Performance of pneumonia severity index and CURB-65 in predicting 30-day mortality in patients with COVID-19

Comparing the efficacy of corticosteroids among patients with community-acquired pneumonia in the ICU versus non-ICU settings: A systematic review and meta-analysis

BACKGROUND

Despite the potential of corticosteroids in treating community-acquired pneumonia (CAP), conflicting evidence exists regarding their effect on mortality. To address this gap and provide new insights, we conducted a pre-specified subgroup meta-analysis of corticosteroid use in CAP patients, focusing on the ICU versus non-ICU subsets.

METHODS

We searched PubMed, Cochrane Central Register of Controlled Trials and SCOPUS from inception to May 2023 for randomized controlled trials (RCTs). The primary outcomes of interest were mortality, need for mechanical ventilation, need for ICU admission, and treatment failure. Secondary outcomes analysed were the need for hospital readmission, length of hospital stay, length of ICU stay, gastrointestinal (GI) bleeding, secondary infections, and hyperglycaemic events. The results were analysed through the random-effects model. A p-value < 0.05 was considered significant.

RESULTS

Eighteen randomized controlled trials (n = 4472) analyzing patients withCAP were included. Our results suggest that corticosteroids significantly reduced the incidence of mortality (RR: 0.66; 95 % CI: 0.54, 0.81; P = <0.0001) and need for mechanical ventilation (RR: 0.57; 95 % CI: 0.44, 0.73; P = <0.00001). It was also observed that corticosteroids significantly decrease the lengths of ICU (MD: -1.67; 95 % CI: -2.97, -0.37; P = 0.01) and hospital stay (MD: -1.94; 95 % CI: -2.89, -0.98; P = 0.0001), while increasing the number of hyperglycemic events (RR: 1.68; 95 % CI: 1.32, 2.12; P = <0.0001) and hospital readmissions (RR: 1.19; 95 % CI: 1.04, 1.37; P = 0.01).

CONCLUSIONS

The results of this meta-analysis demonstrate that corticosteroids yield improved outcomes in CAP patients with regard to reduced mortality and the need for mechanical ventilation. It highlights the need for further large-scale RCTs with the proposed, specific stratifications.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction

BACKGROUND

There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking.

METHODS

A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed.

RESULTS

A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis.

CONCLUSION

Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

Investigation of the relationship between coagulation parameters and mortality in COVID-19 infection

This study, which included patients over the age of 18 who were diagnosed with coronavirus disease 2019 (COVID-19) in the emergency clinic, aims to determine the relationship between coagulation parameters and mortality. Epidemiologic data such as age, gender, medical history, vital parameters at emergency department admission, clinical findings, coagulation parameters such as d-dimer, prothrombin time (PT), active partial thromboplastin time (aPTT), international normalized ration (INR), fibrinogen, and platelet were evaluated. Patients with positive computerized tomography (CT) findings and positive polymerase chain reaction (PCR) together were included in the study. It was revealed that d-dimer, fibrinogen, INR, and PT values were higher in the elderly group. It was shown that there was a significant relationship between hospitalization days (ward or intensive care unit) and d-dimer levels. It was observed that d-dimer, fibrinogen elevation was significantly associated with prognosis by increasing mortality, and that platelet and aPTT values were also associated with prognosis and were lower in the mortality group. On the other hand, in receiver operating characteristic (ROC) analysis, the sensitivity and specificity data were 80.3%/80.0% for d-dimer, 70.5%/72.2% for fibrinogen, 58.2%/59.4% for aPTT, and 59.7%/59.2% for platelet, respectively. The overall classification success was 88.6% and mortality prediction success was 37.7% in the regression model of some coagulation parameters (d-dimer, fibrinogen, aPTT, and platelet) which were effective on prognosis. In conclusion, it was determined that d-dimer, fibrinogen, aPTT, and platelet parameters were directly associated with mortality and when these coagulation parameters were used together with the clinical, vital, and demographic data of the patients, the success of mortality prediction increased significantly.

Predicting COVID-19 prognosis in hospitalized patients based on early status

IMPORTANCE

COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help efficiently allocate resources, including ventilators and intensive care unit beds, particularly when hospital systems are strained. Our PLABAC and PRABLE models are unique because they accurately assess a COVID-19 patient's risk of death from only age and five commonly ordered laboratory tests. This simple design is important because it allows these models to be used by clinicians to rapidly assess a patient's risk of decompensation and serve as a real-time aid when discussing difficult, life-altering decisions for patients. Our models have also shown generalizability to external populations across the United States. In short, these models are practical, efficient tools to assess and communicate COVID-19 prognosis.

Treatment effect of remdesivir on the mortality of hospitalised COVID-19 patients in Switzerland across different patient groups: a tree-based model analysis

AIMS OF THE STUDY

Remdesivir has shown benefits against COVID-19. However, it remains unclear whether, to what extent, and among whom remdesivir can reduce COVID-19-related mortality. We explored whether the treatment response to remdesivir differed by patient characteristics.

METHODS

We analysed data collected from a hospital surveillance study conducted in 21 referral hospitals in Switzerland between 2020 and 2022. We applied model-based recursive partitioning to group patients by the association between treatment levels and mortality. We included either treatment (levels: none, remdesivir within 7 days of symptom onset, remdesivir after 7 days, or another treatment), age and sex, or treatment only as regression variables. Candidate partitioning variables included a range of risk factors and comorbidities (and age and sex unless included in regression). We repeated the analyses using local centring to correct the results for the propensity to receive treatment.

RESULTS

Overall (n = 21,790 patients), remdesivir within 7 days was associated with increased mortality (adjusted hazard ratios 1.28-1.54 versus no treatment). The CURB-65 score caused the most instability in the regression parameters of the model. When adjusted for age and sex, patients receiving remdesivir within 7 days of onset had higher mortality than those not treated in all identified eight patient groups. When age and sex were included as partitioning variables instead, the number of groups increased to 19-20; in five to six of those branches, mortality was lower among patients who received early remdesivir. Factors determining the groups where remdesivir was potentially beneficial included the presence of oncological comorbidities, male sex, and high age.

CONCLUSIONS

Some subgroups of patients, such as individuals with oncological comorbidities or elderly males, may benefit from remdesivir.

Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

The mortality of COVID-19 patients has left the world devastated. Many scoring systems have been developed to predict the mortality of COVID-19 patients, but several scoring components cannot be carried out in limited health facilities. Herein, the authors attempted to create a new and easy scoring system involving mean arterial pressure (MAP), PF Ratio, or SF ratio-respiration rate (SF Ratio-R), and lymphocyte absolute, which were abbreviated as MPL or MSLR functioning, as a predictive scoring system for mortality within 30 days for COVID-19 patients. Of 132 patients with COVID-19 hospitalized between March and November 2021, we followed up on 96 patients. We present bivariate and multivariate analyses as well as the area under the curve (AUC) and Kaplan-Meier charts. From 96 patients, we obtained an MPL score of 3 points: MAP < 75 mmHg, PF Ratio < 200, and lymphocyte absolute < 1500/µL, whereas the MSLR score was 6 points: MAP < 75 mmHg, SF Ratio < 200, lymphocyte absolute < 1500/µL, and respiration rate 24/min. The MPL cut-off point is 2, while the MSLR is 4. MPL and MSLR have the same sensitivity (79.1%) and specificity (75.5%). The AUC value of MPL vs. MSLR was 0.802 vs. 0.807. The MPL ≥ 2 and MSLR ≥ 4 revealed similar predictions for survival within 30 days (p < 0.05). Conclusion: MPL and MSLR scores are potential predictors of mortality in COVID-19 patients within 30 days in a resource-limited country.

The Battle of the Pneumonia Predictors: A Comprehensive Meta-Analysis Comparing the Pneumonia Severity Index (PSI) and the CURB-65 Score in Predicting Mortality and the Need for ICU Support

The CURB-65 (confusion, uremia, respiratory rate, blood pressure, age ≥ 65 years) score and the pneumonia severity index (PSI) are widely used and recommended in predicting 30-day mortality and the need for intensive care support in community-acquired pneumonia. This study aims to compare the performance of these two severity scores in both mortality prediction and the need for intensive care support. A systematic review and meta-analysis was carried out, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, and PubMed, Scopus, ScienceDirect, and Google Scholar were searched for articles published from 2012 to 2022. The reference lists of the included studies were also searched to retrieve possible additional studies. Twenty-five studies reporting prognostic information for CURB 65 and PSI were identified. ReviewManager (RevMan) 5.4.1 was used to produce risk ratios, and a random effects model was used to pool them. Both PSI and CURB-65 showed a high strength in identifying high-risk patients. However, CURB-65 was slightly better in early mortality prediction and had more sensitivity (96.7%) and specificity (89.3%) in predicting admission to intensive care support. Thus, CURB-65 seems to be the preferred tool in predicting mortality and the need for admission into intensive care support.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Background

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.

Methods

Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.

Results

The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.

Conclusion

Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

Neutrophil/Lymphocyte Ratio (NLR) and Lymphocyte/Monocyte Ratio (LMR) - Risk of Death Inflammatory Biomarkers in Patients with COVID-19

Aim

The aim of our retrospective study was search for new prognostic parameters, which can help quickly and cheaply identify patients with risk for severe course of SARS-CoV-2 infection.

Materials and Methods

The following peripheral blood combination biomarkers were calculated: NLR (neutrophil/lymphocytes ratio), LMR (lymphocyte/monocyte ratio), PLR (platelet/lymphocyte ratio), dNLR (neutrophils/(white blood cells - neutrophils)), NLPR (neutrophil/(lymphocyte × platelet ratio)) in 374 patients who were admitted to the Temporary Hospital no 2 of Clinical Hospital in Bialystok (Poland) with COVID-19. The patients were divided into four groups depending on the severity of the course of COVID-19 using MEWS classification.

Results

The NLR and dNLR were significantly increased with the severity of COVID-19, according to MEWS score. The AUC for the assessed parameters was higher in predicting death in patients with COVID-19: NLR (0.656, p=0.0018, cut-off=6.22), dNLR (0.615, p=0.02, cut-off=3.52) and LMR (0.609, p=0.03, cut-off=2.06). Multivariate COX regression analysis showed that NLR median above 5.56 (OR: 1.050, P=0.002), LMR median below 2.23 (OR: 1.021, P=0.011), and age >75 years old (OR: 1.072, P=0.000) had a significant association with high risk of death during COVID-19.

Conclusion

Our results indicate that NLR, dNLR, and LMR calculated on admission to the hospital can quickly and easy identify patients with risk of a more severe course of COVID-19. Increase NLR and decrease LMR have a significant predictive value in COVID-19 patient's mortality and might be a potential biomarker for predicting death in COVID-19 patients.

Validation of the Barthel Index as a Predictor of In-Hospital Mortality among COVID-19 Patients

In order to predict the high mortality due to COVID-19, simple, useful and remote instruments are required. To assess the validity of the baseline Barthel Index score as a predictor of in-hospital mortality among COVID-19 patients, a validation study of a clinical prediction tool in a cohort of patients with COVID-19 was conducted. The primary variable was mortality and the Barthel Index was the main explanatory variable. Demographic, clinical and laboratory variables were collected. Other mortality predictor scores were also assessed: Pneumonia Severity Index, CURB-65 and A-DROP. The Receiver Operating Characteristic Area under the Curve (ROC AUC), sensitivity and specificity were calculated for both the Barthel Index and the other predictor scores. An analysis of the association between the main variables was conducted, adjusting by means of three multivariate models. Three hundred and twelve patients were studied. Mortality was 16.4%. A mortality Odds Ratio (OR) of 5.95 was associated with patients with a Barthel Index ≤ 90. The model number 3 was developed to predict in-hospital mortality before COVID-19 infection occurs. It exhibits an OR of 3.44, a ROC AUC of 0.792, a sensitivity of 74.5% and a specificity of 73.9%. The Baseline Barthel Index proved useful in our population as a predictor of in-hospital mortality due to COVID-19.

Effectiveness of Zingiber officinale to reduce inflammation markers and the length of stay of patients with community-acquired pneumonia: An open-label clinical trial

Examination of the interleukin 6 (IL-6) and procalcitonin levels, and neutrophil-lymphocyte ratio (NLR) might could help to diagnosis and predict the duration of therapy and prognosis of pneumonia cases. Zingiber officinale var rubrum could be used as an adjunct therapy in infectious diseases as it has anti-inflammatory activity. The aim of study was to assess the effect of Z. officinale on levels of IL-6 dan procalcitonin, NLR, and the length of hospitalization of patients with community-acquired pneumonia (CAP). An open-label clinical trial was conducted among CAP cases regardless of the etiology at Dr Moewardi Hospital and Universitas Sebelas Maret Hospital, Surakarta, Indonesia from July to September 2022. A total of 30 inpatient CAP cases were recruited and were randomly divided into two groups: (1) received Z. officinale capsule 300 mg daily for five days in addition to CAP standard therapy; and (2) received CAP standard therapy only, as control group. The data were compared using a paired Student t-test, Chi-squared test, Mann-Whitney test and Wilcoxon signed-rank test as appropriate. In Z. officinale group, the mean difference between post-and pre-treatment as follow: IL-6 level was 9.93 pg/mL, procalcitonin level -471.31 ng/mL, and NLR value -4.01. In control group, the difference was 18.94 pg/mL for IL-6, 339.39 ng/mL for procalcitonin, and 1.56 for NLR. The change of IL-6 was not statistically significant between treatment and control groups with p=0.917. The changes of procalcitonin level and NLR were significant between treatment and control group with p=0.024 and p=0.007, respectively, of which the treatment had better improvement. In addition, our data indicated that the length of stay was not statistically significant between the treatment and control groups (4.13 vs 4.47 days, p=0.361). In conclusion, Z. officinale could reduce serum inflammatory markers such as procalcitonin and NLR but it has little impact in reducing IL-16 level and the length of hospitalization of CAP patients.

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.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Severity of Illness Scores and Biomarkers for Prognosis of Patients with Coronavirus Disease 2019

The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

Predictors of Mortality in COVID-19 patients: An observational study

Objectives

To identify the factors that affect outcome in COVID-19 patients in the Pakistani population.

Methods

A total of 225 patients of COVID-19 RT-PCR proven were included during November, 2020 to June, 2021 in this cross-sectional study. They were stratified into different disease severity categories as per WHO guidelines. The characteristics of survivors and non survivors were recorded and then compared to draw conclusions.

Results

Mean age was 59 years. Majority of the patients were male (68%) and the overall mortality rate was 30.1%. The non survivors were more likely to be female, had a greater number of comorbidities, had a higher respiratory rate and lower oxygen saturations at presentation and had a greater frequency of invasive mechanical ventilation. Non survivors had higher values of TLC, CRP, D-dimers and lower values of Hemoglobin and Platelets. The non survivors had higher incidence of ARDS, Septic shock and Multiorgan involvement. A higher CURB-65 score was observed in non survivors as compared to those who survived. Multivariate analysis showed that female gender, presence of and higher number of comorbid conditions and a higher CURB-65 score was linked with mortality.

Conclusion

Results are compatible with international studies; increasing age, number of comorbid conditions and high inflammatory markers are associated with increased mortality. Our study had an exception that female gender had higher mortality as compared to men.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg’s analysis (p = 0.835) and Egger’s analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Comparison Between Physiological Scores SIPF, CURB-65, and APACHE II as Predictors of Prognosis and Mortality in Hospitalized Patients with COVID-19 Pneumonia: A Multicenter Study, Saudi Arabia

Background

A coronavirus pandemic (COVID-19) is associated with catastrophic effects on the world with high morbidity and mortality. We aimed to evaluate the accuracy of physiological shock index (SIPF) (shock index and hypoxemia), CURB -65, acute physiology, and chronic health assessment II (APACHE II) as predictors of prognosis and in-hospital mortality in patients with COVID-19 pneumonia.

Methods

In Saudi Arabia, a multicenter retrospective study was conducted on hospitalized adult patients confirmed to have COVID-19 pneumonia. Information needed to calculate SIPF, CURB-65, and APACHE II scores were obtained from medical records within 24 hours of admission.

Results

The study included 1131 COVID-19 patients who met the inclusion criteria. They were divided into two groups: (A) the ICU group (n=340; 30.1%) and (B) the ward group (n=791; 69.9%). The most common concomitant diseases of patients at initial ICU admission were hypertension (71.5%) and diabetes (62.4%), and most of them were men (63.8%). The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001). The SIPF score showed a significantly higher ability to predict both ICU admission and mortality in patients with COVID-19 pneumonia compared with APACHE II and CURB -65; (AUC 0.89 vs 0.87; p < 0.001) and (AUC 0.89 vs 0.84; p < 0.001) for ICU admission and (AUC 0.90 vs 0.65; p < 0.001) and (AUC 0.90 vs 0.80; p < 0.001) for mortality, respectively.

Conclusion

The ability of the SIPF score to predict ICU admission and mortality in COVID-19 pneumonia is higher than that of APACHE II and CURB-65. The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001).

Comparison of CURB-65 Pneumonia Severity Score, Quick COVID-19 Severity Index, and Brescia-COVID Respiratory Severity Scale in Emergently Hospitalized COVID-19 Patients with Pneumonia

Objective

This study aimed to assess the performance of the CURB-65, the quick COVID-19 severity index (qCSI), and the Brescia-COVID respiratory severity scale (BCRSS) scores in predicting ICU (intensive care unit) hospitalization and in-hospital mortality in emergently hospitalized patients with COVID-19 pneumonia.

Materials and Methods

We retrospectively reviewed the emergently hospitalized 258 patients with COVID-19 pneumonia consecutively. The required sample size was calculated to compare the areas under the two ROC (receiver operating characteristic) curves (AUC) using the MedCalc 20.0 program (MedCalc Software Ltd., Ostend, Belgium). In addition, we actualized ROC analyses of the CURB-65, the qCSI, and the BCRSS scores and compared the ROC curves of these three scores.

Results

The median age of the patients was 73, and 63.6% (n=164) were male. Of 258 patients, 29.5% (n=76) were hospitalized in the intensive care unit (ICU), and 15.9% (n=41) died. The CURB-65 and the qCSI scores predicted ICU admission at a moderate level (p≤0.001; AUC values were 0.743 and 0.723, respectively). However, the predictive effect of the BCRSS score for ICU admission was lower (p≤0.001; AUC value was 0.667). The CURB-65 predicted in-hospital mortality at a moderate level ( p≤0.001; AUC value was 0.762). However, the predictive effect of the qCSI and the BCRSS scores for in-hospital mortality were lower ( p≤0.001 and p=0.012, respectively; AUC values were 0.655 and 0.612, respectively).

Conclusion

The CURB-65 score predicted ICU hospitalization and in-hospital mortality better than the qCSI and the BCRSS scores. Also, the qCSI score predicted ICU admission better than the BCRSS score.The predictive effect of the BCRSS score was the lowest. We recommend future studies to evaluate the value and utility of COVID-19 risk classification models.

Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations

Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.

External Validation of COVID-19 Risk Scores during Three Waves of Pandemic in a German Cohort-A Retrospective Study

Several risk scores were developed during the COVID-19 pandemic to identify patients at risk for critical illness as a basic step to personalizing medicine even in pandemic circumstances. However, the generalizability of these scores with regard to different populations, clinical settings, healthcare systems, and new epidemiological circumstances is unknown. The aim of our study was to compare the predictive validity of qSOFA, CRB65, NEWS, COVID-GRAM, and 4C-Mortality score. In a monocentric retrospective cohort, consecutively hospitalized adults with COVID-19 from February 2020 to June 2021 were included; risk scores at admission were calculated. The area under the receiver operating characteristic curve and the area under the precision-recall curve were compared using DeLong's method and a bootstrapping approach. A total of 347 patients were included; 23.6% were admitted to the ICU, and 9.2% died in a hospital. NEWS and 4C-Score performed best for the outcomes ICU admission and in-hospital mortality. The easy-to-use bedside score NEWS has proven to identify patients at risk for critical illness, whereas the more complex COVID-19-specific scores 4C and COVID-GRAM were not superior. Decreasing mortality and ICU-admission rates affected the discriminatory ability of all scores. A further evaluation of risk assessment is needed in view of new and rapidly changing epidemiological evolution.

Frontline workers: Mediators of mucosal immunity in community acquired pneumonia and COVID-19

The current COVID-19 pandemic has highlighted a need to further understand lung mucosal immunity to reduce the burden of community acquired pneumonia, including that caused by the SARS-CoV-2 virus. Local mucosal immunity provides the first line of defence against respiratory pathogens, however very little is known about the mechanisms involved, with a majority of literature on respiratory infections based on the examination of peripheral blood. The mortality for severe community acquired pneumonia has been rising annually, even prior to the current pandemic, highlighting a significant need to increase knowledge, understanding and research in this field. In this review we profile key mediators of lung mucosal immunity, the dysfunction that occurs in the diseased lung microenvironment including the imbalance of inflammatory mediators and dysbiosis of the local microbiome. A greater understanding of lung tissue-based immunity may lead to improved diagnostic and prognostic procedures and novel treatment strategies aimed at reducing the disease burden of community acquired pneumonia, avoiding the systemic manifestations of infection and excess morbidity and mortality.

Autoimmune and immunoserological markers of COVID-19 pneumonia: Can they help in the assessment of disease severity

Background

Immune dysregulation and associated inefficient anti-viral immunity during Coronavirus Disease 2019 (COVID-19) can cause tissue and organ damage which shares many similarities with pathogenetic processes in systemic autoimmune diseases. In this study, we investigate wide range autoimmune and immunoserological markers in hospitalized patients with COVID-19.

Methods

Study included 51 patients with confirmed Severe Acute Respiratory Syndrome Coronavirus 2 infection and hospitalized due to COVID-19 pneumonia. Wide spectrum autoantibodies associated with different autoimmune inflammatory rheumatic diseases were analyzed and correlated with clinical and laboratory features and pneumonia severity.

Results

Antinuclear antibodies (ANA) positivity was found in 19.6%, anti-cardiolipin IgG antibodies (aCL IgG) in 15.7%, and anti-cardiolipin IgM antibodies (aCL IgM) in 7.8% of patients. Positive atypical x anti-neutrophil cytoplasmic antibodies (xANCA) were detected in 10.0% (all negative for Proteinase 3 and Myeloperoxidase) and rheumatoid factor was found in 8.2% of patients. None of tested autoantibodies were associated with disease or pneumonia severity, except for aCL IgG being significantly associated with higher pneumonia severity index (p = 0.036). Patients with reduced total serum IgG were more likely to require non-invasive mechanical ventilation (NIMV) (p < 0.0001). Serum concentrations of IgG (p = 0.003) and IgA (p = 0.032) were significantly lower in this group of patients. Higher total serum IgA (p = 0.009) was associated with mortality, with no difference in serum IgG (p = 0.115) or IgM (p = 0.175). Lethal outcome was associated with lower complement C4 (p = 0.013), while there was no difference in complement C3 concentration (p = 0.135).

Conclusion

Increased autoimmune responses are present in moderate and severe COVID-19. Severe pneumonia is associated with the presence of aCL IgG, suggesting their role in disease pathogenesis. Evaluation of serum immunoglobulins and complement concentration could help assess the risk of non-invasive mechanical ventilation NIMV and poor outcome.

The relevance of chest X-ray radiologic severity index and CURB-65 score with the death event in hospitalized patient with COVID-19 pneumonia

Background

The global pandemic respiratory infection COVID-19 has had a high mortality rate since early 2020 with a broad spectrum of symptoms and giving a high burden. This study used the chest X-ray radiologic severity index method to radiologically assess the degree of lung lesions and the CURB-65 score to clinically assess COVID-19 pneumonia patients associated with the incidence of death in hospitalized patients.

Results

The research data were carried out from March 2020 to April 2021 based on patient medical records and chest X-rays at Doctor Kariadi General Hospital Semarang Indonesia. One hundred and five subjects were collected that fulfilled the inclusion and exclusion criteria. The CURB-65 score ≥ 2 had a significant relationship to the death event with a prevalence interval of 2.98 (95% CI, p = 0.000). The radiologic severity index ≥ 22.5 in initial chest X-ray signified a prevalence ratio of 2.24 (CI 95%, p = 0.004) and the radiologic severity index ≥ 29.5 in the second chest X-ray signified a prevalence ratio of 4.53 for the incidence of death (95% CI, p = 0.000). The combination of CURB-65 and the first chest X-ray radiologic severity index resulted in a prevalence ratio of 27.44, and the combination of CURB-65 and the second chest X-ray radiologic severity index resulted in a prevalence ratio of 60.2 which were significant for the mortality of hospitalized COVID-19 pneumonia patients.

Conclusions

Chest X-ray radiologic severity index and CURB-65 score have a significant relevance with the death event in hospitalized patients with COVID-19 pneumonia and can thus be used as a predictor of mortality.

External validation of risk scores to predict in-hospital mortality in patients hospitalized due to coronavirus disease 2019

BACKGROUND

The coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Prediction models that accurately estimate mortality risk in hospitalized patients could assist medical staff in treatment and allocating limited resources.

AIMS

To externally validate two promising previously published risk scores that predict in-hospital mortality among hospitalized COVID-19 patients.

METHODS

Two prospective cohorts were available; a cohort of 1028 patients admitted to one of nine hospitals in Lombardy, Italy (the Lombardy cohort) and a cohort of 432 patients admitted to a hospital in Leiden, the Netherlands (the Leiden cohort). The endpoint was in-hospital mortality. All patients were adult and tested COVID-19 PCR-positive. Model discrimination and calibration were assessed.

RESULTS

The C-statistic of the 4C mortality score was good in the Lombardy cohort (0.85, 95CI: 0.82-0.89) and in the Leiden cohort (0.87, 95CI: 0.80-0.94). Model calibration was acceptable in the Lombardy cohort but poor in the Leiden cohort due to the model systematically overpredicting the mortality risk for all patients. The C-statistic of the CURB-65 score was good in the Lombardy cohort (0.80, 95CI: 0.75-0.85) and in the Leiden cohort (0.82, 95CI: 0.76-0.88). The mortality rate in the CURB-65 development cohort was much lower than the mortality rate in the Lombardy cohort. A similar but less pronounced trend was found for patients in the Leiden cohort.

CONCLUSION

Although performances did not differ greatly, the 4C mortality score showed the best performance. However, because of quickly changing circumstances, model recalibration may be necessary before using the 4C mortality score.

Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.

Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates

Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19.

Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences.

Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 > 2 led to a "number needed to (NNT) diagnose" that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model.

Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making.

The role of a noninvasive index 'Spo2/ Fio2' in predicting mortality among patients with COVID-19 pneumonia

INTRODUCTION

Noninvasive risk assessment is crucial in patients with COVID-19 in emergency department. Since limited data is known about the role of noninvasive parameters, we aimed to evaluate the role of a noninvasive parameter 'SpO2/FiO2' in independently predicting 30-day mortality in patients with COVID-19 and its prognostic utility in combination with a noninvasive score 'CRB-65'.

METHODS

A retrospective study was performed in a tertiary training and research hospital, which included 272 patients with COVID-19 pneumonia diagnosed with polymerase chain reaction in emergency department. Data on characteristics, vital signs, and laboratory parameters were recorded from electronic medical records. The primary outcome of the study was 30-day mortality, and we assessed the discriminative ability of SpO2/FiO2 in predicting mortality in patients with COVID-19 pneumonia and its prognostic utility in combination with conventional pneumonia risk assessment scores.

RESULTS

Multivariate analysis revealed that only SpO2/FiO2 level was found to be an independent parameter associated with 30-day mortality (OR:0.98, 95% CI: 0.98-0.99, p = 0.003). PSI and CURB-65 were found to be better scores than CRB-65 in predicting 30-day mortality (AUC: 0.79 vs 0.72, p = 0.04; AUC: 0.76 vs 0.72, p = 0.01 respectively). Both SpO2/FiO2 combined with CRB-65 and SpO2/FiO2 combined with CURB-65 have good discriminative ability and seemed to be more favorable than PSI in predicting 30-days mortality (AUC: 0.83 vs 0.75; AUC: 0.84 vs 0.75), however no significant difference was found (p = 0.21 and p = 0.06, respectively).

CONCLUSION

SpO2/FiO2 is a promising index in predicting mortality. Addition of SpO2/FiO2 to CRB-65 improved the role of CRB-65 alone, however it performed similar to PSI. The combined noninvasive model of SpO2/FiO2 and CRB-65 may help physicians quickly stratify COVID-19 patients on admission, which is expected to be particularly important in hospitals still stressed by pandemic volumes.

Patient experience and healthcare utilization for a COVID-19 telemedicine home monitoring program offered in English and Spanish

Background

Telemedicine is a vital component of the healthcare system’s response to COVID-19. In March of 2020, Providence health system rapidly implemented a telemedicine home monitoring program (HMP) for COVID-19 patients that included use of at-home pulse oximeters and thermometers and text-based surveys to monitor symptoms. By June 2020, Providence updated the HMP to be offered in Spanish. This program was implemented before COVID-19 testing was readily available and therefore was offered to all patients suspected of having COVID-19. This study examines engagement, experience, and utilization patterns for English and Spanish-speaking patients engaged in the COVID-19 HMP.

Methods

A retrospective review of program data was used to understand HMP patient engagement (responsiveness to three daily text to monitor symptoms), satisfaction with the program (likelihood to recommend the program) as well as comfort using home monitoring devices and comfort recovering from home. To understand impact on care for COVID-19 confirmed cases, we used electronic health records to measure patterns in healthcare use for COVID-19 positive HMP participants and non-HMP propensity weighted controls. All patients enrolled in the COVID-19 HMP from March–October 2020 were included in the study. Patients tested for COVID-19 during the time window and not enrolled in HMP were included in the propensity-weighted comparison group. Descriptive and regression analyses were performed overall and stratified by English and Spanish speakers.

Results

Of the 4,358 HMP participants, 75.5% identified as English speakers and 18.2% identified as Spanish speakers. There was high level of responsiveness to three daily text-based surveys monitoring symptoms engagement (>80%) and a high level of comfort using the home monitoring devices (thermometers and pulse oximeters) for English- and Spanish-speaking participants (97.3% and 99.6%, respectively). The majority of English (95.7%) and Spanish-speaking (100%) patients felt safe monitoring their condition from home and had high satisfaction with the HMP (76.5% and 83.6%, respectively). English and Spanish-speaking COVID-19 positive HMP participants had more outpatient and emergency departments (ED) encounters than non-participants 7 and 30 days after their positive test.

Conclusion

This widely implemented HMP provided participants with a sense of safety and satisfaction and its use was associated with more outpatient care and ED encounters. These outcomes were comparable across English and Spanish-speakers, highlighting the importance and potential impact of language-concordant telemedicine.

Development of COVID-19 severity assessment score in adults presenting with COVID-19 to the emergency department

BACKGROUND

Critically-ill Covid-19 patients require extensive resources which can overburden a healthcare system already under strain due to a pandemic. A good disease severity prediction score can help allocate resources to where they are needed most.

OBJECTIVES

We developed a Covid-19 Severity Assessment Score (CoSAS) to predict those patients likely to suffer from mortalities within 28 days of hospital admission. We also compared this score to Quick Sequential Organ Failure Assessment (qSOFA) in adults.

METHODS

CoSAS includes the following 10 components: Age, gender, Clinical Frailty Score, number of comorbidities, Ferritin level, D-dimer level, neutrophil/lymphocyte ratio, C-reactive Protein levels, systolic blood pressure and oxygen saturation. Our study was a single center study with data collected via chart review and phone calls. 309 patients were included in the study.

RESULTS

CoSAS proved to be a good score to predict Covid-19 mortality with an Area under the Curve (AUC) of 0.78. It also proved better than qSOFA (AUC of 0.70). More studies are needed to externally validate CoSAS.

CONCLUSION

CoSAS is an accurate score to predict Covid-19 mortality in the Pakistani population.

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\textsc {TransMED}}$$\end{document}TRANSMED) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\textsc {TransMED}}$$\end{document}TRANSMED’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\textsc {TransMED}}$$\end{document}TRANSMED’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

Clinical analysis on diagnostic accuracy of Bosch Vivalytic SARS-CoV-2 point-of-care test and evaluation of cycle threshold at admission for COVID-19 risk assessment

BACKGROUND

Point-of-care (POC) polymerase chain reaction (PCR) tests have the ability to improve testing efficiency in the Coronavirus disease 2019 (COVID-19) pandemic. However, real-world data on POC tests is scarce.

OBJECTIVE

To evaluate the efficiency of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) POC test in a clinical setting and examine the prognostic value of cycle threshold (CT) on admission on the length of hospital stay (LOS) in COVID-19 patients.

METHODS

Patients hospitalised between January and May 2021 were included in this prospective cohort study. Patients' nasopharyngeal swabs were tested for SARS-CoV-2 with Allplex™2019-nCoV (Seegene Inc.) real-time (RT) PCR assay as gold standard as well as a novel POC test (Bosch Vivalytic SARS-CoV-2 [Bosch]) and the SARS-CoV-2 Rapid Antigen Test (Roche) accordingly. Clinical sensitivity and specificity as well as inter- and intra-assay variability were analyzed.

RESULTS

120 patients met the inclusion criteria with 46 (38%) having a definite COVID-19 diagnosis by RT-PCR. Bosch Vivalytic SARS-CoV-2 POC had a sensitivity of 88% and specificity of 96%. The inter- and intra- assay variability was below 15%. The CT value at baseline was lower in patients with LOS ≥ 10 days when compared to patients with LOS < 10 days (27.82 (± 4.648) vs. 36.2 (25.9-39.18); p = 0.0191). There was a negative correlation of CT at admission and LOS (r[44]s = - 0.31; p = 0.038) but only age was associated with the probability of an increased LOS in a multiple logistic regression analysis (OR 1.105 [95% CI, 1.03-1.19]; p = 0.006).

CONCLUSION

Our data indicate that POC testing with Bosch Vivalytic SARS-CoV-2 is a valid strategy to identify COVID-19 patients and decrease turnaround time to definite COVID-19 diagnosis. Also, our data suggest that age at admission possibly with CT value as a combined parameter could be a promising tool for risk assessment of increased length of hospital stay and severity of disease in COVID-19 patients.

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.

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer–Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer–Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.

Registration: ClinicalTrials.gov Identifier: NCT04463706.

Development and Validation of the RCOS Prognostic Index: A Bedside Multivariable Logistic Regression Model to Predict Hypoxaemia or Death in Patients with SARS-CoV-2 Infection

Introduction

Previous COVID-19 prognostic models have been developed in hospital settings and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis.

Methods

The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death.

Results

4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count, and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892–0.922), and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where the admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925–0.97) and the Brier score was 0.0188.

Conclusions

The RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource-limited settings with poor healthcare infrastructure such as India. However, implementation in other settings is needed to cross-validate and verify our findings.

Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people’s lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.

Novel biomarker-based score (SAD-60) for predicting mortality in patients with COVID-19 pneumonia: a multicenter retrospective cohort of 1013 patients

Background: The aim was to explore a novel risk score to predict mortality in hospitalized patients with COVID-19 pneumonia. Methods: This was a retrospective, multicenter study. Results: A total of 1013 patients with COVID-19 were included. The mean age was 60.5 ± 14.4 years, and 581 (57.4%) patients were male. In-hospital death occurred in 124 (12.2%) patients. Multivariate analysis revealed peripheral capillary oxygen saturation (SpO2), albumin, D-dimer and age as independent predictors. The mortality score model was given the acronym SAD-60, representing SpO2, Albumin, D-dimer, age ≥60 years. The SAD-60 score (0.776) had the highest area under the curve compared with CURB-65 (0.753), NEWS2 (0.686) and qSOFA (0.628) scores. Conclusion: The SAD-60 score has a promising predictive capacity for mortality in hospitalized patients with COVID-19.

Neurological Prognostic Factors in Hospitalized Patients with COVID-19

We aimed to search whether neurological symptoms or signs (NSS) and the MEWS (Modified Early Warning Score) score were associated with in-hospital mortality or oxygen requirement during the first 14 days of hospitalization in COVID-19 patients recruited at the University Hospital in Krakow, Poland. The detailed clinical questionnaires on twenty NSS were either filled out by patients prospectively or retrospectively assessed by neurologists based on daily medical records. NSS were considered high or low-risk if they were associated with increased or decreased mortality in the univariable analysis. This cohort study included 349 patients with COVID-19 (median age 64, interquartile range (51–77), women 54.72%). The presence of high-risk NSS (decreased level of consciousness, delirium, seizures, and symptoms of stroke or transient ischemic attack) or its combination with the absence of low-risk NSS (headache, dizziness, decreased mood, and fatigue) increased the risk of in-hospital mortality in SARS-CoV-2 infection 3.13 and 7.67-fold, respectively. The presence of low-risk NSS decreased the risk of in-hospital mortality in COVID-19 patients more than 6-fold. Death in patients with SARS-CoV-2 infection, apart from NSS, was predicted by older age, neoplasm, and higher MEWS scores on admission. High-risk NSS or their combination with the absence of low-risk NSS increased the risk of oxygen requirement during hospitalization in COVID-19 patients 4.48 and 1.86-fold, respectively. Independent predictors of oxygen therapy during hospitalization in patients with SARS-CoV-2 infection were also older age, male sex, neoplasm, and higher MEWS score on admission.

CURB-65 as a predictor of 30-day mortality in patients hospitalized with COVID-19 in Ecuador: COVID-EC study

Objective

This article aims to assess the utility of CURB-65 in predicting 30-day mortality in adult patients hospitalized with COVID-19.

Methods

This work is a cohort study conducted between March 1 and April 30, 2020 in Ecuador.

Results

A total of 247 patients were included (mean age 60 ± 14 years, 70% men, overall mortality 41.3%). Patients with CURB-65 ≥ 2 had a higher mortality rate (57 vs. 17%, p < .001) that was associated with other markers of risk: advanced age, hypertension, overweight/obesity, kidney failure, hypoxemia, requirement for mechanical ventilation, or onset of respiratory distress.

Conclusions

CURB-65  ≥ 2 was associated with higher 30-day mortality on the univariate (Kaplan–Meier estimator) and multivariate (Cox regression) analysis.

Clinical characteristics of COVID-19 in young patients differ from middle-aged and elderly patients

INTRODUCTION

Coronavirus disease-2019 (COVID-19) has spread worldwide. The study aimed to understand the clinical characteristics of young COVID-19 patients.

MATERIAL AND METHODS

Ninety patients with severe COVID-19 infection in western Chongqing were collected from 21 January to 14 March 2020. They were divided into 4 groups based on age: youth (< 39 years), middle-aged (39-48 years), middle-elderly aged (49-60 years), and elderly (> 60 years). The clinical symptoms, laboratory findings, imaging findings, and treatment effects were compared among the groups.

RESULTS

There were 22, 27, 19, and 22 cases in the youth, middle-aged, middle-elderly, and elderly groups, respectively. There were no significant differences with respect to gender or smoking status among the four groups. The clinical indicators of severe disease in the youth group were significantly different from the other three groups, and included the lymphocyte count (p < 0.001), C-reactive protein level (p = 0.03), interleukin-6 level (p = 0.01), chest computed tomography (CT) findings (p < 0.001), number of mild cases (p = 0.02), education level (p < 0.001), and CD4 + T lymphocyte level (p = 0.02) at the time of admission, and the pneumonia severity index (PSI) at the time of discharge (p < 0.001). The complications (p < 0.001) among the youth group were also significantly different from the other groups.

CONCLUSIONS

Young patients have milder clinical manifestations, which may be related to higher education level, higher awareness and higher acceptance of the prevention and control of the COVID-19 epidemic, as well as their good immune function.

Simple Coagulation Profile as Predictor of Mortality in Adults Admitted with COVID-19: A Meta-Analysis

Context: COVID-19 severe manifestations must be detected as soon as possible. One of the essential poor characteristics is the involvement of coagulopathy. Simple coagulation parameters, including prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (aPTT), and platelet, are widely accessible in many health centers. Objectives: This meta-analysis aimed to determine the association between simple coagulation profiles and COVID-19 in-hospital mortality. Method: We systematically searched five databases for studies measuring simple coagulation parameters in COVID-19 on admission. The random-effects and inverse-variance weighting were used in the study, which used a standardized-mean difference of coagulation profile values. The odds ratios were computed using the Mantel-Haenszel formula for dichotomous variables. Results: This meta-analysis comprised a total of 30 studies (9,175 patients). In our meta-analysis, we found that non-survivors had a lower platelet count [SMD = -0.56 (95% CI: -0.79 to -0.33), P < 0.01; OR = 3.00 (95% CI: 1.66 to 5.41), P < 0.01], prolonged PT [SMD = 1.22 (95%CI: 0.71 to 1.72), P < 0.01; OR = 1.86 (95%CI: 1.43 to 2.43), P < 0.01], prolonged aPTT [SMD = 0.24 (95%CI: -0.04 to 0.52), P = 0.99], and increased INR [SMD = 2.21 (95%CI: 0.10 to 4.31), P = 0.04] than survivors. Conclusions: In COVID-19 patients, abnormal simple coagulation parameters on admission, such as platelet, PT, and INR, were associated with mortality outcomes.

A new predictor for indicating clinical severity and prognosis in COVID-19 patients: Frontal QRS-T angle

Objective

COVID-19; It spread rapidly around the world and led to a global pandemic. Indicators of poor prognosis are important in the treatment and follow-up of COVID-19 patients and have always been a matter of interest to researchers. The aim of this study was to investigate the relationship between frontal QRS-T angle values and clinical severity and prognosis in COVID-19 patients.

Methods

This prospective case-control study was conducted with 130 COVID-19 patients whose diagnosis was confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR) and 100 healthy controls. The CURB-65 score was used as the clinical severity score.

Results

A total of 130 patients and 100 healthy controls were included in the study. When the patient and control groups were compared a significant difference was found between QT (378.07 ± 33.75 vs. 368.63 ± 19.65, p < 0.001), QTc (410.79 ± 28.19 vs. 403.68 ± 11.70, p < 0.001), QRS time (95.04 ± 21.67 vs. 91.42 ± 11.08, p < 0.001) and frontal QRS-T angle (36.57 ± 22.86 vs. 22.72 ± 14.08, p < 0.001). According to clinical severity scoring, QT (370.27 ± 25.20 vs. 387.75 ± 40.19, p = 0.003), QTc (402.18 ± 19.92 vs. 421.48 ± 33.08, p < 0.001), frontal QRS-T angle (32.25 ± 18.79 vs. 41.94 ± 26.27), p = 0.0.16) parameters were found to be significantly different. Age (odds ratio [OR], 1.201; 95% confidence interval [CI], 1.111–1.298; p < 0.001) and frontal QRS-T angle ([OR], 1.045; 95% [CI], 1.015–1.075; p = 0.003) values were found to be an independent predictor for the severity of the disease. Frontal QRS-T angle ([OR], 1.101; 95% [CI], 1.030–1.176; p = 0.004), and CRP ([OR], 1.029; 95% [CI], 1.007–1.051; p = 0.01) parameters were found to be independent predictors for the mortality of the disease. As a mortality indicator; for the frontal QRS-T angle of ≥44.5°, specificity and sensitivity were 93.8% and 84.2%, respectively.

Conclusion

Frontal QRS-T angle can be used as a reproducible, convenient, inexpensive, new and powerful predictor in determining the clinical severity and prognosis of COVID-19 patients.

The Relationship between Routine Blood Parameters and the Prognosis of COVID-19 Patients in the Emergency Department

The aim of this study is to investigate the routine blood parameters of COVID-19 patients at the time of admission to the emergency department and their relationship with the severity of the disease and prognosis. A total of 500 patients, who were diagnosed with severe COVID-19 and hospitalized in the intensive care unit between 01.04.2020 and 01.02.2021 in the emergency department of a pandemic hospital, were retrospectively analyzed. Demographic, clinical, and laboratory data of the patients were obtained from the hospital registry system. Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) were calculated using neutrophil, lymphocyte, monocyte, and platelet counts. These patients were divided into two groups: survivors and deceased. All parameters obtained from routine blood analysis were statistically compared between these two groups. While 280 out of 500 patients survived, 220 died. Of all patients, the mean age was 67 years and 51.8% were males. There was a significant difference between the two groups in terms of age, gender, length of hospital stay, need for mechanical ventilation, white blood cell, neutrophil, lymphocyte, monocyte, eosinophil, platelet counts, CRP, ferritin, procalcitonin values, NLR, MLR, and PLR (p < 0.001 for all). While NLR alone and MLR + NEU and NLR + PLR + MLR combinations had the highest AUC values (0.930, 0.947, and 0.939, respectively), MLR and PLR alone showed the lowest AUC values (0.875 and 0.797, respectively). The sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) in the prediction of death according to the cutoff values of the parameters have been determined. A significant correlation was determined between age, NLR, MLR, and PLR and duration of hospital stay (p < 0.001 for all). Routine blood parameters and NLR, MLR, and PLR can assist emergency physicians to identify the severity and early prognosis of COVID-19 patients.

Clinical Management of Adult Patients with COVID-19 Outside Intensive Care Units: Guidelines from the Italian Society of Anti-Infective Therapy (SITA) and the Italian Society of Pulmonology (SIP)

INTRODUCTION

The Italian Society of Anti-Infective Therapy (SITA) and the Italian Society of Pulmonology (SIP) constituted an expert panel for developing evidence-based guidance for the clinical management of adult patients with coronavirus disease 2019 (COVID-19) outside intensive care units.

METHODS

Ten systematic literature searches were performed to answer ten different key questions. The retrieved evidence was graded according to the Grading of Recommendations Assessment, Development, and Evaluation methodology (GRADE).

RESULTS AND CONCLUSION

The literature searches mostly assessed the available evidence on the management of COVID-19 patients in terms of antiviral, anticoagulant, anti-inflammatory, immunomodulatory, and continuous positive airway pressure (CPAP)/non-invasive ventilation (NIV) treatment. Most evidence was deemed as of low certainty, and in some cases, recommendations could not be developed according to the GRADE system (best practice recommendations were provided in similar situations). The use of neutralizing monoclonal antibodies may be considered for outpatients at risk of disease progression. For inpatients, favorable recommendations were provided for anticoagulant prophylaxis and systemic steroids administration, although with low certainty of evidence. Favorable recommendations, with very low/low certainty of evidence, were also provided for, in specific situations, remdesivir, alone or in combination with baricitinib, and tocilizumab. The presence of many best practice recommendations testified to the need for further investigations by means of randomized controlled trials, whenever possible, with some possible future research directions stemming from the results of the ten systematic reviews.

Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study

INTRODUCTION

Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED).

METHODS

In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC).

RESULTS

We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models.

CONCLUSION

The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.

Impact of Asthma on Severity and Outcomes in COVID-19

BACKGROUND

We conducted this systematic review to evaluate whether asthma increases the risk of severe disease and adverse outcomes among subjects with COVID-19.

METHODS

We queried the PubMed and Embase databases for studies indexed through December 2020. We included studies providing data on severe disease, hospitalization, ICU care, need for mechanical ventilation, or mortality among subjects with COVID-19 with and without asthma. We calculated the relative risk for each reported outcome of interest and used random effects modeling to summarize the data.

RESULTS

We retrieved 1,832 citations, and included 90 studies, in our review. Most publications reported data retrieved from electronic records of retrospective subject cohorts. Only 25 studies were judged to be of high quality. Subjects with asthma and COVID-19 had a marginally higher risk of hospitalization (summary relative risk 1.13, 95% CI 1.03-1.24) but not for severe disease (summary relative risk 1.17, 95% CI 0.62-2.20), ICU admission (summary relative risk 1.13, 95% CI 0.96-1.32), mechanical ventilation (summary relative risk 1.05, 95% CI 0.85-1.29), or mortality (summary relative risk 0.92, 95% CI 0.82-1.04) as compared to subjects with COVID-19 without asthma.

CONCLUSIONS

Comorbid asthma increases risk of COVID-19-related hospitalization but not severe disease or other adverse outcomes in subjects with COVID-19.