Body temperature correlates with mortality in COVID-19 patients

2023 Beijing Health Data Science Summit

The 5th annual Beijing Health Data Science Summit, organized by the National Institute of Health Data Science at Peking University, recently concluded with resounding success. This year, the summit aimed to foster collaboration among researchers, practitioners, and stakeholders in the field of health data science to advance the use of data for better health outcomes. One significant highlight of this year's summit was the introduction of the Abstract Competition, organized by Health Data Science, a Science Partner Journal, which focused on the use of cutting-edge data science methodologies, particularly the application of artificial intelligence in the healthcare scenarios. The competition provided a platform for researchers to showcase their groundbreaking work and innovations. In total, the summit received 61 abstract submissions. Following a rigorous evaluation process by the Abstract Review Committee, eight exceptional abstracts were selected to compete in the final round and give presentations in the Abstract Competition. The winners of the Abstract Competition are as follows:•First Prize: "Interpretable Machine Learning for Predicting Outcomes of Childhood Kawasaki Disease: Electronic Health Record Analysis" presented by researchers from the Chinese Academy of Medical Sciences, Peking Union Medical College, and Chongqing Medical University (presenter Yifan Duan).•Second Prize: "Survival Disparities among Mobility Patterns of Patients with Cancer: A Population-Based Study" presented by a team from Peking University (presenter Fengyu Wen).•Third Prize: "Deep Learning-Based Real-Time Predictive Model for the Development of Acute Stroke" presented by researchers from Beijing Tiantan Hospital (presenter Lan Lan). We extend our heartfelt gratitude to the esteemed panel of judges whose expertise and dedication ensured the fairness and quality of the competition. The judging panel included Jiebo Luo from the University of Rochester (chair), Shenda Hong from Peking University, Xiaozhong Liu from Worcester Polytechnic Institute, Liu Yang from Hong Kong Baptist University, Ma Jianzhu from Tsinghua University, Ting Ma from Harbin Institute of Technology, and Jian Tang from Mila-Quebec Artificial Intelligence Institute. We wish to convey our deep appreciation to Zixuan He and Haoyang Hong for their invaluable assistance in the meticulous planning and execution of the event. As the 2023 Beijing Health Data Science Summit comes to a close, we look forward to welcoming all participants to join us in 2024. Together, we will continue to advance the frontiers of health data science and work toward a healthier future for all.

Prevalence of fever and its associated risk factors among patients hospitalised with coronavirus disease 2019 (COVID-19) at the Eastern Regional Hospital, Koforidua, Ghana

BACKGROUND

In Ghana, temperature check at various points of entry was adopted as a means of screening people for coronavirus disease 2019 without taking into consideration data on the local prevalence of fever associated with the disease. Our objective was to assess fever prevalence and its associated risk factors among patients hospitalised with coronavirus disease 2019 at the Eastern Regional Hospital, Koforidua in Ghana.

METHODS

We reviewed medical records of 301 coronavirus disease 2019 patients who were admitted at the Eastern Regional Hospital, Koforidua between May 5, 2020, and August 31, 2021. Data collected on a pre-designed extraction sheet was processed, entered and analysed using Microsoft excel 2019 and Stata/IC version 16.1 software. Prevalence of fever was estimated and a multivariable logistic regression model was fitted to establish risk factors associated with fever among hospitalised coronavirus disease 2019 patients. A relationship was accepted to be significant at 5% level of significance.

RESULTS

The prevalence of fever among hospitalised coronavirus disease 2019 patients was 21.6% (95% CI, 17.1%-26.7%). Risk factors associated with fever were age group [0-19 years (AOR, 5.75; 95% CI, 1.46-22.68; p = 0.013); 20-39 years (AOR, 3.22; 95% CI, 1.42-7.29; p = 0.005)], comorbidity (AOR, 2.18; 95% CI, 1.04-4.59; p = 0.040), and disease severity [moderate (AOR, 3.89; 95% CI, 1.44-10.49; p = 0.007); severe (AOR, 4.08; 95% CI, 1.36-12.21; p = 0.012); critical (AOR, 4.85; 95% CI, 1.03-22.85; p = 0.046)].

CONCLUSIONS

The prevalence of fever was low among hospitalised coronavirus disease 2019 patients at the Eastern Regional Hospital, Koforidua. However, there was an increasing risk of fever as the disease severity progresses. Fever screening may be utilised better in disease of higher severity; it should not be used alone especially in mild disease.

Association of body temperature and mortality in critically ill patients: an observational study using two large databases

BACKGROUND

Body temperature (BT) is routinely measured and can be controlled in critical care settings. BT can impact patient outcome, but the relationship between BT and mortality has not been well-established.

METHODS

A retrospective cohort study was conducted based on the MIMIC-IV (N = 43,537) and eICU (N = 75,184) datasets. The primary outcome and exposure variables were hospital mortality and first 48-h median BT, respectively. Generalized additive models were used to model the associations between exposures and outcomes, while adjusting for patient age, sex, APS-III, SOFA, and Charlson comorbidity scores, temperature gap, as well as ventilation, vasopressor, steroids, and dialysis usage. We conducted subgroup analysis according to ICU setting, diagnoses, and demographics.

RESULTS

Optimal BT was 37 °C for the general ICU and subgroup populations. A 10% increase in the proportion of time that BT was within the 36-38 °C range was associated with reduced hospital mortality risk in both MIMIC-IV (OR 0.91; 95% CI 0.90-0.93) and eICU (OR 0.86; 95% CI 0.85-0.87). On the other hand, a 10% increase in the proportion of time when BT < 36 °C was associated with increased mortality risk in both MIMIC-IV (OR 1.08; 95% CI 1.06-1.10) and eICU (OR 1.18; 95% CI 1.16-1.19). Similarly, a 10% increase in the proportion of time when BT > 38 °C was associated with increased mortality risk in both MIMIC-IV (OR 1.09; 95% CI 1.07-1.12) and eICU (OR 1.09; 95% CI 1.08-1.11). All patient subgroups tested consistently showed an optimal temperature within the 36-38 °C range.

CONCLUSIONS

A BT of 37 °C is associated with the lowest mortality risk among ICU patients. Further studies to explore the causal relationship between the optimal BT and mortality should be conducted and may help with establishing guidelines for active BT management in critical care settings.

Record Setting Temperatures in the Setting of SARS-CoV-2 Suggests Dexmedetomidine Drug-Induced Fever: A Case Report

Patients infected with COVID-19 can develop coinfections or acute respiratory disorder that result in ventilation. Dexmedetomidine is a common medication used to sedate ventilated patients in the intensive care unit and for nonintubated patients prior to a surgical procedure. As a highly selective alpha-2 agonist, dexmedetomidine provides sedation while reducing the need for anxiolytics or opioids. However, previous case reports suggest dexmedetomidine can induce fever in a variety of conditions. The purpose of this case report is to describe a patient who acquired a fever of 42.6°C in the setting of COVID-19 after administration of dexmedetomidine.

Transmission dynamics, responses, and clinical features for the first 1100 COVID-19 cases in South Batinah, Oman: Major lessons from a provincial perspective

Objectives

This study was aimed at exploring and analyzing the epidemiological profile, surveillance, and response to COVID-19, including transmission dynamics and cluster formation.

Methodology

This was a retrospective analysis of surveillance data, including contact tracing, risk factors, and clinical information. Binary logistic regressions were used to assess the likelihood of admission, cluster formation, and of each individual being an index patient. Clusters were demonstrated through geographic data systems, network analysis, and visualization software.

Results

A total of 1100 COVID-19 cases were diagnosed from 20 March to 7 June 2020, of which 144 (13.1%) were asymptomatic. The median time from symptom onset to admission was 7 days (IQR, 4.5-10), and the median symptom duration was 5 days (IQR, 3-9). Eighty-nine clusters containing 736 patients were identified. The surveillance and control actions were divided into three phases. Clusters began to form in phase 2 and became more pronounced in phase 3. Patients ≥50 years of age and patients presenting with fever had relatively higher odds of admission: OR = 12.85 (95% CI 5.13-32.19) and 2.53 (95% CI 1.24-5.17), respectively. Cluster formation was observed among females, asymptomatic patients, and people living in Awabi: OR = 2.3 (95% CI 1.7-3.1), 6.39 (95% CI 2.33-17.2), and 3.54 (95% CI 2.06-6.07), respectively. Patients working in the police and defense sectors had higher odds of being an index patient: OR = 7.88 (95% CI 3.35-18.52).

Conclusion

Case-based interventions should be supported by population-wide measures, particularly movement restrictions. Establishing prevention teams or district units, or primary care will be crucial for the control of future pandemics. Prevention should always be prioritized for vulnerable populations.

Predicting mortality among patients with severe COVID-19 pneumonia based on admission vital sign indices: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. Vital sign indices-shock index (SI; heart rate [HR]/systolic blood pressure [SBP]), shock index age (SIA, SI × age), MinPulse (MP; maximum HR-HR), Pulse max index (PMI; HR/maximum HR), and blood pressure-age index (BPAI; SBP/age)-are better predictors of mortality in patients with trauma compared to traditional vital signs. We hypothesized that these vital sign indices may serve as predictors of mortality in patients with severe COVID-19 pneumonia. This study aimed to describe the association between vital sign indices at admission and COVID-19 pneumonia mortality and to modify the CURB-65 with the best performing vital sign index to establish a new mortality prediction tool.

METHODS

This retrospective study was conducted at a tertiary care center in southern Thailand. Adult patients diagnosed with COVID-19 pneumonia were enrolled in this study between January 2020 and July 2022. Patient demographic and clinical data on admission were collected from an electronic database. The area under the receiver operating characteristic (AUC) curve analysis was used to assess the predictive power of the resultant multivariable logistic regression model after univariate and multivariate analyses of variables with identified associations with in-hospital mortality.

RESULTS

In total, 251 patients with COVID-19 pneumonia were enrolled in this study. The in-hospital mortality rate was 27.9%. Non-survivors had significantly higher HR, respiratory rate, SIA, and PMI and lower MP and BPAI than survivors. A cutoff value of 51 for SIA (AUC, 0.663; specificity, 80%) was used to predict mortality. When SIA was introduced as a modifier for the CURB-65 score, the new score (the CURSIA score) showed a higher AUC than the Acute Physiology and Chronic Health Evaluation II and CURB-65 scores (AUCs: 0.785, 0.780, and 0.774, respectively) without statistical significance.

CONCLUSIONS

SIA and CURSIA scores were significantly associated with COVID-19 pneumonia mortality. These scores may contribute to better patient triage than traditional vital signs.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Body temperature as a predictor of mortality in COVID-19

It remains uncertain if body temperature (BT) is a useful prognostic indicator in coronavirus disease 2019 (COVID-19). We investigated the relationship between BT and mortality in COVID-19 patients. We used a de-identified database that prospectively collected information from patients screened for COVID-19 at the Mount Sinai facilities from February 28, 2020 to July 28, 2021. All patients diagnosed with COVID-19 that had BT data were included. BT at initial presentation, maximum BT during hospitalization, comorbidity, and vaccination status data were extracted. Mortality rate was assessed as a primary outcome. Among 24,293 cases, patients with initial BT below 36 °C had higher mortality than those with BT of 36-37 °C (p < 0.001, odds ratio 2.82). Initial BT > 38 °C was associated with high mortality with an incremental trend at higher BT. In 10,503 in-patient cases, a positive association was observed between mortality and maximum BT except in patients with BT < 36 °C. Multiple logistic regression analyses including the comorbidities revealed that maximum BT was an independent predictor of mortality. While vaccination did not change the distribution of maximum BT, mortality was decreased in vaccinated patients. Our retrospective cohort study suggests that high maximum BT is an independent predictor of higher mortality in COVID-19 patients.

Effect of elevated temperature on SARS-CoV-2 viability

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide disruption of global health putting healthcare workers at high risk. To reduce the transmission of SARS-CoV-2, in particular during treating the patients, our team aims to develop an optimized isolation chamber. The present study was conducted to evaluate the role of temperature elevation against SARS-CoV-2 viability, where the information would be used to build the isolation chamber. 0.6 mL of the Indonesian isolate of SARS-CoV-2 strain 20201012747 (approximately 10 13 PFU/mL) was incubated for one hour with a variation of temperatures: 25, 30, 35, 40, 45, 50, 55, 60, and 65°C in digital block heater as well as at room temperature (21-23°C) before used to infect Vero E6 cells. The viability was determined using a plaque assay. Our data found a significant reduction of the viral viability from 10 13 PFU/mL to 10 9 PFU/mL after the room temperature was increase to 40°C. Further elevation revealed that 55°C and above resulted in the total elimination of the viral viability. Increasing the temperature 40°C to reduce the SARS-CoV-2 survival could create mild hyperthermia conditions in a patient which could act as a thermotherapy. In addition, according to our findings, thermal sterilization of the vacant isolation chamber could be conducted by increasing the temperature to 55°C. In conclusion, elevating the temperature of the isolation chamber could be one of the main variables for developing an optimized isolation chamber for COVID-19 patients.

Prognostic Role of Clinical Features of Moderate Forms of COVID-19 Requiring Hospitalization

INTRODUCTION

We aimed to characterize the clinical features of moderate forms of COVID-19 requiring hospitalization and potentially identify predictors for unfavorable outcomes.

METHODS

Pooled anonymized clinical data from 452 COVID-19 patients hospitalized in two regional Romanian respiratory disease centers during the Alpha and Delta variant outbreaks were included in the analysis.

RESULTS

Cough and shortness of breath were the most common clinical features; older patients exhibited more fatigue and dyspnea and fewer upper airway-related symptoms such as smell loss or sore throat. The presence of confusion, shortness of breath and age over 60 years were significantly associated with worse outcomes (odds ratios 5.73, 2.08 and 3.29, respectively).

CONCLUSION

The clinical picture on admission may have a prognostic role for moderate forms of COVID-19. Clear clinical definitions and developing adequate informational infrastructure allowing complex data sharing and analysis might be useful for fast research response should a similar outbreak occur in the future.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Increased insulin resistance due to Long COVID is associated with depressive symptoms and partly predicted by the inflammatory response during acute infection

OBJECTIVE

Some months after the remission of acute COVID-19, some individuals show depressive symptoms, which are predicted by increased peak body temperature (PBT) and decreased blood oxygen saturation (SpO2). The present study aimed to examine data on whether long COVID is associated with increased insulin resistance (IR) in association with neuroimmune and oxidative (NIO) processes during the acute infectious and long COVID phases.

METHODS

This case-control, retrospective cohort study used the Homeostasis Model Assessment 2 (HOMA2) calculator© to compute ß-cell function (HOMA2%B) and insulin sensitivity (HOMA2%S) and resistance (HOMA2-IR) and administered the Beck Depression Inventory (BDI) and Hamilton Depression Rating Scale (HAMD) to 86 patients with long COVID and 39 controls.

RESULTS

Long COVID (3-4 months after the acute infection) is accompanied by increased HOMA2-IR, fasting blood glucose (FBG), and insulin levels; 33.7% of the patients vs. 0% of the controls had HOMA2-IR values > 1.8, suggesting IR. Increased IR was predicted by PBT during acute infection and associated with depressive symptoms above and beyond the effects of NIO pathways (nucleotide-binding domain, leucine-rich repeat, and pyrin domain-containing protein 3 [NLRP3] inflammasome, myeloperoxidase [MPO], protein oxidation). There were no significant associations between increased IR and the activated NIO pathways during long COVID.

CONCLUSION

Long COVID is associated with new-onset IR, which may contribute to onset of depressive symptoms due to long COVID by enhancing overall neurotoxicity.

Clinical courses of 24,563 hospitalized COVID-19 patients during the first 12 months of the pandemic in the Central City of Iran

This study was designed and implemented to analyze and establish documents related to the above cases in the first to third COVID-19 epidemic waves for the use of researchers and doctors during and after the epidemic. The current case series study was conducted on 24,563 thousand hospitalized COVID-19 patients by examining their clinical characteristics within a one-year period from the beginning of the pandemic on 02.22.2020 to 02.14.2021, which included the first to the third waves, based on gender and severity of COVID-19. The mean age of the participants was 56 ± 20.71, and 51.8% were male. Out of a total of 24,563 thousand hospitalized COVID-19 patients until February 2021, there were 2185 mortalities (9.8%) and 2559 cases of severe COVID-19 (13.1%). The median length of hospitalization from the time of admission to discharge or death in the hospital (IQR: 13-41) was estimated to be 21 days. The rate of hospital mortality was higher in severe (37.8%) than in non-severe (4.8%) cases of COVID-19, While the risk of severe cases increased significantly in the third (HR = 1.65, 95% CI: 1.46-1.87, P < 0.001) and early fourth waves (HR = 2.145, 95% CI: 1.7-2.71, P < 0.001). Also, the risk of contracting severe COVID-19 increased significantly in patients aged ≥ 65 years old (HR = 2.1, 95% CI 1.1.93-2.72, P < 0.001). As shown by the results, the rates of hospital mortality (9.3% vs. 8.5%) and severe cases of COVID-19 (13.6% vs. 12.5%) were higher among men than women (P < 0.01). In our study, the mortality rate and severity of COVID-19 were within the scope of global studies. Men experienced higher severity and mortality than women. The was a significantly higher prevalence of old age and underlying diseases in individuals with severe COVID-19. Our data also showed that patients with a previous history of COVID-19 had a more severe experience of COVID-19, while most of these patients were also significantly older and had an underlying disease.

Lowered oxygen saturation and increased body temperature in acute COVID-19 largely predict chronic fatigue syndrome and affective symptoms due to Long COVID: A precision nomothetic approach

BACKGROUND

Long coronavirus disease 2019 (LC) is a chronic sequel of acute COVID-19. The exact pathophysiology of the affective, chronic fatigue and physiosomatic symptoms (labelled as "physio-affective phenome") of LC has remained elusive.

OBJECTIVE

The current study aims to delineate the effects of oxygen saturation (SpO2) and body temperature during the acute phase on the physio-affective phenome of LC.

METHOD

We recruited 120 LC patients and 36 controls. For all participants, we assessed the lowest SpO2 and peak body temperature during acute COVID-19, and the Hamilton Depression and Anxiety Rating Scale (HAMD/HAMA) and Fibro Fatigue (FF) scales 3-4 months later.

RESULTS

Lowered SpO2 and increased body temperature during the acute phase and female sex predict 60.7% of the variance in the physio-affective phenome of LC. Using unsupervised learning techniques, we were able to delineate a new endophenotype class, which comprises around 26.7% of the LC patients and is characterised by very low SpO2 and very high body temperature, and depression, anxiety, chronic fatigue, and autonomic and gastro-intestinal symptoms scores. Single latent vectors could be extracted from both biomarkers, depression, anxiety and FF symptoms or from both biomarkers, insomnia, chronic fatigue, gastro-intestinal and autonomic symptoms.

CONCLUSION

The newly constructed endophenotype class and pathway phenotypes indicate that the physio-affective phenome of LC is at least in part the consequence of the pathophysiology of acute COVID-19, namely the combined effects of lowered SpO2, increased body temperature and the associated immune-inflammatory processes and lung lesions.

The oxygen dissociation curve of blood in COVID-19-An update

An impressive effect of the infection with SARS-Co-19 is the impairment of oxygen uptake due to lung injury. The reduced oxygen diffusion may potentially be counteracted by an increase in oxygen affinity of hemoglobin. However, hypoxia and anemia associated with COVID-19 usually decrease oxygen affinity due to a rise in [2,3-bisphosphoglycerate]. As such, COVID-19 related changes in the oxygen dissociation curve may be critical for oxygen uptake and supply, but are hard to predict. A Pubmed search lists 14 publications on oxygen affinity in COVID-19. While some investigations show no changes, three large studies found an increased affinity that was related to a good prognosis. Exact causes remain unknown. The cause of the associated anemia in COVID-19 is under discussion. Erythrocytes with structural alterations of membrane and cytoskeleton have been observed, and virus binding to Band 3 and also to ACE2 receptors in erythroblasts has been proposed. COVID-19 presentation is moderate in many subjects suffering from sickle cell disease. A possible explanation is that COVID-19 counteracts the unfavorable large right shift of the oxygen dissociation curve in these patients. Under discussion for therapy are mainly affinity-increasing drugs.

Long-COVID post-viral chronic fatigue and affective symptoms are associated with oxidative damage, lowered antioxidant defenses and inflammation: a proof of concept and mechanism study

The immune-inflammatory response during the acute phase of COVID-19, as assessed using peak body temperature (PBT) and peripheral oxygen saturation (SpO2), predicts the severity of chronic fatigue, depression and anxiety symptoms 3-4 months later. The present study was performed to examine the effects of SpO2 and PBT during acute infection on immune, oxidative and nitrosative stress (IO&NS) pathways and neuropsychiatric symptoms of Long COVID. This study assayed SpO2 and PBT during acute COVID-19, and C-reactive protein (CRP), malondialdehyde (MDA), protein carbonyls (PCs), myeloperoxidase (MPO), nitric oxide (NO), zinc, and glutathione peroxidase (Gpx) in 120 Long COVID individuals and 36 controls. Cluster analysis showed that 31.7% of the Long COVID patients had severe abnormalities in SpO2, body temperature, increased oxidative toxicity (OSTOX) and lowered antioxidant defenses (ANTIOX), and increased total Hamilton Depression (HAMD) and Anxiety (HAMA) and Fibromylagia-Fatigue (FF) scores. Around 60% of the variance in the neuropsychiatric symptoms of Long COVID (a factor extracted from HAMD, HAMA and FF scores) was explained by OSTOX/ANTIOX ratio, PBT and SpO2. Increased PBT predicted increased CRP and lowered ANTIOX and zinc levels, while lowered SpO2 predicted lowered Gpx and increased NO production. Lowered SpO2 strongly predicts OSTOX/ANTIOX during Long COVID. In conclusion, the impact of acute COVID-19 on the symptoms of Long COVID is partly mediated by OSTOX/ANTIOX, especially lowered Gpx and zinc, increased MPO and NO production and lipid peroxidation-associated aldehyde formation. The results suggest that post-viral somatic and mental symptoms have a neuroimmune and neuro-oxidative origin.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis.

Chronic Fatigue, Depression and Anxiety Symptoms in Long COVID Are Strongly Predicted by Neuroimmune and Neuro-Oxidative Pathways Which Are Caused by the Inflammation during Acute Infection

BACKGROUND

Long-term coronavirus disease 2019 (long COVID) is associated with physio-somatic (chronic fatigue syndrome and somatic symptoms) and affective (depression and anxiety) symptoms. The severity of the long COVID physio-affective phenome is largely predicted by increased peak body temperature (BT) and lowered oxygen saturation (SpO2) during the acute infectious phase. This study aims to delineate whether the association of BT and SpO2 during the acute phase and the long COVID physio-affective phenome is mediated by neurotoxicity (NT) resulting from activated immune-inflammatory and oxidative stress pathways.

METHODS

We recruited 86 patients with long COVID (3-4 months after the acute phase) and 39 healthy controls and assessed serum C-reactive protein (CRP), caspase 1, interleukin (IL) 1β, IL-18, IL-10, myeloperoxidase (MPO), advanced oxidation protein products (AOPPs), total antioxidant capacity (TAC), and calcium (Ca), as well as peak BT and SpO2 during the acute phase.

RESULTS

Cluster analysis revealed that a significant part (34.9%) of long COVID patients (n = 30) show a highly elevated NT index as computed based on IL-1β, IL-18, caspase 1, CRP, MPO, and AOPPs. Partial least squares analysis showed that 61.6% of the variance in the physio-affective phenome of long COVID could be explained by the NT index, lowered Ca, and peak BT/SpO2 in the acute phase and prior vaccinations with AstraZeneca or Pfizer. The most important predictors of the physio-affective phenome are Ca, CRP, IL-1β, AOPPs, and MPO.

CONCLUSION

The infection-immune-inflammatory core of acute COVID-19 strongly predicts the development of physio-affective symptoms 3-4 months later, and these effects are partly mediated by neuro-immune and neuro-oxidative pathways.

Clinical characteristics and the risk of hospitalization of patients with coronavirus disease 2019 quarantined in a designated hotel in Japan

The aim was to investigate the clinical characteristics of coronavirus disease 2019 (COVID-19) patients who were admitted to a designated hotel, and to clarify the risk factors for hospitalization of such patients with clinical deterioration. The medical records of COVID-19 patients who were admitted to the designated hotel in Mie Prefecture, Japan, between August 2020 and September 2021 were reviewed retrospectively. Of the 1,087 COVID-19 patients who were admitted to the designated hotel, 936 patients (32.1± 12.8 years, 61.3% male) were recruited after excluding patients under the age of 15 years (n = 33), those admitted from the hospital (n = 111, 10.2%), COVID-19 vaccinated patients (n = 4, 0.4%), and those who were discharged to their own home due to social disorders (n = 3). During the study period, 884 patients (94.4%) were discharged to their own home with improving symptoms, whereas 52 patients (5.6%) were hospitalized for a deteriorating clinical condition. The logistic regression analyses showed that older age (≥ 40 years), higher body mass index (≥ 25 kg/m2), hypertension were the risk factors for hospitalization. As the new risk scale score based on the results of the odds ratios increased, the hospitalization rate increased significantly: 2.0% at 0-1 points, 9.7% at 2-3 points, and 28.8% at 4-5 points (p < 0.001). None of the 52 hospitalized patients died, and none developed serious complications from COVID-19 after hospitalization. In conclusion, the designated accommodation program for COVID-19 patients was safe, especially for those with a low risk for hospitalization.

An efficacy and safety report based on randomized controlled single-blinded multi-centre clinical trial of ZingiVir-H, a novel herbo-mineral formulation designed as an add-on therapy in adult patients with mild to moderate COVID-19

OBJECTIVE

Coronaviruses, hence named because of the crown-like spikes on the viral envelope, are members of Coronaviridae family and Order Nidovirales. SARS-CoV-2 is the seventh human pathogenic coronavirus identified after HCoV-229E, HCoV-OC43, SARS-CoV (SARS-CoV-1), HCoV-NL63, CoV-HKU1, and MERS-CoV. SARS-Cov-2 is highly similar to SARS-CoV. COVID-19 is the corresponding acute disease caused by SARS-CoV-2 that was initially reported in Wuhan, China towards the end of 2019 and spread to millions of humans globally. Unfortunately, limited studies were available on the efficacy of antiviral drugs to treat COVID-19 at the time of this study. ZingiVir-H is an Ayurvedic formulation for use in early therapy of viral disease. This clinical trial was planned to investigate (1) the efficacy and safety of ZingiVir-H and (2) the efficacy of ZingiVir-H as an add-on therapy to the standard of care in hospitalized adults diagnosed with COVID-19.

METHODS

A total of 123 eligible subjects as per inclusion criteria were randomized within the study. Three subjects later declined to participate in the study and four subjects didn't meet inclusion criteria, which brought the final evaluable subject count to 116 for the efficacy and safety endpoint analysis. Thus, a total of 116 patients were equally randomised into two groups, namely, ZingiVir-H and Placebo for this clinical trial. The study patients were assigned to receive either ZingiVir-H or Placebo along with the standard of care as per the National Indian COVID-19 treatment protocol. The time interval until a negative RT-PCR obtained, was evaluated during treatment with ZingiVir-H or Placebo for ten days. Liver and kidney function tests were regularly assessed to ensure the safety profile of ZingiVir-H.

RESULTS

The study found that patients who were administered ZingiVir-H had a median recovery time of 5 days (95% confidence interval (CI) 5-5) when compared to 6 days (95% CI 5-6) in those who received Placebo. Besides, in Ordinal Scale analysis of all the patients treated with ZingiVir-H demonstrated significant redistribution to a better clinical status from ordinal scale 5 to 6 and 7 within five to seven days when compared to that of placebo treatment. The time required for clinical improvement and the number of days needed for hospitalization was significantly less in the ZingiVir-H treated group when compared to placebo. The absence of liver and kidney function changes affirmed the safety profile of ZingiVir-H. No serious adverse events were reported in ZingiVir-H treated patients.

CONCLUSION

We found that ZingiVir-H is effective and safe in managing COVID-19 infections and delaying the disease progression from mild to moderate and moderate to severe. To the best of our knowledge, this is the first clinical trial report on the efficacy/safety of a herbo-mineral Ayurvedic drug against COVID-19 as of yet.

TRIAL REGISTRATION

Clinical Trial Registry of India CTRI/2020/04/024883. Registered on 28/04/2020.

Vital Signs Sensing Gown Employing ECG-Based Intelligent Algorithms

This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can be assessed by monitoring vital signs, which typically include blood pressure, body temperature, heart rate, and respiration rate. The miniaturized monitoring device is composed of a compact circuit which can acquire two kinds of physiological signals including bioelectrical potentials and skin surface temperature. These two signals were pre-processed in the circuit and transmitted to the intelligent computation platform for further analysis using three algorithms, which incorporate R-wave detection, ECG-derived respiration, and core body temperature estimation. After the processing, the derived vital signs would be displayed on a portable device screen, including ECG signals, heart rate (HR), respiration rate (RR), and core body temperature. An experiment for validating the performance of the intelligent computation platform was conducted in clinical practices. Thirty-one participants were recruited in the study (ten healthy participants and twenty-one clinical patients). The results showed that the relative error of HR is lower than 1.41%, RR is lower than 5.52%, and the bias of core body temperature is lower than 0.04 °C in both healthy participant and clinical patient trials. In this study, a miniaturized monitoring device and three algorithms which derive vital signs including HR, RR, and core body temperature were integrated for developing the vital signs sensing gown. The proposed sensing gown outperformed the commonly used equipment in terms of usability and price in clinical practices. Employing algorithms for estimating vital signs is a continuous and non-invasive approach, and it could be a novel and potential device for home-caring and clinical monitoring, especially during the pandemic.

Fever temperatures modulate intraprotein dynamics and enhance the binding affinity between monoclonal antibodies and the Spike protein from SARS-CoV-2

Fever is a typical symptom of most infectious diseases. While prolonged fever may be clinically undesirable, mild reversible fever (<39℃, 312 K) can potentiate the immune responses against pathogens. Here, using molecular dynamics and free energy calculations, we investigated the effect of febrile temperatures (38℃ to 40℃, 311 K to 313 K) on the immune complexes formed by the SARS-CoV-2 spike protein with two neutralizing monoclonal antibodies. In analyzing the conformational dynamics of the interactions between the antibodies and the spike protein under different thermal conditions, we found that, at mild fever temperatures (311–312 K), the binding affinities of the two antibodies improve when compared to the physiological body temperature (37℃, 310 K). Furthermore, only at 312 K, antibodies exert distinct mechanical effects on the receptor binding domains of the spike protein that may hinder SARS-CoV-2 infectivity. Enhanced antibody binding affinity may thus be obtained using appropriate temperature conditions.

Lowered Quality of Life in Long COVID Is Predicted by Affective Symptoms, Chronic Fatigue Syndrome, Inflammation and Neuroimmunotoxic Pathways

The physio-affective phenome of Long COVID-19 is predicted by (a) immune-inflammatory biomarkers of the acute infectious phase, including peak body temperature (PBT) and oxygen saturation (SpO2), and (b) the subsequent activation of immune and oxidative stress pathways during Long COVID. The purpose of this study was to delineate the effects of PBT and SpO2 during acute infection, as well as the increased neurotoxicity on the physical, psychological, social and environmental domains of health-related quality of life (HR-QoL) in people with Long COVID. We recruited 86 participants with Long COVID and 39 normal controls, assessed the WHO-QoL-BREF (World Health Organization Quality of Life Instrument-Abridged Version, Geneva, Switzerland) and the physio-affective phenome of Long COVID (comprising depression, anxiety and fibromyalgia-fatigue rating scales) and measured PBT and SpO2 during acute infection, and neurotoxicity (NT, comprising serum interleukin (IL)-1β, IL-18 and caspase-1, advanced oxidation protein products and myeloperoxidase, calcium and insulin resistance) in Long COVID. We found that 70.3% of the variance in HR-QoL was explained by the regression on the physio-affective phenome, lowered calcium and increased NT, whilst 61.5% of the variance in the physio-affective phenome was explained by calcium, NT, increased PBT, lowered SpO2, female sex and vaccination with AstraZeneca and Pfizer. The effects of PBT and SpO2 on lowered HR-QoL were mediated by increased NT and lowered calcium yielding increased severity of the physio-affective phenome which largely affects HR-QoL. In conclusion, lowered HR-Qol in Long COVID is largely predicted by the severity of neuro-immune and neuro-oxidative pathways during acute and Long COVID.

Low Oxygen Saturation of COVID-19 in Patient Case Fatalities, Limpopo Province, South Africa

On 1 August 2020, South Africa’s Minister of Health announced that more than half of a million coronavirus cases were confirmed in the country. South Africa was by far the hardest-hit country in Africa, accounting for half of all infections reported across the continent. The prevalence of underlying conditions such as fever and blood oxygen saturation (SpO2) has been known known to be a significant determinant in the hospitalisation of COVID-19 patients. Low oxygen saturation on admission was reported as a strong predictor of in-hospital mortality in COVID-19 patients. The study sought to assess the association between body temperature and other clinical risk factors with low SpO2 among COVID-19 inpatient case fatalities. A quantitative retrospective study was carried out in Limpopo Province, employing secondary data from the Limpopo Department of Health (LDoH) on COVID-19 inpatients case fatalities across all districts in the province. The chi-square test and Pearson’s correlation coefficient were used to assess the relationship between body temperature and clinical risk factors with SpO2 levels. The findings of this paper indicated that age (older age), chills, sore throat, anosmia, dysgeusia, myalgia/body aches, diarrhoea and HIV/AIDS were associated with low SpO2 in-hospital mortality in COVID-19 patients. Nasal prongs and a face mask with a reservoir for respiratory support cannula were commonly used patient interfaces to provide supplemental oxygen, with the use of only a high-flow nasal cannula (HFNC) being minimal (7%). The majority of COVID-19 inpatient fatalities had normal body temperature (<38 °C) and SpO2, with no correlation between the two variables. Considering temperature screening as a possible strategy to combat the spread of COVID-19 or suspicious COVID-19 cases appeared, then, to be a pointless exercise. This study aimed to recommend new clinical criteria for detecting COVID-19 cases.

Admission vital signs as predictors of COVID-19 mortality: a retrospective cross-sectional study

Background

COVID-19 remains a major healthcare concern. Vital signs are routinely measured on admission and may provide an early, cost-effective indicator of outcome – more so in developing countries where such data is scarce. We sought to describe the association between six routinely measured admission vital signs and COVID-19 mortality, and secondarily to derive potential applications for resource-limited settings.

Methods

Retrospective analysis of consecutive patients admitted to King Edward VIII Hospital, South Africa, with COVID-19 during June–September 2020 was undertaken. The sample was subdivided into survivors and non-survivors and comparisons made in terms of demographics and admission vital signs. Univariate and multivariate analysis of predictor variables identified associations with in-hospital mortality, with the resulting multivariate regression model evaluated for its predictive ability with receiver operating characteristic (ROC) curve analysis.

Results

The 236 participants enrolled comprised 153(77.54%) survivors and 53(22.46%) non-survivors. Most participants were Black African(87.71%) and female(59.75%) with a mean age of 53.08(16.96) years. The non-survivor group demonstrated a significantly lower median/mean for admission oxygen saturation (%) [87(78–95) vs. 96(90–98)] and diastolic BP (mmHg) [70.79(14.66) vs. 76.3(12.07)], and higher median for admission respiratory rate (breaths/minute) [24(20–28) vs. 20(20–23)] and glucose (mmol/l) [10.2(6.95–16.25) vs. 7.4(5.5–9.8)]. Age, oxygen saturation, respiratory rate, glucose and diastolic BP were found to be significantly associated with mortality on univariate analysis. A log rank test revealed significantly lower survival rates in patients with an admission oxygen saturation < 90% compared with ≥90% (p = 0.001). Multivariate logistic regression revealed a significant relationship between age and oxygen saturation with in-hospital mortality (OR 1.047; 95% CI 1.016–1.080; p = 0.003 and OR 0.922; 95% CI 0.880–0.965; p = 0.001 respectively). A ROC curve analysis generated an area under the curve (AUC) of 0.778 (p < 0.001) when evaluating the predictive ability of oxygen saturation, respiratory rate, glucose and diastolic BP for in-hospital death. This improved to an AUC of 0.832 (p < 0.001) with the inclusion of age.

Conclusion

A multivariate regression model comprising admission oxygen saturation, respiratory rate, glucose and diastolic BP (with/without age) demonstrated promising predictive capacity, and may provide a cost-effective means for early prognostication of patients admitted with COVID-19 in resource-limited settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00631-7.

Heat Treatment Promotes Ubiquitin-Mediated Proteolysis of SARS-CoV-2 RNA Polymerase and Decreases Viral Load

Despite extensive efforts, COVID-19 pandemic caused by the SARS-CoV-2 virus is still at large. Vaccination is an effective approach to curb virus spread, but several variants (e.g., delta, delta plus, omicron, and IHU) appear to weaken or possibly escape immune protection. Thus, novel and quickly scalable approaches to restrain SARS-CoV-2 are urgently needed. Multiple evidences showed thermal sensitivity of SARS-CoV-2 and negative correlation between environmental temperature and COVID-19 transmission with unknown mechanism. Here, we reveal a potential mechanism by which mild heat treatment destabilizes the wild-type RNA-dependent RNA polymerase (also known as nonstructural protein 12 (NSP12)) of SARS-CoV-2 as well as the P323L mutant commonly found in SARS-CoV-2 variants, including omicron and IHU. Mechanistically, heat treatment promotes E3 ubiquitin ligase ZNF598-dependent NSP12 ubiquitination leading to proteasomal degradation and significantly decreases SARS-CoV-2 RNA copy number and viral titer. A mild daily heat treatment maintains low levels of both wild-type and P323L mutant of NSP12, suggesting clinical potential. Collectively, this novel mechanism, heat-induced NSP12 degradation, suggests a prospective heat-based intervention against SARS-CoV-2.

Real-World Efficacy of Regdanvimab on Clinical Outcomes in Patients with Mild to Moderate COVID-19

Background: This study aims to evaluate the real-world effectiveness of regdanvimab on clinical outcomes in patients with mild to moderate coronavirus disease 2019 (COVID-19). Methods: This retrospective observational study included 152 patients (89 received regdanvimab and 63 did not) diagnosed with mild to moderate COVID-19 between August 2021 and October 2021 and admitted to Armed Forces Goyang Hospital. We collected information on the use of regdanvimab, remdesivir, dexamethasone, and supplemental oxygen; symptom severity score (SSS); and laboratory test results. A linear mixed-effects model was used to test the effectiveness of regdanvimab usage on SSS and the results of laboratory tests. A multivariate logistic regression model was used to calculate the odds ratio (OR) for additional therapeutic options, such as remdesivir, dexamethasone, and supplemental oxygen. Results: The patients who received regdanvimab were older, showed a higher rate of vaccination, and had a higher Charlson comorbidity index, initial body temperature, and percentages of pneumonia at admission. The use of regdanvimab showed no interactive effects on the SSS and laboratory findings. Older age, male sex, obesity, high initial body temperature, and the presence of pneumonia at admission were associated with increased ORs for the use of these additional treatments. The use of regdanvimab reduced the probability of requiring additional therapies such as remdesivir, dexamethasone, and oxygen supplementation by 90.3% (95% confidence interval (CI), 60.3–97.6), 85.8% (95% CI, 34.2–96.9), and 89.8% (95% CI, 48.3–98), respectively. Conclusions: Regdanvimab usage was well tolerated and was associated with a decreased probability of requiring remdesivir, dexamethasone, and oxygen therapy. However, changes in SSS were not significantly different by the drug usage.

Development of a repeated-measures predictive model and clinical risk score for mortality in ventilated COVID-19 patients

PURPOSE

The COVID-19 pandemic has caused intensive care units (ICUs) to reach capacities requiring triage. A tool to predict mortality risk in ventilated patients with COVID-19 could inform decision-making and resource allocation, and allow population-level comparisons across institutions.

METHODS

This retrospective cohort study included all mechanically ventilated adults with COVID-19 admitted to three tertiary care ICUs in Toronto, Ontario, between 1 March 2020 and 15 December 2020. Generalized estimating equations were used to identify variables predictive of mortality. The primary outcome was the probability of death at three-day intervals from the time of ICU admission (day 0), with risk re-calculation every three days to day 15; the final risk calculation estimated the probability of death at day 15 and beyond. A numerical algorithm was developed from the final model coefficients.

RESULTS

One hundred twenty-seven patients were eligible for inclusion. Median ICU length of stay was 26.9 (interquartile range, 15.4-52.0) days. Overall mortality was 42%. From day 0 to 15, the variables age, temperature, lactate level, ventilation tidal volume, and vasopressor use significantly predicted mortality. Our final clinical risk score had an area under the receiver-operating characteristics curve of 0.9 (95% confidence interval [CI], 0.8 to 0.9). For every ten-point increase in risk score, the relative increase in the odds of death was approximately 4, with an odds ratio of 4.1 (95% CI, 2.9 to 5.9).

CONCLUSION

Our dynamic prediction tool for mortality in ventilated patients with COVID-19 has excellent diagnostic properties. Notwithstanding, external validation is required before widespread implementation.

Comparative Analysis of Human Body Temperatures Measured with Noncontact and Contact Thermometers

Body temperature measurement is one of the basic methods in clinical diagnosis. The problems of thermometry—interpretation of the accuracy and repeatability of various types of thermometers—are still being discussed, especially during the current pandemic in connection with the SARS-CoV-2 virus responsible for causing the COVID-19 disease. The aim of the study was to compare surface temperatures of the human body measured by various techniques, in particular a noncontact thermometer (infrared) and contact thermometers (mercury, mercury-free, electronic). The study included 102 randomly selected healthy women and men (age 18–79 years). The Bland–Altman method was used to estimate the 95% reproducibility coefficient, i.e., to assess the degree of conformity between different attempts. Temperatures measured with contact thermometers in the armpit are higher than temperatures measured without contact at the frontal area of the head. The methods used to measure with contact thermometers and a noncontact infrared thermometer statistically showed high measurement reliability. In order to correctly interpret the result of measuring human body temperature, it is necessary to indicate the place of measurement and the type of thermometer used.

Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes

OBJECTIVES

Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019.

DESIGN

Retrospective observational study.

SETTING

Four hospitals within an academic healthcare system from March 2020 to February 2021.

PATIENTS

All adult patients hospitalized with coronavirus disease 2019.

INTERVENTIONS

Using a validated group-based trajectory model, we classified patients into four previously defined temperature trajectory subphenotypes using oral temperature measurements from the first 72 hours of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes.

MEASUREMENTS AND MAIN RESULTS

The 5,903 hospitalized coronavirus disease 2019 patients were classified into four subphenotypes: hyperthermic slow resolvers (n = 1,452, 25%), hyperthermic fast resolvers (1,469, 25%), normothermics (2,126, 36%), and hypothermics (856, 15%). Hypothermics had abnormal coagulation markers, with the highest d-dimer and fibrin monomers (p < 0.001) and the highest prevalence of cerebrovascular accidents (10%, p = 0.001). The prevalence of venous thromboembolism was significantly different between subphenotypes (p = 0.005), with the highest rate in hypothermics (8.5%) and lowest in hyperthermic slow resolvers (5.1%). Hyperthermic slow resolvers had abnormal inflammatory markers, with the highest C-reactive protein, ferritin, and interleukin-6 (p < 0.001). Hyperthermic slow resolvers had increased odds of mechanical ventilation, vasopressors, and 30-day inpatient mortality (odds ratio, 1.58; 95% CI, 1.13-2.19) compared with hyperthermic fast resolvers. Over the course of the pandemic, we observed a drastic decrease in the prevalence of hyperthermic slow resolvers, from representing 53% of admissions in March 2020 to less than 15% by 2021. We found that dexamethasone use was associated with significant reduction in probability of hyperthermic slow resolvers membership (27% reduction; 95% CI, 23-31%; p < 0.001).

CONCLUSIONS

Hypothermics had abnormal coagulation markers, suggesting a hypercoagulable subphenotype. Hyperthermic slow resolvers had elevated inflammatory markers and the highest odds of mortality, suggesting a hyperinflammatory subphenotype. Future work should investigate whether temperature subphenotypes benefit from targeted antithrombotic and anti-inflammatory strategies.

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

Understanding the cross-talk between mediators of infertility and COVID-19

COVID-19 is the ongoing health emergency affecting individuals of all ages around the globe. Initially, the infection was reported to affect pulmonary structures. However, recent studies have delineated the impacts of COVID-19 on the reproductive system of both men and women. Hence, the present review aims to shed light on the distribution of SARS-CoV-2 entry factors in various reproductive organs. In addition, impacts of COVID-19 mediators like disrupted renin angiotensin system, oxidative stress, cytokine storm, fever, and the mental stress on reproductive physiology have also been discussed. For the present study, various keywords were used to search literature on PubMed, ScienceDirect, and Google Scholar databases. Articles were screened for relevancy and were studied in detail for qualitative synthesis of the review. Through our literature review, we found a multitude of effects of COVID-19 mediators on reproductive systems. Studies reported expression of receptors like ACE-2, TMPRSS2, and CD147 in the testes, epididymis, prostrate, seminal vesicles, and ovarian follicles. These proteins are known to serve as major SARS-CoV-2 entry factors. The expression of lysosomal cathepsins (CTSB/CTSL) and/ neuropilin-1 (NRP-1) are also evident in the testes, epididymis, seminal vesicles, fallopian tube, cervix, and endometrium. The binding of viral spike protein with ACE-2 was found to alter the renin-angiotensin cascade, which could invite additional infertility problems. Furthermore, COVID-19 mediated cytokine storm, oxidative stress, and elevated body temperature could be detrimental to gametogenesis, steroidogenesis, and reproductive cycles in patients. Finally, social isolation, confinement, and job insecurities have fueled mental stress and frustration that might promote glucocorticoid-mediated subnormal sperm quality in men and higher risk of miscarriage in women. Hence, the influence of COVID-19 on the alteration of reproductive health and fertility is quite apparent.

Carbohydrates-based diagnosis, prophylaxis and treatment of infectious diseases: Special emphasis on COVID-19

COVID-19 pandemic is taking a dangerous turn due to unavailability of approved and effective vaccines and therapy. Currently available diagnostic techniques are time-consuming, expensive, and maybe impacted by the mutations produced in the virus. Therefore, investigation of novel, rapid, and economic diagnosis techniques, prophylactic vaccines and targeted efficacious drug delivery systems as treatment strategy is imperative. Carbohydrates are essential biomolecules which also act as markers in the realization of immune systems. Moreover, they exhibit antiviral, antimicrobial, and antifungal properties. Carbohydrate-based vaccines and therapeutics including stimuli sensitive systems can be developed successfully and used effectively to fight COVID-19. Thus, carbohydrate-based diagnostic, prophylactic and therapeutic alternatives could be promising to defeat COVID-19 propitiously. Morphology of SARS-CoV-2 and its relevance in devising combat strategies has been discussed. Carbohydrate-based approaches for tackling infectious diseases and their importance in the design of various diagnostic and treatment modalities have been reviewed.

Predictors of Mortality in Hospitalized COVID-19 Patients Complicated With Hypotension and Hypoxemia: A Retrospective Cohort Study

Introduction: COVID-19 patients with hypotension and hypoxemia had a significantly worse outcome. The purpose of this research was to ascertain the risk factors affecting the prognoses of these patients and to develop appropriate prognostic prediction tools.

Methods: From March 1, 2020, to April 16, 2020, a retrospective cohort analysis of COVID-19 patients with hypotension and hypoxemia was performed. The univariate and multivariate analyses were performed to identify the associated risk factors influencing the prognosis of COVID-19 patients with hypotension and hypoxemia, and the selected variables were then utilized to construct and validate the prediction model for these patients.

Results: Three hundred and twenty-seven COVID-19 patients with hypotension and hypoxemia who met the inclusion and exclusion criteria were included in this study. Age, temperature, troponin, and blood glucose were related to mortality in COVID-19 patients with hypotension and hypoxemia in both univariate and multivariate analyses. The MFP model (multiple fractional polynomial model), full model, and stepwise model were utilized to build the prediction model, and their AUCs were, respectively, 0.902 (0.868, 0.936), 0.902 (0.868, 0.936), and 0.902 (0.868, 0.936). Because the sample size for this research was limited, we utilized bootstrapping for internal validation. The AUCs of Bootstrap full and Bootstrap stepwise were 0.902 (0.867, 0.936) and 0.902 (0.868, 0.936), respectively.

Conclusion: Age, temperature, troponin, and blood glucose levels were associated with mortality in COVID-19 patients with hypotension and hypoxemia. Additionally, the prediction model developed using the variables above showed a high predictive value for predicting the prognosis of these individuals.

Prognosis of patients hospitalized with a diagnosis of COVID-19 pneumonia in a tertiary hospital in Turkey

BACKGROUND

SARS-CoV2/COVID-19 emerged in China and caused a global pandemic in 2020. The mortality rate has been reported to be between 0% and 14.6% in all patients. In this study, we determined the clinical and laboratory parameters of COVID-19 related morbidity and mortality in our hospital.

OBJECTIVES

Investigate the relationship between demographic, clinical, and laboratory parameters on COVID-19-related morbidity and mortality.

DESIGN

Retrospective observational study.

SETTINGS

Tertiary care hospital.

PATIENTS AND METHODS

Patients diagnosed with COVID-19 pneumonia from March until the end of December were included in the study.

MAIN OUTCOME MEASURES

The relationship between demographic, clinical, and laboratory parameters and the morbidity and mortality rates of patients diagnosed with COVID-19.

SAMPLE SIZE

124 patients RESULTS: The mortality rate was 9.6% (12/124). Coronary artery disease (P<.0001) diabetes mellitus (P=.04) fever (>38.3°C) at presentation (P=.04) hypertension (P<.0001), and positive smoking history (P<.0001) were significantly associated with mortality. Patients who died were older, had a higher comorbid disease index, pneumonia severity index, fasting blood glucose, baseline serum creatinine, D-dimer, and had lower baseline haemoglobin, SaO₂, percentage of lymphocyte counts and diastolic blood pressure. Patients admitted to the ICU were older, had a higher comorbidity disease index, pneumonia severity index, C-reactive protein, WBC, D-dimer, creatinine, number of antibiotics used, longer O₂ support duration, lower hemoglobin, lymphocyte (%), and baseline SaO₂ (%).

CONCLUSIONS

Our results were consistent with much of the reported data. We suggest that the frequency, dosage, and duration of steroid treatment should be limited.

LIMITATIONS

Low patient number, uncertain reason of mortality, no standard treatment regimen, limited treatment options, like ECMO.

CONFLICT OF INTEREST

None.

Effects of dexamethasone on immune dysfunction and ventilator-associated pneumonia in COVID-19 acute respiratory distress syndrome: an observational study

Dexamethasone improves survival of patients with COVID-19 acute respiratory distress syndrome, but might shorten the delay between the start of invasive mechanical ventilation and the occurrence of ventilator-associated pneumonia, suggesting possible worsening of COVID-19-induced immune dysfunction with this treatment. In a prospective observational study, we found that mechanically ventilated patients with COVID-19 treated with dexamethasone presented earlier ventilator-associated pneumonia, had significantly lower monocyte Human Leukocyte Antigen-DR expression and number of circulating CD4 + cells after ICU admission, than those not treated with corticoids.

Home-Based Physiological Monitoring of Patients with COVID-19

The COVID-19 pandemic has necessitated the rise of telehealth modalities to relieve the incredible stress the pandemic has placed on the healthcare system. This rise has seen the emergence of new software, applications, and hardware for home-based physiological monitoring, leading to the promise of innovative predictive and therapeutic practices. This article is a literature-based review of the most promising technologies and advances regarding home-based physiological monitoring of patients with COVID-19. We conclude that the applications currently on the market, while helping stem the flow of patients to the hospital during the pandemic, require additional evidence related to improvement in patient outcomes. However, new devices and technology are a promising and successful venture into home-based monitoring with clinical implications reaching far into the future.

Characteristics and outcome of COVID-19 cases in Saudi Arabia: Review of six-months of data (March-August 2020)

Background

This study presents the demographic, epidemiological, and clinical characteristics of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia (KSA). It identifies the important predictors of the disease prognosis.

Methods

The study reviewed and analysed a sample of 307,010 confirmed symptomatic COVID-19 cases, between March and August 2020, available in the health electronic surveillance system (HESN) of the Ministry of Health of KSA. Descriptive and univariate analyses were conducted.

Results

The overall estimated prevalence of symptomatic COVID-19 cases in KSA between March and August 2020 was 6.1% . The estimated incidence proportion was 879.7 per 100,000 population. The overall case fatality ratio was 2.0%. Males represented 63.9% , with a mean age of 35.1 ± 16.6 years. Young adults (16-39 years) were the most affected ages (53.3%). Fever (90.5%) with a mean body temperature of 37.4 ± 2.0 Celsius, cough (90%), and sore throat (77.4%) were the most prevalent symptoms. A history of contact with a confirmed COVID-19 case was reported in 98.8% of patients.Males (2.1%) and elderly cases aged 65-99 years (25.6%) had the highest association with mortality (p < .001). Among the clinical characteristics investigated, low oxygen saturation (SpO₂ ≤ 93%) had the highest association with hospital admission (50.8%) and mortality (19.1%) (p < .001). Cases with cardiovascular diseases (28.6%) and malignancy (28%) demonstrated the highest associations with mortality compared to other underlying diseases (p < .001).

Conclusions

In KSA, the prevalent symptoms of COVID-19 are fever, cough, and sore throat. Makkah and Almadinah regions are significantly associated with highest burden of mortality. The low level of oxygen saturation, high fever, old age, and underlying cardiovascular disease are the most important predictors for prognosis.

Fever management in critically ill COVID-19 patients: a retrospective analysis

BACKGROUND

Fever has been reported as a common symptom in COVID-19 patients. The aim of the study was to describe the characteristics of COVID-19 critically ill patients with fever and to assess if fever management had an impact on some physiologic variables.

METHODS

This is a retrospective monocentric cohort analysis of critically ill COVID-19 patients admitted to the Department of Intensive Care Unit (ICU) of Erasme Hospital, Brussels, Belgium, between March 2020 and May 2020. Fever was defined as body temperature ≥ 38° C during the ICU stay. We assessed the independent predictors of fever during ICU stay. We reported the clinical and physiological variables before and after the first treated episode of fever during the ICU stay.

RESULTS

A total of 72 critically ill COVID-19 patients were admitted to the ICU over the study period and were all eligible for the final analysis; 53 (74%) of them developed fever, after a median of 4 [0-13] hours since ICU admission. In the multivariable analysis, male gender (OR 5.41 [C.I. 95% 1.34-21.92]; p=0.02) and low PaO2/FiO2 ratio (OR 0.99 [C.I. 95% 0.99-1.00]; p=0.04) were independently associated with fever. After the treatment of the first febrile episode, heart rate and respiratory rate significantly decreased together with an increase in PaO2 and SaO2.

CONCLUSIONS

In our study, male gender and severe impairment of oxygenation were independently associated with fever in critically ill COVID-19 patients. Fever treatment reduced heart rate and respiratory rate and improved systemic oxygenation.

Smart Bracelet System for Temperature Monitoring and Movement Tracking Analysis

In several epidemic diseases, one of the main symptoms exhibited by people is abnormal body temperature. Therefore, monitoring body temperature is crucial for preventing the spread of infectious diseases and facilitating timely responses. This study presents a wearable bracelet that can be used as a temperature monitoring and trajectory analysis system. The temperature sensor in the bracelet can effectively monitor the body temperature of the wearer in the target scene, and the data transmission between the bracelet and the data collector can effectively detect the movement range and trajectory of the wearer. Through these, the whole set can also detect the direct and indirect contact of the wearer in any period of time, which is very helpful for the prevention and control of infectious diseases and the isolation of potentially infected persons.

Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients

OBJECTIVES

Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day.

METHODS

For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients' COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3.

RESULTS

Data from 117 patients were included. The models' AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI -0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI -0.021 to 0.097, p=0.10) between model 3 and 2.

CONCLUSIONS

By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

Gut Microbiota Dysbiosis Correlates with Abnormal Immune Response in Moderate COVID-19 Patients with Fever

Background

Most COVID-19 patients are moderate, and fever is the most common clinical manifestation and associated with poorer prognosis. Gut microbiota may also play important roles in COVID-19 pathogenesis. However, the association between gut microbiota and fever in individuals with moderate COVID-19 remains unclear.

Methods

We compared the clinical features and laboratory results of 187 moderate COVID-19 patients with fever and without fever and identified several inflammatory markers in patients with fever. Then, we performed gut metagenome-wide association study for 31 individuals to identify the microbes and their epitopes which have potential role in fever and hyperinflammation.

Results

Among 187 moderate COVID-19 patients, 127 (67.9%) patients presented with fever. Lymphocytes, CD3+ T cells, CD4+ T cells and the ratio of CD4+ T cells to CD8+ T cells were significantly reduced, while AST, LDH, CRP, IL-6 and IL-10 were significantly elevated in patients with fever. Gut microbiome composition was significantly altered in patients with fever compared with those with non-fever. Opportunistic pathogens such as Enterococcus faecalis and Saccharomyces cerevisiae were enriched in patients with fever. E. faecalis was positively correlated with LDH and D-dimer and negatively correlated with CD8+T cells and IL-4, while S. cerevisiae was positively correlated with diarrhea symptom. Furthermore, several species with anti-inflammatory and protective effects, such as Bacteroides fragilis and Eubacterium ramulus, were enriched in patients with non-fever. B. fragilis was positively correlated with lymphocytes, and E. ramulus was negatively correlated with LDH, AST and IL-6. Finally, we found that several bacterial epitopes of GroEL, a homolog of human HSP60, were enriched in patients with fever and positively correlated with IL-6, IL-10, WBC, neutrophils, D-dimer, LDH, CRP, and E. faecalis.

Conclusion

Gut microbiota dysbiosis correlates with abnormal immune response in moderate COVID-19 patients with fever.

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

The COVID-19 outbreak is in its growing stage due to the lack of standard diagnosis for the patients. In recent times, various models with machine learning have been developed to predict and diagnose novel coronavirus. However, the existing models fail to take an instant decision for detecting the COVID-19 patient immediately and cannot handle multiple medical sensor data for disease prediction. To handle such challenges, we propose an intelligent health monitoring and prediction framework, namely the iCovidCare model for predicting the health status of COVID-19 patients using the ensemble Random Forest (eRF) technique in edge networks. In the proposed framework, a rule-based policy is designed on the local edge devices to detect the risk factor of a patient immediately using monitoring Temperature sensor values. The real-time health monitoring parameters of different medical sensors are transmitted to the centralized cloud servers for future health prediction of the patients. The standard eRF technique is used to predict the health status of the patients using the proposed data fusion and feature selection strategy by selecting the most significant features for disease prediction. The proposed iCovidCare model is evaluated with a synthetic COVID-19 dataset and compared with the standard classification models based on various performance matrices to show its effectiveness. The proposed model has achieved 95.13% accuracy, which is higher than the standard classification models.

Diabetes is most important cause for mortality in COVID-19 hospitalized patients: Systematic review and meta-analysis

The presence of SARS-CoV-2 was officially documented in Europe at the end of February 2020. Despite many observations, the real impact of COVID-19 in the European Union (EU), its underlying factors and their contribution to mortality and morbidity outcomes were never systematically investigated. The aim of the present work is to provide an overview and a meta-analysis of main predictors and of country differences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-associated mortality rate (MR) in hospitalized patients. Out of 3714 retrieved articles, 87 studies were considered, including 35,486 patients (mean age 60.9 ± 8.2 years) and 5867 deaths. After adjustment for confounders, diabetes mellitus was the best predictors of MR in an age- and sex-dependent manner, followed by chronic pulmonary obstructive diseases and malignancies. In both the US and Europe, MR was higher than that reported in Asia (25[20;29] % and 20[17;23] % vs. 13[10;17]%; both p < 0.02). Among clinical parameters, dyspnea, fatigue and myalgia, along with respiratory rate, emerged as the best predictors of MR. Finally, reduced lymphocyte and platelet count, along with increased D-dimer levels, all significantly contributed to increased mortality. The optimization of glucose profile along with an adequate thrombotic complications preventive strategy must become routine practice in diseased SARS-CoV-2 infected patients.

Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID-19 Patients

PURPOSE

While fever may be a presenting symptom of COVID-19, fever at hospital admission has not been identified as a predictor of mortality. However, hyperthermia during critical illness among ventilated COVID-19 patients in the ICU has not yet been studied. We sought to determine mortality predictors among ventilated COVID-19 ICU patients and we hypothesized that fever in the ICU is predictive of mortality.

MATERIALS AND METHODS

We conducted a retrospective cohort study of 103 ventilated COVID-19 patients admitted to the ICU between March 14 and May 27, 2020. Final follow-up was June 5, 2020. Patients discharged from the ICU or who died were included. Patients still admitted to the ICU at final follow-up were excluded.

RESULTS

103 patients were included, 40 survived and 63(61.1%) died. Deceased patients were older {66 years[IQR18] vs 62.5[IQR10], (p = 0.0237)}, more often male {48(68%) vs 22(55%), (p = 0.0247)}, had lower initial oxygen saturation {86.0%[IQR18] vs 91.5%[IQR11.5], (p = 0.0060)}, and had lower pH nadir than survivors {7.10[IQR0.2] vs 7.30[IQR0.2] (p < 0.0001)}. Patients had higher peak temperatures during ICU stay as compared to hospital presentation {103.3°F[IQR1.7] vs 100.0°F[IQR3.5], (p < 0.0001)}. Deceased patients had higher peak ICU temperatures than survivors {103.6°F[IQR2.0] vs 102.9°F[IQR1.4], (p = 0.0008)}. Increasing peak temperatures were linearly associated with mortality. Febrile patients who underwent targeted temperature management to achieve normothermia did not have different outcomes than those not actively cooled. Multivariable analysis revealed 60% and 75% higher risk of mortality with peak temperature greater than 103°F and 104°F respectively; it also confirmed hyperthermia, age, male sex, and acidosis to be predictors of mortality.

CONCLUSIONS

This is one of the first studies to identify ICU hyperthermia as predictive of mortality in ventilated COVID-19 patients. Additional predictors included male sex, age, and acidosis. With COVID-19 cases increasing, identification of ICU mortality predictors is crucial to improve risk stratification, resource management, and patient outcomes.

An emergency system for monitoring pulse oximetry, peak expiratory flow, and body temperature of patients with COVID-19 at home: Development and preliminary application

BACKGROUND

COVID-19 is characterized by a rapid change in the patient's condition, with major changes occurring over a few days. We aimed to develop and evaluate an emergency system for monitoring patients with COVID-19, which may be useful in hospitals where more severe patients stay in their homes.

METHODOLOGY/PRINCIPAL FINDINGS

The system consists of the home-based patient unit, which is set up around the patient and the hospital unit, which enables the medical staff to telemonitor the patient's condition and help to send medical recommendations. The home unit allows the data transmission from the patient to the hospital, which is performed using a cell phone application. The hospital unit includes a virtual instrument developed in LabVIEW® environment that can provide a real-time monitoring of the oxygen saturation (SpO2), beats per minute (BPM), body temperature (BT), and peak expiratory flow (PEF). Abnormal events may be fast and automatically identified. After the design details are described, the system is validated by a 30-day home monitoring study in 12 controls and 12 patients with COVID-19 presenting asymptomatic to mild disease. Patients presented reduced SpO2 (p<0.0001) and increased BPM values (p<0.0001). Three patients (25%) presented PEF values between 50 and 80% of the predicted. Three of the 12 monitored patients presented events of desaturation (SpO2<92%). The experimental results were in close agreement with the involved pathophysiology, providing clear evidence that the proposed system can be a useful tool for the remote monitoring of patients with COVID-19.

CONCLUSIONS

An emergency system for home monitoring of patients with COVID-19 was developed in the current study. The proposed system allowed us to quickly respond to early abnormalities in these patients. This system may contribute to conserving hospital resources for those most in need while simultaneously enabling early recognition of patients under acute deterioration, requiring urgent assessment.

Exploring the rationale for thermotherapy in COVID-19

Increased transmissibility of the pandemic severe acute respiratory coronavirus 2 (SARS-CoV-2) has been noted to occur at lower ambient temperatures. This is seemingly related to a better replication of most respiratory viruses, including SARS-CoV-2, at lower-than-core body temperatures (i.e., 33 °C vs 37 °C). Also, intrinsic characteristics of SARS-CoV-2 make it a heat-susceptible pathogen. Thermotherapy has successfully been used to combat viral infections in plants which could otherwise result in great economic losses; 90% of viruses causing infections in plants are positive-sense single-stranded ribonucleic acid (+ssRNA) viruses, a characteristic shared by SARS-CoV-2. Thus, it is possible to envision the use of heat-based interventions (thermotherapy or mild-temperature hyperthermia) in patients with COVID-19 for which moderate cycles (every 8-12 h) of mild-temperature hyperthermia (1-2 h) have been proposed. However, there are potential safety and mechanistic concerns which could limit the use of thermotherapy only to patients with mild-to-moderate COVID-19 to prevent disease progression rather than to treat patients who have already progressed to severe-to-critical COVID-19. Here, we review the characteristics of SARS-CoV-2 which make it a heat-susceptible virus, potential host mechanisms which could be enhanced at higher temperatures to aid viral clearance, and how thermotherapy could be investigated as a modality of treatment in patients with COVID-19 while taking into consideration potential risks.

Predictors of Intensive Care Unit Admission or Death in Patients with Coronavirus Disease 2019 Pneumonia in Istanbul, Turkey

We aimed to determine the predictors of intensive care unit (ICU) admission or death in patients with Coronavirus Disease 2019 (COVID-19) pneumonia. This retrospective and single-center study includes patients aged ≥18 years who were diagnosed with COVID-19 pneumonia (laboratory and radiologically confirmed) between March 9 and April 8, 2020. Our composite endpoint was ICU admission or in-hospital death. To evaluate the factors in the composite endpoint, univariate and multivariate logistic regression analyses were performed. A total of 336 patients with COVID-19 pneumonia were recorded. The median age was 54 years [interquartile range (IQR): 21] and 187 (55.7%) were male. Fifty-one (15.2%) patients were admitted to the ICU. In-hospital death occurred in 33 (9.8%) patients. In univariate analysis, 17 parameters were associated with the composite endpoint and procalcitonin had the highest ODDs ratio (OR=36.568 CI=5.145-259.915). Our results revealed that body temperature (OR=1.489 CI=1.023-2.167, p=0.037), peripheral capillary oxygen saturation (SpO2) (OR=0.835 CI=0.773-0.901, p<0.001), and consolidation (>25%) in chest computed tomography (OR=3.170 CI=1.218-8.252, p=0.018) at admission were independent predictors. As a result, increased body temperature, decreased SpO2, a high level of procalcitonin, and degree of consolidation in chest computed tomography may predict a poor prognosis and have utility in the management of patients.