Prognosis of COVID-19 pneumonia can be early predicted combining Age-adjusted Charlson Comorbidity Index, CRB score and baseline oxygen saturation

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

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.

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.

Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset

The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48-71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

Cytokine Levels and Severity of Illness Scoring Systems to Predict Mortality in COVID-19 Infection

Various scoring systems and cytokines have been cited as predicting disease severity in COVID-19 infection. This study analyzed the link between mortality rate, levels of cytokines, and scoring systems such as the Glasgow Coma Scale (GCS), Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and Charlson Comorbidity Index in patients infected with COVID-19. Adult patients infected with COVID-19 were followed up in the intensive care unit (ICU) and analyzed prospectively. We measured serum cytokine levels (Interleukin-10 (IL-10), Interleukin-8 (IL-8), Interleukin-6 (IL-6), Interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α) and High mobility group box 1 (HMGB-1)) and recorded GCS, APACHE II, SOFA, and Charlson comorbidity index scores on admission to the ICU. Receiver operating curve (ROC) analysis was performed to predict mortality from IL-1β, IL-6 IL-10, IL-8, TNF-α, and HMGB-1 values. Study participants were grouped as follows: Group A, survivors, and Group B, deceased, during the 28-day follow-up. The mean age was 65.69 (±13.56) in Group A (n = 36) and 70.85 (±10.06) in Group B (n = 27). The female/male ratio was 23/40. Age, sex, body mass index (BMI), comorbid illnesses, GCS, APACHE II, SOFA, and Charlson scores, duration of hospitalization or ICU admission, therapeutic choices, and lymphocyte, PMNL, NLR, platelet, D-dimer, fibrinogen, GGT, CRP, procalcitonin, and lactate levels were similar between the groups. The frequency of acute kidney injury (AKI) was higher in Group B (p = 0.005). Serum IL-10, IL-8, IL-6, IL-1β, TNF-α, HMGB-1, ferritin, and LDH values were higher, and PaO₂/FiO₂ was lower in Group B than in Group A. ROC analysis showed that there was an association between serum IL-1β (>1015.7), serum IL-6 (>116.7), serum IL-8 (>258.4), serum IL-10 (>247.5), serum TNF-α (>280.7), and serum HMGB-1 (>23.5) and mortality. AKI gave rise to a greater risk of mortality (odds ratio: 7.081, p = 0.014). Mortality was associated with serum IL-10, IL-8, IL-6, IL-1β, TNF-α, and HMGB-1 but not with GCS, APACHE II, SOFA, or Charlson comorbidity index scores. AKI increased the risk of mortality by seven times. Our findings suggest that cytokine levels (serum IL-10, IL-8, IL-6, IL-1β, TNF-α, and HMGB-1) were predictors of mortality in COVID-19 infection. In addition, our results might give an opinion about the course of COVID-19 infection.

Age-adjusted Charlson comorbidity index predicts postoperative mortality in elderly patients with hip fracture: A prospective cohort

Background

This study aimed to evaluate the clinical association between the age-adjusted Charlson comorbidity index (aCCI) and postoperative mortality in elderly patients.

Materials and methods

Elderly patients with hip fractures were screened from January 2015 to September 2019. After demographic and clinical characteristics were collected, linear and non-linear multivariate Cox regression models were used to identify the association between the aCCI and mortality. All analyses were performed using EmpowerStats and R software.

Results

A total of 2,657 patients were included in the study, and the mean follow-up duration was of 38.97 months. The mean aCCI score was 4.24 ± 1.09, and 977 (34.14%) died of all-cause mortality. The fully-adjusted linear multivariate Cox regression models showed the aCCI to be associated with mortality [hazard ratio (HR) = 1.31, 95% confidence interval (CI):1.21-1.41, P < 0.0001]. Patients in Q2 showed greater mortality (HR = 1.60, 95% CI: 1.23-2.09; P = 0.0005) than those in Q1; patients in Q3 showed greater mortality (HR = 2.18, 95% CI: 1.66-2.87; P < 0.001) than those in Q1. In addition, the P-value for the trend also showed a linear association in the three models (P < 0.0001). In the sensitivity analysis, propensity score matching was used, and the results were stable.

Conclusion

The mortality risk of hip fractures increased by 31% when the aCCI increased by one unit. aCCI score was shown to be a good predictor of three-year mortality following hip fracture.

Clinical trial registration

http://www.chictr.org.cn/showproj.aspx?proj=152919, identifier ChiCTR2200057323.

Molnupiravir Outpatient Treatment for Adults with COVID-19 in a Real-World Setting-A Single Center Experience

BACKGROUND

Molnupiravir is approved for the treatment of adult patients with mild to moderate COVID-19. The main goal of the treatment is to reduce hospitalization and mortality rate. This study aimed at the all-cause hospitalization and all-cause death assessment in patients at high risk of severe COVID-19 treated with molnupiravir.

METHODS

This was a prospective, observational single center study. Non-hospitalized patients with SARS-CoV-2 infection, COVID-19 symptoms with the onset of up to 5 days, and at high risk of severe COVID-19 illness received molnupiravir based on attending physician decisions.

RESULTS

In total, 107 patients were enrolled. Adverse events were reported in 28.0% of patients, with nausea and abdominal pain being the most commonly observed. No treatment-emergent AEs resulted in therapy discontinuation. Overall, 15 patients required hospitalization. During the observation, 2.8% (n = 3) of patients subsequently died. All deaths were considered to be related to COVID-19 complications. Age over 65 years, heart failure, and ischemic heart disease showed a significant correlation with the severe course of COVID-19.

CONCLUSION

Molnupiravir may be perceived as an alternative treatment for patients with immunosuppression and advanced chronic kidney disease. Nevertheless, further studies are required to conclusively establish a role for molnupiravir in future COVID-19 treatment recommendations.