Abstract
The utility of lung ultrasound (LUS) in evaluation of coronavirus disease (COVID-19) with pneumonia has not yet been elucidated. The main objective of study is to determine whether LUS can effectively predict the prognosis in intensive care unit (ICU), including mortality and disease severity. It’s also aimed to determine whether LUS will provide a threshold value to predict mortality in COVID-19 cases. In this prospective observational study, 90 patients admitted to the ICU with COVID-19 pneumonia and respiratory failure were included. A LUS cutoff score of 21 on admission demonstrated sensitivity of 97% and specificity of 68% for predicting mortality. Baseline LUS scores were found to be significantly higher in nonsurvivor group(P < .001) whereas APACHE II, sequential organ failure assessment (SOFA), charlson comorbidity index (CCI), nutrition risk in critically ill (NUTRIC) scores, serum lactate, procalcitonin, ferritin, D-dimer levels and heart rate were also significantly found to be higher in nonsurvivor group(P < .05). Overall mean progression-free-survival (PFS) rate was significantly longer in patients with LUS scores < 21, (mean-survival 23.8 days) compared to those with LUS scores ≥ 21 (mean-survival 12.5 days) (P < .05). Multivariate Cox-regression analysis identified a LUS score ≥ 21 was an independent risk factor for mortality during ICU stay (P = .002). LUS performed at ICU admission can serve as a prognostic indicator for patients with COVID-19 pneumonia. By identifying high-risk groups and monitoring these patients closely using LUS, healthcare providers may enhance resource utilization and potentially improve patient outcomes.
Keywords: COVID-19, intensive care unit, lung ultrasound, mortality, prognosis, risk stratification
1. Introduction
The 2019 coronavirus disease (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), represents one of the most significant global public health crises in recent decades.[1] The lungs are predominantly affected in COVID-19 patients, with initial CT scans typically revealing bilateral multilobar ground-glass opacification with a peripheral or posterior distribution, predominantly in the lower lobes, and less frequently in the right middle lobe.[2] In addition to acute respiratory failure, which is the primary reason for intensive care unit (ICU) admission and mortality, patients may also experience multi-organ involvement, including hemodynamic instability, cardiac damage, renal dysfunction, and coagulopathy. ICU mortality rates for COVID-19 patients have been reported to range from 50% to 97%.[3–5] Factors such as older age, multiple comorbidities, elevated sequential organ failure assessment (SOFA) score, lymphopenia, and increased levels of troponin and D-dimer are associated with poorer outcomes.[6] Given the unpredictable clinical course, early prognosis prediction is crucial for optimizing healthcare resource allocation.
Lung ultrasound (LUS) is a rapid, repeatable, and noninvasive diagnostic tool widely utilized in ICU settings.[7–9] It provides quantitative results with high sensitivity (94%) and specificity (96%) for diagnosing pneumonia in adults.[10,11] During the COVID-19 pandemic, its importance increased due to the high risk of disease transmission.[12–14]
According to WHO guidelines, auscultation carries a significant risk of nosocomial transmission,[15,16] while the American College of Radiology advised reserving CT for hospitalized symptomatic patients to mitigate the risk of infection spread to healthcare staff and other patients.[17] Consequently, LUS has been extensively employed in various studies for diagnosing and monitoring COVID-19 patients with its several advantages including easy to learn and perform, high repeatability, no transportation or radiation risk and better visualizing the typical peripheral involvement of the disease.[18,19] However, the use of LUS for predicting ICU prognosis upon admission has received comparatively less attention. Given the lack of definitive information regarding the use of LUS in predicting outcomes for COVID-19 patients, the studies need to assess whether LUS can effectively forecast prognosis in patients with COVID-19 pneumonia and respiratory failure in the ICU. The main objective of this study is to determine whether LUS can effectively predict the prognosis of patients in the ICU, including mortality and disease severity, thereby contributing to improved risk stratification and management strategies for critically ill patients with COVID-19 pneumonia. We also aimed to determine whether LUS will provide a threshold value to predict mortality in COVID-19 cases.
2. Methods
2.1. Study design and study population
This prospective, single-center observational study was conducted from April 15, 2020, to November 15, 2020, across 2 medical ICUs at Cukurova University Hospital, which has a total of 25 beds. The Instutional Research Ethics Committee approved the study protocol (number 98/41). Informed consent was obtained from first-degree relatives or official guardians of all patients.
All patients requiring ICU admission for COVID-19 pneumonia and respiratory failure were subsequently screened (n = 345). The exclusion criteria were as follows: negative RT-PCR (n = 95), tuberculosis (n = 1); bronchiectasis (n = 3); malignancy (n = 14); hospitalization for reasons other than pneumonia (n = 15); failure to do ultrasound in the first 24 hours (n = 29); absence of adequate initial US image (n = 22); pneumothorax (n = 3); subcutaneous emphysema (n = 1); known pregnancy (n = 1); age < 18 years (n = 1); prone positioning (n = 30); heart failure (n = 11); pleural effusion (n = 3); refused to participate (n = 3) and 26 of the study population had at least one of the exclusion criteria (n = 26). At last, 95 were enrolled within 24 hours of ICU admission. Figure 1 shows the flow chart of the study.
Figure 1.
Flow chart of the study.
2.2. Outcomes
The aim of this study was to assess the prognostic significance of LUS in patients with COVID-19 pneumonia. The primary outcome was mortality during the ICU stay, with a comparative analysis of clinical and laboratory data, as well as LUS scores, between nonsurviving (deceased) and surviving patients. Secondary outcome is identifying a cutoff value for LUS and evaluating the relationship between LUS scores and various clinical and laboratory data.
2.3. Clinical data collection
Demographic data, comorbidities, and illness severity-assessed using APACHE II scores,[20] SOFA scores,[21] body mass index (BMI), nutrition risk in critically ill (NUTRIC) scores,[22] and various laboratory tests (including lactate, procalcitonin, C-reactive protein, ferritin, complete blood count, D-dimer, liver, and kidney function tests): were prospectively recorded at 24 hours of ICU admission. Additionally, the oxygen saturation ratio (ROX) index, arterial oxygen partial pressure to fractional inspired oxygen ratio (PaO2/FiO2), vital signs, sepsis presence,[23] and use of sedation, catecholamines, and neuromuscular blockers were documented. Prognostic evaluation included mortality, need for mechanical ventilation (MV), MV duration, and lengths of ICU and hospital stay.
2.4. LUS imaging protocol
LUS was performed within the first 24 hours of ICU admission. The examinations were conducted by the same sonographer, who was blinded to the patients’ clinical information, using portable ultrasound equipment general electric (GE) with a 1 to 6-MHz convex transducer. A predefined 6-site method was employed for each lung, which included scans of the anterior, antero-lateral, and postero-lateral aspects of the thorax (Fig. 2).
Figure 2.
Lung ultrasound regions. Each hemithorax was divided into 6 quadrants. Anterior axillary and posterior axillary lines were determined, the thorax was divided into 3 regions as anterior-lateral-posterior. A total of 6 regions were created by drawing a single line perpendicular to the anterior-posterior axillary line from the fourth intercostal space, creating the upper and lower regions. A total of 12 lung regions were scored.
A total of 12 regions were evaluated for ultrasound patterns: A-lines (normal reverberation artifacts of the pleural line indicating normal lung aeration when accompanied by lung sliding), B-lines (vertical hyperechoic reverberation artifacts originating from the pleural line), and consolidation (tissue-like pattern). Each region was assigned a score using a point system:
Score 0: Horizontal A-lines or well-spaced B-lines < 3 (Fig. 3A).
Score 1: Well-spaced B-lines ≥ 3 (Fig. 3B).
Score 2: Multiple coalescent B-lines (Fig. 3C).
Score 3: lung consolidation with or without air bronchograms (Fig. 3D)
Figure 3.
Lung ultrasound scoring system. (A) Score 0: horizontal A-lines or well-spaced B-lines < 3. (B) Score 1: well-spaced B-lines ≥ 3. (C) Score 2: multiple coalescent B-lines. (D) Score 3: lung consolidation with or without air bronchograms.
A LUS of 0 indicated normal findings, while a score of 36 represented the worst possible condition.[24]
The term “survivor” refers to patients who remained alive throughout their intensive care follow-up, whereas “nonsurvivor” describes those who did not survive during the intensive care period.
2.5. Statistical analysis
Alternative cutoff points for the LUS scores were assessed using area under the ROC curve (AUC) statistics.
The prognostic impact of risk factors on progression-free survival (PFS) was analyzed using both univariate and multivariable analyses. Two PFS rates were calculated as the time from begining of the hospital entry or from ICU entry to the time of any documented clinical progression, relapse or death from any cause. The Kaplan–Meier method was used to estimate the mean-median PFS rates. Log-rank test was used to compare the survival distributions between groups. Cox regression models (forward stepwise) were used to estimate survival rates, failure rates, and hazard ratios. The Chi-square test or Student t-test were used for group comparisons. Results were reported as mean ± SD, median, number (n), and percent (%), with a P-value < .05 considered significant. Analyses were conducted using SPSS v22.0.
The study protocol was approved by the Institutional Research Ethics Committee of Cukurova University Hospital (approval number 98/41). Written informed consent was obtained from all participants or their primary legal representatives. All procedures performed in the study involving human participants were in accordance with the ethical standards of the hospital, national research committee and the 1964 Helsinki declaration.
3. Results
The study involved 90 patients, with an average age of 62.5 ± 13.2 years, of whom 64.4% were male. Upon admission to the ICU, 4 patients (4.4%) required intubation, 4 patients (4.4%) were receiving noninvasive MV, 31 patients (34.4%) were on high-flow oxygen therapy, and 51 patients (56.6%) were supported with low-flow oxygen.
ROC curve analysis was utilized to evaluate the predictive value of the baseline (within 24 hours of admission to ICU) LUS for mortality during hospitalization. The analysis revealed that the area under the ROC curve for the LUS score was .85 (P < .001) (Fig. 4A). A cutoff value of 21.0 for the LUS on admission demonstrated a sensitivity of 97% and a specificity of 68% for predicting mortality in patients with COVID-19. Based on this cutoff, 52 patients were classified into group 1 with an LUS ≥ 21, while 38 patients were classified into group 2 with an LUS score < 21. Patients in group 1 (LUS ≥ 21) were older and had higher APACHE II scores, SOFA scores, NUTRIC scores, CCI scores, and procalcitonin levels compared to those in group 2 (LUS < 21) (P < .05). Table 1 presents the clinical characteristics based on the LUS score at ICU admission. The duration of MV, ICU length of stay, and hospital length of stay were comparable between the 2 groups.
Figure 4.
(A) ROC curve analysis. (B) Survival curve according to LUS score for the total population.
Table 1.
Clinical characteristics according to the lung ultrasound score groups at ICU admission.
| Characteristics | LUS ≥ 21 (n = 52) mean ± SD |
LUS < 21 (n = 38) mean ± SD |
P-value |
|---|---|---|---|
| Sex (male)* | 28 (48.3) | 30 (51.7) | .016 |
| Tobaccouse* | 15 (60) | 10 (40) | .817 |
| Alcohol* | 2 (66.7) | 1 (33.3) | 1.000 |
| Age (yr) | 65.04 ± 13.02 | 58.66 ± 12.85 | .023 |
| BMI (kg/m2) | 30.60 ± 7.05 | 28.09 ± 4.49 | .042 |
| Symptom duration (d) | 7.06 ± 5.03 | 7.71 ± 3.82 | .504 |
| Intubation during hospitalization* | 34 (89.5) | 4 (10.5) | <.001 |
| APACHE II score | 22.19 ± 8.75 | 14.76 ± 6.75 | <.001 |
| SOFA score | 6.19 ± 3.71 | 3.63 ± 1.95 | <.001 |
| CCI | 4.20 ± 2.34 | 2.43 ± 2.34 | .002 |
| NUTRIC score | 4.70 ± 2.04 | 2.68 ± 2.07 | <.001 |
| Rox index | 6.7 ± 4.0 | 7.8 ± 4.9 | .247 |
| Lactate (mmol/L) | 2.16 ± 1.30 | 1.90 ± 0.86 | .285 |
| Procalcitonin (ng/mL) | 2.31 ± 5.71 | 0.38 ± 0.74 | .020 |
| CRP (mg/L) | 122.63 ± 83.78 | 120.79 ± 93.70 | .922 |
| Ferritin (mg/L) | 809.84 ± 944.16 | 678.43 ± 642.79 | .461 |
| WBC (103/µL) | 10.14 ± 7.64 | 11.70 ± 6.00 | .301 |
| Hb (g/dL) | 12.34 ± 2.21 | 12.79 ± 2.58 | .381 |
| Lymphocyte (103/µL) | 0.73 ± 0.43 | 0.79 ± 0.33 | .484 |
| D-dimer (mg/L) | 2.44 ± 3.08 | 2.17 ± 4.88 | .754 |
| PaO2/FiO2 ratio | 142.6 ± 77.9 | 150.7 ± 83.8 | .640 |
| Presence of sepsis* | 30 (83.3) | 6 (16.7) | <.001 |
| Use of sedation* | 30 (88.2) | 4 (11.8) | <.001 |
| Use of neuromuscular blocker* | 22 (100) | 0 (0) | <.001 |
| Use of catecholamines* | 28 (93.3) | 2 (6.7) | <.001 |
| Duration of MV (d) | 5.29 ± 4.48 | 8.5 ± 4.65 | .186 |
ABP = arterial blood pressure, APACHE II = acute physiology and chronic health evaluation II, BMI = body mass index, CCI = charlson comorbidity index, CRP = C-reactive protein, ICU = intensive care unit, LUS = lung ultrasound score, MV = mechanical ventilation, PaO2/FiO2 ratio = the ratio of arterial oxygen partial pressure to fractional inspired oxygen; ROX index = ratio of oxygen saturation as measured by pulse oximetry SpO2/FiO2 to respiratory rate, SOFA = sequential organ failure assessment.
n (%).
The mortality rate in the ICU was 37.8%. However, higher LUS at baseline were significantly associated with increased mortality (P < .001). When comparing survivors and nonsurvivors, it was found that nonsurvivors had significantly higher APACHE II scores, SOFA scores, CCI scores, NUTRIC scores, serum lactate levels, procalcitonin levels, ferritin levels, D-dimer levels, and heart rates (P < .05). However, there were no significant differences between the 2 groups in terms of gender, BMI, PaO2/FiO2 ratio, ROX index, body temperature, respiratory rate, mean arterial blood pressure, C-reactive protein levels, white blood cell count, hemoglobin levels, or lymphocyte counts (P > .05). Among the patients, 34 (89.5%) in the deceased group and 4 (10.5%) in the survivor group were intubated. Table 2 presents the patient characteristics for the entire cohort as well as comparisons between the survivor and nonsurvivor groups.
Table 2.
Baseline characteristics of the study according to the mortality outcome at ICU admission.
| Parameter | Death n = 34 mean ± SD |
Alive n = 56 mean ± SD |
P-value | AllPatients n = 90 mean ± SD (min–max) |
|---|---|---|---|---|
| Sex (male)* | 18 (31.0) | 40 (69.0) | .111 | 58 (64.4) |
| Tobaccouse* | 11 (44.0) | 14 (56.0) | .475 | 25 (27.8) |
| Alcohol* | 1 (1.1) | 2 (2.2) | 1.000 | 3 (3.3) |
| Age (yr) | 64.6 ± 10.6 | 60.9 ± 14.5 | .207 | 62.5 ± 13.19 (27–89) |
| BMI (kg/m2) | 30.7 ± 7.0 | 28.8 ± 5.6 | .157 | 29.5 ± 6.2 (18–50) |
| Symptom duration (d) | 6.41 ± 5.18 | 7.89 ± 4.05 | .135 | 7.33 ± 4.54 (1–30) |
| Intubation during hospitalization* | 34 (89.5) | 4 (10.5) | <.001 | 38 (42.2) |
| LUS | 25.2 ± 3.2 | 18.6 ± 5.5 | <.001 | 21.2 (6–32) |
| APACHE II score | 23.3 ± 7.9 | 16.5 ± 8.2 | <.001 | 19.0 ± 8.7 (3–46) |
| SOFA score | 7.1 ± 3.5 | 3.8 ± 2.4 | <.001 | 5.1 ± 3.3 (1–16) |
| CCI | 4.9 ± 2.2 | 2.7 ± 2.3 | <.001 | 3.4 ± 2.5 (0–9) |
| NUTRIC score | 5.2 ± 1.58 | 3.1 ± 2.2 | <.001 | 3.8 ± 2.27 (0–9) |
| Rox index | 6.0 ± 3.6 | 7.8 ± 4.8 | .063 | 7.1 ± 4.4 (2.1–23.1) |
| Lactate (mmol/L) | 2.36 ± 1.5 | 1.86 ± 0.8 | .040 | 2.05 ± 1.1 (0.5–8.2) |
| Procalcitonin (ng/mL) | 3.1 ± 6.8 | 0.5 ± 1.0 | .032 | 1.5 ± 4.4 (0.02–33) |
| Ferritin (mg/L) | 960.1 ± 1064.6 | 604.8 ± 598.5 | .047 | 736.5 ± 815.9 (5.5–4992) |
| CRP (mg/L) | 126.5 ± 85.7 | 119.0 ± 89.3 | .696 | 121.8 ± 87.6 (2–424) |
| WBC (103/µL) | 10.87 ± 9.08 | 10.76 ± 5.46 | .941 | 10.80 ± 7.00 (0.8–53.7) |
| Hb (g/dL) | 12.39 ± 2.24 | 12.62 ± 2.46 | .666 | 12.53 ± 2.37 (3–17.6) |
| Lymphocyte (103/µL) | 0.75 ± 0.45 | 0.75 ± 0.35 | .945 | 0.75 ± 0.39 (0.1–1.8) |
| PaO2/FiO2 ratio | 131.8 ± 76.8 | 154.4 ± 81.3 | .194 | 145.9 ± 79.9 (55–461) |
| Temperature (°C) | 36.9 ± 0.7 | 36.8 ± 0.5 | .364 | 36.8 ± 0.3 (36.0–40.1) |
| D-dimer (mg/L) | 2.9 ± 3.6 | 1.5 ± 2.5 | .046 | 2.0 ± 3.0 (0.08–17.38) |
| Heart rate (bpm) | 99.4 ± 16.8 | 90.7 ± 14.8 | .012 | 93.9 ± 16.1 (55–140) |
| Respiratory rate | 32.2 ± 6.4 | 29.6 ± 6.6 | .069 | 30.5 ± 6.6 (18–45) |
| Mean ABP (mm Hg) | 82.3 ± 21.2 | 82.4 ± 9.4 | .978 | 82.2 ± 14.8 (48–150) |
| Presence of sepsis* | 26 (72.2) | 10 (27.8) | <.001 | 36 (40) |
| Use of sedation* | 25 (73.5) | 9 (26.5) | <.001 | 34 (37.8) |
| Use of neuromuscular blocker* | 21 (95.5) | 1 (4.5) | <.001 | 22 (24.4) |
| Use of catecholamines* | 25 (83.3) | 5 (16.7) | <.001 | 30 (33.3) |
ABP = arterial blood pressure, APACHE II = acute physiology and chronic health evaluation II, BMI = body mass index, CCI = charlson comorbidity index, CRP = C-reactive protein, LUS = lung ultrasound score, PaO2/FiO2 ratio = the ratio of arterial oxygen partial pressure to fractional inspired oxygen, ROX index = ratio of oxygen saturation as measured by pulse oximetry SpO2/FiO2 to respiratory rate, SOFA = sequential organ failure assessment.
n(%).
The median follow-up duration was 12 days (range 1–60) for hospital stay and 7 days (range 1–25) for ICU stay. The estimated overall survival was 15.3 days for ICU stay and 33.4 days for hospital stay. PFS rate was significantly longer for patients with a baseline LUS score ≥ 21, (mean-survival of 23.8 days) compared to for those with a LUS score < 21 (12.5 days) (P < .05) for ICU stay. The survival curves are depicted in Figure 4B.
Cox regression analysis identified significantly associated factors with prognosis during hospitalization, including age, gender, APACHE II score, LUS, comorbidity, ferritin levels, procalcitonin levels, D-dimer levels at admission, as well as the presence of sepsis and the use of sedation, catecholamines, and neuromuscular blockers. These significant parameters were then included in a multivariate Cox regression model. The multivariate analysis revealed that a LUS score ≥ 21 were independent risk factor for mortality during ICU stay (Table 3).
Table 3.
Results of multivariate Cox regression analysis for mortality.
| B | OR | 95% CI | P-value | |
|---|---|---|---|---|
| Age | −0.15 | 0.99 | 0.96 to 1.02 | .327 |
| Gender (female) | 0.47 | 1.6 | 0.74 to 3.45 | .234 |
| Comorbidity (yes) | 1.33 | 3.8 | 0.83 to 17.28 | .085 |
| LUS (<21) | 3.27 | 26.22 | 3.36 to 204.47 | .002 |
| APACHE II score | −0.07 | 0.99 | 0.95 to 1.04 | .738 |
| Ferritin | 0.00 | 1.00 | 1.0 to 1.0 | .179 |
| Procalcitonin | 0.23 | 0.99 | 0.96 to 1.02 | .327 |
APACHE II = acute physiology and chronic health evaluation II, LUS = lung ultrasound score.
4. Discussion
This present study demonstrated that an LUS score ≥ 21 upon ICU admission is an independent predictor of mortality in patients with COVID-19 pneumonia and respiratory failure. Notably, 97.1% of patients with elevated LUS scores in our study group experienced mortality. A high LUS score at admission can be utilized to identify patients at increased risk, thereby potentially aiding in clinical decision-making.
During the COVID-19 pandemic, LUS gained widespread use globally for the diagnosis, risk stratification, and monitoring of patients.[25] Although computed tomography (CT) is considered the gold standard for diagnosing pneumonia, there has been documented high agreement between CT and LUS for diagnosing critically ill patients with COVID-19 pneumonia.[26–29] Several studies have highlighted the value of LUS for both diagnosing and assessing prognosis outside of the ICU during the pandemic. For instance, Bonadia et al conducted a prospective observational study involving 36 patients and demonstrated that LUS could effectively detect COVID-19 pneumonia and predict patients at risk for ICU admission and mortality.[30] Subsequent reports in emergency departments confirmed these findings,[31] although these studies employed different scoring methods and were conducted outside the ICU setting.
Two previous studies employed the same LUS system as used in our research. In the first study, Ji et al explored the prognostic significance of LUS in hospitalized patients with COVID-19 pneumonia. They analyzed 280 patients and found that an LUS > 12 was predictive of adverse outcomes, including mortality or ARDS, with over 90% sensitivity and specificity. Although their cutoff value was lower than ours, their study was conducted in a non-ICU population, with LUS evaluations occurring a median of 7 days (range 3–10 days) after admission.[32] In contrast, we performed LUS examinations within 24 hours of ICU admission, which may account for observing higher LUS scores in our ICU cohort. Similarly, Yasuka et al utilized the same LUS scoring system to predict outcomes in 105 patients hospitalized on general wards within 24 hours of admission. They demonstrated that a low LUS score (<5) had a 100% negative predictive value for the need for intensive respiratory support.[33] However, their study, like Ji et al’s, did not specifically focus on ICU patients.
Since LUS evaluation has not yet become a standard practice, there are only a few studies with limited sample sizes that focus on its prognostic value specifically in ICU patients. Deng et al retrospectively assessed 128 critically ill COVID-19 patients, conducting LUS evaluations within 24 hours of hospitalization. They found that an LUS score above 10.5 points had a sensitivity of 97.4% and a specificity of 75% for identifying critical-care patients. However, their study employed an 8-zone LUS examination protocol with a maximum score of 24, which did not include the posterior lung regions.[34] However, the posterior region is known as a main involved part of the lung in COVID-19 pneumonia patients. Thus, the higher cutoff value observed in our study could be due to differences in the study designs, patient populations and timing of LUS measurements. In the study by Deng et al, only 42 out of 128 patients were admitted to the ICU, and their analysis did not include ICU severity scores, which limits direct comparison with our ICU-focused cohort.[34]
In another study, Lichter et al retrospectively analyzed 120 patients who underwent LUS within 24 hours of admission, including 75 patients with milder conditions. They identified an optimal LUS score cutoff of 18, with a sensitivity of 62% and specificity of 74% for predicting mortality and the need for invasive MV.[35] Our study, which exclusively included severely ill ICU patients with COVID-19 pneumonia, found a comparable cutoff value. Notably, our study, with the largest cohort of ICU patients to date, provides a new and specific cutoff value for LUS that predicts mortality within 24 hours of ICU admission. Early prediction of ICU prognosis is extremely substantial for risk stratification, guiding management decisions and use of health care sources during a pandemic.
Univariate analysis of the entire study group revealed that nonsurvivors had higher APACHE II, SOFA, CCI, and NUTRIC scores, as well as elevated levels of serum lactate, procalcitonin, ferritin, and D-dimer. While these findings align with expectations for an ICU population, the multivariate analysis identified a higher LUS score as the only independent predictor of mortality. Elevated ferritin levels are known to correlate with poorer outcomes in COVID-19 patients, reflecting its role as a marker of stored iron and its association with severe inflammatory responses and worse prognosis.[36]
Several limitations of this study should be acknowledged. First, the exclusion of a number of patients could introduce bias into the results. Second, the study did not compare LUS scores with chest CT, which was beyond its scope but is important for validating LUS against a gold standard imaging modality. Third, while LUS is known to be highly effective in detecting peripheral lung involvement, central lesions may have been missed. However, it’s well known that COVID-19 pneumonia mainly involved peripheral region at lung so we don’t think that this has significantly affected our results. Finally, the study did not conduct a power analysis at the outset due to the pandemia conditions. However, post hoc power analysis indicates that the study has a power of over 80%. Additionally, because the data is sourced from a single tertiary care center which were the most severely patients referred, generalizing the results to other settings who has followed less severe patients may be challenging. The findings may not fully represent the broader population or different healthcare environments, underscoring the need for multicenter studies to validate and extend the applicability of our results.
LUS is an effective tool for early predicting prognosis in ICU patients with COVID-19 pneumonia. An LUS score ≥ 21 within 24 hours is an independent predictor of mortality in this cohort. Identifying high-risk patients using LUS score can facilitate closer monitoring and optimize healthcare resource utilization, potentially improving patient outcomes.
Author contributions
Conceptualization: Sinem Bayrakçi, Gülşah Seydaoğlu, Emre Karakoç, Oya Baydar Toprak, Ezgi Özyilmaz.
Data curation: Sinem Bayrakçi, Nazire Ateş Ayhan, Ahmet Firat, Yurdaer Bulut.
Formal analysis: Sinem Bayrakçi, Gülşah Seydaoğlu.
Funding acquisition: Sinem Bayrakçi, Ahmet Firat, Yurdaer Bulut, Oya Baydar Toprak, Ezgi Özyilmaz.
Investigation: Emre Karakoç.
Methodology: Sinem Bayrakçi, Nazire Ateş Ayhan, Ahmet Firat, Yurdaer Bulut, Emre Karakoç, Oya Baydar Toprak, Ezgi Özyilmaz.
Project administration: Emre Karakoç, Ezgi Özyilmaz.
Resources: Sinem Bayrakçi, Nazire Ateş Ayhan, Oya Baydar Toprak.
Software: Sinem Bayrakçi, Nazire Ateş Ayhan, Oya Baydar Toprak, Ezgi Özyilmaz.
Supervision: Ahmet Firat, Yurdaer Bulut, Gülşah Seydaoğlu.
Validation: Sinem Bayrakçi, Gülşah Seydaoğlu, Emre Karakoç, Ezgi Özyilmaz.
Visualization: Sinem Bayrakçi, Ahmet Firat, Yurdaer Bulut.
Writing – original draft: Sinem Bayrakçi.
Writing – review & editing: Sinem Bayrakçi, Nazire Ateş Ayhan, Emre Karakoç, Oya Baydar Toprak, Ezgi Özyilmaz.
Abbreviations:
- ABP
- arterial blood pressure
- APACHE II
- acute physiology and chronic health evaluation II
- BMI
- body mass index
- CCI
- Charlson comorbidity index
- COVID-19
- coronavirus disease
- GE
- general electric
- ICU
- intensive care unit
- LUS
- lung ultrasound
- MV
- mechanical ventilation
- NUTRIC
- nutrition risk in critically ill
- PaO2/FiO2 =
- arterial oxygen partial pressure to fractional inspired oxygen ratio
- PFS
- progression-free-survival
- ROX
- oxygen saturation ratio
- SOFA
- sequential organ failure assessment
Cukurova University Hospital, The Institutional Research Ethics Committee approved the study protocol (number 98/ 41).
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Bayrakçi S, Ateş Ayhan N, Firat A, Bulut Y, Seydaoğlu G, Karakoç E, Baydar Toprak O, Özyilmaz E. The role of early lung ultrasound score measurement in determining prognosis in COVID-19 ICU patients with respiratory failure. Medicine 2025;104:17(e42010).
This article has not been presented at any congresses or scientific meetings. It has not been sent to any scientific journal other than this journal.
Contributor Information
Ahmet Firat, Email: ben.firat@hotmail.com.
Yurdaer Bulut, Email: bulutyurdaer@gmail.com.
Gülşah Seydaoğlu, Email: gulsahseydaoglu@gmail.com.
Emre Karakoç, Email: emre1967@yahoo.com.
Oya Baydar Toprak, Email: oyabaydarr@yahoo.com.tr.
Ezgi Özyilmaz, Email: ezgiozyilmaz@hotmail.com.
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