Risk-factors for re-admission and outcome of patients hospitalized with confirmed COVID-19

Use of NIR in COVID-19 Screening: Proof of Principles for Future Application

The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.

Effectiveness of sarcopenia screening markers in predicting out-of-hospital death in the oldest (≥80 years) older

OBJECTIVE

The goal of this investigation was to elucidate the correlation between sarcopenia screening indicators (aspartate transaminase/alanine transaminase (AST/ALT) and creatinine/cystatin C*100 (Cr/CysC*100)) and the risk of out-of-hospital (OFH) death among the very advanced age (≥80 years) population.

METHODS

We conducted a retrospective cohort investigation, involving internal medicine inpatients aged ≥80 years of age, who sought treatment at a teaching hospital in western China. We obtained OFH mortality information from telephonic interviews. Subsequently, we employed Cox proportional hazards models to analyze the links between AST/ALT and Cr/CysC*100 and OFH all-cause mortality among the very advanced age (≥80 years old) population.

RESULTS

In all, we recruited 398 subjects, among which 51.51% were male. The median age of OFH deceased male patients was 85 years, and the same for female patients was 87 years. The total quantity of OFH deaths was 164 (41.21%). Among the oldest male population, those who died OFH exhibited enhanced AST/ALT, relative to those who survived (death vs. survival: 1.5 vs 1.3, P=0.008). However, among the oldest female, there was no difference in AST/ALT between patients who expired OFH, and those who survived. Among the oldest elders (male and female), Cr/CysC*100 did not significantly differ between surviving and OFH deceased patients. Additional analysis involving the Cox proportional hazards model revealed that among the oldest male population, an enhanced AST/ALT denoted an augmented risk of OFH death (hazard ratios (HRs) =1.797, 95%CI: 1.2-2.691). However, Cr/CysC*100 was not correlated with OFH mortality risk. Among the oldest female population, neither AST/ALT nor Cr/CysC*100 was correlated with OFH mortality risk.

CONCLUSIONS

Enhanced AST/ALT was correlated with an augmented OFH mortality risk among the oldest male, but not female population. Alternately, Cr/CysC*100 was not linked to OFH mortality risk among any population.

The effect of Fingolimod on patients with moderate to severe COVID-19

Hyper-inflammation, cytokine storm, and recruitment of immune cells lead to uncontrollable endothelial cell damage in patients with coronavirus disease 2019 (COVID-19). Sphingosine 1-phosphate (S1P) signaling is needed for endothelial integrity and its decreased serum level is a predictor of clinical severity in COVID-19. In this clinical trial, the effect of Fingolimod, an agonist of S1P, was evaluated on patients with COVID-19. Forty patients with moderate to severe COVID-19 were enrolled and divided into two groups including (1) the control group (n = 21) receiving the national standard regimen for COVID-19 patients and (2) the intervention group (n = 19) that prescribed daily Fingolimod (0.5 mg) for 3 days besides receiving the standard national regimen for COVID-19. The hospitalization period, re-admission rate, intensive care unit (ICU) administration, need for mechanical ventilation, and mortality rate were assessed as primary outcomes in both groups. The results showed that re-admission was significantly decreased in COVID-19 patients who received Fingolimod compared to the controls (p = .04). In addition, the hemoglobin levels of the COVID-19 patients in the intervention group were increased compared to the controls (p = .018). However, no significant differences were found regarding the intubation or mortality rate between the groups (p > .05). Fingolimod could significantly reduce the re-admission rate after hospitalization with COVID-19. Fingolimod may not enhance patients' outcomes with moderate COVID-19. It is necessary to examine these findings in a larger cohort of patients with severe to critical COVID-19.

Microbiological and Clinical Findings of SARS-CoV-2 Infection after 2 Years of Pandemic: From Lung to Gut Microbiota

Early recognition and prompt management are crucial for improving survival in COVID-19 patients, and after 2 years of the pandemic, many efforts have been made to obtain an early diagnosis. A key factor is the use of fast microbiological techniques, considering also that COVID-19 patients may show no peculiar signs and symptoms that may differentiate COVID-19 from other infective or non-infective diseases. These techniques were developed to promptly identify SARS-CoV-2 infection and to prevent viral spread and transmission. However, recent data about clinical, radiological and laboratory features of COVID-19 at time of hospitalization could help physicians in early suspicion of SARS-CoV-2 infection and distinguishing it from other etiologies. The knowledge of clinical features and microbiological techniques will be crucial in the next years when the endemic circulation of SARS-CoV-2 will be probably associated with clusters of infection. In this review we provide a state of the art about new advances in microbiological and clinical findings of SARS-CoV-2 infection in hospitalized patients with a focus on pulmonary and extrapulmonary characteristics, including the role of gut microbiota.

Re-Admission of COVID-19 Patients Hospitalized with Omicron Variant-A Retrospective Cohort Study

In accordance with previous publications, re-admission rates following hospitalization of patients with COVID-19 is 10%. The aim of the current study was to describe the rates and risk factors of hospital re-admissions two months following discharge from hospitalization during the fifth wave due to the dominant Omicron variant. A retrospective cohort study was performed in Rabin Medical Center, Israel, from November 2021 to February 2022. The primary outcome was re-admissions with any diagnosis; the secondary outcome was mortality within two months of discharge. Overall, 660 patients were hospitalized with a diagnosis of COVID-19. Of the 528 patients discharged from a primary hospitalization, 150 (28%) were re-admitted. A total of 164 patients (25%) died throughout the follow-up period. A multi-variable analysis determined that elevated creatinine was associated with a higher risk of re-admissions. Rates of re-admissions after discharge during the Omicron wave were considerably higher compared to previous waves. A discharge plan for surveillance and treatment following hospitalization is of great importance in the management of pandemics.

SARS-CoV-2 does not infect pigs, but this has to be verified regularly

For successful xenotransplantation, freedom of the xenocraft donor from certain viral infections that may harm the organ recipient is important. A novel human coronavirus (CoV) with a respiratory tropism, designated as SARS-CoV-2, was first identified in January 2020 in China, but likely has been circulating unnoticed for some time before. Since then, this virus has reached most inhabited areas, resulting in a major global pandemic which is still ongoing. Due to a high number of subclinical infections, re-infections, geographic differences in diagnostic tests used, and differences in result reporting programs, the percentage of the population infected with SARS-CoV-2 at least once has been challenging to estimate. With continuous ongoing infections in people and an overall high viral load, it makes sense to look into possible viral spillover events in pets and farm animals, who are often in close contact with humans. The pig is currently the main species considered for xenotransplantation and hence there is interest to know if pigs can become infected with SARS-CoV-2 and if so what the infection dynamics may look like. This review article summarizes the latest research findings on this topic. It would appear that pigs can currently be considered a low risk species, and hence do not pose an immediate risk to the human population or xenotransplantation recipients per se. Monitoring the ever-changing SARS-CoV-2 variants appears important to recognize immediately should this change in the future.

Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies

In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Introduction

The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features.

Material and methods

The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics.

Results

Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%.

Conclusion

The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

We aimed to determine the characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients.

METHODS

PubMed, Embase, Web of Science, and Scopus databases were searched to retrieve articles on readmitted COVID-19 patients, available up to September 25, 2021. All studies comparing characteristics of readmitted and non-readmitted COVID-19 patients were included. We also included articles reporting the reasons for readmission in COVID-19 patients. Data were pooled and meta-analyzed using random or fixed-effect models, as appropriate. Subgroup analyses were conducted based on the place and duration of readmission.

RESULTS

Our meta-analysis included 4823 readmitted and 63,413 non-readmitted COVID-19 patients. The re-hospitalization rate was calculated at 9.3% with 95% Confidence Interval (CI) [5.5%-15.4%], mostly associated with respiratory or cardiac complications (48% and 14%, respectively). Comorbidities including cerebrovascular disease (Odds Ratio (OR) = 1.812; 95% CI [1.547-2.121]), cardiovascular (2.173 [1.545-3.057]), hypertension (1.608 [1.319-1.960]), ischemic heart disease (1.998 [1.495-2.670]), heart failure (2.556 [1.980-3.300]), diabetes (1.588 [1.443-1.747]), cancer (1.817 [1.526-2.162]), kidney disease (2.083 [1.498-2.897]), chronic pulmonary disease (1.601 [1.438-1.783]), as well as older age (1.525 [1.175-1.978]), male sex (1.155 [1.041-1.282]), and white race (1.263 [1.044-1.528]) were significantly associated with higher readmission rates (P < 0.05 for all instances). The mortality rate was significantly lower in readmitted patients (OR = 0.530 [0.329-0.855], P = 0.009).

CONCLUSIONS

Male sex, white race, comorbidities, and older age were associated with a higher risk of readmission among previously admitted COVID-19 patients. These factors can help clinicians and policy-makers predict, and conceivably reduce the risk of readmission in COVID-19 patients.