ORIGINAL ARTICLE |
https://doi.org/10.5005/jp-journals-10071-23917 |
Arterial Blood Gas as a Predictor of Mortality in COVID Pneumonia Patients Initiated on Noninvasive Mechanical Ventilation: A Retrospective Analysis
1–5Department of Anaesthesia and Critical Care, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
Corresponding Author: Nidhi Gupta, Department of Anaesthesia and Critical Care, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India, Phone: +91 9760672721, e-mail: nidhigupta.doon@gmail.com
How to cite this article: Gupta B, Jain G, Chandrakar S, Gupta N, Agarwal A. Arterial Blood Gas as a Predictor of Mortality in COVID Pneumonia Patients Initiated on Noninvasive Mechanical Ventilation: A Retrospective Analysis. Indian J Crit Care Med 2021;25(8):866–871.
Source of support: Nil
Conflict of interest: None
ABSTRACT
Background: The alveolar–arterial oxygen (A–a) gradient measures the difference between the oxygen concentration in alveoli and the arterial system, which has considerable clinical utility.
Materials and methods: It was a retrospective, observational cohort study involving the analysis of patients diagnosed with acute COVID pneumonia and required noninvasive mechanical ventilation (NIV) over a period of 3 months. The primary objective was to investigate the utility of the A–a gradient (pre-NIV) as a predictor of 28-day mortality in COVID pneumonia. The secondary objective included the utility of other arterial blood gas (ABG) parameters (pre-NIV) as a predictor of 28-day mortality. The outcome was also compared between survivors and nonsurvivors. The outcome variables were analyzed by receiver-operating characteristic (ROC) curve, Youden index, and regression analysis.
Results: The optimal criterion for A–a gradient to predict 28-day mortality was calculated as ≤430.43 at a Youden index of 0.5029, with the highest area under the curve (AUC) of 0.755 (p <0.0001). On regression analysis, the odds ratio for the A–a gradient was 0.99. A significant difference was observed in ABG predictors, including PaO2, PaCO2, A–a gradient, AO2, and arterial–alveolar (a–A) (%) among nonsurvivors vs survivors (p-value <0.001). The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion were higher among nonsurvivors; however, a significant difference was achieved with the vasopressor need (p <0.001).
Conclusion: This study demonstrated that the A–a gradient is a significant predictor of mortality in patients initiated on NIV for worsening respiratory distress in COVID pneumonia. All other ABG parameters also showed a significant AUC for predicting 28-day mortality, although with variable sensitivity and specificity.
Key messages: COVID-19 pneumonia shows an initial presentation with type 1 respiratory failure with increased A–a gradient, while a subsequent impending type 2 respiratory failure requires invasive ventilation.
A significant difference was observed in ABG predictors, including PaO2, PaCO2, A–a gradient, AO2, and a–A (%) among nonsurvivors vs survivors. (p-value <0.001).
The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion need were higher among nonsurvivors than survivors; however, a significant difference was achieved with the vasopressor need (p <0.001).
Keywords: COVID-19 Acute Respiratory Distress Syndrome, Critically ill adults, Mortality predictors.
INTRODUCTION
Severe acute respiratory syndrome–related coronavirus-2 (SARS-CoV-2) is a virus-targeted respiratory disorder with a broad range of symptoms ranging from asymptomatic cases to severe pneumonia or acute respiratory distress syndrome (ARDS)1; the reported mortality rate is as high as 78% in critically ill patients.2 For efficient use of limited healthcare resources, early detection of risk factors and timely management are critical to minimize the adverse outcome.3
The alveolar–arterial oxygen gradient (A–a gradient), which measures the difference between the oxygen concentration in alveoli and the arterial system, has considerable clinical utility in narrowing the differential diagnosis for the cause of hypoxemia.4 Although a perfect system should have a zero A–a gradient, the underlying heterogeneity in capillary perfusion and alveolar ventilation from the apical to basal lung units leads to a physiological A–a gradient throughout the lung. It is further affected by demographic characteristics and various pathological processes. The pulmonary disorders that alter the ventilation–perfusion ratio or disturb the oxygen transfer from the alveoli to pulmonary circulation compromise the arterial oxygenation and increase this gradient.
The recent literature on COVID-19 pneumonia shows an initial presentation with type 1 respiratory failure with increased A–a gradient, while a subsequent impending type 2 respiratory failure requires invasive ventilation.5 A higher A–a gradient at the initiation of noninvasive mechanical ventilation (NIV) signifies a higher degree of ventilation–perfusion mismatch or interstitial pathology. So, we hypothesized that it should reflect subsequent mortality in such patients. Thus, we aimed to assess the utility of ABG parameters as a predictor of 28-day mortality in COVID pneumonia patients initiated on NIV.
MATERIALS AND METHODS
After institutional ethical clearance (via letter no AIIMS/IEC/21/17, dated January 9, 2021), under a retrospective, observational cohort design, this study involved the collection, classification, and analysis of patients aged >18 years of either sex, admitted to this tertiary care institute in between July and September 2020 (3-month data), diagnosed with acute COVID pneumonia, and required NIV. We excluded those with any previous history of cardiopulmonary disease, renal disorder, psychiatric illness, pregnancy, lactation, recent hospitalization (past 3 months), immunocompromised status, or malignancy. We also followed the patient’s data till discharge or death or 28 days since admission, whichever occurred first, through hospital records.
We categorized a patient as COVID-positive case on getting a positive reverse-transcriptase–polymerase chain reaction report from a nasal or pharyngeal swab specimen. According to the institution guidelines, patients were classified as moderate- or severe based on the presence of hypoxia (SpO2 <93%) or radiological evidence of pneumonia or ARDS and organ impairment and shock. A high-resolution computed tomographic scan was done for COVID-19 patients with inconclusive chest X-ray and persistent symptoms. The indication for initiation of NIV included standard protocol as followed in our tertiary care institute. The indications included symptoms of ARDS, such as dyspnea at rest, respiratory rate >25 breath/minute, use of accessory respiratory muscles, or paradoxical breathing, and signs of disturbed alveolar gas exchange in ABG parameters, including partial pressure of carbon dioxide (PaCO2) >45 mm Hg, arterial power of hydrogen (pH) <7.25, the partial pressure of oxygen (PaO2) <60 mm Hg or ratio of the partial pressure of oxygen or fraction of inspired oxygen (PaO2/FiO2) <200. The indication for initiation of invasive mechanical ventilation included the following: poor patient compliance/leaking interface with no improvement in respiratory condition despite all efforts, Glasgow coma scale <9 or deterioration >3, hemodynamic instability, cardiac arrhythmia, the inability of the NIV to improve respiratory distress or gas exchange disturbance within 2 hours of initiation, apnea or respiratory rate (RR)>35 breaths/minute, or inability to maintain a patent airway.
The data collected comprised stay and 28-day mortality. The ABG parameters included PaO2, PaCO2, bicarbonate (HCO3), a fraction of inspired oxygen concentration (FiO2), PaO2/FiO2, A–a gradient, respiratory index, and arterial–alveolar (a–A) oxygen ratio, by following equations:
- A–a ratio was calculated by the following equations:
PaO2 = FiO2 (PB − 47) − PaCO2/R
PaO2 = 0.21 × (760 − 47) − PaCO2/0.8
PaO2 = 150 − PaCO2/0.8
Here PaO2 = partial pressure of alveolar oxygen, PaCO2 = partial pressure of arterial carbon dioxide, FiO2 = fraction of inspired oxygen, Patm = atmospheric pressure (at sea level = 760 mm Hg), PH2O = water partial pressure in alveolus, 100% saturated (at sea level = 47 mm Hg) and R = respiratory quotient (normally 0.8). - Respiratory index was calculated by dividing pO2 from A–a gradient:
Respiratory index (RI) (RI = pO2(A − a)/pO2(a)) - PaO2/FiO2 ratio was calculated by dividing alveolar oxygen to inspired oxygen
- a–A ratio was calculated as a ratio of arterial and alveolar PaO2.
AIMS AND OBJECTIVES
The primary objective was to investigate the utility of A–a gradient (pre-NIV) as a predictor of 28-day mortality in COVID pneumonia patients initiated on NIV.
The secondary objective included the utility of other ABG parameters (pre-NIV) as a predictor of 28-day mortality in the above patients. The outcome studied in terms of ABG parameters and other variables like the vasopressor requirement, the need for renal replacement therapy, total parenteral requirement, and blood transfusion was compared between survivors and nonsurvivors.
The sample size was calculated using Medcalc statistical software version 14.8.1. Taking into account a receiver-operating characteristic (ROC) curve value of 0.70 as significant for pre-NIV A–a gradient as a predictor of 28-day mortality, a null hypothesis ROC curve value of 0.50, an alpha error of 0.05, an 80% power, and a survival: mortality ratio of 3:1, we required sample size of 88 (66:22). To increase the significance of results, we aim to recruit all eligible cases for the assessment period. The statistical analysis was performed with Statistical Package for the Social Sciences version 23.0 software (SPSS, IBM Corp. Armonk, New York, United States). The data were expressed as means and standard deviation (SD) or as number and percentage. The outcome variables were analyzed by a ROC curve, Youden index, and regression analysis. Continuous data were analyzed by Student’s t-test. The Chi-square test analyzed the categorical parameters. A p-value <0.05 denotes statistical significance.
RESULTS
We included 165 patients in the final analysis. Thirty-four patients expired (nonsurvivors), while the remaining (survivors) had a successful outcome. The mean age of included patients was 53.3 years; males were 61% and females 39%, most had no comorbidities, while others had hypertension or diabetes. The mean pre-NIV A–a gradient was 403.83 (range: 379.66–427.99). The mean FiO2 patients were receiving at this time point was 69.7% (range: 21–100%) through nasal prongs, face masks, venturi masks, or rebreathing masks. The mean pre-NIV PaO2 was 56.03 mm Hg (range: 28–96 mm Hg), and the mean PaCO2 was 29.7 mm Hg (range: 18–53 mm Hg), suggesting type I respiratory failure in the majority of the patients. The mean pH was 7.432 (range 7.28–7.54), while the mean HCO3 was 21.48 mEq/L (range 12.5–34), and the mean pO2/FiO2 ratio was 89.87 (22.3–217.85) (Table 1). Mean ICU stay was 14.2 days.
| Parameters | Mean and SD | 95% CI |
|---|---|---|
| Age (years) | 53.30 ± 12.99 | 51.305–55.301 |
| ICU length of stay (days) | 14.20 ± 6.80 | 4–40 |
| A–a gradient | 403.83 ± 157.21 | 379.663–427.998 |
| PaO2/FiO2 | 89.88 ± 3.42 | 22.3–217.86 |
| Respiratory index | 8.35 ± 0.40 | 1.39–28.99 |
| A–A (%) | 26.08 ± 2.75 | 3.58–250 |
| FiO2 | 69.70 ± 22.02 | 66.318–73.088 |
| HCO3 | 21.48 ± 12.98 | 19.489–23.482 |
| K+ | 3.74 ± 0.48 | 3.672–3.823 |
| Na+ | 135.07 ± 6.46 | 134.085–136.073 |
| PaCO2 | 29.692 ± 6.08 | 28.756–30.627 |
| PaO2 | 56.03 ± 19.54 | 53.033–59.042 |
| PaO2/FiO2 | 89.87 ± 43.94 | 83.119–96.632 |
| pH | 7.432 ± 0.06 | 7.423–7.442 |
For validity analysis, we plotted a ROC curve for each ABG parameter. The optimal criterion for A–a gradient to predict 28-day mortality was calculated as ≤430.43 (sensitivity: 67.94%, specificity: 82.35%) at a Youden index of 0.5029, with the highest area under the curve (AUC) of 0.755 (p <0.0001). On regression analysis (Table 2), the odds ratio (OR) for the A–a gradient was 0.99, indicating no further increase in the log-odds of mortality with an increase in A–a gradient above the threshold value.
| Parameter | AUC | YI | Criterion | Sensitivity | Specificity | +LR | −LR | p-value |
|---|---|---|---|---|---|---|---|---|
| A–a gradient | 0.76 | 0.5 | ≤430.43 | 67.94 (59.2–75.8) | 82.35 (65.5–93.2) | 3.85 | 0.39 | <0.0001 |
| PaO2/FiO2 | 0.58 | 0.31 | >110 | 31.30 (23.5–40.0) | 100.00 (89.7–100.0) | 0.69 | 0.0793 | |
| Respiratory index | 0.60 | 0.35 | ≤4.96 | 35.11 (27.0–43.9) | 100 (89.7–100.0) | 0.65 | 0.0194 | |
| A–A (%) | 0.60 | 0.35 | >24.99 | 35.11 (27.0–43.9 | 100.00 (89.7–100.0) | 0.65 | 0.0194 | |
| PaO2 | 0.67 | 0.39 | ≤54 | 56.49 (47.6–65.1) | 82.35 (65.5–93.2) | 3.2 | 0.53 | 0.0014 |
| PaCO2 | 0.73 | 0.44 | >24.9 | 88.55 (81.8–93.4) | 55.88 (37.9–72.8) | 2.01 | 0.2 | <0.0001 |
| pH | 0.66 | 0.44 | ≤7.47 | 79.39 (71.4–86.0) | 64.71 (46.5–80.3) | 2.25 | 0.32 | 0.0076 |
| HCO3 | 0.66 | 0.43 | >19.7 | 61.07 (52.2–69.5) | 82.35 (65.5–93.2) | 3.46 | 0.47 | 0.0016 |
The optimal criterion for PaO2/FiO2 to predict 28-day mortality was measured as >110 (sensitivity 31.3%, specificity 100%) at a Youden index of 0.31 and AUC of 58% (p-value: 0.0793). The optimal criterion for the respiratory index to predict 28-day mortality was found to be ≤4.96 (sensitivity 35.11%, specificity 100%) at a Youden index of 0.35 and AUC of 60% (p-value: 0.0194). The optimal criterion for a–A percentage to predict 28-day mortality was observed to be >24.9 (sensitivity 35%, specificity 100%) at a Youden index of 0.35 and AUC of 60% (p-value: 0.0194) The optimal criterion for PaCO2 to predict 28-day mortality was observed to be >24.9 (sensitivity 88.5%, specificity 55.9%) at a Youden index of 0.388 and AUC of 72.9% (p-value <0.0001) The optimal criterion of PaO2 to predict 28-day mortality was observed to be ≤54 (sensitivity 56.5%, specificity: 82.5%, % LR+ 3.2) at a Youden index of 0.44 and AUC of 66.7% (p-value: 0.0014). The optimal pH criterion to predict 28-day mortality was observed to be ≤7.468 (sensitivity 79.4%, specificity: 64.7%, %LR+ 2.25, LR-0.32) at a Youden index of 0.44 and AUC of 66% (p-value: 0.0076). The optimal criterion of HCO3 to predict 28-day mortality was observed to be >19.7 (sensitivity: 61.1%, specificity: 82.4%) at a Youden index of 0.4342 and an AUC of 66.3% (p-value: 0.0016) (Table 3 and Fig. 1).
Figs 1A to H: Receiver-operating characteristic curve for (A) a gradient; (B) pO2/FiO2; (C) respiratory index; (D) a–A (%); (E) PaO2; (F) PaCO2; (G) pH; (H) HCO3
| Variable | Odds ratio | 95% CI | p-value |
|---|---|---|---|
| A–a gradient | 0.9934 | 0.9904–0.9964 | <0.0001 |
| Sample size | 165 | Nonsurvivors: 34 (20.61%) | |
| Survivors: 131 (79.39%) | |||
A significant difference was observed in ABG predictors, including PaO2, PaCO2, A–a gradient, AO2, and a–A (%) among nonsurvivors vs survivors (p-value <0.001). The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion need were higher among nonsurvivors than survivors; however, a significant difference was achieved with the vasopressor need (p <0.001) (Table 4).
| Parameters | Survivors (n = 131) | Nonsurvivors (n = 34) | p-value |
|---|---|---|---|
| Age | 51.34 ± 13.09 | 60.97 ± 9.34 | 0.154 |
| pH | 7.43 ± 0.0595 | 7.46 ± 0.061 | 0.012 |
| pCO2 (mm Hg) | 30.67 ± 6.08 | 25.93 ± 4.49 | 0.000 |
| pO2 (mm Hg) | 53.77 ± 19.29 | 64.79 ± 18.24 | 0.003 |
| HCO3 (mm Hg) | 22.21 ± 14.37 | 18.70 ± 3.7 | 0.162 |
| A–a gradient based on pO2 | 375.49 ± 157.15 | 513.04 ± 100.31 | 0.000 |
| ICU length of stay | 13.927 ± 6.86 | 15.206 ± 6.61 | 0.334 |
| respiratory index | 8.27 ± 5.6 | 8.69 ± 3.22 | 0.676 |
| AO2 | 321.71 ± 161.2 | 448.25 ± 99.92 | 0.000 |
| a–A (%) | 28.90 ± 39.21 | 15.19 ± 5.75 | 0.000 |
| Mechanical ventilation days | 12.00 ± 1.2 | 11.40 ± 5.56 | 0.917 |
| Vasopressor requirement | 0.130 ± 0.33 | 0.515 ± 0.50 | 0.000 |
| CRRT requirement | 0.137 ± 0.345 | 0.176 ± 0.387 | 0.568 |
| TPN requirement | 0.092 ± 0.28 | 0.176 ± 0.38 | 0.159 |
| Blood transfusion requirement | 0.099 ± 0.30 | 0.206 ± 0.410 | 0.09 |
DISCUSSION
This study demonstrated that the A–a gradient is a significant predictor of mortality in patients initiated on NIV for worsening respiratory distress in COVID pneumonia. The optimal criterion for the A–a gradient to predict above was observed to be ≤430.43. All other ABG parameters also showed a significant AUC for predicting 28-day mortality, although with variable sensitivity and specificity.
It is known that increased cardiopulmonary vascular shunt or altered alveolar diffusion barrier can substantially affect the A–a gradient.6 Given the impact of COVID-19 interstitial pulmonary involvement on gas exchange, ventilation–perfusion mismatch,7 and shunting, we decided to test ABG parameters’ validity, particularly A–a gradient, in predicting mortality. The available data indicate that around 40% of COVID-19 patients develop ARDS (20% severe cases), with 5–10% requiring ICU admission and invasive ventilation. Although most patients survive an acute illness, a subset develops fibro-proliferative response marked by fibroblast aggregation and deposition of collagen and other extracellular matrix components in the lung. Historically, the occurrence of severe fibro-proliferative pulmonary8 disease has been associated with a poor prognosis, causing high mortality and/or prolonged ventilation dependence. In the present study, we found that the A–a gradient effectively predicted mortality in moderate to severe COVID-19 patients. Previous studies9 have shown that the A–a gradient was higher among nonsurvivors than survivors who had community-acquired pneumonia (AUC 0.78), with a mean A–a gradient of 148.64 and 90.16 among nonsurvivors and survivors, respectively. In another published literature, AaDO2 (A–a gradient) and AaDO2 augmentation displayed good accuracy (AUC: 0.952 and 0.810, respectively) to predict ICU admission in patients with COVID-19. However, their cutoff value was 56.6 ± 17.5 in the ICU group and 25.9 ± 9.7 in the non-ICU group; given small sample size, strong emphasis cannot be laid upon results. Farina et al.10 also investigated the role of A–a gradient in predicting the need for hospitalization, the survival rate, and identifying pneumonia in patients with SARS-CoV-2 infection. They reported that out of 168 patients with AaDO2 ≤27, only 3 (1.8%) required readmission within 7 days; it could be attributed to the inclusion of patients with less disease severity. In our study, all patients were in respiratory failure requiring NIV, which could account for higher cutoff values of observed mean A–a gradient.
In recent years, the a–A tension ratio (PaO2/PaO2), sometimes denoted a–APO2, has been used as an index of pulmonary gas exchange. While the A–a tension difference (A–aPO2) is known to depend strongly on the FiO2, a–APO2 is less dependent on the extrapulmonary factors.11 The PaO2/FiO2 ratio is also widely used as a clinical indicator of hypoxemia, though its diagnostic utility is controversial. The optimal criterion for PaO2/FiO2, respiratory index, and a–A percentage to predict 28-day mortality in our study were also found to have significant differences, as expected. We observed a considerable difference in ABG predictors, including PaO2, PaCO2, A–a gradient, AO2, and a–A (%) among nonsurvivors vs survivors (p-value <0.001), which was expected in the disease. The hallmark of disease severity in COVID-19 patients is hypoxemia, although the associated symptoms, including respiratory distress, develop late in the disease (“silent hypoxemia”12). We calculated a criterion value of >24.9, ≤54, and ≤7.468 for PaCO2, pO2, and pH, respectively, to predict 28-day mortality in patients developing respiratory distress and requiring NIV. Tendon et al.13 also demonstrated a significant difference in PaO2 among survivors and nonsurvivors; a statistical difference was not observed for PaCO2 and pH.
We also compared morbidity predictors, and patient course during the ICU stay, and higher need for vasopressor requirement, continuous renal replacement therapy requirement, total parenteral nutrition requirement, and blood transfusion need among nonsurvivors. The underlying cause is multifactorial, including hypovolemia (fever, restricted fluid administration to prevent overload), vasodilation (sepsis, deep sedation during mechanical ventilation), and right or/and left ventricular dysfunction (mechanical ventilation with high positive end-expiratory pressure, pulmonary embolism, and circulating cytokines decreasing contractility, myocarditis). Although the requirement for vasopressor support is quite common in COVID-19 ICU patients, the detailed hemodynamic profile or phenotype remains poorly documented. A similar duration of ICU stay was due to the development of the fibro-proliferative phase requiring oxygen supplementation, and it is further reflected by the similar need for parenteral nutrition, blood transfusion, and renal replacement therapy.
There were several limitations in this study. Although we followed a standard protocol for initiating invasive and NIV, changing guidelines from time to time concerning steroid therapy, antiviral therapy initiation, and self-proning protocol may have affected the patient outcome. Another limitation included the delayed availability of high-flow nasal cannula, which can also affect the outcome. The fact that the research was performed at one center is also a limitation of the study, restricting the findings’ generalizability.
CONCLUSION
The A–a gradient is a significant predictor of mortality in patients initiated on NIV for worsening respiratory distress in COVID pneumonia. All other ABG parameters also showed a significant AUC for predicting 28-day mortality, although with variable sensitivity and specificity.
HIGHLIGHTS
The routine analysis of these simple, quick, readily accessible, and cost-effective ABG parameters, especially the A–a gradient, reliably predicted the poor prognosis of patients at NIV initiation. It will aid in the early initiation of NIV and prognostication of adverse outcomes.
ORCID
Bhavna K Gupta https://orcid.org/0000-0002-3108-0408
Gaurav Jain https://orcid.org/0000-0002-1205-7237
Saurabh Chandrakar https://orcid.org/0000-0003-2449-2153
Nidhi Gupta https://orcid.org/0000-0001-7952-5317
Ankit Agarwal https://orcid.org/0000-0003-4963-7101
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