Covid-19: contribution of clinical characteristics and laboratory features for early detection of patients with high risk of severe evolution

Hemogram-based decision tree models for discriminating COVID-19 from RSV in infants

OBJECTIVE

Decision trees are efficient and reliable decision-making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID-19) and respiratory syncytial virus (RSV) infection in infants.

METHODS

A cross-sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS-CoV-2) infection and 44 infants with RSV infection. In total, 23 hemogram-based instances were used to construct the decision tree models via 10-fold cross-validation method.

RESULTS

The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one.

CONCLUSION

Random forest and optimized forest models might have significant clinical applications, helping to speed up decision-making when SARS-CoV-2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.

The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis

Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

An integrated framework for identifying clinical-laboratory indicators for novel pandemics: COVID-19 and MIS-C

During the critical early stages of an emerging pandemic, limited availability of pathogen-specific testing can severely inhibit individualized risk screening and pandemic tracking. Standard clinical laboratory tests offer a widely available complementary data source for first-line risk screening and pandemic surveillance. Here, we propose an integrated framework for developing clinical-laboratory indicators for novel pandemics that combines population-level and individual-level analyses. We apply this framework to 7,520,834 clinical laboratory tests recorded over five years and find clinical-lab-test combinations that are strongly associated with SARS-CoV-2 PCR test results and Multisystem Inflammatory Syndrome in Children (MIS-C) diagnoses: Interleukin-related tests (e.g. IL4, IL10) were most strongly associated with SARS-CoV-2 infection and MIS-C, while other more widely available tests (ferritin, D-dimer, fibrinogen, alanine transaminase, and C-reactive protein) also had strong associations. When novel pandemics emerge, this framework can be used to identify specific combinations of clinical laboratory tests for public health tracking and first-line individualized risk screening.

The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis

Background: The neutrophil–lymphocyte count ratio (NLR) has emerged as a potential prognostic tool for different diseases. In the current coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk scarification and the optimal utilization of limited healthcare resources. However, there is no consensus regarding the optimal value of NLR, and the association with disease severity and mortality. Thus, this study aims to systematically analyze the current evidence of the utility of baseline NLR as a predictive tool for mortality, disease severity in COVID-19 patients. Methods: A compendious screening of electronic databases up to June 15, 2021, was done after enlisting the protocol in PROSPERO (CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are included for this review as per the PRISMA statement. Results: We retrieved a total of 13112 and 12986 COVID-19 patients for survivability and severity over 90 studies. The expired and critically sick patients had elevated baseline NLR on admission, in comparison to survivors and noncritical patients. (SMD = 3.82; 95% CI: 2.79-4.85; I² = 100% and SMD = 1.42; 95% CI: 1.22-1.63; I² = 95%, respectively). The summary receiver operating curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87; I² = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84; I² = 79.7%) were also suggestive of its significant predictive value. Conclusions: The elevated NLR on admission in COVID-19 patients is associated with poor outcomes.

Precision Medicine and Public Health: New Challenges for Effective and Sustainable Health

The development of high-throughput omics technologies represents an unmissable opportunity for evidence-based prevention of adverse effects on human health. However, the applicability and access to multi-omics tests are limited. In Italy, this is due to the rapid increase of knowledge and the high levels of skill and economic investment initially necessary. The fields of human genetics and public health have highlighted the relevance of an implementation strategy at a national level in Italy, including integration in sanitary regulations and governance instruments. In this review, the emerging field of public health genomics is discussed, including the polygenic scores approach, epigenetic modulation, nutrigenomics, and microbiomes implications. Moreover, the Italian state of implementation is presented. The omics sciences have important implications for the prevention of both communicable and noncommunicable diseases, especially because they can be used to assess the health status during the whole course of life. An effective population health gain is possible if omics tools are implemented for each person after a preliminary assessment of effectiveness in the medium to long term.

Clinical characteristics and analysis of risk factors for disease progression of COVID-19: A retrospective Cohort Study

Objective: Since December 2019, an outbreak of coronavirus disease 2019 (COVID 19) has been experienced from Wuhan, China to the world. A retrospective cohort study was conducted to summarize the clinical characteristics of patients with COVID-19 and to explore the risk factors affecting the disease duration in Jiangan Fangcang shelter hospital, Wuhan, China. Methods: Clinical characteristics of 409 patients with COVID-19 were retrospectively analyzed. We describe the clinical characteristics and distribution of discharge time or transfer time for each patient. Then we performed univariate and multivariate Cox regression analysis to identify potential risk factors for progression from non-severe to severe COVID-19 or death. Results: The median disease duration of all patients was 23 days (IQR 19-28). The main symptoms of the patient were fever (95.6%), cough (74.3%), tiredness (21.5%), and so on. Comorbidities mainly included hypertension (30.6%) diabetes (17.6%) and heart disease (12.5%). The univariate Cox regression analysis showed that old age, number of symptoms, the combination of hypertension, heart disease and pulmonary disease were associated with the progression of disease. The multivariate Cox regression analysis showed that old age (HR: 7.294; 95% CI: 1.442-36.888; P = 0.016), the combination of hypertension (HR: 2.230; 95% CI: 1.090-4.562; P = 0.028) and heart disease (HR: 2.650; 95% CI: 1.079-6.510; P = 0.034) were independent risk factors for progression of COVID-19. Conclusions: The age of the patient, the combination of hypertension and heart disease were independent risk factors for the progression of COVID-19. Cautions should be raised for patients with these risk factors.

Evolution of COVID-19 in patients with autoimmune rheumatic diseases

The characteristics of COVID-19 patients with autoimmune rheumatic diseases (AIRD) have rarely been reported. Patients with AIRD have suppressed immune defense function, which may increase their susceptibility to COVID-19. However, the immunosuppressive agents AIRD patients routinely used may be beneficial for protecting the cytokine storm caused by SARS-CoV-2. In this retrospective study, we included all confirmed cases in Huoshenshan Hospital from February 4 to April 9. Data were extracted from electronic medical records and were analyzed for clinical and laboratory features using SPSS (version 25.0). Of 3059 patients, 21 had the comorbidities with systematic lupus erythematosus (SLE) and/or rheumatoid arthritis (RA), including 5 with SLE, 15 with RA, and 1 with Rhupus. The proportion was 57.1% for severe cases, 61.9% for either severe or critical cases, and 4.8% for critical cases. The main manifestations, ARDS and ICU admission rate, as well as the mortality and length of hospital stay of COVID-19 in AIRD patients were similar to COVID-19 patients in the general population. Our preliminary experience shows that patients with AIRD tend to have a higher risk of SARS-CoV-2 infection, and may be at risk for a severe but less likely critical disease course. Further investigation is needed to understand the immunological features of these diseases.

Combined Interventions for Severe Novel Coronavirus Disease (COVID-19): Experience from 350 Patients

Purpose

To summarize the clinical features and effective therapy of severe COVID-19 patients.

Patients and Methods

In this retrospective, multicenter study, the medical records of COVID-19 patients in Hunan, from January 21, 2020 to February 19, 2020 were reviewed.

Results

Of the 350 COVID-19 patients, 13.7% were severe cases. On admission, compared with non-severe patients, more severe patients had a neutrophil/lymphocyte ratio > 3 (58.3% vs 33.8%, P=0.001), D-dimer > 1 mg/L (41.7% vs 13.6%, P<0.0001), higher level of CRP (39.1 mg/L, IQR18.1–75.9 vs 13.4 mg/L, IQR5.0–32.8, P<0.0001), and multiple pneumonia on CT (77.1% vs 18.2%, P<0.0001). All severe patients received oxygen support. 95.8% of them received antivirals, and the most frequent therapy was lopinavir and ritonavir plus human interferon-α2b. Moxifloxacin was used in 70.8% severe patients. The total dosage of methylprednisolone sodium succinate was 640 mg (IQR 360–960) in severe patients, and the duration of use was 8.5 days (IQR 6.8–11.3). The total dosage of immunoglobulin was 80 g (IQR, 60–140) in severe patients, and the duration was 8.0 days (IQR, 6.0–11.5). As of March 15, 2020, 95.8% of the severe patients had been discharged and only two deaths occurred.

Conclusion

The rate of severe cases and mortality of COVID-19 in Hunan are lower than those in Wuhan. In addition to antivirals and oxygen support, timely interventions including corticosteroids, immunoglobulin, and antibiotics, contribute to improving the prognosis of severe COVID-19 patients.