The healthier healthcare management models for COVID-19

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis

BACKGROUND

The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans.

METHODS

This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments.

RESULTS

The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively.

CONCLUSIONS

The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

Comparison of Clinical Features, Complete Blood Count Parameters, and Outcomes between Two Distinct Waves of COVID-19: A Monocentric Report from Italy

BACKGROUND

Since the beginning of the SARS-CoV-2 pandemic, the ability to predict the trajectory of the disease has represented a major challenge for clinicians. There is recent evidence that complete blood cell count (CBC)-derived inflammation indexes have predictive value in COVID-19. We aimed to describe any changes in the clinical features, CBC-derived ratios, and outcomes of patients admitted to our hospital across two temporally distinct waves.

METHODS

We retrospectively assessed and compared the clinical characteristics and blood cell count values of patients hospitalized during the second and fourth waves of COVID-19, and explored any outcome differences in terms of the level of respiratory support required and transfer to intensive care.

RESULTS

We observed that fourth-wave patients were older, less male-predominant, and carried more comorbidities compared to the second-wave patients but, nevertheless, experienced more favorable outcomes. A strong internal correlation was documented for both waves between outcomes and CBC-derived ratios, with the fourth-wave cases displaying lower admission values of the neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). No significant differences were found for lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI).

CONCLUSIONS

We observed that both admission values of CBC-derived indexes and adverse respiratory outcomes decreased from the second to the fourth wave of COVID-19. These data represent a contribution to the existing knowledge on the role of CBC-derived indexes as a potential tool to help clinicians to quickly differentiate in-hospital patients at increased risk of serious illness and death.

Progress in COVID research and developments during pandemic

The pandemic respiratory disease COVID-19 has spread over the globe within a small span of time. Generally, there are two important points are being highlighted and considered towards the successful diagnosis and treatment process. The first point includes the reduction of the rate of infections and the next one is the decrease of the death rate. The major threat to public health globally progresses due to the absence of effective medication and widely accepted immunization for the COVID-19. Whereas, understanding of host susceptibility, clinical features, adaptation of COVID-19 to new environments, asymptomatic infection is difficult and challenging. Therefore, a rapid and an exact determination of pathogenic viruses play an important role in deciding treatments and preventing pandemic to save the people's lives. It is urgent to fix a standardized diagnostic approach for detecting the COVID-19. Here, this systematic review describes all the current approaches using for screening and diagnosing the COVID-19 infectious patient. The renaissance in pathogen due to host adaptability and new region, facing creates several obstacles in diagnosis, drug, and vaccine development process. The study shows that adaptation of accurate and affordable diagnostic tools based on candidate biomarkers using sensor and digital medicine technology can deliver effective diagnosis services at the mass level. Better prospects of public health management rely on diagnosis with high specificity and cost-effective manner along with multidisciplinary research, specific policy, and technology adaptation. The proposed healthcare model with defined road map represents effective prognosis system.

Advances in Organ-on-a-Chip Materials and Devices

The organ-on-a-chip (OoC) paves a way for biomedical applications ranging from preclinical to clinical translational precision. The current trends in the in vitro modeling is to reduce the complexity of human organ anatomy to the fundamental cellular microanatomy as an alternative of recreating the entire cell milieu that allows systematic analysis of medicinal absorption of compounds, metabolism, and mechanistic investigation. The OoC devices accurately represent human physiology in vitro; however, it is vital to choose the correct chip materials. The potential chip materials include inorganic, elastomeric, thermoplastic, natural, and hybrid materials. Despite the fact that polydimethylsiloxane is the most commonly utilized polymer for OoC and microphysiological systems, substitute materials have been continuously developed for its advanced applications. The evaluation of human physiological status can help to demonstrate using noninvasive OoC materials in real-time procedures. Therefore, this Review examines the materials used for fabricating OoC devices, the application-oriented pros and cons, possessions for device fabrication and biocompatibility, as well as their potential for downstream biochemical surface alteration and commercialization. The convergence of emerging approaches, such as advanced materials, artificial intelligence, machine learning, three-dimensional (3D) bioprinting, and genomics, have the potential to perform OoC technology at next generation. Thus, OoC technologies provide easy and precise methodologies in cost-effective clinical monitoring and treatment using standardized protocols, at even personalized levels. Because of the inherent utilization of the integrated materials, employing the OoC with biomedical approaches will be a promising methodology in the healthcare industry.