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Article

Lung Injury in COVID-19 Has Pulmonary Edema as an Important Component and Treatment with Furosemide and Negative Fluid Balance (NEGBAL) Decreases Mortality

by
Jose L. Francisco Santos
1,*,
Patricio Zanardi
1,
Veronica Alo
1,
Vanina Dos Santos
1,
Leonardo Bovone
1,
Marcelo Rodriguez
2,
Federico Magdaleno
3,
Virginia De Langhe
3,
Andrea Villoldo
1,
Romina Martinez Souvielle
1,
Julieta Alconcher
4,
Diego Quiros
4,
Claudio Milicchio
4 and
Eduardo Garcia Saiz
4
1
Intensive Care Unit, Clínica Colón, Mar del Plata, Buenos Aires 7600, Argentina
2
Cardiology Service, Clínica Colón, Mar del Plata, Buenos Aires 7600, Argentina
3
Diagnostic Imaging Service, Hospital Privado del Sur. Bahía Blanca, Buenos Aires 8000, Argentina
4
Diagnostic Imaging Service, Clínica Colón, Mar del Plata, Buenos Aires 7600, Argentina
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(4), 1542; https://doi.org/10.3390/jcm12041542
Submission received: 13 December 2022 / Revised: 9 February 2023 / Accepted: 11 February 2023 / Published: 15 February 2023
(This article belongs to the Special Issue Pulmonary and Critical Care Practice in the Pandemic of COVID-19)
Browse Figures
Review Reports Versions Notes

Abstract

:
The SARS-CoV2 promotes dysregulation of Renin–Angiotensin–Aldosterone. The result is excessive retention of water, producing a state of noxious hypervolemia. Consequently, in COVID-19 injury lung is pulmonary edema. Our report is a case–control study, retrospective. We included 116 patients with moderate–severe COVID-19 lung injury. A total of 58 patients received standard care (Control group). A total of 58 patients received a standard treatment with a more negative fluid balance (NEGBAL group), consisting of hydric restriction and diuretics. Analyzing the mortality of the population studied, it was observed that the NEGBAL group had lower mortality than the Control group, p = 0.001. Compared with Controls, the NEGBAL group had significantly fewer days of hospital stay (p < 0.001), fewer days of ICU stay (p < 0.001), and fewer days of IMV (p < 0.001). The regressive analysis between PaO2/FiO2BAL and NEGBAL demonstrated correlation (p = 0.04). Compared with Controls, the NEGBAL group showed significant progressive improvement in PaO2/FiO2 (p < 0.001), CT score (p < 0.001). The multivariate model, the vaccination variables, and linear trends resulted in p = 0.671 and quadratic trends p = 0.723, whilst the accumulated fluid balance is p < 0.001. Although the study has limitations, the promising results encourage more research on this different therapeutic approach, since in our research it decreases mortality.

1. Introduction

Since the beginning of the COVID-19 pandemic, the inflammatory pattern, generated by a cytokine storm [1,2,3,4] and a “COVID viral pneumonia” [5,6] has been imposed as pathophysiology of lung injury [7,8].
The COVID-19 cytokine/pneumonia hypothesis is undoubtedly fine and elegant, but it fails to explain such recurrent findings as sinus bradycardia [9], anemia [10], normotension [11], afebrile course [12,13] and increased vascular diameters [14].
Moreover, based on this approach, inflammatory-pneumonia viral, antivirals [15,16], convalescent plasma [17], tocilizumab [18,19], IL-1 receptor antagonists [20], monoclonal antibodies [21,22,23,24], were rehearsed, but only exhibit failures [12,15,16,17,18,19,20,21,22,23,24,25,26].
Thus, the pathophysiology of COVID is far from being clarified [25,27,28,29].
In addition to this, there is wide evidence that the second phase of COVID-19 would be the product of progressive hypervolemia/pulmonary edema [30,31,32,33] and not an aggravation of “pneumonia”.
Based on the foregoing, we challenged the cytokines/pneumonia paradigm.
We proposed to distinguish between what the lung injury COVID-19 appears to be -pneumonia, from what it is—pulmonary edema [33].
The hypervolemia/pulmonary edema hypothesis has a simple biological plausibility: dysregulation of the RAAS. SARS CoV 2 blocks ACE2 [34,35], induces its downregulation [36,37], generates an imbalance in the RAAS [38,39,40,41] with a harmful increase in angiotensin II [42,43] and a decrease in angiotensin 1–7 [44,45] and a harmful increase in aldosterone [46]. The result is a decrease in urine output [40] and excessive retention of sodium and water, producing a state of noxious hypervolemia with subsequent pulmonary edema [30,31].
In this setting of detrimental overload, a different approach emerged: lung injury in COVID-19 is a pulmonary edema with a “dual phase” [31,47]. A “first phase” of viral pneumonitis [25]—increase of the alveolar capillary membrane permeability. However, the “second phase” is most harmful and stealthy: high pressure pulmonary edema, caused by increase of hydrostatic pressure [48,49,50] secondary to volume overload phase 2, secondary to dysregulation of the RAAS [34,51]. This results in a “dual phase” that triggers acute pulmonary edema. If this pulmonary edema is not resolved, then a “third phase” comes, with the typical ARDS [52].
A new hypothesis that could explain COVID-19 lung injury must rely on evidence. Such evidences are:
Pathological anatomy: there are multiple descriptions of severe capillary congestion and alveolar flooding [47,53,54,55].
Clinical: in a second phase, many patients evolve afebrile [12,13], normocardic and, normotensive [4,11,33]. We would be facing a tempest without thunderbolts or lightning.
There are multiple hypotheses for sinus bradycardia in COVID-19 [9,56] but one explanation is enough: with stable cardiac output and in the presence of hypervolemia, systolic volume increases and, according to Frank Starling’s Law, there is a compensatory decrease in the heart rate.
Biochemical: Anemia is dilutional and the increased hypervolemia leads to more anemia and therefore worse prognosis [10,57]. Something similar occurs with lymphopenia [58,59,60], which would have a dilutional component.
Tomographic: in chest tomography (CT) hypervolemia is confirmed by dilation of the great vessels [33,61], of the cardiac axis, without heart failure [33,61], and of the pulmonary vessels [14]. In addition, the rapid improvement of the CT Score with negative fluid balance (NEGBAL) is strong evidence that there is more pulmonary edema than pneumonia [33].
Cardiotechnics: dilation of the right ventricle [62,63] and increased end-diastolic volume [31] are clear manifestations of hypervolemia.
Techniques thermodilution: the increase in the rate of extravascular lung water (EVLW) in COVID-19 has already been demonstrated [30,31].
Therapeutic: improved oxygenation, decreased cardiac axis and great vessels, and increased hematocrit are results obtained with negative balance [33].
Despite this evidence, the role of hypervolemia and edema in lung injury in COVID-19 continues to be underestimated in daily practice. Taking into consideration what was previously stated, we detected pulmonary edema and, consequently, in the presence of pulmonary edema in COVID-19 patients, we established a standard treatment, consisting of oral hydric restriction and diuretics [48,64,65] called NEGBAL (negative fluid balance).
In this research, we compared the evolution of patients with moderate and severe COVID-19 infection into two groups, a control group and a group that received the NEGBAL approach.
At the time of submission of our study, excepting our previous report [33], we did not find any literature that proposed the model of volume overload pulmonary edema secondary to the dysregulation of the RAAS in COVID-19.

2. Materials and Methods

2.1. Ethical Approval

The study was conducted according to the guidelines of the Declaration of Helsinki and its later amendments, and approved by the Ethics in Investigation Committee of INSTITUTO DE INVESTIGACIONES CLINICAS, Mar del Plata, Argentina (OHRP: IRB 00008222, date of approval: 3 September 2021).

2.2. Study Design and Setting

This single-center retrospective, observational case–control design study took place during the period of 8 July 2020 to 15 February 2022. One-to-one matching was included, all with confirmed diagnosis of COVID-19, admitted to our high complexity center in Mar del Plata, Argentina.
A one-to-one case control ratio was designed.
The cases were all patients who had received the NEGBAL approach since we started implementing it, 24 June 2021 to 15 February 2022.
Control patients were selected among those who did not receive the NEGBAL approach, since they were admitted to the Intensive Care Unit before 24 June 2021, when the NEGBAL strategy was implemented.

2.3. Data Compilation

We reviewed data from medical records: demographic; clinical, laboratory; blood gas; chest tomography (CT); the oxygen therapy support; and mechanical ventilation (IMV) requirements, all of which were reviewed and recorded by investigators. Clinical outcomes were followed up to 1 May 2022.
In this case–control study, we compared fluid management applied during eight days in patients with acute lung injury for COVID-19: a conservative strategy for the control group, and NEGBAL approach for the NEGBAL group.
The control group received standard treatment. It was based on dexamethasone 6 mg/day, enoxaparin 40 mg/day, oxygen supplement (nasal cannula, reservoir mask, HFNC), invasive or non-invasive mechanical ventilation and standard care.
The NEGBAL group underwent, in addition to the standard care, a treatment with furosemide in continuous intravenous infusion: the NEGBAL approach.
Inclusion criteria were as follows: (1) age older than 18 years; (2) confirmed diagnosis of COVID-19 through real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay with samples obtained from nasopharyngeal swab or positive antinucleocapsid IgM antibodies; (3) PaO2/FiO2 (ratio of arterial oxygen partial pressure to fractional inspired oxygen) < 300; and (4) tomographic evidence of acute pulmonary edema, defined as ground glass infiltration, dilated superior vena cava, large pulmonary arteries, and dilated right ventricle or dilated cardiac axis.
Exclusion criteria were as follows: (1) patients with indication for diuretics for another reason; (2) renal failure; (3) cardiac failure; (4) hepatic failure; (5) hypernatremia or hyponatremia; (6) hypotension, use of inotropics or shock; (7) receiving antivirals or monoclonal antibodies.
CTs were performed upon admission to ICU, CT controls were scheduled for day 4 (+/− 1 day), day 8 (+/− 1 day), and day 12 (+/− 1 day). A 1.5 mm slice thickness and 1.5 mm interval were used for the axial image.
For the evaluation of CT infiltrates, the scoring was as described by Pan F et al. [66]. The measurement of the superior vena cava diameter (Ø svc) in the CT was performed just above the arch of the azygos veins.

2.4. Negative Fluid Balance (NEGBAL) Approach

We established a treatment NEGBAL approach: It consisted of oral hydric restriction and diuretics (20 mg of furosemide, intravenous bolus, followed by furosemide in endovenous continuous infusion, starting at 60 mg/day). The objective was to achieve a negative fluid balance, between 1000 to 1500 mL/day adjusted to body surface area, with a final target of 10% of body weight in 8 days. The presence of hypotension (systolic blood pressure less than 100 mmHg for 30 min), use of inotropics or elevated serum creatinine higher to 2.0 mg/dl was considered a cause of suspension of NEGBAL. All patients were followed until either death or complete recovery and discharge were reached.
The primary clinical outcome was mortality within 28 days after ICU admission. Secondary outcomes were days of invasive mechanical ventilation; days of ICU stay, and days of hospital stay, to measure accumulated fluid balance at day 8 (AccFluBal), PaO2/FiO2, CT Score, diameter SVC, hematocrit and lymphocytes evolution.
Safety outcomes included treatment-emergent adverse events, serious adverse events, and discontinuations of NEGBAL approach.

2.5. Statistical Analysis Plan

2.5.1. Data Source and Description

We analyzed data from 116 patients treated for COVID-19 disease. A first group of 58 patients was treated with the standard treatment (CONTROL group) and a second group was treated following the NEGBAL approach (NEGBAL group). At this stage, we compared data from both populations as to inquire whether there was any statistically significant difference between the evolution of those patients treated with NEGBAL protocol and those treated with the standard treatment.

2.5.2. Baseline Data

Descriptive data are presented as means (std) or medians (with range) for continuous data depending on the distribution of the variable. Categorical variables are presented as frequencies and percentages.

2.5.3. Primary Analysis

The variables analyzed were: age, weight, sex, vaccination status for 1st and 2nd doses obesity diabetes instead COPD or tabaquism, cardiopathy instead, HR (heart rate), SBP (systolic blood pressure), DBP (diastolic blood pressure), temperature instead, RR (respiratory rate), APACHE (acute physiology and chronic health evaluation score), BMI (body mass index), SSD (Symptom Start Date), PO2, FiO2, PaFiO2, PCO2, HCO3, creatinine instead lymphocytes instead, plasmatic sodium instead, hematocrit instead, arterial hypertension, BNP (pro-B-type Natriuretic Peptides), troponin instead TGO (glutamic oxaloacetic transaminase), TGP (glutamic pyruvic transaminase), ALP (alkaline phosphatase), total bilirubin instead, direct bilirubin instead.
The references NEGBAL and CONTROL refer to which group the variable under consideration belongs and ADM means that the value of the variable was observed at the moment of admission.
The data consisted of: PaFiO2, creatinine and hematocrit measurements for at the moment of admission, at days 4 and 8 and at discharge; leukocytes at the moment of admission; accumulated hydric balance after the 8 days; hospitalization days, ICU days, days under IMV and IMV (invasive mechanical ventilation) use; and result of the treatment (RES, alive or dead); CT score and superior vena cava diameter (CT SCORE and SVC, respectively) measurements previous to the treatment, at the moment of admission, at days 4, 8 and 12.
The comparison between numerical variables was made via Welch’s two samples t-test with null hypothesis that the difference of the means equals 0. The comparison between categorical variables was carried out mostly via a Person’s χ2 test, except for the case of the CARDIOP variable, for which a Fisher’s test was used.
Five types of analyses were performed:
  • An intrinsic analysis via paired differences t-tests regarding the evolution of the variables PaFiO2, creatinine and hematocrit at the moment of admission, days 4 and 8 and discharge and CT SCORE and VSC (superior vena cava) previous to the treatment, at the moment of admission, and at days 4, 8 and 12 for the same group;
  • A comparative analysis via unpaired differences t-tests regarding the same variables as above (as well as the variable leukocytes at the moment of admission) but comparing for each day the values of both groups, together with a comparison (again using unpaired difference t-tests) of the variables accumulated hydric balance (ACC HYD BAL) and hospitalization days between groups and a Pearson’s χ2 test for the result;
  • A simple regression model to assess the correlation between PaFiO2 and ACC HYD BAL in both groups.
  • A multivariate regression model to assess the correlation between PaFiO2 and ACC HYD BAL and vaccination (VAC) for the whole population.
  • Paired differences t-tests comparing the values of the variable ACC HYD BAL for survivors and non survivors within each group and for the whole population.

2.5.4. Results

For each numerical variable, two tables and one plot are provided: the corresponding statistical summary (IQR standing for interquartile range and sd for standard deviation) for the Control and BLANEG groups and the t-test for difference of the means and the boxplot for both samples. For the categorical variables, a contingency table is provided, and on its caption the result of the χ2 test (Fisher in the case of CARDIOP).
The results are divided in subsections according to the analyses detailed in the previous paragraph: intrinsic evolution, day by day comparison and regression.
All analyses were performed using R software, version 4.1.1 (R Foundation for Statistical Computing).
(Please see Supplemental Materials: Statistics Nº 1 and Nº 2).

3. Results

3.1. Population Analysis

A total of 293 patients with COVID-19 were referred to our ICU. After excluding 89 patients with PaO2/FiO2 > 300, 27 for prior indication of diuretics, 6 with shock, 9 with limitation of therapeutic effort, and 46 with hepatic, kidney or heart failure; we included in the final analysis 116 patients withCOVID-19 lung injury. There were not any dropouts in the study population.
Twenty eight patients died during hospitalization and 88 were discharged. Mean (SD) age was 59.7 (±13.6) years, ranging from 22 to 91. Forty were female (34.5%) p = 0.55. Among the reported population, 92 (79.3%) had comorbidities; the most common ones were obesity (50.9%) and hypertension (40.5%), followed by diabetes mellitus (25.9%) and COPD (17.4%).
Fifty two patients had received the first dose of vaccine (44.8%) and 27, (23.2%) the second dose. The mean (SD) BMI was 30.1 (5.16). The mean (SD) hematocrit was 39.8% (4.91%). The mean (SD) PaO2/FiO2 on admission was 164 (70) and the mean (SD) CT score was 15.7 (4.63).
There were no significant differences in demographics, clinical, tomographic biochemical and background variables between the cases and the control group at the time of ICU admission, except for the prevalence of vaccinated patients, which was higher in the NEGBAL group (p = 0.01). (See demographic information and biochemical data at admission information of both groups in Table 1).
The percentage of the patients with moderate or severe lung injury at admission were:
In the Control group: 33% (n = 19) of moderate lung injury and 67% (n = 39) of severe lung injury. In the NEGBAL group: 31% (n = 18) of moderate lung injury and 69% (n = 40) of severe lung injury.
The causes of death in the population studied were:
Control group: 22 deaths: 5 secondary to refractory distress, 6 due to sepsis, 8 due to refractory distress and sepsis and 3 due to multiple organ failure.
NEGBAL Group: 6 deaths: 4 secondary to sepsis, 1 due to coronary disease and 1 due to ventilator-associated pneumonia.
No patient in the NEGBAL group presented criteria for suspension of the NEGBAL protocol. In other words, no patient had hypotension, no patient had tachycardia, no patient had renal failure, nor sodium disorders.
For the evolutionary analysis, we compared day of admission, day 4, day 8 and day of discharge.

3.2. Primary Outcome

Analyzing mortality: it is observed that the NEGBAL group has lower mortality than the control group, p = 0.001. See Figure 1A.

3.3. Secondary Outcomes

Compared with controls, NEGBAL has significantly fewer days of hospital stay, fewer days of ICU stay, and fewer days of IMV (see Figure 1B–D)
Comparing accumulated fluid balance (AccFluBal) at day 8: Both groups had negative fluid balance; however, the NEGBAL group had significantly higher negative AccFluBal −8027 ± 2516 mL versus AccFluBal Control, −685 ± 5045 mL (see Figure 2A).
Comparing within the control group, the AccFluBal at day 8 between survivors and non-survivors. We observed that survivors in the control group developed negative fluid balance, (spontaneous, without diuretics -”auto-NEGBAL”-) that was significantly higher −3565 ± 1000 mL than non-survivors: +580 ± 1000 mL, with p < 0.001. (see Figure 2B).
The linear regression analysis of the NEGBAL group showed a statistically significant correlation between PaO2/FiO2 and accumulated fluid balance (AccFluBal) p = 0.04 (see Figure 2C)
The linear regression analysis of the control group also showed a statistically significant correlation between PaO2/FiO2 and accumulated fluid balance (AccFluBal) p = 0.007 (see Figure 2D)
Regarding the multivariate model, the p−value corresponding to the whole model was p < 0.001. The p−values for the variables AccFluBal and vaccination (Vac) account for whether or not such variables are significant explaining the variation of the variable PaO2/FiO2. The results obtained for the variable AccFluBal was p < 0.001. As for the variable Vac, the p values were 0.67 for the linear and 0.72 for the quadratic fit, respectively.

3.4. Complementary Analysis

We analyzed the temporal evolution of PaO2/FiO2 between admission, day 4 and 8. In the NEGBAL group, PaO2/FiO2 showed significant progressive improvement coinciding with the application of the NEGBAL approach, while control showed no evolutionary differences in PaO2/FiO2 (Figure 3A,B).
We compared PaO2/FiO2 between NEGBAL and control. Significant differences were detected with better oxygenation in NEGBAL at days 4 and 8. (Figure 3A,B).
We analyzed the temporal evolution of the CT score between admission, days 4 and 8. In NEGBAL, the CT score showed a significant progressive decrease, coinciding with the application of the NEGBAL approach. Meanwhile, the control group did not show significant evolutionary differences in the CT score. (Figure 3C,D).
We compared the CT score between NEGBAL and control between admission, day 4 and 8. Significant differences were detected with a greater decrease in the CT score in the NEGBAL group. (Figure 3C,D).
We analyzed the temporal evolution of the diameter of the SVC (Ø SVC) and hematocrit (HCT) between admission, day 4 and 8 can be seen in Figure 3E–H, respectively.

3.5. Analysis Variable Security

We found no differences in safety measurements between both groups.
No patient in the NEGBAL group presented criteria for suspension of the approach.
More detailed results analysis can be found at Table 2.

4. Discussion

Our study has some limitations. First, due to the study design, it is retrospective and one-center only. Second, the interpretation of our findings might be limited by the sample size. Third, a weakness of this study is not being able to report the variants of SARS-CoV2 that affected each of the patients. Unfortunately, viral typing is out of the investigators reach and the means we currently have.
Unlike high-altitude pulmonary edema (HAPE), oxygen administration alone may be ineffective in severe cases of COVID-19 due to extensive alveolar cell compromise that reduces the gas exchange surface area. It is also important to note that alveolar destruction by pneumolysis induced by initial intraalveolar viral replication hinders spontaneous resorption of pulmonary edema.
The focus on the link between SARS-CoV2 and RAAS imbalance was mentioned by Rothlin et al. The use of ACEI/ARBs is promoted to “block” the ACE2 receptor, and decrease the proinflammatory activity of angiotensin II, although ACEI/ARBs would have a protective effect and would not be useful in advanced conditions [29].
The NEGBAL group comparison to the control group describes homogeneous groups without significant differences, except for the prevalence of vaccinated people. This was due to the fact that the advance of vaccination in our country coincided with the application of the NEGBAL approach. However, this did not affect the results; first, because the objective of the vaccines is not the improvement of lung injury, but its prevention [67,68]; second, multivariate analysis did not show that vaccination reverses lung injury.
Our population upon admission to the ICU presented itself without clinical evidence of cytokine storm. However, our study does describe several clinical, biochemical and tomographic findings consistent with the model hypervolemia/pulmonary edema:
The control group showed that there is a tendency to maintain the Ø SVC or even a progressive increase, not significant, of the Ø SVC—evidence of hypervolemia—and associated with progressive drop in PaO2/FiO2 and an increase in the CT score—evidence of pulmonary edema.
On the contrary, in the NEGBAL group, fluid overload was reversed, through negative balance, observing a significant progressive decrease in the Ø SVC—evidence of resolution of hypervolemia—and progressive improvement of PaO2/FiO2, which is positively correlated in the linear regression analysis with the magnitude of the negative balance.
CT score also improved—evidence of resolution of the pulmonary edema—which, as an expected consequence, resulted in a significant decrease in IMV requirements in the NEGBAL group.
Signs of hypervolemia are also described by Lang et al. [14], Eslami et al. [61], and Argulian and Yu described right ventricle dilation [62,63]. Although the deregulation of the RAAS in COVID-19 is out of the question, its role in the genesis of pulmonary injury is underestimated, despite the fact that the relationship between hypervolemia and pulmonary edema in COVID-19 is described by Villard et al. [46] when she demonstrated that “elevated levels of aldosterone (excessive fluid retention) in COVID-19 are associated with greater pulmonary injury (pulmonary edema)”. Even Rysz [40] described the genesis of hypervolemia in pigs, generated by ACE 2 deficiency and high levels of angiotensin II, measuring water retention and detecting a decrease of eighty percent in urine output.
The control group showed a decrease in the hematocrit and lymphocytes level, also coinciding with an increase Ø SVC. These three simultaneous observations can only be explained in a simple way by the presence of hypervolemia that generates anemia and lymphopenia by a dilutional phenomenon. On the contrary, in the NEGBAL group, as the negative balance progressed, the hypervolemia was corrected, and the desired result appeared: Ø SVC decreased and hematocrit and lymphocytes increased [33,69].
On admission, the study population had normal heart rate or sinus bradycardia associated with decreased hematocrit and increased Ø SVC. This association has no simpler explanation than the presence of hypervolemia. Consequently, sinus bradycardia in COVID-19 is the result of Frank Starling’s Law [70]. In fact, Rhi Shi [31] observed that COVID-19 patients had a higher end-diastolic volume, but lower lactate than non-COVID-19 patients. This demonstrated hypervolemia, not hemodynamic failure.
This early improvement in the CT score and PaO2/FiO2 with NEGBAL can be attributed to resolution of the edema. Furthermore, it is unusual for a “pneumonia” to improve tomographically on day 4. Sibbald et al. [48] described the relationship between hypervolemia and pulmonary edema. Similarly, Rasch et al. [30] reported “that COVID-19 lung injury has up to five times more EVLW index -2600 mL- than normal lungs -500 mL”-. Rhi Shi et al. [31] reported the same facts.
We noted that within the control group, the surviving subgroup had a higher spontaneous negative fluid balance during hospitalization (without diuretics, “auto-NEGBAL”), compared to the non-surviving control subgroup, which had a positive fluid balance. This “auto-NEGBAL” could result from the natural regeneration of ACE2 [71], restoring the RAAS and euvolemia, naturally.
The NEGBAL group quickly and effectively resolved the pattern of hypervolemia/pulmonary edema in COVID-19 and this was remarkable when comparing the hospital evolution in both groups: the NEGBAL group has fewer days of MV, shorter ICU stay and shorter hospital stay than the control group.
Finally, we are convinced that the resolution of the hypervolemia/pulmonary edema pattern through the NEGBAL approach is the reason why the analysis of the primary outcome showed that COVID-19 patients in the NEGBAL group have lower mortality than the control group and less use of IMV, confirmed with the multivariate analysis showing the relationship between better PaO2/FiO2 and negative fluid balance. This agrees with many authors describing the benefit of negative fluid balance in lung injury of any kind [64,65,69,72,73,74].

5. Conclusions

Our study has limitations. In any case, the hypervolemia/pulmonary edema hypothesis has the advantage that it is easy to objectify, and it explains and resolves signs and observations associated with COVID-19 in a simple way. The negative fluid balance in our study showed an improvement in oxygenation, shorter hospital stay, less use of IMV and lower mortality, with the advantage of being an inexpensive and accessible therapy. We invite you to challenge this hypothesis and its therapeutic line, instead of being passive subjects.

Supplementary Materials

The following Statistical Reports can be downloaded at: https://www.mdpi.com/article/10.3390/jcm12041542/s1. Supplement S1: Table S1: AGE summary; Figure S1: Boxplot for AGE; Table S2: Paired differences t-test between AGE CONTROL and AGE NEGBAL; Table S3: WEIGHT summary; Table S4: Paired differences t-test between WEIGHT CONTROL and WEIGHT NEGBAL; Figure S2: Boxplot for WEIGHT; Table S5: SEX frequencies; Table S6: Pearson’s Chi-squared test (Yates’ continuity correction); Table S7: COVID vaccine, 1st dose frequencies; Table S8: Pearson’s Chi-squared test (Yates’ continuity correction); Table S9: COVID vaccine, 2nd dose frequencies; Table S10: Pearson’s Chi-squared test (Yates’ continuity correction); Table S11: OBESITY frequencies; Table S12: Pearson’s Chi-squared test (Yates’ continuity correction); Table S13: HTA frequencies; Table S14: Pearson’s Chi-squared test (Yates’ continuity correction); Table S15: DBT frequencies; Table S16: Pearson’s Chi-squared test (Yates’ continuity correction; Table S17: EPOC frequencies; Table S18: Pearson’s Chi-squared test (Yates’ continuity correction; Table S19: CARDIOP frequencies; Table S20: Fisher’s Exact Test; Table S21: HR ADM summary; Table S22: Paired differences t-test between HR ADM CONTROL and HR ADM NEGBAL; Figure S3: Boxplot for HR ADM.; Table S23: SBP ADM summary; Figure S4: Boxplot for SBP ADM.; Table S24: Paired differences t-test between SBP ADM CONTROL and SBP ADM NEGBAL.; Table S25: DBP ADM summary; Figure S5: Boxplot for DBP ADM.; Table S26: Paired differences t-test between DBP ADM CONTROL and DBP ADM NEGBAL.; Table S27: TEMP ADM summary; Figure S6: Boxplot for TEMP ADM.; Table S28: Paired differences t-test between TEMP ADM CONTROL and TEMP ADM NEGBAL; Table S29: RR ADM summary; Table S30: Paired differences t-test between RR ADM CONTROL and RR ADM NEGBAL; Table S31: APACHE ADM summary; Figure S7: Boxplot for RR ADM.; Figure S8: Boxplot for APACHE ADM.; Table S32: Paired differences t-test between APACHE ADM CONTROL and APACHE ADM NEGBAL; Table S33: BMI summary; Figure S9: Boxplot for BMI; Table S34: Paired differences t-test between BMI CONTROL and BMI NEGBAL; Table S35: PO2 ADM summary; Table S36: Paired differences t-test between PO2 ADM CONTROL and PO2 ADM NEGBAL; Figure S10: Boxplot for PO2 ADM; Table S37: FiO2 ADM summary; Figure S11: Boxplot for FiO2 ADM.; Table S38: Paired differences t-test between FiO2 ADM CONTROL and FiO2 ADM NEGBAL; Table S39: PaFiO2 ADM summary; Figure S12: Boxplot for PaFiO2 ADM; Table S40: Paired differences t-test between PaFiO2 ADM CONTROL and PaFiO2 ADM NEGBAL; Table S41: PCO2 ADM summary; Figure S13: Boxplot for PCO2 ADM; Table S42: Paired differences t-test between PCO2 ADM CONTROL and PCO2 ADM NEGBAL; Table S43: HCO3 ADM summary; Table S44: Paired differences t-test between HCO3 ADM CONTROL and HCO3 ADM NEGBAL; Figure S14: Boxplot for HCO3 ADM; Table S45: CREAT ADM summary; Figure S15: Boxplot for CREAT ADM; Table S46: Paired differences t-test between CREAT ADM CONTROL and CREAT ADM NEGBAL; Table S47: LYMP ADM summary; Table S48: LYMP D4 summary; Table S49: Paired differences t-test between LYMP ADM CONTROL and LYMP D4 CONTROL; Figure S16: Boxplot for LYMP ADM v LYMP D4 for CONTROL group; Figure S17: Boxplot for LYMP ADM v LYMP D4 for NEGBAL group; Table S50: Paired differences t-test between LYMP ADM NEGBAL and LYMP D4 NEGBAL; Table S51: Paired differences t-test between LYMP ADM CONTROL and LYMP ADM NEGBAL; Figure S18: Boxplot for LYMP ADM CONTROL v NEGBAL group; Figure S19: Boxplot for LYMP D4 CONTROL v NEGBAL group; Table S52: Paired differences t-test between LYMP D4 CONTROL and LYMP D4 NEGBAL; Table S53: NAP ADM summary; Figure S20: Boxplot for NAP ADM; Table S54: Paired differences t-test between NAP ADM CONTROL and NAP ADM NEGBAL; Table S55: HTO ADM summary; Table S56: Paired differences t-test between HTO ADM CONTROL and HTO ADM NEGBAL; Figure S21: Boxplot for HTO ADM; Table S57: AHT frequencies; Table S58: Pearson’s Chi-squared test (Yates’ continuity correction); Table S59: BNP ADM summary; Table S60: Paired differences t-test between BNP ADM CONTROL and BNP ADM NEGBAL; Table S61: TROPO ADM summary; Figure S22: Boxplot for BNP ADM; Figure S23: Boxplot for TROPO ADM; Table S62: Paired differences t-test between TROPO ADM CONTROL and TROPO ADM NEGBAL; Table S63: TGO ADM summary; Figure S24: Boxplot for TGO ADM; Table S64: Paired differences t-test between TGO ADM CONTROL and TGO ADM NEGBAL; Table S65: TGP ADM summary; Table S66: Paired differences t-test between TGP ADM CONTROL and TGP ADM NEGBAL; Figure S25: Boxplot for TGP ADM; Table S67: FAL ADM summary; Figure S26: Boxplot for FAL ADM; Table S68: Paired differences t-test between FAL ADM CONTROL and FAL ADM NEGBAL; Table S69: BIT ADM summary; Figure S27: Boxplot for BIT ADM; Table S70: Paired differences t-test between BIT ADM CONTROL and BIT ADM NEGBAL; Table S71: BID ADM summary; Figure S28: Boxplot for BID ADM; Table S72: Paired differences t-test between BID ADM CONTROL and BID ADM NEGBAL; Table S73: Avergage value, maximum, minimum and standard deviation of selected characteristics for the whole population; Table S74: Percentages of age ranges for the whole population and for CONTROL and NEGBAL groups and p−value of the comparison CONTROL v NEGBAL; Table S75: Proportion of whole population for selected categorical variables. Supplement S2: Figure S1: Boxplot for PAFI ADM, PAFI D4, PAFI D8 and PAFI DIS for the CONTROL group; Table S1: Evolution of PAFI CONTROL group; Table S2: Evolution of PAFI NEGBAL group; Table S3: Results for the paired differences between PAFI ADM and PAFI D04, PAFI ADM and PAFI D08 and PAFI ADM and PAFI DIS for the CONTROL group; Table S4: Results for the paired differences between PAFI ADM and PAFI D4, PAFI ADM and PAFI D8 and PAFI ADM and PAFI DIS for the NEGBAL group; Figure S2: Boxplot for PAFI ADM, PAFI D4, PAFI D8 and PAFI DIS for the NEGBAL group; Table S5: Evolution of HEMATOCRIT CONTROL group; Table S6: Evolution of HEMATOCRIT NEGBAL group; Table S7: Results for the paired differences between HCT ADM and HCT D4, HCT ADM and HCT D8 and HCT ADM and HCT DIS for the CONTROL group; Table S8: Results for the paired differences between HCT ADM and HCT D4, HCT ADM and HCT D8 and HCT ADM and HCT DIS for the NEGBAL group; Figure S3: Boxplot for HCT ADM, HCT D4, HCT D8 and HCT DIS for the CONTROL group; Figure S4: Boxplot for HCT ADM, HCT D4, HCT D8 and HCT DIS for the NEGBAL group; Figure S5: Boxplot for CREAT ADM, CREAT D4, CREAT D8 and CREAT DIS for the CONTROL group; Table S9: Evolution of CREATININE CONTROL group; Table S10: Evolution of CREATININE NEGBAL group; Table S11: Results for the paired differences between CREAT ADM and CREAT D4, CREAT ADM and CREAT D8 and CREAT ADM and CREAT DIS for the CONTROL group; Table S12: Results for the paired differences between CREAT ADM and CREAT D4, CREAT ADM and CREAT D8 and CREAT ADM and CREAT DIS for the NEGBAL group; Figure S6: Boxplot for CREAT ADM, CREAT D4, CREAT D8 and CREAT DIS for the NEGBAL group; Table S13: CTSCORE previous to the treatment and on the day of admission, the fourth, the eight and the twelfth days for the CONTROL group; Table S14: CTSCORE previous to the treatment and on the day of admission, the fourth, the eight and the twelfth days for the NEGBAL group; Table S15: Results for the paired differences between CTSCORE PRE and CTSCORE ADM, CTSCORE ADM and CTSCORE D4, CTSCORE ADM and CTSCORE D8 and CTSCORE ADM CTSCORE D12 for the CONTROL group; Table S16: Results for the paired differences between CTSCORE PRE and CTSCORE ADM, CTSCORE ADM and CTSCORE D4, CTSCORE ADM and CTSCORE D8 and CTSCORE ADM CTSCORE D12 for the NEGBAL group; Figure S7: Boxplot for CTSCORE PRE, CTSCORE ADM, CTSCORE D4, CTSCORE D8 and CTSCORE D12 for the CONTROL group; Figure S8: Boxplot for CTSCORE PRE, CTSCORE ADM, CTSCORE D4, CTSCORE D8 and CTSCORE D12 for the NEGBAL group; Figure S9: Boxplot for SVC PRE, SVC ADM, SVC D4, SVC D8 and SVC D12 for the CONTROL group; Table S17: SVC previous to the treatment and on the day of admission, the fourth, the eight and the twelfth days for the CONTROL group; Table S18: SVC previous to the treatment and on the day of admission, the fourth, the eight and the twelfth days for the NEGBAL group; Table S19: Results for the paired differences between SVC PRE and SVC ADM, SVC ADM and SVC D4, SVC ADM and SVC D8 and SVC ADM SVC D12 for the CONTROL group; Table S20: Results for the paired differences between SVC PRE and SVC ADM, SVC ADM and SVC D4, SVC ADM and SVC D8 and SVC ADM SVC D12 for the NEGBAL group; Figure S10: Boxplot for SVC PRE, SVC ADM, SVC D4, SVC D8 and SVC D12 for the NEGBAL group; Table S21: Results for the paired differences of PAFI D4, PAFI D8 and PAFI DIS between groups; Figure S11: Boxplot for PAFI D4 CONTROL and PAFI D4 NEGBAL. Table S22: Results for the paired differences of HCT D4, HCT D8 and HCT DIS between groups; Figure S12: Boxplot for PAFI D8 CONTROL and PAFI D8 NEGBAL; Figure S13: Boxplot for PAFI DIS CONTROL and PAFI DIS NEGBAL; Table S23: Results for the paired differences of CREAT D4, CREAT D8 and CREAT DIS between groups; Figure S14: Boxplot for HCT D4 CONTROL and HCT D4 NEGBAL; Figure S15: Boxplot for HCT D8 CONTROL and HCT D8 NEGBAL; Figure S16: Boxplot for HCT DIS CONTROL and HCT DIS NEGBAL; Figure S17: Boxplot for CREAT D4 CONTROL and CREAT D4 NEGBAL; Figure S18: Boxplot for CREAT D8 CONTROL and CREAT D8 NEGBAL; Figure S19: Boxplot for CREAT DIS CONTROL and CREAT DIS NEGBAL; Table S24: Results LEUC ADM CONTROL and LEUC ADM NEGBAL; Table S25: Results for the paired differences between LEUC ADM CONTROL and LEUC ADM NEGBAL; Figure S20: Boxplot for LEUC ADM CONTROL and LEUC ADM NEGBAL; Table S26: Results for the paired differences of CTSCORE ADM, CTSCORE D4, CTSCORE D8 and CTSCORE D12 between groups; Table S27: Results for the paired differences of SVC ADM, SVC D4, SVC D8 and SVC D12 between groups; Figure S21: Boxplot for CTSCORE ADM CONTROL and CTSCORE ADM NEGBAL; Figure S22: Boxplot for CTSCORE D4 CONTROL and CTSCORE D4 NEGBAL; Figure S23: Boxplot for CTSCORE D8 CONTROL and CTSCORE D8 NEGBAL; Figure S24: Boxplot for CTSCORE D12 CONTROL and CTSCORE D12 NEGBAL; Figure S25: Boxplot for SVC ADM CONTROL and SVC ADM NEGBAL; Figure S26: Boxplot for SVC D4 CONTROL and SVC D4 NEGBAL; Figure S27: Boxplot for SVC D8 CONTROL and SVC D8 NEGBAL; Figure S28: Boxplot for SVC D12 CONTROL and SVC D12 NEGBAL; Figure S29: Boxplot for ACC HYD BAL CONTROL and ACC HYD BAL NEGBAL; Table S28: Results of Accumulated Hydric Balance of CONTROL group; Table S29: ACC HYD BAL for survivors of the CONTROL group; Table S30: ACC HYD BAL for non survivors of the CONTROL group; Figure S30: Boxplot for HOSP DAYS CONTROL and HOSP DAYS NEGBAL; Table S31: Results of Accumulated Hydric Balance of NEGBAL group; Table S32: Results for the paired differences of ACC HYD BAL between groups; Figure S31: Boxplot for ICU DAYS CONTROL and ICU DAYS NEGBAL; Figure S32: Boxplot for IMV DAYS CONTROL and IMV DAYS NEGBAL; Table S33: Results of HOSP DAYS, ICU DAYS and IMV DAYS in CONTROL group; Table S34: Results of HOSP DAYS, ICU DAYS and IMV DAYS in NEGBAL group; Table S35: Results for the paired differences of HOSP DAYS, ICU DAYS and IMV DAYS between groups; Table S36: Result frequencies; Table S37: Pearson’s χ2 test (Yates’ continuity correction); Figure S33: Mosaic plot for the result of the treatment; Table S38: Results for the paired differences of ACC HYD BAL between survivors and non-survivors within groups and in the whole population. Regression. Figure S34: Scatter plot and regression line for ACC HYD BAL CONTROL and PAFI DIS CONTROL, p = 0.007238; Figure S35: Scatter plot and regression line for ACC HYD BAL NEGBAL and PAFI DIS NEGBAL, p = 0.0429; Figure S36: Stripchart for PAFI v VAC. Figure S37: Boxplot for ACC HYD BAL CONTROL SUR and ACC HYD BAL CONTROL NONSUR; Figure S38: Boxplot for ACC HYD BAL NEGBAL SUR and ACC HYD BAL NEGBAL NONSUR; Figure S39: Boxplot for ACC HYD BAL SUR and ACC HYD BAL NONSUR. Table S39: Result IMV use; Figure S40: Boxplot for ICU from SSD CONTROL and ICU from SSD NEGBAL.

Author Contributions

Conceptualization, J.L.F.S.; data curation, P.Z., V.A., V.D.S., L.B., R.M.S. and A.V.; formal analysis, M.R.; investigation, J.L.F.S., L.B., V.A., F.M. and V.D.L.; methodology, J.L.F.S., P.Z., V.A. and M.R.; project administration, J.L.F.S.; resources, F.M., V.D.L., J.A., D.Q., C.M. and E.G.S.; software, M.R.; supervision, J.L.F.S., P.Z. and V.A.; validation, J.L.F.S., P.Z., V.A., L.B. and M.R.; visualization, P.Z., V.A. and V.D.S.; writing—original draft, J.L.F.S., V.A., and L.B.; writing—review and editing, J.L.F.S., V.A., M.R. and A.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics in Investigation Committee of INSTITUTO DE INVESTIGACIONES CLINICAS, Mar del Plata, Argentina (OHRP: IRB 00008222, date of approval: 3 September 2021). Registration: ClinicalTrials.gov ID: NCT05304702. Date: 17 March 2022.

Informed Consent Statement

Patient consent was waived since it is a retrospective observational study in which data were obtained from routine diagnostic studies and standard conventional therapeutic for pulmonary edema.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. More data is contained within the supplementary material. The visualization of the tomographies is available in an online website.

Acknowledgments

The research team wants to thank Jorge Guzman, Juan C. Staltari, Agustina Palacios, Alejandro Santi, Cristian Hernandez, Graciela Tivani, and Ignacio Mackinon for their collaboration and Hernan Vivas and Natalia Torres for providing statistical analysis, the Ethics Committee, Luciana Leguiza, Soraya Barrionuevo, José Luis Derdoy and Hernán Martinez, the Internal Medical Staff of Clinica Colón, Mariana Eguren for article translation, Ramiro Santos for figures design, and Jeronimo Santos for Emmanuel Kant perspectivism and to the memory of Raul Dzienisi.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sun, X.; Wang, T.; Cai, D.; Hu, Z.; Chen, J.; Liao, H.; Zhi, L.; Wei, H.; Zhang, Z.; Qiu, Y.; et al. Cytokine storm intervention in the early stages of COVID-19 pneumonia. Cytokine Growth Factor Rev. 2020, 53, 38–42. [Google Scholar] [CrossRef] [PubMed]
  2. Mangalmurti, N.; Hunter, C.A. Cytokine Storms: Understanding COVID-19. Immunity 2020, 53, 19–25. [Google Scholar] [CrossRef] [PubMed]
  3. Hu, B.; Huang, S.; Yin, L. The cytokine storm and COVID-19. J. Med. Virol. 2021, 93, 250–256. [Google Scholar] [CrossRef] [PubMed]
  4. Ragab, D.; Salah Eldin, H.; Taeimah, M.; Khattab, R.; Salem, R. The COVID-19 Cytokine Storm; What We Know So Far. Front. Immunol. 2020, 11, 1446. [Google Scholar] [CrossRef] [PubMed]
  5. Gattinoni, L.; Gattarello, S.; Steinberg, I.; Busana, M.; Palermo, P.; Lazzari, S.; Romitti, F.; Quintel, M.; Meissner, K.; Marini, J.J.; et al. COVID-19 pneumonia: Pathophysiology and management. Eur. Respir. Rev. 2021, 30, 210138. [Google Scholar] [CrossRef] [PubMed]
  6. Wu, C.; Chen, X.; Cai, Y.; Xia, J.; Zhou, X.; Xu, S.; Huang, H.; Zhang, L.; Zhou, X.; Du, C.; et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients with Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern. Med. 2020, 180, 934–943. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. McGonagle, D.; Sharif, K.; O’Regan, A.; Bridgewood, C. The Role of Cytokines including Interleukin-6 in COVID-19 induced Pneumonia and Macrophage Activation Syndrome-Like Disease. Autoimmun. Rev. 2020, 19, 102537. [Google Scholar] [CrossRef]
  8. Chen, X.; Zhao, B.; Qu, Y.; Chen, Y.; Xiong, J.; Feng, Y.; Men, D.; Huang, Q.; Liu, Y.; Yang, B.; et al. Detectable Serum Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load (RNAemia) Is Closely Correlated With Drastically Elevated Interleukin 6 Level in Critically Ill Patients with Coronavirus Disease 2019. Clin. Infect. Dis. 2020, 71, 1937–1942. [Google Scholar] [CrossRef]
  9. Amaratunga, E.A.; Corwin, D.S.; Moran, L.; Snyder, R. Bradycardia in Patients with COVID-19: A Calm Before the Storm? Cureus 2020, 12, e8599. [Google Scholar] [CrossRef]
  10. Tao, Z.; Xu, J.; Chen, W.; Yang, Z.; Xu, X.; Liu, L.; Chen, R.; Xie, J.; Liu, M.; Wu, J.; et al. Anemia is associated with severe illness in COVID-19: A retrospective cohort study. J. Med. Virol. 2021, 93, 1478–1488. [Google Scholar] [CrossRef]
  11. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef]
  12. Wang, Y.; Zhang, D.; Du, G.; Du, R.; Zhao, J.; Jin, Y.; Fu, S.; Gao, L.; Cheng, Z.; Lu, Q.; et al. Remdesivir in adults with severe COVID-19: A randomised, double-blind, placebo-controlled, multicentre trial. Lancet 2020, 395, 1569–1578. [Google Scholar] [CrossRef] [PubMed]
  13. Guan, W.J.; Ni, Z.Y.; Hu, Y.; Liang, W.H.; Qu, C.Q.; He, J.X.; Liu, L.; Shan, H.; Lei, C.L.; Hui, D.S.C.; et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
  14. Lang, M.; Som, A.; Carey, D.; Reid, N.; Mendoza, D.P.; Flores, E.J.; Li, M.D.; Shepard, J.-A.O.; Little, B.P. Pulmonary Vascular Manifestations of COVID-19 Pneumonia. Radiol. Cardiothorac. Imaging 2020, 2, e200277. [Google Scholar] [CrossRef] [PubMed]
  15. Ansems, K.; Grundeis, F.; Dahms, K.; Mikolajewska, A.; Thieme, V.; Piechotta, V.; Metzendorf, M.-I.; Stegemann, M.; Benstoem, C.; Fichtner, F. Remdesivir for the treatment of COVID-19. Cochrane Database Syst. Rev. 2021, 8, CD014962. [Google Scholar] [CrossRef]
  16. Cao, B.; Wang, Y.; Wen, D.; Liu, W.; Wang, J.; Fan, G.; Ruan, L.; Song, B.; Cai, Y.; Wei, M.; et al. A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Severe COVID-19. N. Engl. J. Med. 2020, 382, 1787–1799. [Google Scholar] [CrossRef]
  17. Simonovich, V.A.; Pratx, L.D.B.; Scibona, P.; Beruto, M.V.; Vallone, M.G.; Vázquez, C.; Savoy, N.; Giunta, D.H.; Pérez, L.G.; Sánchez, M.D.L.; et al. A Randomized Trial of Convalescent Plasma in COVID-19 Severe Pneumonia. N. Engl. J. Med. 2021, 384, 619–629. [Google Scholar] [CrossRef]
  18. Lan, S.H.; Lai, C.C.; Huang, H.T.; Chang, S.P.; Lu, L.C.; Hsueh, P.R. Tocilizumab for severe COVID-19: A systematic review and meta-analysis. Int. J. Antimicrob. Agents 2020, 56, 106103. [Google Scholar] [CrossRef]
  19. Huang, E.; Jordan, S.C. Tocilizumab for COVID-19—The Ongoing Search for Effective Therapies. N. Engl. J. Med. 2020, 383, 2387–2388. [Google Scholar] [CrossRef]
  20. Davidson, M.; Menon, S.; Chaimani, A.; Evrenoglou, T.; Ghosn, L.; Graña, C.; Henschke, N.; Cogo, E.; Villanueva, G.; Ferrand, G.; et al. Interleukin-1 blocking agents for treating COVID-19. Cochrane Database Syst. Rev. 2022, 1, CD015308. [Google Scholar] [CrossRef]
  21. Shanmugaraj, B.; Siriwattananon, K.; Wangkanont, K.; Phoolcharoen, W. Perspectives on monoclonal antibody therapy as potential therapeutic intervention for Coronavirus disease-19 (COVID-19). Asian Pac. J. Allergy Immunol. 2020, 38, 10–18. [Google Scholar] [CrossRef] [PubMed]
  22. Yang, L.; Liu, S.; Liu, J.; Zhang, Z.; Wan, X.; Huang, B.; Chen, Y.; Zhang, Y. COVID-19: Immunopathogenesis and Immunotherapeutics. Signal Transduct. Target Ther. 2020, 5, 128. [Google Scholar] [CrossRef] [PubMed]
  23. Lundgren, J.D.; Grund, B.; Barkauskas, C.E.; Holland, T.L.; Gottlieb, R.L.; Sandkovsky, U.; Brown, S.M.; Knowlton, K.U.; Self, W.H.; Files, D.C.; et al. A Neutralizing Monoclonal Antibody for Hospitalized Patients with COVID-19. N. Engl. J. Med. 2021, 384, 905–914. [Google Scholar] [CrossRef] [PubMed]
  24. Deb, P.; Molla, M.M.A.; Saif-Ur-Rahman, K.M. An update to monoclonal antibody as therapeutic option against COVID-19. Biosaf. Health 2021, 3, 87–91. [Google Scholar] [CrossRef]
  25. Remy, K.E.; Mazer, M.; Striker, D.A.; Ellebedy, A.H.; Walton, A.H.; Unsinger, J.; Blood, T.M.; Mudd, P.A.; Yi, D.J.; Mannion, D.A.; et al. Severe immunosuppression and not a cytokine storm characterizes COVID-19 infections. JCI Insight 2020, 5, e140329. [Google Scholar] [CrossRef]
  26. Group, R.C. Lopinavir-ritonavir in patients admitted to hospital with COVID-19 (RECOVERY): A randomised, controlled, open-label, platform trial. Lancet 2020, 396, 1345–1352. [Google Scholar] [CrossRef]
  27. Sinha, P.; Matthay, M.A.; Calfee, C.S. Is a “Cytokine Storm” Relevant to COVID-19? JAMA Intern. Med. 2020, 180, 1152–1154. [Google Scholar] [CrossRef]
  28. Leisman, D.E.; Ronner, L.; Pinotti, R.; Taylor, M.D.; Sinha, P.; Calfee, C.S.; Hirayama, A.V.; Mastroiani, F.; Turtle, C.J.; Harhay, M.O.; et al. Cytokine elevation in severe and critical COVID-19: A rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir. Med. 2020, 8, 1233–1244. [Google Scholar] [CrossRef]
  29. Rothlin, R.P.; Duarte, M.; Pelorosso, F.G.; Nicolosi, L.; Salgado, M.V.; Vetulli, H.M.; Spitzer, E. Angiotensin Receptor Blockers for COVID-19: Pathophysiological and Pharmacological Considerations About Ongoing and Future Prospective Clinical Trials. Front. Pharmacol. 2021, 12, 603736. [Google Scholar] [CrossRef]
  30. Rasch, S.; Schmidle, P.; Sancak, S.; Herner, A.; Huberle, C.; Schulz, D.; Mayr, U.; Schneider, J.; Spinner, C.D.; Geisler, F.; et al. Increased extravascular lung water index (EVLWI) reflects rapid non-cardiogenic oedema and mortality in COVID-19 associated ARDS. Sci. Rep. 2021, 11, 11524. [Google Scholar] [CrossRef]
  31. Shi, R.; Lai, C.; Teboul, J.-L.; Dres, M.; Moretto, F.; De Vita, N.; Pham, T.; Bonny, V.; Mayaux, J.; Vaschetto, R.; et al. COVID-19 ARDS is characterized by higher extravascular lung water than non-COVID-19 ARDS: The PiCCOVID study. Crit. Care 2021, 25, 186. [Google Scholar] [CrossRef] [PubMed]
  32. Cui, X.; Chen, W.; Zhou, H.; Gong, Y.; Zhu, B.; Lv, X.; Guo, H.; Duan, J.; Zhou, J.; Marcon, E.; et al. Pulmonary Edema in COVID-19 Patients: Mechanisms and Treatment Potential. Front. Pharmacol. 2021, 12, 664349. [Google Scholar] [CrossRef] [PubMed]
  33. Santos, J.L.F.; Zanardi, P.; Alo, V.; Rodriguez, M.; Magdaleno, F.; De Langhe, V.; Dos Santos, V.; Murialdo, G.; Villoldo, A.; Coria, M.; et al. Pulmonary Edema in COVID-19 Treated with Furosemide and Negative Fluid Balance (NEGBAL): A Different and Promising Approach. J. Clin. Med. 2021, 10, 5599. [Google Scholar] [CrossRef] [PubMed]
  34. Yan, R.; Zhang, Y.; Li, Y.; Xia, L.; Guo, Y.; Zhou, Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science 2020, 367, 1444–1448. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Qiu, Y.; Zhao, Y.-B.; Wang, Q.; Li, J.-Y.; Zhou, Z.-J.; Liao, C.-H.; Ge, X.-Y. Predicting the angiotensin converting enzyme 2 (ACE2) utilizing capability as the receptor of SARS-CoV-2. Microbes Infect. 2020, 22, 221–225. [Google Scholar] [CrossRef] [PubMed]
  36. Mehrabadi, M.E.; Hemmati, R.; Tashakor, A.; Homaei, A.; Yousefzadeh, M.; Hemati, K.; Hosseinkhani, S. Induced dysregulation of ACE2 by SARS-CoV-2 plays a key role in COVID-19 severity. Biomed. Pharmacother. 2021, 137, 111363. [Google Scholar] [CrossRef] [PubMed]
  37. Verdecchia, P.; Cavallini, C.; Spanevello, A.; Angeli, F. The pivotal link between ACE2 deficiency and SARS-CoV-2 infection. Eur. J. Intern. Med. 2020, 76, 14–20. [Google Scholar] [CrossRef]
  38. Choi, M.; Aiello, E.A.; Ennis, I.L.; Villa-Abrille, M.C. The RAAS and SARS-CoV-2: A riddle to solve. Hipertens Riesgo Vasc. 2020, 37, 169–175. [Google Scholar] [CrossRef]
  39. Vaduganathan, M.; Vardeny, O.; Michel, T.; McMurray, J.J.V.; Pfeffer, M.A.; Solomon, S.D. Renin-Angiotensin-Aldosterone System Inhibitors in Patients with COVID-19. N. Engl. J. Med. 2020, 382, 1653–1659. [Google Scholar] [CrossRef]
  40. Rysz, S.; Al-Saadi, J.; Sjöström, A.; Farm, M.; Jalde, F.C.; Plattén, M.; Eriksson, H.; Klein, M.; Vargas-Paris, R.; Nyrén, S.; et al. COVID-19 pathophysiology may be driven by an imbalance in the renin-angiotensin-aldosterone system. Nat. Commun. 2021, 12, 2417. [Google Scholar] [CrossRef]
  41. Duarte, M.; Pelorosso, F.; Nicolosi, L.N.; Salgado, M.V.; Vetulli, H.; Aquieri, A.; Azzato, F.; Castro, M.; Coyle, J.; Davolos, I.; et al. Telmisartan for treatment of COVID-19 patients: An open multicenter randomized clinical trial. EClinicalMedicine 2021, 37, 100962. [Google Scholar] [CrossRef] [PubMed]
  42. Liu, Y.; Yang, Y.; Zhang, C.; Huang, F.; Wang, F.; Yuan, J.; Wang, Z.; Li, J.; Li, J.; Feng, C.; et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci. China Life Sci. 2020, 63, 364–374. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Wu, Z.; Hu, R.; Zhang, C.; Ren, W.; Yu, A.; Zhou, X. Elevation of plasma angiotensin II level is a potential pathogenesis for the critically ill COVID-19 patients. Crit. Care 2020, 24, 290. [Google Scholar] [CrossRef] [PubMed]
  44. Santos, R.A.; Ferreira, A.J.; Simões ESilva, A.C. Recent advances in the angiotensin-converting enzyme 2-angiotensin(1-7)-Mas axis. Exp. Physiol. 2008, 93, 519–527. [Google Scholar] [CrossRef] [PubMed]
  45. Henry, B.M.; Benoit, J.L.; Berger, B.A.; Pulvino, C.; Lavie, C.J.; Lippi, G.; Benoit, S.W. Coronavirus disease 2019 is associated with low circulating plasma levels of angiotensin 1 and angiotensin 1,7. J. Med. Virol. 2021, 93, 678–680. [Google Scholar] [CrossRef] [PubMed]
  46. Villard, O.; Morquin, D.; Molinari, N.; Raingeard, I.; Nagot, N.; Cristol, J.-P.; Jung, B.; Roubille, C.; Foulongne, V.; Fesler, P.; et al. The Plasmatic Aldosterone and C-Reactive Protein Levels, and the Severity of COVID-19: The Dyhor-19 Study. J. Clin. Med. 2020, 9, 2315. [Google Scholar] [CrossRef]
  47. Polak, S.B.; Van Gool, I.C.; Cohen, D.; von der Thüsen, J.H.; van Paassen, J. A systematic review of pathological findings in COVID-19: A pathophysiological timeline and possible mechanisms of disease progression. Mod. Pathol. 2020, 33, 2128–2138. [Google Scholar] [CrossRef]
  48. Sibbald, W.J.; Short, A.K.; Warshawski, F.J.; Cunningham, D.G.; Cheung, H. Thermal dye measurements of extravascular lung water in critically ill patients. Intravascular Starling forces and extravascular lung water in the adult respiratory distress syndrome. Chest 1985, 87, 585–592. [Google Scholar] [CrossRef]
  49. Casey, J.D.; Semler, M.W.; Rice, T.W. Fluid Management in Acute Respiratory Distress Syndrome. Semin. Respir. Crit. Care Med. 2019, 40, 57–65. [Google Scholar] [CrossRef]
  50. Ware, L.B.; Matthay, M.A. Clinical practice. Acute pulmonary edema. N. Engl. J. Med. 2005, 353, 2788–2796. [Google Scholar] [CrossRef] [Green Version]
  51. Imai, Y.; Kuba, K.; Rao, S.; Huan, Y.; Guo, F.; Guan, B.; Yang, P.; Sarao, R.; Wada, T.; Leong-Poi, H.; et al. Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature 2005, 436, 112–116. [Google Scholar] [CrossRef] [PubMed]
  52. Gattinoni, L.; Chiumello, D.; Rossi, S. COVID-19 pneumonia: ARDS or not? Crit. Care 2020, 24, 154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Menter, T.; Haslbauer, J.D.; Nienhold, R.; Savic, S.; Hopfer, H.; Deigendesch, N.; Frank, S.; Turek, D.; Willi, N.; Pargger, H.; et al. Postmortem examination of COVID-19 patients reveals diffuse alveolar damage with severe capillary congestion and variegated findings in lungs and other organs suggesting vascular dysfunction. Histopathology 2020, 77, 198–209. [Google Scholar] [CrossRef] [PubMed]
  54. Mason, R.J. Thoughts on the alveolar phase of COVID-19. Am. J. Physiol. Lung Cell Mol. Physiol. 2020, 319, L115–L120. [Google Scholar] [CrossRef] [PubMed]
  55. Carsana, L.; Sonzogni, A.; Nasr, A.; Rossi, R.S.; Pellegrinelli, A.; Zerbi, P.; Rech, R.; Colombo, R.; Antinori, S.; Corbellino, M.; et al. Pulmonary post-mortem findings in a series of COVID-19 cases from northern Italy: A two-centre descriptive study. Lancet Infect. Dis. 2020, 20, 1135–1140. [Google Scholar] [CrossRef] [PubMed]
  56. Umeh, C.; Giberson, C.; Kumar, S.; Aseri, M.; Barve, P. A Multicenter Retrospective Analysis on the Etiology of Bradycardia in COVID-19 Patients. Cureus 2022, 14, e21294. [Google Scholar] [CrossRef]
  57. Hariyanto, T.I.; Kurniawan, A. Anemia is associated with severe coronavirus disease 2019 (COVID-19) infection. Transfus. Apher. Sci. 2020, 59, 102926. [Google Scholar] [CrossRef]
  58. Henry, B.M.; de Oliveira, M.H.S.; Benoit, S.; Plebani, M.; Lippi, G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): A meta-analysis. Clin. Chem. Lab. Med. 2020, 58, 1021–1028. [Google Scholar] [CrossRef] [Green Version]
  59. Yang, X.; Yu, Y.; Xu, J.; Shu, H.; Xia, J.; Liu, H.; Wu, Y.; Zhang, L.; Yu, Z.; Fang, M.; et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir. Med. 2020, 8, 475–481. [Google Scholar] [CrossRef] [Green Version]
  60. Paliogiannis, P.; Zinellu, A.; Scano, V.; Mulas, G.; De Riu, G.; Pascale, R.M.; Arru, L.B.; Carru, C.; Pirina, P.; Mangoni, A.A.; et al. Laboratory test alterations in patients with COVID-19 and non COVID-19 interstitial pneumonia: A preliminary report. J. Infect. Dev. Ctries 2020, 14, 685–690. [Google Scholar] [CrossRef]
  61. Eslami, V.; Abrishami, A.; Zarei, E.; Khalili, N.; Baharvand, Z.; Sanei-Taheri, M. The Association of CT-measured Cardiac Indices with Lung Involvement and Clinical Outcome in Patients with COVID-19. Acad. Radiol. 2021, 28, 8–17. [Google Scholar] [CrossRef] [PubMed]
  62. Argulian, E.; Sud, K.; Vogel, B.; Bohra, C.; Garg, V.P.; Talebi, S.; Lerakis, S.; Narula, J. Right Ventricular Dilation in Hospitalized Patients with COVID-19 Infection. JACC Cardiovasc. Imaging 2020, 13, 2459–2461. [Google Scholar] [CrossRef] [PubMed]
  63. Li, Y.-L.; Zheng, J.-B.; Jin, Y.; Tang, R.; Li, M.; Xiu, C.-H.; Dai, Q.-Q.; Zuo, S.; Wang, H.-Q.; Wang, H.-L.; et al. Acute right ventricular dysfunction in severe COVID-19 pneumonia. Rev. Cardiovasc. Med. 2020, 21, 635–641. [Google Scholar] [CrossRef] [PubMed]
  64. Mitchell, J.P.; Schuller, D.; Calandrino, F.S.; Schuster, D.P. Improved outcome based on fluid management in critically ill patients requiring pulmonary artery catheterization. Am. Rev. Respir. Dis. 1992, 145, 990–998. [Google Scholar] [CrossRef]
  65. Martin, G.S.; Mangialardi, R.J.; Wheeler, A.P.; Dupont, W.D.; Morris, J.A.; Bernard, G.R. Albumin and furosemide therapy in hypoproteinemic patients with acute lung injury. Crit. Care Med. 2002, 30, 2175–2182. [Google Scholar] [CrossRef]
  66. Pan, F.; Ye, T.; Sun, P.; Gui, S.; Liang, B.; Li, L.; Zheng, D.; Wang, J.; Hesketh, R.L.; Yang, L.; et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology 2020, 295, 715–721. [Google Scholar] [CrossRef] [Green Version]
  67. Pilishvili, T.; Gierke, R.; Fleming-Dutra, K.E.; Farrar, J.L.; Mohr, N.M.; Talan, D.A.; Krishnadasan, A.; Harland, K.K.; Smithline, H.A.; Hou, P.C.; et al. Effectiveness of mRNA COVID-19 Vaccine among U.S. Health Care Personnel. N. Engl. J. Med. 2021, 385, e90. [Google Scholar] [CrossRef]
  68. Tenforde, M.W.; Self, W.H.; Adams, K.; Gaglani, M.; Ginde, A.A.; McNeal, T.; Ghamande, S.; Douin, D.J.; Talbot, H.K.; Casey, J.D.; et al. Association between mRNA Vaccination and COVID-19 Hospitalization and Disease Severity. JAMA 2021, 326, 2043–2054. [Google Scholar] [CrossRef]
  69. Wiedemann, H.P.; Wheeler, A.P.; Bernard, G.R.; Thompson, B.T.; Hayden, D.; Deboisblanc, B.; Connors, A.F.J.; Hite, R.D.; Harabin, A.L. Comparison of two fluid-management strategies in acute lung injury. N. Engl. J. Med. 2006, 354, 2564–2575. [Google Scholar] [CrossRef] [Green Version]
  70. Noble, M.I. The Frank—Starling curve. Clin. Sci. Mol. Med. 1978, 54, 1–7. [Google Scholar] [CrossRef] [Green Version]
  71. García-Ayllón, M.; Moreno-Pérez, O.; García-Arriaza, J.; Ramos-Rincón, J.; Cortés-Gómez, M.; Brinkmalm, G.; Andrés, M.; León-Ramírez, J.; Boix, V.; Gil, J.; et al. Plasma ACE2 species are differentially altered in COVID-19 patients. FASEB J. 2021, 35, e21745. [Google Scholar] [CrossRef] [PubMed]
  72. Silversides, J.A.; Major, E.; Ferguson, A.J.; Mann, E.E.; McAuley, D.F.; Marshall, J.C.; Blackwood, B.; Fan, E. Conservative fluid management or deresuscitation for patients with sepsis or acute respiratory distress syndrome following the resuscitation phase of critical illness: A systematic review and meta-analysis. Intensive Care Med. 2017, 43, 155–170. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Alsous, F.; Khamiees, M.; DeGirolamo, A.; Amoateng-Adjepong, Y.; Manthous, C.A. Negative fluid balance predicts survival in patients with septic shock: A retrospective pilot study. Chest 2000, 117, 1749–1754. [Google Scholar] [CrossRef] [PubMed]
  74. Jaffee, W.; Hodgins, S.; McGee, W.T. Tissue Edema, Fluid Balance, and Patient Outcomes in Severe Sepsis: An Organ Systems Review. J. Intensive Care Med. 2018, 33, 502–509. [Google Scholar] [CrossRef]
Jcm 12 01542 g001 550
Figure 1. Box plot of comparative analysis of mortality and length of stay. (A) Mortality. (B) Stay in Hospital. (C) Stay in ICU. (D) Days in IMV.
Figure 1. Box plot of comparative analysis of mortality and length of stay. (A) Mortality. (B) Stay in Hospital. (C) Stay in ICU. (D) Days in IMV.
Jcm 12 01542 g001
Jcm 12 01542 g002 550
Figure 2. Accumulated fluid balance analysis. (A) Box plot of accumulated fluid balance CONTROL vs. NEGBAL. (B) Box plot of accumulated fluid balance CONTROL survivor vs. non-survivor. (C) Regression line for ACC FLU BAL CONTROL and PAFI DIS CONTROL. (D) Regression line for ACC FLU BAL NEGBAL and PAFI DIS NEGBAL.
Figure 2. Accumulated fluid balance analysis. (A) Box plot of accumulated fluid balance CONTROL vs. NEGBAL. (B) Box plot of accumulated fluid balance CONTROL survivor vs. non-survivor. (C) Regression line for ACC FLU BAL CONTROL and PAFI DIS CONTROL. (D) Regression line for ACC FLU BAL NEGBAL and PAFI DIS NEGBAL.
Jcm 12 01542 g002
Jcm 12 01542 g003 550
Figure 3. Box plot of comparative analysis of PaO2/FiO2, CT score, diameter SVC and hematocrit. (A) PaO2/FiO2 CONTROL; (B) PaO2/FiO2 NEGBAL; (C) CT Score CONTROL; (D) CT Score NEGBAL; (E) Ø SVC CONTROL; (F) Ø SVC NEGBAL; (G) HCT CONTROL; (H) HCT NEGBAL.
Figure 3. Box plot of comparative analysis of PaO2/FiO2, CT score, diameter SVC and hematocrit. (A) PaO2/FiO2 CONTROL; (B) PaO2/FiO2 NEGBAL; (C) CT Score CONTROL; (D) CT Score NEGBAL; (E) Ø SVC CONTROL; (F) Ø SVC NEGBAL; (G) HCT CONTROL; (H) HCT NEGBAL.
Jcm 12 01542 g003
Table
Table 1. Comparative analysis of population with COVID-19 at ICU admission.
Table 1. Comparative analysis of population with COVID-19 at ICU admission.
VariableTotal PopulationCONTROL Group
n = 58		NEGBAL Group
n = 58		p Value
Demographics—Pathological antecedents Variables
Age59.6 ± 13.659.45 ± 12.0459.86 ± 15.14 (100)0.87 (−5.44, 4.61)
Obesity59 (50.8%)30 (51.7)291
BMI30.1 ± 5.1630.25 ± 5.4629.93 ± 4.890.74 (−1.58, 2.22)
AHT47 (40.5%)24231
DBT30 (25.8%)17130.52
COPD20 (17.3%)10100.52
COVID-19 Day8.41 ± 38.93 ± 2.78 ± 3.20.75
VAC-152 (44.8%)18340.01
VAC-227 (22.4%)5220.01
Clinical variables
Heart Rate83.5 ± 1584.55 ± 14.982.5 ± 15.190.46 (−3.48, 7.58)
Systolic BP127 ± 19.2127.2 ± 20.77128.3 ± 17.750.75 (−3.48, 7.58)
Diastolic BP77.4 ± 12.476.79 ± 12.4778.14 ± 12.40.56 (−5.92, 3.23)
Temperature36.5 ± 0.8636.63 ± 0.8836.43 ± 0.840.20 (−0.11, 0.52)
Resp. Rate24.9 ± 4.5825.34 ± 4.6124.47 ± 4.560.30 (−0.80, 2.56)
APACHE II8.01 ± 3.267.6 ± 3.048.43 ± 3.440.17 (−2.02, 0.36)
Biochemical variables
Po273.3.± 18.873.99 ± 21.5572.8 ± 15.970.73 (−5.79, 8.17)
Pafio2164 ± 70.2164.81 ± 73.64163.6 ± 67.260.92 (−24.6, 27.1)
Pco233.28 ± 5.1533.75 ± 5.4032.81 ± 4.900.32 (−0.95, 2.83)
HCO321.8 ± 3.0621.64 ± 3.0123.14 ± 3.120.38 (−1.62, 0.63)
Creatinine9.01 ± 2.229.41 ± 2.388.75 ± 2.010.11 (−0.15, 1.47)
Hematocrit39.7 ± 4.940.38 ± 5.2239.20 ± 4.540.19 (−0.62, 2.97)
Lymphocytes 959 ± 665 963 ± 773 955 ± 540 0.42 (−345, 145)
Troponina10.4 ± 8.3510.84 ± 8.539.99 ± 8.250.63 (−2.73, 4.43)
proBNP224 ± 235277 ± 246.19188.80 ± 222.860.11 (−22.4,198)
GOT49.9 ± 37.753.84 ± 38.8545.15 ± 36.390.26 (−6.56, 23.9)
GPT57.2 ± 6860.69 ± 64.6053.38 ± 71.960.58 (−20.18, 35.3)
Chest Tomography Score and Diameter Superior Vena Cava variables
CT Score15.7 ± 4.6315.8 ± 4.1415.6 ± 5.080.80 (−1.51,1.94)
Ø SVC17.7 ± 2.9718.1 ± 2.5517.3 ± 3.290.14 (−0.28,1.93)
Abbreviations: BMI: Body Mass Index; DBT: Diabetes; COPD: Chronic Obstructive Pulmonary Disease; COVID-19 Day: Day since the beginning of COVID-19, in which it was admitted to ICU; VAC-1: Vaccine first dose; VAC-2: Vaccine second dose; BP: Blood Pressure; HCT: Hematocrit at admission; AHT: Arterial Hypertension; LYMP: Lymphocytes; Pafio2: PaO2/FiO2 ratio; proBNP: pro-B-type Natriuretic Peptides; GOT; glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase; CT Score: Chest Tomography Score; Ø SVC: diameter Superior Vena Cava.
Table
Table 2. Comparative analysis during evolution of COVID-19.
Table 2. Comparative analysis during evolution of COVID-19.
VariableCONTROL Group
n = 58		NEGBAL Group
n = 58		p Value
Biochemical variables
PaPafio2 day 4175 ± 90.5249 ± 100<0.001 (−109, −37.8)
PaPafio2 day 8 183 ± 87 290 ± 101 <0.001 (−146, −67.2)
PaPafio2 day dis. 235 ± 116 344 ± 97.6 <0.001 (−141, −61)
HCT day 4 39.1 ± 5.21 43.1 ± 5.21 <0.001 (−5.90, −2)
HCT day 8 38.5 ± 4.67 42.5 ± 5.23 <0.001 (−6.38, −2.05)
LYMP day 4 697 ± 402 1056 ± 735 0.002 (−586, −130)
CREAT day dis. 13.1 ± 9.8 11.6 ± 8.2 0.37 (−1.84, 4.85)
Chest Tomography Score and Diameter Superior Vena Cava
CT Score day 416.4 ± 4.3110.6 ± 5.96<0.001 (3.31, 6.8)
CT Score day 816.3 ± 5.798.77 ± 5.57<0.001 (4.18, 10.9)
Ø SVC day 418.9 ± 3.113.8 ± 4.09<0.001 (3.29, 6.8)
Ø SVC day 817.8 ± 3.4212.7 ± 3.43<0.001 (3.14, 7.21)
Accumulated Fluid Balance
ACC FLU BAL−685 ± 1000−7898 ± 1000<0.001 (5419, 8453)
Hospital stay, ICU stay and IMV days
Hospital Stay20.5 ± 13.213 ± 13.2<0.001 (3.15, 11.8)
ICU Stay15.7 ± 139 ± 8.91<0.001 (2.77, 10.3)
Days of IMV use4 ± 10.21 ± 6.4<0.001 (4.20, 10.5)
Patients IMV use319<0.001
Mortality
Mortality2260.001
Accumulated Fluid Balance CONTROL group
Control GroupSURVIVORS
Control group		NON SURVIVORS
Control group		p value
ACC FLU BAL−3685 ± 1000+5898 ± 1000<0.001 (−7648,−2688)
Abbreviations: SD: Standard Deviation; Pafio2: PaO2/FiO2 ratio; HCT: Hematocrit; LYMP: Lymphocytes; CREAT: creatinine; CT Score: Chest Tomography Score; Ø SVC: diameter Superior Vena Cava; ACC FLU BAL: Accumulated Fluid Balance at day 8; IMV: Invasive Mechanical Ventilation.
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MDPI and ACS Style

Santos, J.L.F.; Zanardi, P.; Alo, V.; Dos Santos, V.; Bovone, L.; Rodriguez, M.; Magdaleno, F.; De Langhe, V.; Villoldo, A.; Martinez Souvielle, R.; et al. Lung Injury in COVID-19 Has Pulmonary Edema as an Important Component and Treatment with Furosemide and Negative Fluid Balance (NEGBAL) Decreases Mortality. J. Clin. Med. 2023, 12, 1542. https://doi.org/10.3390/jcm12041542

AMA Style

Santos JLF, Zanardi P, Alo V, Dos Santos V, Bovone L, Rodriguez M, Magdaleno F, De Langhe V, Villoldo A, Martinez Souvielle R, et al. Lung Injury in COVID-19 Has Pulmonary Edema as an Important Component and Treatment with Furosemide and Negative Fluid Balance (NEGBAL) Decreases Mortality. Journal of Clinical Medicine. 2023; 12(4):1542. https://doi.org/10.3390/jcm12041542

Chicago/Turabian Style

Santos, Jose L. Francisco, Patricio Zanardi, Veronica Alo, Vanina Dos Santos, Leonardo Bovone, Marcelo Rodriguez, Federico Magdaleno, Virginia De Langhe, Andrea Villoldo, Romina Martinez Souvielle, and et al. 2023. "Lung Injury in COVID-19 Has Pulmonary Edema as an Important Component and Treatment with Furosemide and Negative Fluid Balance (NEGBAL) Decreases Mortality" Journal of Clinical Medicine 12, no. 4: 1542. https://doi.org/10.3390/jcm12041542

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