ORIGINAL ARTICLE |
https://doi.org/10.5005/jp-journals-11010-1053 |
Association of Dynamic Changes in Illness Severity Scores Biochemical and Inflammatory Markers with Outcomes in Invasively Ventilated COVID-19 in Resource-limited Settings: A Time-course Study
1–3,5,6Department of Intensive Care Unit, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, Maharashtra, India
4Department of Intensive Care Unit, Manipal Hospital, Baner, Pune, Maharashtra, India
7Department of Intensive Care Unit, Yashoda Super Speciality Hospital, Kaushambi, Ghaziabad, Uttar Pradesh, India
8Symbiosis Institute of Health Sciences, Symbiosis International University, Pune, Maharashtra, India
9Department of Nutrition and Biostatistics, NutriCanvas, Mumbai, Maharashtra, India
Corresponding Author: Sonali Vadi, Department of Intensive Care Unit, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, Maharashtra India, Phone: 022-42696969, e-mail: sonali.vadi@kokilabenhospitals.com
Received on: 24 May 2023; Accepted on: 18 July 2023; Published on: 30 October 2023
ABSTRACT
Background and objectives: No information is available from India on the kinetics of biochemical and inflammatory markers on the outcomes of invasively ventilated coronavirus disease 2019 (COVID-19) patients.
Patients and: methods: In a retrospective study on invasively ventilated COVID-19 patients we performed a time-course design to look at the development of organ dysfunction and its association with survival.
Results: A total of 156 patients were studied. Higher acute physiology and chronic health evaluation (APACHE) II scores on day 1 and Sequential Organ Failure Assessment (SOFA)/Simplified Acute Physiology Score (SAPS) II scores were noted in nonsurvivors. Nonsurvivors had significantly higher blood urea nitrogen, neutrophil-lymphocyte ratio, serum ferritin, interleukin (IL)—6, and D dimer levels as compared to survivors (p < 0.05) as a baseline. A higher percentage of nonsurvivors had serum sodium, serum chloride, blood urea nitrogen (BUN), and serum creatinine levels both above and below limits as compared to survivors. A significantly higher percentage of nonsurvivors had higher arterial pressure of carbon dioxide (PaCO2), lower platelet and absolute lymphocyte counts, and multiorgan dysfunction syndrome (MODS) as compared to survivors (p < 0.05). Positive fluid balance was associated with higher mortality.
Conclusion: Variables recorded at baseline and their dynamics during the period of invasive ventilation were correlated with survival.
How to cite this article: Vadi S, Pednekar A, Bajpe S, et al. Association of Dynamic Changes in Illness Severity Scores Biochemical and Inflammatory Markers with Outcomes in Invasively Ventilated COVID-19 in Resource-limited Settings: A Time-course Study. Indian J Respir Care 2023;12(3):222–229.
Source of support: Nil
Conflict of interest: None
Keywords: Acute respiratory failure, Blood biochemistry trends, Coronavirus disease 2019, Invasive mechanical ventilation, Survivors vs nonsurvivors
STUDY HIGHLIGHTS
We report the first time-course of illness severity scores and a spectrum of biochemical and inflammatory markers and their association with survival in invasively ventilated coronavirus disease 2019 (COVID-19) acute respiratory failure (C-ARF) from India.
In resource-limited settings, routine and economical biomarkers such as absolute lymphocyte count, platelet count, blood urea nitrogen, and serum creatinine can be used to monitor the severity of COVID-19 infection and triage-scarce healthcare.
INTRODUCTION
Coronavirus disease of 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) portrays a spectrum of clinical infliction, severe of which is multiorgan dysfunction syndrome (MODS) often progressing to mortality. 35–50% of these patients have been reported to have a fatal outcome.1 It is difficult to ascertain the timing of the tipping point in C-ARF as these patients display hyperinflammation2 from an early stage. Alterations in biochemical parameters commensurate with the severity of infection. Studies have evaluated outcomes of C-ARF using biochemical values at baseline. In order to recognize the presence and extent of severity of inflammation in the tissues, hematological, biochemistry, and immunological biomarkers are monitored3 to guide clinical decision-making.4 Identifying at-risk patients can guide timely interventions.5 Costly cytokine analysis6 is not routinely performed in the majority of laboratories in India. Notably, COVID-19 patients require prolonged invasive mechanical ventilation and intensive care unit (ICU) stay.
For C-ARF patients, no data is available from India on the sequel of organ dysfunction and biochemical markers by tracking kinetics using time-course design. We hypothesized that sequential trends rather than baseline snapshots7 of clinical and laboratory parameters would more accurately reflect ICU outcomes in these patients. We aimed to look at basic blood biochemistry (inflammatory, renal, respiratory, severity of illness scoring) of C-ARF patients and their clinical progression in relation to the evolution of the illness. These basic blood biomarkers can be employed even in resource-limited settings.
METHODS
Study Design and Setting
This retrospective, chart-review study was conducted in the ICU of a tertiary care hospital in Mumbai. We enrolled patients with COVID-19-associated C-ARF who required invasive ventilation from 23rd March 2020 to 31st March 2021, corresponding to the first wave. The study was approved by the Institutional Ethics Committee (047/2021).
The aim was to evaluate the kinetics of blood biochemical parameters in invasively ventilated C-ARF patients to help prognosticate.
Study Population and Observation Period
Patients aged >18 years of age with SARS-CoV-2 as confirmed by quantitative real-time polymerase chain reaction on nasopharyngeal swabs and invasively ventilated for C-ARF were included in the study. Patients invasively ventilated for reasons other than C-ARF, for example, stroke, surgical patients, and following seizures for airway protection were excluded. Patients were followed up until the day of discharge or death. All these patients had only one admission.
Interventions
All patients received standard of care as clinically indicated. Medical management included glucocorticoids, tocilizumab, remdesivir, anticoagulants, antibiotics, and vasoactive agents. Invasively ventilated patients were administered sedatives and muscle relaxants as per ICU protocol. Intubated patients with moderate and severe hypoxemia were prone to ventilation.
Variables, Data Sources, and Definitions
Data were entered into a predesigned online data acquisition system. Patient confidentiality was safeguarded by allocating a de-identified code.
The recorded data included patient-related variables [age, gender, comorbidities, clinical severity score variables (Sequential Organ Failure Assessment (SOFA) score for first 10 days, Simplified Acute Physiology Score (SAPS) II score for first 10 days), and fluid balance for first 10 days]; laboratory variables {[worst daily values for first 10 days—serum sodium, serum chloride, blood urea nitrogen, serum creatinine, platelet counts, absolute lymphocyte counts, neutrophil-lymphocyte ratio, C-reactive protein (CRP)], highest values of D dimer, interleukin (IL)—6, serum ferritin, lowest values of serum albumin}, respiratory parameters (worst daily values of arterial pressure of carbon dioxide (PaCO2) for first 10 days of invasive mechanical ventilation); treatment variables (high-flow nasal oxygen, noninvasive ventilation, vasoactive agents, renal replacement therapy, COVID-19-specific medications—steroids, remdesivir, tocilizumab, convalescent plasma); outcome-related variables—day of intubation, duration of invasive ventilation, duration of ICU stay, duration of hospital stay, and outcome (alive, unhealed, and deceased). Unhealed patients were critically ill patients who were discharged against medical advice.
Results of all quantitative biochemical parameters were validated using internal and external quality control procedures as per the norms of our laboratory.
Statistical Analysis
The statistical sample size was not calculated a priori, and the number included consecutively treated patients during the study period as per the inclusion and exclusion criteria.
Data were analyzed using Statistical Package for the Social Sciences version 25 for Windows (version 25, 2017, IBM Corporation, Armonk, New York, United States). Data presented as mean (minimum–maximum) or frequency (%). Cross tabulations were computed based on survival and compared using Fisher’s exact test or Chi-squared test. Mann-Whitney U test was used to analyze the difference in continued parameters between survivors and nonsurvivors. A univariate general linear model was used to analyze the difference in the prevalence of MODS and the use of inotropes-adjusted means of blood biochemistry over 10 days. Data of the univariate model is presented as mean ± standard error. Scatter plots with the evolution trend line for the biomarker during follow-up according to the isotonic regression method were plotted using MedCalc software (version 20.218, MedCalc Software Ltd, Belgium), p < 0.05 was considered to be significant.
Unhealed patients were counted as deceased for the analysis.
RESULTS
A total of 156 patients were analyzed. 15 patients who were ventilated for reasons other than C-ARF were excluded.
Patient Characteristics
The overall mean age of study participants was 65 (55–72) years. Age of nonsurvivors [67 (57.5–67) years] was significantly higher as compared to survivors [58 (48.5–68.8) years] (p < 0.05). Table 1 gives the demographic information of study participants. A higher percentage of participants were males (74.4%). Of 156, 27.6% were in septic shock and 25% had MODS. A significantly higher percentage of nonsurvivors had MODS as compared to survivors (p < 0.05). No significant differences were observed in prevalence of comorbidities (p > 0.05).
| Survivors (n = 38) | Nonsurvivors (n = 118) | Total (n = 156) | p-value | |
|---|---|---|---|---|
| Gender | ||||
| Males | 27 (71.1%) | 89 (75.4%) | 116 (74.4%) | 0.670 |
| Females | 11 (28.9%) | 29 (24.6%) | 40 (25.6%) | |
| Diagnosis | ||||
| Septic shock | 8 (21.1%) | 35 (29.7%) | 43 (27.6%) | 0.404 |
| Multiple organ dysfunction | 5 (13.2%) | 34 (28.8%) | 39 (25%) | 0.053 |
| Comorbidity | ||||
| Overweight/obese | 25 (65.8%) | 61 (51.7%) | 86 (55.1%) | 0.139 |
| Diabetes mellitus | 23 (60.5%) | 59 (50%) | 82 (52.6%) | 0.270 |
| Hypertension | 23 (60.5%) | 57 (48.3%) | 80 (51.3%) | 0.199 |
| Coronary artery disease | 8 (21.1%) | 29 (24.6%) | 37 (23.7%) | 0.827 |
| Chronic kidney disease | 4 (10.5%) | 13 (11%) | 17 (10.9%) | 0.999 |
| Tuberculosis | 2 (5.3%) | 3 (2.5%) | 5 (3.2%) | 0.596 |
| Cerebrovascular accident | 4 (10.5%) | 7 (5.9%) | 11 (7.1%) | 0.464 |
| Liver disease | 2 (5.3%) | 7 (5.9%) | 9 (5.8%) | 0.878 |
| Malignancy | 0 (0%) | 7 (5.9%) | 7 (4.5%) | 0.196 |
| Neurological issues | 3 (7.9%) | 8 (6.8%) | 11 (7.1%) | 0.730 |
| Pulmonary issues | 3 (7.9%) | 11 (9.3%) | 14 (9%) | 0.789 |
| Hypothyroidism | 4 (10.5%) | 5 (4.2%) | 9 (5.8%) | 0.223 |
| Organ transplant | 1 (2.6%) | 4 (3.4%) | 5 (3.2%) | 0.817 |
| Other diseases | 1 (2.6%) | 10 (8.5%) | 11 (7.1%) | 0.297 |
| No comorbidity | 5 (13.2%) | 18 (15.3%) | 23 (14.7%) | 0.999 |
Data presented as frequency (percentage)
Treatment Received
Table 2 gives the treatment received by study participants. A significantly higher percentage of nonsurvivors received inotropes as compared to survivors (p < 0.05). Nonsurvivors had a significantly lesser duration of ventilation, duration of ICU stay, and duration of hospital stay as compared to survivors (p < 0.05).
| Survivors (n = 38) | Nonsurvivors (n = 118) | Total (n = 156) | p-value | |
|---|---|---|---|---|
| Data presented as frequency (percentage) | ||||
| Inotropes | 27 (71.1%) | 107 (90.7%) | 134 (85.9%) | 0.003 |
| Noninvasive ventilation | 5 (13.2%) | 24 (20.3%) | 29 (18.6%) | 0.472 |
| HFNO | 22 (57.9%) | 65 (55.1%) | 87 (55.8%) | 0.762 |
| Tocilizumab | 16 (42.1%) | 55 (46.6%) | 71 (45.5%) | 0.709 |
| Steroids | 3 (7.9%) | 9 (7.6%) | 12 (7.7%) | 0.999 |
| Remdesivir | 10 (26.3%) | 39 (33.1%) | 49 (31.4%) | 0.548 |
| Convalescent plasma | 1 (2.6%) | 2 (1.7%) | 3 (1.9%) | 0.715 |
| Renal replacement therapy | 1 (2.6%) | 26 (22%) | 27 (17.3%) | 0.005 |
| Data presented as mean (minimum–maximum) | ||||
| Day of intubation | 4 (1–12) | 4 (1–28) | 4 (1–28) | 0.673 |
| Duration of ventilation (days) | 24 (1–120) | 12 (1–120) | 15 (1–120) | 0.001 |
| Duration of ICU stay (days) | 37 (7–190) | 15 (1–66) | 21 (1–190) | 0.001 |
| Duration of hospital stay (days) | 44 (11–190) | 17 (1–120) | 23 (1–190) | 0.001 |
Table 3 gives on-admission blood biochemistry of study participants. Nonsurvivors had significantly higher on-admission BUN, NLR, serum ferritin, IL-6, and D-dimer as compared to survivors (p < 0.05). On the other hand, nonsurvivors had significantly lower on-admission absolute lymphocyte count as compared to survivors (p < 0.05). No other significant differences were observed in on-admission blood biochemistry between survivors and nonsurvivors (p > 0.05).
| Survivors (n = 38) | Nonsurvivors (n = 118) | Total (n = 156) | p-value | |
|---|---|---|---|---|
| Glycosylated hemoglobin | 7.6 (6.2–9.7) | 7.6 (6.3–9.4) | 7.6 (6.3–9.5) | 0.874 |
| Serum sodium (mEq/L) | 138.5 (135–141.3) | 136 (131.8–141) | 137 (132–141) | 0.157 |
| Serum chloride (mEq/L) | 100.8 (97–104.1) | 98.4 (93.2–104) | 99.4 (94.2–104.1) | 0.081 |
| Serum potassium (mEq/L) | 4.5 (4–4.8) | 4.6 (4–5.1) | 4.5 (4.0–5.0) | 0.389 |
| Bicarbonate (mmol/L) | 20.8 (16.5–24.2) | 21.5 (18.8–25.5) | 21.3 (18–25) | 0.171 |
| PaCO2 (mm Hg) | 39.4 (31.9–46.7) | 38.7 (32.5–48.7) | 38.7 (32.4–48.4) | 0.998 |
| Blood urea nitrogen (BUN) (mg/dL) | 19.4 (16.6–25.5) | 25.7 (17.2–39.7) | 24.2 (17–36.1) | 0.012 |
| Serum creatinine (mg/dL) | 1 (0.76–1.25) | 1.02 (0.81–1.77) | 1.02 (0.79–1.5) | 0.218 |
| Neutrophil-lymphocyte ratio | 11.2 (4.9–18) | 17 (7.9–35.7) | 14.9 (7–30.8) | 0.015 |
| Absolute lymphocyte count (103/μL) | 0.8 (0.5–1.4) | 0.6 (0.4–1.0) | 0.7 (0.4–1.1) | 0.044 |
| Platelet count (103/μL) | 236 (184–313) | 217 (141.3–301.5) | 226 (157.3–307.5) | 0.209 |
| B-type natriuretic peptide (pg/mL) | 1503 (977–3391) | 2821 (646.8–10541) | 2041 (684–6826) | 0.259 |
| Serum albumin (g/dL) | 2.7 (2.3–3.3) | 2.6 (2.1–3.1) | 2.6 (2.1–3.1) | 0.350 |
| IL-6 (pg/mL) | 54.1 (16.2–192.9) | 113 (43.9–292.3) | 84.1 (34.9–256.5) | 0.010 |
| Serum ferritin (mcg/L) | 660.8 (233–1226.5) | 1139.8 (425.4–2060.4) | 959.8 (386.1–1843.8) | 0.033 |
| D-dimer (ng/mL) | 995.5 (655.2–1705.5) | 1741 (1050.4–4560.5) | 1562.7 (888.5–3621.9) | 0.001 |
Data presented as median (25–75th quartile)
Blood Parameter Variations Over First 10 Days of Admission
Serum Sodium, Serum Chloride, Blood Urea Nitrogen, and Serum Creatinine
Table 4 gives the kinetic of serum sodium, serum chloride, blood urea nitrogen, and serum creatinine over the first 10 days of admission. A significantly higher percentage of nonsurvivors had serum sodium and chloride levels both lower and above the limits as compared to survivors (p < 0.05). A significantly higher percentage of nonsurvivors had higher blood urea nitrogen (BUN) and serum creatinine levels as compared to survivors (p < 0.05).
| N | Median | 25–75th quartile | Reference value | % of time <lower limit | % of time >upper limit | p-value** | |
|---|---|---|---|---|---|---|---|
| Serum sodium (mEq/L) | |||||||
| Survivors | 364 | 142 | 138–146 | 136–148 | 14.1 (10.5–17.7) | 16 (12.2–19.8) | 0.001 |
| Nonsurvivors | 954 | 142 | 137–148 | 21.3 (18.7–23.9) | 20.9 (18.3–23.5) | ||
| Serum chloride (mEq/L) | |||||||
| Survivors | 364 | 103 | 99.1–107.4 | 98–106 | 19 (15– 23) | 31.3 (26.5–36.1) | 0.001 |
| Nonsurvivors | 952 | 103.4 | 98–108.6 | 26.9 (24.1–29.7) | 35.7 (32.7–38.7) | ||
| Blood urea nitrogen (BUN) (mg/dL) | |||||||
| Survivors | 265 | 24.5 | 18.0–35.6 | 8–23 | 3 (0.9–5.1) | 55 (49–61) | 0.001 |
| Nonsurvivors | 680 | 40.3 | 27–60.4 | 6 (4.2–7.8) | 81.5 (78.6–84.4) | ||
| Serum creatinine (mg/dL) | |||||||
| Survivors | 323 | 0.86 | 0.63–1.24 | 0.51–95 | 10.8 (7.4–14.2) | 37.8 (32.5–43.1) | 0.001 |
| Nonsurvivors | 813 | 1.29 | 0.80–2.27 | 5.5 (3.9–7.1) | 66.5 (63.3–69.7) | ||
**p-value for the prevalence of higher and lower limits of blood levels when compared between survivors and nonsurvivors
Figure 1 shows changes in adjusted (prevalence of MODS and use of inotropes) serum sodium (Fig. 1A), serum chloride (Fig. 1B), BUN (Fig. 1C), and serum creatinine (Fig. 1D) over the first 10 days of admission in survivors and nonsurvivors. No significant differences were observed in adjusted serum sodium and serum chloride levels over the duration of 10 days between survivors and nonsurvivors (p > 0.05). Nonsurvivors had significantly higher adjusted BUN as compared to survivors (p < 0.05). All through the 10 days, nonsurvivors had higher adjusted serum creatinine as compared to survivors. This difference was significant from days 6 to 10 (p < 0.05).
Figs 1A to D: (A) Changes in adjusted serum sodium over the first 10 days of admission; (B) Changes in adjusted serum chloride over the first 10 days of admission; (C) Changes in adjusted BUN over the first 10 days of admission; (D) Changes in adjusted serum creatinine over the first 10 days of admission
Arterial Pressure of Carbon Dioxide (PaCO2), Platelet Count, Absolute Lymphocyte Count, and CRP
Table 5 represents the kinetics of PaCO2, platelet, absolute lymphocyte count, and CRP over the first 10 days of admission. A significantly higher percentage of nonsurvivors had higher PaCO2 levels as compared to survivors (p < 0.05). On the other hand, a significantly higher percentage of nonsurvivors had lower platelet count and lymphocyte counts as compared to survivors (p < 0.05).
| N | Median | 25–75th quartile | Reference value | % of time <lower limit | % of time >upper limit | p-value** | |
|---|---|---|---|---|---|---|---|
| PaCO2 (mm Hg) | |||||||
| Survivors | 355 | 40.8 | 36.9–47.8 | 35–45 | 18 (14–22) | 34.6 (29.7–39.5) | 0.001 |
| Nonsurvivors | 950 | 44.7 | 37.9–57 | 17.9 (15.5–20.3) | 49.2 (46–52.4) | ||
| Platelet count (103/μL) | |||||||
| Survivors | 369 | 232 | 164–312 | 150–410 | 19.8 (15.7–23.9) | 6 (3.6–8.4) | 0.001 |
| Nonsurvivors | 973 | 191 | 127–273 | 34 (31–37) | 3.8 (2.6–5) | ||
| Absolute lymphocyte count (103/μL) | |||||||
| Survivors | 369 | 0.87 | 0.59–1.37 | 1–3 | 58.8 (53.8–63.8) | 1.6 (0.3–2.9) | 0.001 |
| Nonsurvivors | 972 | 0.54 | 0.35–0.88 | 80.9 (78.4–83.4) | 1 (0.4–1.6) | ||
| C-reactive protein (CRP) (mg/dL) | |||||||
| Survivors | 246 | 4.4 | 1.3–11 | 0–5 | NA | 47.6 (41.4–53.8) | 0.064 |
| Nonsurvivors | 732 | 6 | 1.7–14 | NA | 54.4 (50.8–58) | ||
**p-value for the prevalence of higher and lower limits of blood levels when compared between survivors and nonsurvivors
Figure 2 shows changes in adjusted (prevalence of MODS and use of inotropes) PaCO2 (Fig. 2A), platelet count (Fig. 2B), absolute lymphocyte count (Fig. 2C), and CRP (Fig. 2D) over the first 10 days of admission in survivors and nonsurvivors. Nonsurvivors had significantly higher adjusted PaCO2 on day 2, day 4–6, and day 8 to 9 as compared to survivors (p < 0.05). Even though not significant, adjusted platelet count was higher in survivors as compared to nonsurvivors on most days during the duration of 10 days (p > 0.05). Survivors had significantly higher adjusted absolute lymphocyte count as compared to nonsurvivors on day 4, day 6–8, and day 10 (p < 0.05). Nonsurvivors had significantly lower adjusted CRP on day 8 as compared to survivors (p < 0.05). There were no other significant differences in adjusted CRP on any other day between survivors and nonsurvivors (p > 0.05).
Figs 2A to D: (A) Changes in adjusted PaC02 over the first 10 days of admission; (B) Changes in adjusted platelet over the first 10 days of admission; (C) Changes in adjusted absolute lymphocyte count over the first 10 days of admission; (D) Changes in adjusted CRP over the first 10 days of admission
Illness Severity Score Variation Over First 10 Days of Hospital Stay
Sequential Organ Failure Assessment (SOFA) Score, SAPS II Score
Table 6 gives the kinetic of SOFA, SAPS II, and fluid balance over the first 10 days of admission. The mean SOFA, SAPS II, and fluid balance were significantly higher in nonsurvivors as compared to survivors (p < 0.05).
| N | Median | 25–75th quartile | p-value** | |
|---|---|---|---|---|
| SOFA | ||||
| Survivors | 332 | 4 | 3–6 | 0.001 |
| Nonsurvivors | 869 | 6 | 4–10 | |
| SAPS II | ||||
| Survivors | 333 | 28 | 23–35 | 0.001 |
| Nonsurvivors | 869 | 38 | 30–49 | |
| Fluid balance | ||||
| Survivors | 349 | 515 | −35–1138 | 0.013 |
| Nonsurvivors | 939 | 648 | 100–1291 | |
**p for comparison of median levels over a duration of 10 days
Figure 3 shows changes in adjusted (prevalence of MODS and use of inotropes) SOFA (Fig. 3A) and SAPS II (Fig. 3B) scores over the first 10 days of admission in survivors and nonsurvivors. Adjusted SOFA was higher in nonsurvivors as compared to survivors throughout the 10 days. This difference was significant on day 2 and 10. Adjusted SAPS II was higher in nonsurvivors as compared to survivors throughout the 10 days. This difference was significant on day 2 and from day 8 to 10.
Figs 3A to D: (A) Changes in adjusted PaC02 over the first 10 days of admission; (B) Changes in adjusted platelet over the first 10 days of admission; (C) Changes in adjusted absolute lymphocyte count over the first 10 days of admission; (D) Changes in adjusted CRP over the first 10 days of admission
Cumulative Fluid Balance
Figure 3 shows adjusted (prevalence of MODS and use of inotropes) cumulative fluid balance (Fig. 3C) over the first 10 days of admission in survivors and nonsurvivors. Even though not significant adjusted fluid balance was higher during day 2 and 8 in a nonsurvivor group.
Acute Physiology and Chronic Health Evaluation (APACHE) Score
Table 6 gives the APACHE II score on day 1. Nonsurvivors had significantly higher APACHE II scores as compared to survivors (p < 0.05).
DISCUSSION
Key Findings
Using a time-course design on a retrospective cohort of SARS-CoV-2 naïve patients, we have analyzed inflammatory and biochemical variables over time in patients with C-ARF. Rather than a snapshot, we analyzed dynamic changes in blood biochemistry during the acute phase of COVID-19.
We noted males to be more affected in terms of mortality following COVID-19. Overweight/obese, diabetes mellitus, and hypertension were more prevalent comorbidities in nonsurvivors. The average age of the study population was 65 years with those who succumbed being a decade older than the survivors.
On admission variables that were distinct for survivors vs nonsurvivors were BUN (p:0.012), NLR (p:0.015), and absolute lymphocyte count (p:0.044).
In our study, factors independently associated with inferior ICU outcomes (in those requiring inotrope support and developed MODS) were BUN, serum creatinine, PaCO2, platelet count, and absolute lymphocyte counts. D dimer, IL-6, and serum ferritin levels were significantly higher in the nonsurvivor group indicating the level of severity of inflammation in this group. These were monitored ad libitum. Raised inflammatory markers have been reported to be associated with unfavorable outcomes in C-ARF.8 As time is of the essence when managing C-ARF patients, monitoring these trends will help identify those enroute to deterioration and adverse outcomes.
Acute kidney injury predicted COVID-19-related mortality in our study with 17.3% of patients requiring renal replacement therapy. 10.9% of our cohort had chronic kidney disease at baseline. Mechanisms of acute kidney injury in COVID-19 include9 acute tubular injury—direct viral invasion due to renal tropism, inflammation and injury caused by cytokine response, organ cross-talk, for example, acute myocardial injury caused by the virus can lead to hypotension and renal hypoperfusion, mechanical ventilation with ventilator-induced lung injury and consequences of high positive-end expiratory pressure, hypoxia, and nephrotoxic medications; vascular injury—endothelitis, thrombotic microangiopathy, microthrombi; glomerular injury- glomerulitis; and interstitial injury—acute interstitial nephritis.
We found higher adjusted cumulative fluid balance on day 2 and 8 in our cohort. A multicenter observational analysis10 has shown positive fluid balance to be an adverse prognostic factor in invasively ventilated COVID-19 patients. Our results add to the existing evidence in ARDS patients11 that positive fluid balance is associated with higher mortality. Increased extravascular lung water12 and increased vascular permeability as seen in COVID-19 ARDS patients with impaired alveolar fluid clearance13 increases duration of ventilation.
High PaCO2 trends were associated with higher mortality in our cohort. Reduced respiratory compliance in patients with C-ARF, intrapulmonary shunting secondary to alveolar damage, and increased dead space14 following ventilation of under-perfused alveoli secondary to pulmonary microvascular occlusion (reflected by high D dimer) may have contributed to persistently severe respiratory acidosis despite low tidal volume ventilation strategy seems to be plausible reasons.15
Studies16,17 have reported SOFA as a better predictor of mortality in critically ill COVID-19 patients. User-friendly tools for continuously evaluating organ function during ICU stay, SOFA, and SAPS II scores look at organ dysfunction in various organ systems- neurological, respiratory, cardiac, hepatic, renal, and hematologic. Change in the clinical condition of C-ARF patients can be unremitting and these daily scoring appropriately reflected the oxygenation, ventilation support, and renal injury (22% of nonsurvivors required renal replacement therapy) in our cohort. Given the multitude of organ involvement with evolving illness, SOFA and SAPS II scores help prognostication. A combination of clinical severity scores with biochemistry parameters in a clinical context helps stratify severity and risk of death in C-ARF.
Limitations and Strengths
The limitation of this study is its retrospective nature. Data and outcomes were recorded during the highest surge of the pandemic which may bias the results towards higher mortality as the study period was in the early phase of the pandemic when there was limited understanding of the illness, it was a strain on the healthcare system and also the availability of certain COVID- 19 specific medications. A small sample size of our cohort needs confirmation with larger prospective studies. Details of cumulative fluid balance for patients transferred from other hospitals were not available. Information on this probably could have shown a more significant association between a positive balance and mortality. A study including patients in the mild and moderate COVID-19 illness category will afford a clearer picture in relation to the development of severe illness requiring invasive ventilation. Larger, multi-center studies will help validate our findings.
Our study has several strengths. The parameters analyzed were basic, ones that are easily available even in resource-limited settings. We studied biochemical parameters at baseline as well as tracked them that provided dynamic information about a patient’s clinical progress. We highlighted a strong association between acute kidney injury and C-ARF-related in-hospital mortality. Our results will provide a base for the development of prognostic scoring systems for COVID-19 patients.
The higher mortality rate seen in our cohort could be attributed to the peak surge of the contagion as well as the treatment received pre-COVID-19 ICU transfer. Many of these patients were transferred ventilated from other facilities several days after receiving primary treatment. Transfer patients are representative of complex cases.
CONCLUSION
In this retrospective cohort study, monitoring the kinetics of blood biochemical variables correlated with MODS in parallel to a severe inflammatory response. Time-course data collection helps identify patterns of variations amongst survivors vs nonsurvivors as well as helps predict the trajectory of progress. This can guide treatment customization.
ORCID
Sonali Vadi https://orcid.org/0000-0002-7341-2407
Ashwini Pednekar https://orcid.org/0000-0003-3917-7498
Sumiran Bajpe https://orcid.org/0000-0001-6315-8436
Suhas Sonawane https://orcid.org/0000-0002-6761-2082
Sagar Shinde https://orcid.org/0000-0003-2071-1881
Yogesh Vaishnav https://orcid.org/0000-0002-7630-5607
Sanjiv Jha https://orcid.org/0000-0003-3778-0509
Priya Wani https://orcid.org/0000-0002-8010-4824
Neha Sanwalka https://orcid.org/0000-0003-3428-3144
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