During a pandemic like COVID-19, hospital resources are constrained and accurate severity triage of the patients is required.

The objective of this study is to estimate the predictive performances of candidate biomarkers for short-term worsening (STW) of COVID-19.

Prospective, multicenter (20 hospitals in Paris) cohort study of consecutive COVID-19 patients with systematic biobanking at admission, during the first waves of COVID-19 in France in 2020 (COVIDeF cohort).

Consecutive COVID-19 patients were screened for inclusion. They were excluded in presence of severity criteria defined by either an ICU admission, mechanical ventilation (including noninvasive ventilation), acute respiratory distress, or in-hospital death before sampling. Routine blood tests measured during usual care and centralized systematic measurement of creatine kinase, C-reactive protein (CRP), procalcitonin, soluble urokinase plasminogen activator receptor (suPAR), high-sensitive troponin T (TnT-hs), N terminal pro-B natriuretic peptide (NT-proBNP), calprotectin, platelet factor 4, mid-regional pro-adrenomedullin (MR-proADM), and proendothelin were performed.

The primary outcome was STW, defined by a severity criteria within 7 days. A backward stepwise logistic regression model and a 'best subset' approach were used to identify independent association, and the area under the receiving operator characteristics (AUROC) was computed.

Five hundred and eleven patients were analyzed, of whom 60 (11.7%) experienced STW. Median time to occurrence of a severity criteria was 3 days. At admission, lower values of eosinophils, lymphocytes, platelets, alanine aminotransferase, and higher values of neutrophils, creatinine, urea, CRP, TnT-hs, suPAR, NT-proBNP, calprotectin, procalcitonin, MR-proADM, and proendothelin were predictive of worsening. Stepwise logistic regression identified three biomarkers significantly associated with worsening: CRP [adjusted odds ratio (aOR): 1.10, 95% confidence interval (95% CI): 1.06-1.15 for a 10-unit increase, AUROC: 0.73 (0.66-0.79)], procalcitonin [aOR: 0.42, 95% CI: 0.22-0.81, AUROC: 0.69 (0.64-0.88)], and MR-proADM [aOR: 2.85, 95% CI: 1.74-4.69, AUROC: 0.75 (0.69-0.81)]. These biomarkers outperformed clinical variables except diabetes and cancer comorbidities.

In this multicenter prospective study that assessed a large panel of biomarkers for COVID-19 patients, CRP, procalcitonin, and MR-proADM were independently associated with the risk of STW.

ClinicalTrials.gov NCT04352348.

At the beginning of December 2022, the Chinese government made major adjustments to the epidemic prevention and control measures. The epidemic infection data and laboratory makers for infected patients based on this period may help with the management and prognostication of COVID-19 patients.

The COVID-19 patients hospitalized during December 2022 were enrolled. Logistic regression analysis was used to screen significant factors associated with mortality in patients with COVID-19. Candidate variables were screened by LASSO and stepwise logistic regression methods and were used to construct logistic regression as the prognostic model. The performance of the models was evaluated by discrimination, calibration, and net benefit.

888 patients were eligible, consisting of 715 survivors and 173 all-cause deaths. Factors significantly associated with mortality in COVID-19 patients were: lactate dehydrogenase (LDH), albumin (ALB), procalcitonin (PCT), age, smoking history, malignancy history, high density lipoprotein cholesterol (HDL-C), lactate, vaccine status and urea. 335 of the 888 eligible patients were defined as ICU cases. Seven predictors, including neutrophil to lymphocyte ratio, D-dimer, PCT, C-reactive protein, ALB, bicarbonate, and LDH, were finally selected to establish the prognostic model and generate a nomogram. The area under the curve of the receiver operating curve in the training and validation cohorts were respectively 0.842 and 0.853. In terms of calibration, predicted probabilities and observed proportions displayed high agreements. Decision curve analysis showed high clinical net benefit in the risk threshold of 0.10-0.85. A cutoff value of 81.220 was determined to predict the outcome of COVID-19 patients via this nomogram.

The laboratory model established in this study showed high discrimination, calibration, and net benefit. It may be used for early identification of severe patients with COVID-19.

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.

East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

The coronavirus disease 2019 (COVID-19) pandemic is ongoing and many drugs have been studied in clinical trials. From a pathophysiological perspective, anti-viral drugs may be more effective in the early stage while immunomodulators may be more effective in severe patients in later stages of infection. While drugs such as lopinavir-ritonavir, hydroxychloroquine and azithromycin have proved to be ineffective in randomized controlled trials, corticosteroids, neutralizing monoclonal antibodies, remdesivir, tocilizumab and baricitinib have been reported to benefit certain groups of patients with COVID-19. In this review, we will present the key clinical evidence and progress in promising COVID-19 therapeutics, as well as summarize the experience and lessons learned from the development of the current therapeutics.

Because of the catastrophic outbreak of global coronavirus disease 2019 (COVID-19) and its strong infectivity and possible persistence, computational repurposing of existing approved drugs will be a promising strategy that facilitates rapid clinical treatment decisions and provides reasonable justification for subsequent clinical trials and regulatory reviews. Since the effects of a small number of conditionally marketed vaccines need further clinical observation, there is still an urgent need to quickly and effectively repurpose potentially available drugs before the next disease peak. In this work, we have manually collected a set of experimentally confirmed virus-drug associations through the publicly published database and literature, consisting of 175 drugs and 95 viruses, as well as 933 virus-drug associations. Then, because the samples are extremely sparse and unbalanced, negative samples cannot be easily obtained. We have developed an ensemble model, EMC-Voting, based on matrix completion and weighted soft voting, a semi-supervised machine learning model for computational drug repurposing. Finally, we have evaluated the prediction performance of EMC-Voting by fivefold crossing-validation and compared it with other baseline classifiers and prediction models. The case study for the virus SARS-COV-2 included in the dataset demonstrates that our model achieves the outperforming AUPR value of 0.934 in virus-drug association's prediction.