Comparative analysis of chest radiography and lung ultrasound to predict intra-hospital prognosis of patients admitted for acute SARS-CoV-2 pneumonia (COVID-19).
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ABSTRACT:
SUBMITTER: Rubio-Gracia J
PROVIDER: S-EPMC8890970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPoint of care lung ultrasound (POCUS) has been recently used to assess prognosis in COVID-19 patients. However, there are no data comparing POCUS and chest-X ray, a technique widely used.Patients and methodsRetrospective analysis in stable COVID-19 patients. Schalekamp radiological lung scale and LUZ-Score ultrasound scale were compared. Primary end-point was in-hospital death and/or need for Intensive Care Unit admission.ResultsA total of 138 patients were included. Median Schalekamp scale was 2 (2) and median LUZ-Score scale was 21 (10). No significant correlation was observed between both techniques. Patients with a LUZ-Score ≥ 21 points at admission had worse lung function and higher concentrations of LDH, CRP and Interleuquine-6. Schalekamp scale failed to identify patients at a higher risk at admission for the primary end-point. Addition of POCUS to a previous clinical model, improved risk prediction (AUC 0.805 [95% CI: 0.662-0.948]; P = <0.001).ConclusionsChest X-ray and POCUS showed no correlation at admission in this analysis. Only POCUS identified a group of patients with greater clinical and analytical involvement. POCUS improved, previous clinical model, while chest X-ray did not add relevant predictive information for the primary endpoint.
| S-EPMC9618450 | biostudies-literature
Project description:BackgroundThe aim of this meta-analysis was to evaluate the diagnostic value of lung ultrasound (LUS) in comparison to chest radiography (CXR) in children with pneumonia.MethodsComputer-based retrieval was performed on PubMed and EMBASE. Quality was evaluated according to the quality assessment of diagnostic accuracy studies-2, and Meta-Disc was adopted to perform meta-analysis. Heterogeneity was assessed using Q and I statistics. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals (CIs) as the primary outcomes were calculated for each index test.ResultsTwenty two studies with a total of 2470 patients met the inclusion criteria. Our results showed that the pooled sensitivity, specificity, and DOR for children with pneumonia diagnosed by LUS were 0.95 (95% CI: 0.94 to 0.96), 0.90 (95% CI: 0.87 to 0.92), and 137.49 (95% CI: 60.21 to 313.98), respectively. The pooled sensitivity, specificity, and DOR for pediatric pneumonia diagnosed by CXR was 0.91 (95% CI: 0.90 to 0.93), 1.00 (95% CI: 0.99 to 1.00), and 369.66 (95% CI: 137.14 to 996.47), respectively. Four clinical signs, including pulmonary consolidation, positive air bronchogram, abnormal pleural line, and pleural effusion were most frequently observed using LUS in the screening of children with pneumonia.ConclusionsThe available evidence suggests that LUS is a reliable, valuable, and alternative method to CXR for the diagnosis of pediatric pneumonia.
| S-EPMC7738074 | biostudies-literature
Project description:ObjectiveThe chest X-ray (CXR) is the most readily available and common imaging modality for the assessment of pneumonia. However, detecting pneumonia from chest radiography is a challenging task, even for experienced radiologists. An artificial intelligence (AI) model might help to diagnose pneumonia from CXR more quickly and accurately. We aim to develop an AI model for pneumonia from CXR images and to evaluate diagnostic performance with external dataset.MethodsTo train the pneumonia model, a total of 157,016 CXR images from the National Institutes of Health (NIH) and the Korean National Tuberculosis Association (KNTA) were used (normal vs. pneumonia = 120,722 vs.36,294). An ensemble model of two neural networks with DenseNet classifies each CXR image into pneumonia or not. To test the accuracy of the models, a separate external dataset of pneumonia CXR images (n = 212) from a tertiary university hospital (Gachon University Gil Medical Center GUGMC, Incheon, South Korea) was used; the diagnosis of pneumonia was based on both the chest CT findings and clinical information, and the performance evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, we tested the change of the AI probability score for pneumonia using the follow-up CXR images (7 days after the diagnosis of pneumonia, n = 100).ResultsWhen the probability scores of the models that have a threshold of 0.5 for pneumonia, two models (models 1 and 4) having different pre-processing parameters on the histogram equalization distribution showed best AUC performances of 0.973 and 0.960, respectively. As expected, the ensemble model of these two models performed better than each of the classification models with 0.983 AUC. Furthermore, the AI probability score change for pneumonia showed a significant difference between improved cases and aggravated cases (Δ = -0.06 ± 0.14 vs. 0.06 ± 0.09, for 85 improved cases and 15 aggravated cases, respectively, P = 0.001) for CXR taken as a 7-day follow-up.ConclusionsThe ensemble model combined two different classification models for pneumonia that performed at 0.983 AUC for an external test dataset from a completely different data source. Furthermore, AI probability scores showed significant changes between cases of different clinical prognosis, which suggest the possibility of increased efficiency and performance of the CXR reading at the diagnosis and follow-up evaluation for pneumonia.
| S-EPMC8049482 | biostudies-literature
Project description:Background: Patients with heart failure (HF) often display dyspnea associated with pulmonary congestion, along with intravascular congestion, both may result in urgent hospitalization and subsequent death. A combination of radiographic pulmonary congestion and plasma volume might screen patients with a high risk of in-hospital mortality in the emergency department (ED). Methods: In the pathway of dyspneic patients in emergency (PARADISE) cohort, patients admitted for acute HF were stratified into 4 groups based on high or low congestion score index (CSI, ranging from 0 to 3, high value indicating severe congestion) and estimated plasma volume status (ePVS) calculated from hemoglobin/hematocrit. Results: In a total of 252 patients (mean age, 81.9 years; male, 46.8%), CSI and ePVS were not correlated (Spearman rho <0 .10, p > 0.10). High CSI/high ePVS was associated with poorer renal function, but clinical congestion markers (i.e., natriuretic peptide) were comparable across CSI/ePVS categories. High CSI/high ePVS was associated with a four-fold higher risk of in-hospital mortality (adjusted-OR, 95%CI = 4.20, 1.10-19.67) compared with low CSI/low ePVS, whereas neither high CSI nor ePVS alone was associated with poor prognosis (all-p-value > 0.10; Pinteraction = 0.03). High CSI/high ePVS improved a routine risk model (i.e., natriuretic peptide and lactate)(NRI = 46.9%, p = 0.02), resulting in high prediction of risk of in-hospital mortality (AUC = 0.85, 0.82-0.89). Conclusion: In patients hospitalized for acute HF with relatively old age and comorbidity burdens, a combination of CSI and ePVS was associated with a risk of in-hospital death, and improved prognostic performance on top of a conventional risk model.
| S-EPMC8787280 | biostudies-literature
Project description:BackgroundWhether digitally recorded lung sounds are associated with radiographic pneumonia or clinical outcomes among children in low-income and middle-income countries is unknown. We sought to address these knowledge gaps.MethodsWe enrolled 1 to 59monthold children hospitalized with pneumonia at eight African and Asian Pneumonia Etiology Research for Child Health sites in six countries, recorded digital stethoscope lung sounds, obtained chest radiographs, and collected clinical outcomes. Recordings were processed and classified into binary categories positive or negative for adventitial lung sounds. Listening and reading panels classified recordings and radiographs. Recording classification associations with chest radiographs with World Health Organization (WHO)-defined primary endpoint pneumonia (radiographic pneumonia) or mortality were evaluated. We also examined case fatality among risk strata.ResultsAmong children without WHO danger signs, wheezing (without crackles) had a lower adjusted odds ratio (aOR) for radiographic pneumonia (0.35, 95% confidence interval (CI): 0.15, 0.82), compared to children with normal recordings. Neither crackle only (no wheeze) (aOR: 2.13, 95% CI: 0.91, 4.96) or any wheeze (with or without crackle) (aOR: 0.63, 95% CI: 0.34, 1.15) were associated with radiographic pneumonia. Among children with WHO danger signs no lung recording classification was independently associated with radiographic pneumonia, although trends toward greater odds of radiographic pneumonia were observed among children classified with crackle only (no wheeze) or any wheeze (with or without crackle). Among children without WHO danger signs, those with recorded wheezing had a lower case fatality than those without wheezing (3.8% vs. 9.1%, p = .03).ConclusionsAmong lower risk children without WHO danger signs digitally recorded wheezing is associated with a lower odds for radiographic pneumonia and with lower mortality. Although further research is needed, these data indicate that with further development digital auscultation may eventually contribute to child pneumonia care.
| S-EPMC7692889 | biostudies-literature
Project description:Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non-COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019-related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.
| S-EPMC7841876 | biostudies-literature
Project description:Respiratory X-ray imaging enhanced by phase contrast has shown improved airway visualization in animal models. Limitations in current X-ray technology have nevertheless hindered clinical translation, leaving the potential clinical impact an open question. Here, we explore phase-contrast chest radiography in a realistic in silico framework. Specifically, we use preprocessed virtual patients to generate in silico chest radiographs by Fresnel-diffraction simulations of X-ray wave propagation. Following a reader study conducted with clinical radiologists, we predict that phase-contrast edge enhancement will have a negligible impact on improving solitary pulmonary nodule detection (6 to 20 mm). However, edge enhancement of bronchial walls visualizes small airways (< 2 mm), which are invisible in conventional radiography. Our results show that phase-contrast chest radiography could play a future role in observing small-airway obstruction (e.g., relevant for asthma or early-stage chronic obstructive pulmonary disease), which cannot be directly visualized using current clinical methods, thereby motivating the experimental development needed for clinical translation. Finally, we discuss quantitative requirements on distances and X-ray source/detector specifications for clinical implementation of phase-contrast chest radiography.
| S-EPMC9910502 | biostudies-literature
Project description:BACKGROUND:Lung ultrasound may be a reasonable alternative to chest radiography for the identification of acute respiratory distress syndrome (ARDS), but the diagnostic performance of lung ultrasound for ARDS is uncertain. We therefore analyzed the clinical outcomes of ARDS diagnosed according to the Berlin Definition, using either chest radiography (Berlin-CXR) or lung ultrasound (Berlin-LUS) as an alternative imaging method. METHODS:This was a retrospective observational study in a 20-bed medical intensive care unit (ICU). Patients who required noninvasive ventilation or invasive ventilation for hypoxemic respiratory failure on ICU admission from August 2014 to March 2017 were included. Both chest radiography and lung ultrasound were performed routinely upon ICU admission. Comparisons were made using either the Berlin-CXR or Berlin-LUS definitions to diagnose ARDS with respect to the patient characteristics and clinical outcomes for each definition. ICU and hospital mortality were the main outcome measures for both definitions. RESULTS:The first admissions of 456 distinct patients were analyzed. Compared with the 216 patients who met the Berlin-CXR definition (ICU mortality 19.4%, hospital mortality 36.1%), 229 patients who met the Berlin-LUS definition (ICU mortality 22.7%, hospital mortality 34.5%) and 79 patients who met the Berlin-LUS but not the Berlin-CXR definition (ICU mortality 21.5%, hospital mortality 29.1%) had similar outcomes. In contrast, the 295 patients who met either definition had higher mortality than the 161 patients who did not meet either definition (ICU mortality 20.0% versus 12.4%, P?=?0.041; hospital mortality 34.2% versus 24.2%, P?=?0.027). Compared with Berlin-CXR, Berlin-LUS had a positive predictive value of 0.66 (95% confidence interval 0.59-0.72) and a negative predictive value of 0.71 (0.65-0.77). Among the 216 Berlin-CXR ARDS patients, 150 patients (69.4%) also fulfilled Berlin-LUS definition. CONCLUSIONS:For the identification of ARDS using the Berlin definition, both chest radiography and lung ultrasound were equally related to mortality. The Berlin definition using lung ultrasound helped identify patients at higher risk of death, even if these patients did not fulfill the conventional Berlin definition using chest radiography. However, the moderate overlap of patients when chest imaging modalities differed suggests that chest radiography and lung ultrasound should be complementary rather than used interchangeably.
| S-EPMC6098581 | biostudies-literature
Project description:PURPOSE:The relationship between lung ultrasound (LUS) and chest computed tomography (CT) scans in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia is not clearly defined. The primary objective of our study was to assess the performance of LUS in determining severity of SARS-CoV-2 pneumonia compared with chest CT scan. Secondary objectives were to test the association between LUS score and location of the patient, use of mechanical ventilation, and the pulse oximetry (SpO2)/fractional inspired oxygen (FiO2) ratio. METHODS:A multicentre observational study was performed between 15 March and 20 April 2020. Patients in the Emergency Department (ED) or Intensive Care Unit (ICU) with acute dyspnoea who were PCR positive for SARS-CoV-2, and who had LUS and chest CT performed within a 24-h period, were included. RESULTS:One hundred patients were included. LUS score was significantly associated with pneumonia severity assessed by chest CT and clinical features. The AUC of the ROC curve of the relationship of LUS versus chest CT for the assessment of severe SARS-CoV-2 pneumonia was 0.78 (CI 95% 0.68-0.87; p?<?0.0001). A high LUS score was associated with the use of mechanical ventilation, and with a SpO2/FiO2 ratio below 357. CONCLUSION:In known SARS-CoV-2 pneumonia patients, the LUS score was predictive of pneumonia severity as assessed by a chest CT scan and clinical features. Within the limitations inherent to our study design, LUS can be used to assess SARS-CoV-2 pneumonia severity.
| S-EPMC7388119 | biostudies-literature
Project description:COVID-19 pneumonia can be severe, with an unpredictable evolution and high mortality prevalence in older patients. The diagnosis is usually performed by RT-PCR or CT chest scan. Lung ultrasonography (LUS) has been proposed as an alternative method to monitor patients with COVID-19 pneumonia. To assess the diagnostic performance of LUS, we performed LUS using a portable device and adapting a protocol already used in Acute Respiratory Syndrome. We used the score obtained with the index we created to assess for LUS diagnostic performance as compared to lung CT chest scan and to predict for oxygen requirements. Daily bedside LUS was easy to perform and microbiologically safe. LUS was 89% sensitive and 100% specific in predicting CT chest scan abnormalities, and 95% sensitive and 67% specific in detecting oxygen requirements. This is the first report on the diagnostic performance of LUS as compared to CT chest scan for the diagnosis of COVID-19 pneumonia and assessments of oxygen requirements by LUS. LUS could help in the orientation of dyspneic patients to intensive care. It could also be proposed when there is limited access to CT scan in the context of a pandemic crisis, or to implement clinical lung examinations for outpatient follow-up.
| S-EPMC7655154 | biostudies-literature