated that 33% of people with COVID-19 infection did not show any symptoms [1]. Although 81% of symptomatic patients show mild symptoms, 14% have severe, 5% have a critical illness, and the death rate in the critically ill group is 49% [2].
Increased blood lactate concentration has been used in critically ill patients to predict disease severity, morbidity, and mortality, identify specific treatments, and monitor the adequacy and timing of medical intervention. Lactate levels, which can be measured at the bedside when necessary and provide rapid results, have been seen as a valuable parameter in critically ill patients [3]. Repeated lactate measurements in critical patient follow-up have a more reliable prognostic value than a single lactate measurement at the beginning [4]. The Sepsis-3 consensus defined high lactate levels (>2 mmol/L) as a diagnostic criterion for septic shock and recommended the use of lactate monitoring in the evaluation of treatment response and disease severity [5]. Current guidelines recommend using lactate and lactate kinetics in COVID-19 disease, although its value is still unclear [6].
Various scoring systems have been established to determine the clinical severity of COVID-19 disease. One of them is the quick COVID Severity Index (qCSI) score, which provides an effective assessment at the bedside. This scoring system, which was created to predict respiratory failure and progression to critical illness in the first 24 h, includes respiratory rate, pulse oximetry, and supplemental oxygen flow rate parameters [7]. An independent validation cohort of this scoring system was conducted [8]. The qCSI scoring system was found to be useful in predicting in-hospital mortality and the need for intensive care [9].
In this study, we aim to investigate the relationship between blood lactate levels and lactate kinetics (lactate clearance and ∆ lactate) in patients with COVID-19 admitted to the emergency department.
2. Material and methods
2.1. Study design
This study was performed as a single-center retrospective study after receiving the ethics committee's approval (approval code: BAEK/2022/2/8) in February 2022 in Samsun Education and Research Hospital.
2.2. Study population
Patients who presented to the emergency department between March 1st, 2020, and January 1st, 2022, aged over 18 years, had positive COVID-19 polymerase chain reaction (PCR) tests, had pneumonia in lung tomography, and were hospitalized in the intensive care unit (ICU) were included in the study. The selection of patients to be admitted to the ICU was determined according to the clinical algorithm of the World Health Organization [10]. Patients younger than 18 years, patients who were COVID-19 positive but had no pneumonia in their tomography, patients hospitalized in ICUs with COVID-19 but for non-COVID reasons, patients with a negative PCR test whose tomography was compatible with COVID-19 pneumonia, patients intubated prior to emergency department admission, patients who received any oxygen support or vasopressor therapy before applying to the emergency department, and patients with missing data were excluded from the study.
2.3. Data collection
All abstractors were trained before the data collection and all abstractors were unaware of the study's hypothesis. In this context, the abstractors are blinded to the patient's group assignment. The abstractors use standardized abstraction forms to guide data collection. Sociodemographic characteristics of the patients, vital signs at the time of the first admission in the emergency department triage area, comorbid diseases, laboratory parameters, and blood lactate levels at the time of admission, and the highest blood lactate levels in the first 24 h of follow-up were recorded using the hospital automation system.
The quick COVID Severity Index (qCSI) score consists of three parameters: Respiratory rate, pulse oximetry, and supplemental oxygen flow rate. The patients' respiratory rate, pulse oximetry, and supplemental oxygen flow rate values were obtained from the file information, and qCSI scores were calculated accordingly. This scoring system is a 12-point scale, and patients were divided into four groups according to their scores: 0–3 low risk, 4–6 low-intermediate risk, 7–9 high-intermediate risk, and >10 high risk [7,9]. The acute physiology and chronic health evaluation (APACHE II) scores calculated at the time of admission to the ICU were recorded. Patients were classified according to their clinical outcome as developing or not developing mortality within 28 days. These patients were compared according to outcomes and changes in blood lactate levels. All data were saved in the data collection form. For interrater-reliability testing, another reviewer reabstracted a sample of charts, blinded to the information obtained by the first correlation reviewer. Periodic meetings were held with chart abstractors for the optimal management of the collected data and for the abstractors' performance monitoring.
2.4. Determination of lactate levels
Lactate concentrations were measured using an ABL90 FLEX (Radiometer Medical Aps Akandevej 21, 2700 Branshe, Denmark), an automated system for immunoassays. The range of normal values for serum lactate concentration was 0.5–1.6 mmol/L.
The lactate levels of all patients included in the study were recorded at the time of admission, at the 6th, 12th, and 24th hours according to the hospital protocol, and also at the time points deemed appropriate by the physician. The lactate levels obtained from the venous blood gases of the patients at the time of admission and the highest blood lactate levels in the first 24 h of follow-up were recorded in mmol/L. The difference between the blood lactate level at the time of admission (0-h lactate) and the highest blood lactate level taken in the first 24 h (2nd highest lactate) was defined as delta (∆) lactate. Lactate clearance (%) was defined as [((0-h lactate – 2nd highest lactate)/0-h lactate) × 100] [11]. In clinical follow-up, negative ∆ lactate and lactate clearance were used as indicators of increased blood lactate levels.
2.5. Outcome measures
The primary outcome of the study was to determine the relationship between lactate and lactate kinetics (lactate clearance and Δ lactate) and mortality in COVID-19 pneumonia. The efficacy of the qCSI score at admission to the emergency department in identifying COVID-19 patients to be transferred from the emergency department to the intensive care unit was its secondary outcome.
2.6. Statistical method(s)
Obtained data were analyzed using IBM SPSS Statistics 25 and MedCalc statistical software (version 20; MedCalc Software, Ostend, Belgium) package programs. Categorical variables are expressed as frequency and percentage. The mean ± standard deviation for the numerical variables that fit the normal distribution, the median (minimum-maximum) for the variables that did not fit the normal distribution; Student's t-test was used to compare numerical data with normal distribution in pairwise group comparisons, the Mann-Whitney U test was used to compare data that did not fit. The Chi-square or Fisher's exact test was used to compare categorical data. Kruskal-Wallis's analysis of variance was used for multiple group comparisons. Receiver operating characteristics (ROC) analysis was performed to determine the best lactate levels in evaluating the clinical course. Optimal cut-off values and sensitivity and specificity values were determined for lactate measurements. The area under the curve (AUC) and 95% confidence intervals (CI) are indicated. We performed a univariable analysis to determine the association between lactate concentrations and the study outcomes. Variables with a p-value of <0.1 or judged as significant confounders were subjected to a backward multivariable logistic regression analysis to evaluate possible independent associations. Finally, adjusted odds ratios (aORs) with 95% CI were calculated to evaluate the association between lactate concentration and mortality. The Hosmer–Lemeshow goodness-of-fit test was performed to evaluate the fit of the logistic regression model. All statistical tests were two-tailed, and the statistical significance level was accepted as p < 0.05 for all analyses.
3. Results
Files of 1410 PCR (+) patients with COVID-19 pneumonia were scanned for the study. After applying the exclusion criteria, 300 patients were included in the study. The working flow chart is presented in Fig. 1 .
Fig. 1.
Study Flow Chart.
The clinical features and laboratory parameters of the patients in our study are presented in Table 1 .
Table 1.
Clınıcal characterıstıcs and laboratory fındıngs of the enrolled patıents.
| Characteristics/Variables | All Patients | Survivors | Non Survivors | p |
|---|---|---|---|---|
| Number of patients | 300 | 157 (52.3) | 143 (47.7) | |
| Age (years) | 74 (64–82) | 72 (61–80) | 76 (66.5–82) | 0.052 |
| Sex | ||||
| Male | 154 (51.3) | 81 (52.6) | 73 (47.4) | 0.925 |
| Female | 146 (48.7) | 76 (52.1) | 70 (47.9) | |
| Comorbidities | ||||
| Comorbidities (+) | 218 (72.7) | 120 (55) | 98 (45) | 0.125 |
| Comorbidities (−) | 82 (27.3) | 37 (45.1) | 45 (54.9) | |
| Hypertension (+) | 177 (59) | 100 (56.5) | 77 (43.5) | 0.083 |
| Hypertension (−) | 123 (41) | 57 (46.3) | 66 (53.7) | |
| Diabetes mellitus (+) | 91 (30.3) | 50 (54.9) | 41 (45.1) | 0.550 |
| Diabetes mellitus (−) | 209 (69.7) | 107 (51.2) | 102 (48.8) | |
| Coronary heart disease (+) | 131 (43.7) | 67 (51.1) | 64 (48.9) | 0.717 |
| Coronary heart disease (−) | 169 (56.3) | 90 (53.3) | 79 (46.7) | |
| Chronic obstructive pulmonary disease (+) | 47 (15.7) | 27 (57.4) | 20 (42.6) | 0.545 |
| Chronic obstructive pulmonary disease (−) | 253 (84.3) | 130 (51.4) | 123 (48.6) | |
| Asthma (+) | 37 (12.3) | 22 (59.5) | 15 (40.5) | 0.453 |
| Asthma (−) | 263 (87.7) | 135 (51.3) | 128 (48.7) | |
| Renal failure (+) | 18 (6) | 12 (66.7) | 6 (33.3) | 0.311 |
| Renal failure (−) | 282 (94) | 145 (51.4) | 137 (48.6) | |
| Vitals signs | ||||
| Systolic blood pressure (mm Hg) | 120 (100–135) | 120 (110–140) | 110 (100−130) | 0.006 |
| Diastolic blood pressure (mm Hg) | 70 (60–80) | 70 (60–80) | 70 (60–70) | <0.001 |
| Heart rate, /min | 90 (80–109) | 91 (80–108) | 90 (81–110) | 0.741 |
| Temperature (°C) | 36.5 (36.3–36.9) | 36.5 (36.3–36.8) | 36.5 (36.3–37) | 0.735 |
| SpO2 (%) | 89.5 (81.5–95) | 92 (84–96) | 88 (80–95) | 0.039 |
| Respiratory rate, /min | 22 (20–27.5) | 22 (19–25) | 24 (20–28) | 0.002 |
| Pulse pressure | 50 (40–60) | 50 (40–60) | 50 (40–60) | 0.440 |
| Mean arterial pressure (mm Hg) | 86.6 (74.6–96.6) | 90 (80–100) | 83.3 (73.3–91.1) | 0.001 |
| Laboratory data on EM admission | ||||
| CRP | 134.9 (65.5–198.9) | 130 (70.7–193.1) | 148.4 (64.7–202.3) | 0.298 |
| White blood cell count (per _L) | 9.9 (6.6–13.4) | 10 (6.7–13.7) | 9.8 (6.6–12.2) | 0.373 |
| Platelets (per L), | 222 (152–304.5) | 242 (175–317) | 197 (141.5–273.5) | 0.001 |
| Lymphocytes | 0.8 (0.5–1.3) | 0.9 (0.6–1.4) | 0.7 (0.5–1.2) | 0.002 |
| Neutrophils | 7.9 (5.4–11) | 8.1 (5.1–11.7) | 7.5 (5.5–10.8) | 0.795 |
| NLR | 9.2 (4.5–16.3) | 7.3 (4.1–14.6) | 10.5 (6–18.5) | 0.01 |
| Characteristics of ICU admission | ||||
| APACHE II score, (median, min-max) | 23.4 (6.6–53.3) | 14.6 (5.8–38.9) | 35.5 (11.1–67.2) | <0.001 |
| Lactate values | ||||
| 0 h Lactate (mmol/L) | 1.7 (1.2–2.5) | 1.8 (1.3–2.6) | 1.6 (1.1–2.4) | 0.129 |
| 2nd highest lactate (mmol/L) | 1.7 (1.25–2.6) | 1.5 (1.1–2) | 2.2 (1.5–3.3) | <0.001 |
| Lactate clearance, % | 0 (−50–32.6) | 15 (−22.2–41.1) | −16.6 (−141.6–23.1) | <0.001 |
| ∆Lactate (mmol/L) | 0 (−0.7–0.7) | 0.2 (−0.3–0.8) | −0.2 (−1.6–0.5) | <0.001 |
| qCSI | ||||
| Low | 47 (15.7) | 35 (74.5) | 12 (25.5) |
<0.001 |
| Low-intermediate | 62 (20.7) | 35 (56.5) | 27 (43.5) | |
| High-intermediate | 74 (24.7) | 41 (55.4) | 33 (44.6) | |
| High | 117 (39) | 46 (39.3) | 71 (60.7) | |
ICU: Intensive Care Unit, APACHE: Acute Physiology and Chronic Health Evaluation, qCSI: quick COVID Severity Index.
Continuous measures are presented as medians with interquartile ranges (25th and 75th percentile). Categorical variables are presented as counts and percentiles.
Of the 300 patients included in the study, 48.7% (n = 146) were female and 51.3% (n = 154) were male. The median age of the patients was 74 years. The median age of the women was 75.5 years, and the median age of the men was 73 years. The age of women was higher than men (p = 0.012).
The mortality rate in the patients included in the study was 47.7% (n = 143). The median age of patients who developed mortality was 76 years, and the median age of patients who did not develop mortality was 72 years. The ages of the groups with and without mortality were similar (p = 0.052). Of the patients who developed mortality, 48.7% (n = 70) were female and 51.3% (n = 73) were male. There was no significant difference between the sexes in terms of mortality (p = 0.925).
Considering the vital signs of the patients, the systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) at the time of admission were significantly lower in the mortality group (p = 0.006, p < 0.001, and p = 0.001, respectively). In addition, oxygen saturation was lower and respiratory rate (RR) was higher in the mortality group (p = 0.039 and p = 0.002, respectively).
Considering the comorbid characteristics of the patients, 218 (72.7%) patients had at least one other disease; the number of people who had no other disease was 82 (27.3%). It was observed that comorbidity did not affect mortality.
When the characteristics of lactate levels in the groups with and without mortality were examined, it was found that the lactate levels at the first admission were similar in the groups with and without mortality. In contrast, the 2nd highest lactate levels were found to be significantly higher in the mortality group (p < 0.001). Lactate clearance and ∆ lactate levels were also found to be lower in the mortality group (p < 0.001) (Table 1).
When the relationship between the APACHE II scores was examined, which showed the expected death rate in the ICU admission and the mortality, the scores of the patients who developed mortality were found to be significantly higher than those who did not (p < 0.001) (Table 1).
In the classification of patients according to their qCSI scores, the rate of low-risk patients with COVID-19 was 15.7% (n = 47), the rate of low-intermediate risk patients was 20.7% (n = 62), the rate of high-intermediate risk patients was 24.7% (n = 72), and the rate of high-risk patients was the rate of patients was determined as 39% (n = 117). In addition, mortality was found to be associated with the qCSI classification (p = 0.001) (Table 1).
The relationship between lactate parameters and mortality was examined in patients who were divided into four groups according to clinical severity scores (qCSI) (Table 2 ). Accordingly, it was determined that lactate kinetics (lactate clearance and ∆ lactate) in patients in the clinically low severity group were lower in the mortality group (p = 0.02 and p = 0.039, respectively). In the low-intermediate and high-intermediate groups, 0-h lactate and 2nd highest lactate levels were found to be higher in the mortality group, and lactate kinetics were similar in the groups with and without mortality. In the group with high clinical severity, 2nd highest lactate levels were found to be higher in the group with mortality (p = 0.010). Lactate kinetics were also found to be significantly lower in the mortality group (p < 0.001). The APACHE II scores of all four groups, which were classified according to the clinical severity scores, were higher in the mortality group (Table 2).
Table 2.
The Correlatıon of the quıck COVID Severıty Index (qCSI) Groups wıth Mortalıty Accordıng to Lactate Levels and APACHE II Scores.
| qCSI | Lactate values | Survivors (n = 35) | Non-survivors (n = 12) | p |
|---|---|---|---|---|
| Low n = 47 |
0-h lactate (mmol/L) | 3.2 (2.6–5.7) | 2.35 (1.8–4) | 0.099 |
| 2nd highest lactate (mmol/L) | 1.3 (0.9–1.6) | 1.3 (1.1–2) | 0.441 | |
| Lactate clearance, % | 63.6 (51.8–72.8) | 50 (44.6–56.7) | 0.002 | |
| ∆ lactate (mmol/L) | 1.9 (1.4–4.1) | 1.05 (0.7–2.3) | 0.039 | |
| APACHE II score, % | 29.1 (7.1–53.3) | 55.12 (39.4–71.6) | 0.034 | |
| Lactate values | Survivors (n = 35) | Non-survivors (n = 27) | P | |
|---|---|---|---|---|
| Low-intermediate n = 62 |
0-h lactate (mmol/L) | 1.9 (1.7–2.35) | 2.7 (2–3.4) | 0.001 |
| 2nd highest lactate (mmol/L) | 1.4 (1–1.6) | 1.8 (1.4–2.3) | 0.001 | |
| Lactate clearance, % | 32.6 (27.7–37.2) | 30.43 (25–34.2) | 0.247 | |
| ∆ lactate (mmol/L) | 0.7 (0.5–0.8) | 0.8 (0.5–1.2) | 0.089 | |
| APACHE II score, % | 7.62 (3.8–23.4) | 46.03 (15.7–67.1) | <0.001 | |
| Lactate values | Survivors (n = 41) | Non-survivors (n = 33) | P | |
|---|---|---|---|---|
| High-intermediate n = 74 |
0-h lactate (mmol/L) | 1.5 (1.2–1.8) | 1.9 (1.4–2.4) | 0.035 |
| 2nd highest lactate (mmol/L) | 1.5 (1.1–1.7) | 1.7 (1.3–2.1) | 0.047 | |
| Lactate clearance, % | 6.25 (−5.6–15.4) | 2.13 (0–12.5) | 0.913 | |
| ∆ lactate (mmol/L) | 0.1 (−0.1–0.2) | 0.1 (0–0.2) | 0.847 | |
| APACHE II score, % | 7.62 (5.8–35.5) | 44.23 (18–60.5) | <0.001 | |
| Lactate values | Survivors (n = 46) | Non-survivors (n = 71) | P | |
|---|---|---|---|---|
| High n = 117 |
0-h Lactate (mmol/L) | 1.4 (1.1–1.8) | 1.2 (0.9–1.7) | 0.057 |
| 2nd highest lactate (mmol/L) | 2.2 (1.7–3) | 3.1 (2–4.2) | 0.010 | |
| Lactate clearance, % | −48.53 (−90-(−27.3)) | −150 (−251.9-(−52.8)) | <0.001 | |
| ∆ lactate (mmol/L) | −0.7 (−1.2-(−0.4)) | −1.6 (−2.9-(−0.7)) | <0.001 | |
| APACHE II score, % | 18.65 (5.8–42.4) | 29.13 (8.7–63.8) | 0.048 |
APACHE: Acute Physiology and Chronic Health Evaluation.
Continuous measures are presented as medians with interquartile ranges (25th and 75th percentile).
Table 3 shows the ROC analysis of lactate levels of all qCSI groups, combined with all lactate parameters (0-h lactate levels, 2nd highest lactate levels, lactate clearance, and ∆ lactate) for predicting mortality. Fig. 2, Fig. 3, Fig. 4, Fig. 5 shows the ROC curve of lactate levels of all qCSI groups separately, combined with all lactate parameters. Only for the high qCSI group, all lactate parameters (0-h lactate, 2nd highest lactate levels, lactate clearance, and ∆ lactate) were statistically significant for predicting mortality.
Table 3.
Predicting mortality using emergency department lactate concentrations and kinetics by using qcsi scoring system.
| qCSI | Lactate values | AUC (%95 CI) | Cut-Off Value | Sensıtıvıty-Specıfıty (%) | p |
|---|---|---|---|---|---|
| Low N = 47 |
0 h Lactate (mmol/L) | 0.654 (0.501–0.786) | ≤2.6 | 66.7–74.3 | 0.121 |
| 2nd highest lactate (mmol/L) | 0.701 (0.550–0.826) | ≤1.1 | 58.9–85.7 | 0.041 | |
| Lactate clearance, % | 0.805 (0.663–0.906) | ≤61.7 | 100–57.1 | <0.001 | |
| ∆Lactate (mmol/L) | 0.590 (0.437–0.732) | >1.1 | 75–45.7 | 0.371 | |
| Low-intermediate N = 62 |
0 h Lactate (mmol/L) | 0.734 (0.607–0.839) | >2.6 | 51.9–85.7 | <0.001 |
| 2nd highest lactate (mmol/L) | 0.744 (0.618–0.847) | >1.7 | 55.6–82.9 | <0.001 | |
| Lactate clearance, % | 0.586 (0.454–0.710) | ≤35.1 | 77.8–48.6 | 0.248 | |
| ∆Lactate (mmol/L) | 0.649 (0.517–0.766) | >0.9 | 37–88.6 | 0.038 | |
| High-intermediate N = 74 |
0 h Lactate (mmol/L) | 0.643 (0.523–0.751) | >1.8 | 51.5–80.5 | 0.029 |
| 2nd highest lactate (mmol/L) | 0.635 (0.514–0.743) | >1.6 | 54.5–73.2 | 0.042 | |
| Lactate clearance, % | 0.507 (0.389–0.626) | > −9.09 | 93.9–17.1 | 0.913 | |
| ∆Lactate (mmol/L) | 0.513 (0.394–0.631) | > −0.2 | 87.9–4.9 | 0.848 | |
| High N = 117 |
0 h Lactate (mmol/L) | 0.607 (0.513–0.696) | ≤1.2 | 54.9–67.4 | 0.040 |
| 2nd highest lactate (mmol/L) | 0.642 (0.548–0.728) | >2.4 | 60.6–67.4 | 0.006 | |
| Lactate clearance, % | 0.748 (0.659–0.824) | ≤ −177.78 | 49.3–100 | <0.001 | |
| ∆Lactate (mmol/L) | 0.707 (616–787) | ≤ −2 | 45.1–93.5 | <0.001 |
AUC: Area Under Curve.
Fig. 2.
ROC curve for predicting mortality using lactate concentrations of low qCSI patients group.
Fig. 3.
ROC curve for predicting mortality using lactate concentrations of low-intermediate qCSI patients group.
Fig. 4.
ROC curve for predicting mortality using lactate concentrations of high-intermediate qCSI patients group.
Fig. 5.
ROC curve for predicting mortality using lactate concentrations of high qCSI patients group.
ROC curves of all clinical severity scores are presented in Fig. 2, Fig. 3, Fig. 4, Fig. 5. Fig. 5 shows the ROC curve of lactate levels of the high qCSI group, combined with all lactate parameters (0-h lactate levels, 2nd highest lactate levels, lactate clearance, and ∆ lactate) for predicting mortality. The AUC of lactate at 0-h for predicting mortality was 0.607 (0.513–0.696) which was statistically and clinically significant. The optimal 0-h lactate cut-off point for mortality was >1.2 mmol/L (54.9% sensitivity, 67.4% specificity). The AUC of lactate for 2nd highest lactate for predicting mortality was 0.642 (95% CI: 0.548–0.728), which was statistically and clinically significant. The optimal lactate cut-off point for mortality was >2.4 mmol/L (60.6% sensitivity, 67.4% specificity). The AUC of lactate clearance for predicting mortality was 0.748 (95% CI: 0.659–0.824), which was statistically and clinically significant. The optimal lactate clearance cut-off point for mortality was ≤ −177.78% (49.3% sensitivity, 100% specificity). The AUC of ∆lactate predicting mortality was 0.707 (95% CI: 0.616–0.787), which was statistically and clinically significant. The optimal ∆ lactate cut-off point for mortality was ≤ −2 mmol/L (45.1% sensitivity, 93.5% specificity).
The univariate and multivariate logistic regression analysis results for the risk factors for mortality in COVID-19 pneumonia are shown in Table 4 . The univariate analysis showed that significant risk factors were SBP, DBP, RR, MAP, platelet count, 2nd highest lactate level, lactate clearance, ∆ lactate, APACHE II scores, and all qCSI groups. To assess the independent factors and the ability of lactate concentrations to predict mortality, we subjected the variables to multivariate logistic regression analysis using a stepwise backward method based on the results of the univariate analysis. Multivariate analysis showed that lactate clearance was associated with mortality in patients with COVID-19 pneumonia after adjusting for confounders (aOR for lactate clearance = 0.985, 95% CI: 0.978–0.991). However, the 0-h lactate level, the 2nd highest lactate level, and ∆ lactate were not associated with mortality. The mortality risk of the high qCSI group was 4.5 times higher than the low qCSI group (p < 0.001 and 95% CI:2.119–9.562). However, there was no statistically significant difference between the qCSI groups in multivariate analysis. This implies that the qCSI contains little additional information for predicting mortality when the relevant variables from the multivariate analysis are accounted for. The goodness-of-fit of the model was 0.231 (Hosmer–Lemeshow test).
Table 4.
Univariable and multivariable⁎ analyses of predictors of mortality.
| Variable | Unadjusted OR | 95% CI | p-value | Adjusted OR | 95% CI | p-value |
|---|---|---|---|---|---|---|
| Systolic Blood Pressure (mmHg) | 0.989 | 0.981–0.998 | 0.017 | – | – | – |
| Diastolic Blood Pressure (mmHg) | 0.973 | 0.957–0.989 | 0.001 | 0.976 | 0.956–0.996 | 0.018 |
| Respiratory rate (breaths/min) | 1.058 | 1.019–1.097 | 0.003 | 1.061 | 1.014–1.110 | 0.010 |
| Mean Arterial Pressure (mmHg) | 0.980 | 0.966–0.993 | 0.003 | – | – | – |
| Platelets (per _L), | 0.997 | 0.995–0.999 | 0.002 | 0.996 | 0.994–0.998 | 0.001 |
| Lymphocytes | 1.016 | 0.946–1.090 | 0.663 | – | – | – |
| 2nd highest lactate (mmol/L) | 1.606 | 1.299–1.984 | <0.001 | – | – | – |
| Lactate clearance, % | 0.994 | 0.987–0.994 | <0.001 | 0.985 | 0.978–0.991 | <0.001 |
| ∆Lactate (mmol/L) | 0.680 | 0.577–0.802 | <0.001 | – | – | – |
| APACHE II score | 1.020 | 1.011–1.028 | <0.001 | 1.022 | 1.012–1.032 | <0.001 |
| Low qCSI | 1 (reference) | 1 (reference) | 1(reference) | 1(reference) | ||
| Low-intermediate qCSI | 2.250 | 0.985–5.138 | 0.054 | 1.747 | 0.674–4.531 | 0.251 |
| High-intermediate qCSI | 2.348 | 1.055–5.225 | 0.037 | 1.186 | 0.445–3.159 | 0.733 |
| High qCSI | 4.502 | 2.119–9.562 | <0.001 | 0.454 | 0.125–1.652 | 0.231 |
CI, confidence interval; OR, odds ratio.
APACHE: Acute Physiology and Chronic Health Evaluation.
*Multivariable logistic regression analysis with a stepwise backward method based on the univariable analysis results. The goodness-of-fit of the multivariable logistic model was tested using the Hosmer–Lemeshow test (p = 0.231). Omnibus: <0.001, nagelkerke r2: 0.381.
4. Discussion
This study was conducted to determine the relationship between lactate and lactate kinetics (lactate clearance and Δ lactate) with disease outcome (mortality) in patients with COVID-19 pneumonia and clinical severity (qCSI) score. It was determined that the 0-h lactate level was not an indicator of mortality. However, 2nd highest lactate level and lactate kinetics were found to be parameters that could be used to predict mortality.
The use of lactate and lactate kinetics in the evaluation of response to treatment in patients with septic shock in ICUs is recommended by current guidelines [12]. It was reported in a study that intensive care treatment based on this basis reduced mortality, length of stay in the ICU, and mechanical ventilation [13]. In a study that included 397 patients, Gwak et al. reported that increased blood lactate levels might indicate mortality in community-acquired pneumonia [14]. Gwak et al. reported that blood lactate levels of patients (11.6%) who developed mortality were 2.4 ± 2.2 mmol/L, whereas it was 1.6 ± 1.2 mmol/L in the group without mortality.
COVID-19 disease has been seen as a unique cause of sepsis and is included in current sepsis guidelines [6]. It has been reported in these guidelines that the measurement of dynamic parameters such as serum lactate levels can guide physicians in the response to fluid therapy in COVID-19 sepsis. In this context, it can be thought that blood lactate levels may be an indicator of critical COVID-19 disease and mortality. In a study conducted by Goodall et al. including 981 patients with COVID-19, the mortality rate was 36%, and lactate levels were found to be an indicator of mortality (aHR 2.67) [15]. In another study, the mortality rate of 235 patients with COVID-19 was found as 8.5%, and the lactate levels of the patients in the mortality group were found to be significantly higher than those in the mortality group [4.1 (2.4–6.4) and 1.8 (1.2–2.7), respectively (p = 0.002)] [16]. However, blood lactate levels from these studies were taken and evaluated simultaneously (at admission).
As in our study, there are a limited number of studies examining the relationship between recurrent lactate measurements and lactate kinetics in COVID-19 pneumonia and mortality. One of these is the study of Vassiliou et al. on 45 ICU patients [17]. In the study, with a mortality rate of 24.4%, it was determined that the lactate level at admission and the mean lactate values taken during the intensive care follow-up were higher in deceased patients than in survivors. In a multicenter, prospective study of 2860 patients aged over 70 years, Bruno et al. reported that patients with lactate levels ≥2 mmol/L had a higher mortality rate than patients with lactate levels <2 mmol/L. Contrary to our study, it was reported that negative lactate clearance was not associated with 30-day mortality [18]. In our study, we examined the mortality rates that developed in the first 28 days and the relationship of these rates with lactate and lactate kinetics in patients with COVID-19 pneumonia who were admitted to the emergency department and then hospitalized in the ICU. In contrast to the study of Vassiliou et al., we found that the blood lactate (0-h lactate) levels at admission were similar in the groups with and without mortality (p = 0.129). In the study of Vassiliou et al., blood lactate levels were examined at the time of presentation and during the following two weeks. Our study also looked at blood lactate levels upon admission to the emergency department. We analyzed the highest lactate level taken within the first 24 h to control blood lactate levels, which is more suitable for emergency medicine practice. According to our study, the highest lactate level (2nd highest lactate), lactate clearance, and ∆ lactate levels measured within 24 h may predict mortality. In two similar studies, including critically ill patients with sepsis, 2nd highest lactate levels and lactate clearance were found to be the best mortality indicators. Our study is supported by the results of these two studies [19,20].
Many scoring systems in community-acquired pneumonia have also been used for patients with COVID-19 [21,22]. However, the clinical spectrum of COVID-19 is wide, ranging from asymptomatic to multi-organ failure, and its clinical course is affected by age and race, leading to the disease being seen as more than pneumonia [9,23]. The course of the disease and high mortality rates have allowed studies to develop specific scoring systems such as qCSI, which has been validated in previous studies [8,9]. We used the qCSI in our study because it is easy to apply during the initial evaluation of the patient in the emergency department. In a retrospective study by Covino et al., it was reported that high qCSI scores might indicate mortality in patients with COVID-19 aged over 60 years [24]. In another study by Ak et al., including 341 patients, it was reported that the qCSI scoring system was an indicator of in-hospital mortality [25]. Similar to these studies, we also found a relationship between the qCSI scoring system and mortality in our study. This supports the study of Rodriguez-Nava et al. [9].
In addition, based on the results of our study, it can be thought that lactate kinetic parameters, which would be evaluated in clinically low severity groups according to the qCSI scoring system, may be an indicator of mortality from the disease. This suggests that calculating lactate kinetics for patients with mild clinical symptoms may guide physicians for patient outcomes. For the low-intermediate and high-intermediate groups, the lactate taken at the time of admission and the highest lactate level within 24 h can be considered predictive parameters for mortality by physicians. According to the qCSI scoring system, elevated 2nd highest lactate levels and negativity in lactate kinetics are considered predictive parameters for mortality for patients with high clinical severity. Evaluation of these parameters can guide the physician in determining the treatment plan for critically ill patients. In our logistic regression analysis to evaluate risk factors for mortality in COVID-19 pneumonia, we found that lactate clearance was a risk factor for mortality. The APACHE II score, an indicator of expected mortality, showed the value of lactate clearance in our study.
Although a limited number of studies have previously compared blood lactate levels with the clinical outcome of patients with COVID-19, our study is the first to evaluate qCSI scores added to lactate levels. When the literature data were examined, no other study was found in which lactate and lactate kinetics were integrated with clinical severity scoring of COVID-19 disease. In this context, we think that our study is original.
4.1. Limitations
The retrospective nature of this study reduced the number of patients whose data could be fully accessed and, therefore, the number of patients in our study. Prospective studies with more patients may reduce data loss and provide better results. Since it is more suitable for emergency medicine practice, only blood lactate levels in the first 24 h were evaluated in this study, and blood lactate levels in the following processes were not evaluated.
4.2. Conclusion
In COVID-19 pneumonia, 2nd highest blood lactate and lactate kinetics (lactate clearance and ∆ lactate) are thought to be prognostic indicators of the disease. In addition, we think that the evaluation of lactate levels, which can be easily studied and gives rapid results, will help physicians in predicting clinical severity, even if the clinical severity is low in COVID-19 severity scoring. High 2nd highest lactate levels and low lactate kinetics in patients with high clinical severity guide physicians in predicting mortality in the disease outcome.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Ethical approval
Our study was approved by the Health Sciences University, Samsun Training and Research Hospital Clinical Research Ethics Committee on February 27th, 2020 (Decision Number: SBUSEAH-KAEK-2020/2/7).
CRediT authorship contribution statement
Metin Yadigaroğlu: Writing – original draft, Supervision, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. Vecdi Vahdet Çömez: Software, Investigation, Formal analysis, Data curation. Yunus Emre Gültekin: Software, Investigation, Formal analysis, Data curation. Yasin Ceylan: Software, Investigation, Formal analysis, Data curation. Hüseyin Tufan Yanık: Software, Project administration, Investigation, Data curation. Nurçin Öğreten Yadigaroğlu: Software, Investigation, Formal analysis, Data curation. Murat Yücel: Writing – review & editing, Writing – original draft, Supervision, Conceptualization. Murat Güzel: Writing – review & editing, Writing – original draft, Supervision, Methodology, Formal analysis, Data curation.
Declaration of Competing Interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Acknowledgments
We would like to thank David Chapman for his contribution to the editing of our article.
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