Pneumonia severity indices predict prognosis in coronavirus disease-2019

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

Introduction

Chest computed tomography (CT) is suitable to assess morphological changes in the lungs. Chest CT scoring systems (CCTS) have been developed and use in order to quantify the severity of pulmonary involvement in COVID-19. CCTS has also been correlated with clinical outcomes. Here we wished to use a validated, relatively simple CTSS to assess chest CT patterns and to correlate CTSS with clinical outcomes in COVID-19.

Patients and methods

Altogether 227 COVID-19 cases underwent chest CT scanning using a 128 multi-detector CT scanner (SOMATOM Go Top, Siemens Healthineers, Germany). Specific pathological features, such as ground-glass opacity (GGO), crazy-paving pattern, consolidation, fibrosis, subpleural lines, pleural effusion, lymphadenopathy and pulmonary embolism were evaluated. CTSS developed by Pan et al. (CTSS-Pan) was applied. CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, A-DROP scores, as well as outcome measures. We compared CTSS-Pan to two other CT scoring systems.

Results

The mean CTSS-Pan in the 227 COVID-19 patients was 14.6 ± 6.7. The need for ICU admission (p < 0.001) and death (p < 0.001) were significantly associated with higher CTSS. With respect to chest CT patterns, crazy-paving pattern was significantly associated with ICU admission. Subpleural lines exerted significant inverse associations with ICU admission and ventilation. Lymphadenopathy was associated with all three outcome parameters. Pulmonary embolism led to ICU admission. In the ROC analysis, CTSS>18.5 significantly predicted admission to ICU (p = 0.026) and CTSS>19.5 was the cutoff for increased mortality (p < 0.001). CTSS-Pan and the two other CTSS systems exerted similar performance. With respect to clinical outcomes, CTSS-Pan might have the best performance.

Conclusion

CTSS may be suitable to assess severity and prognosis of COVID-19-associated pneumonia. CTSS and specific chest CT patterns may predict the need for ventilation, as well as mortality in COVID-19. This can help the physician to guide treatment strategies in COVID-19, as well as other pulmonary infections.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Severity and prognostic factors of SARS-CoV-2-induced pneumonia: The value of clinical and laboratory biomarkers and the A-DROP score

Introduction

Numerous clinical and laboratory scores that include C-reactive protein (CRP), D-dimer, ferritin, lactate dehydrogenase (LDH), interleukin 6 (IL-6), procalcitonin (PCT), blood urea nitrogen (BUN), creatinine levels and oxygenation (PaO₂ and SaO₂) have been used for the prognosis of COVID-19. In addition, composite scores have been developed for the assessment of general state and risk in community-acquired pneumonia (CAP) that may be applied for COVID-19 as well. In this study, we assessed severity and potential prognostic risk factors for unfavorable outcome among hospitalized COVID-19 patients. We also applied the A-DROP general scoring system used in CAP to COVID-19.

Patients and methods

Altogether 233 patients admitted to our center with COVID-19 were included in the study. Clinical status, several laboratory biomarkers described above, indicators of oxygenation were determined at hospital admission. We also applied the A-DROP composite scoring system that includes Age (≥ 70 years in males and ≥ 75 years in females), Dehydration (BUN ≥ 7.5 mmol/l), Respiratory failure (SaO₂ ≤ 90% or PaO₂ ≤ 60 mmHg), Orientation disturbance (confusion) and low blood Pressure (systolic BP ≤ 90 mmHg) to COVID-19.

Results

At the time of admission, most patients had elevated CRP, LDH, ferritin, D-dimer, and IL-6 levels indicating multisystemic inflammatory syndrome (MIS). Altogether 49 patients (21.2%) required admission to ICU, 46 (19.7%) needed ventilation and 40 patients (17.2%) died. In the binary analysis, admission to ICU, the need for ventilation and death were all significantly associated with the duration of hospitalization, history of hypertension or obesity, confusion/dizziness, as well as higher absolute leukocyte and neutrophil and lower lymphocyte counts, elevated CRP, PCT, LDH, ferritin, IL-6, BUN, and creatinine levels, low PaO₂ and SaO₂ and higher A-DROP score at the time of admission (p < 0.05).

Conclusion

Numerous laboratory biomarkers in addition to obesity, dizziness at the time of admission and the history of hypertension may predict the need for ICU admission and ventilation, as well as mortality in COVID-19. Moreover, A-DROP may be a suitable scoring system for the assessment of general health and disease outcome in COVID-19.

Comparison of Pneumonia Severity Indices, qCSI, 4C-Mortality Score and qSOFA in Predicting Mortality in Hospitalized Patients with COVID-19 Pneumonia

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961−0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

Management of COVID-19 Patients in the Emergency Department

COVID-19 is an emerging disease of global public health concern. As the pandemic overwhelmed emergency departments (EDs), a restructuring of emergency care delivery became necessary in many hospitals. Furthermore, with more than 2000 papers being published each week, keeping up with ever-changing information has proven to be difficult for emergency physicians. The aim of the present review is to provide emergency physician with a summary of the current literature regarding the management of COVID-19 patients in the emergency department.

Comparison of demographic and clinical characteristics of hospitalized COVID-19 patients with severe/critical illness in the first wave versus the second wave

Due to current advances and growing experience in the management of coronavirus Disease 2019 (COVID‐19), the outcome of COVID‐19 patients with severe/critical illness would be expected to be better in the second wave compared with the first wave. As our hospitalization criteria changed in the second wave, we aimed to investigate whether a favorable outcome occurred in hospitalized COVID‐19 patients with only severe/critical illness. Among 642 laboratory‐confirmed hospitalized COVID‐19 patients in the first wave and 1121 in the second wave, those who met World Health Organization (WHO) definitions for severe or critical illness on admission or during follow‐up were surveyed. Data on demographics, comorbidities, C‐reactive protein (CRP) levels on admission, and outcomes were obtained from an electronic hospital database. Univariate analysis was performed to compare the characteristics of patients in the first and second waves. There were 228 (35.5%) patients with severe/critical illness in the first wave and 681 (60.7%) in the second wave. Both groups were similar in terms of age, gender, and comorbidities, other than chronic kidney disease. Median serum CRP levels were significantly higher in patients in the second wave compared with those in the first wave [109 mg/L (interquartile range [IQR]: 65–157) vs. 87 mg/L (IQR: 39–140); p < 0.001]. However, intensive care unit admission and mortality rates were similar among the waves. Even though a lower mortality rate in the second wave has been reported in previous studies, including all hospitalized COVID‐19 patients, we found similar demographics and outcomes among hospitalized COVID‐19 patients with severe/critical illness in the first and second wave.

Even though a lower mortality rate in the second wave has been reported in previous studies, including all hospitalized COVID‐19 patients, we found similar AQ4demographics and outcomes among hospitalized COVID‐19 patients with severe/critical illness in the first and second wave.