Response to "Body temperature correlates with mortality in COVID-19 patients"

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.

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.

Comparison between the first and second COVID-19 waves in Internal Medicine wards in Milan, Italy: a retrospective observational study

COVID-19 spread in two pandemic waves in Italy between 2020 and 2021. The aim of this study is to compare the first with the second COVID-19 wave, analyzing modifiable and non-modifiable factors and how these factors affected mortality in patients hospitalized in Internal Medicine wards. Consecutive patients with SARS-CoV-2 infection and dyspnea requiring O2 supplementation were included. The severity of lung involvement was categorized according to the patients' oxygen need. Six hundred and ten SARS-CoV-2 hospitalized patients satisfied the inclusion criteria. The overall estimated 4-week mortality was similar in the two pandemic waves. Several variables were associated with mortality after univariate analysis, but they lacked the significance after multivariable adjustment. Steroids did not exert any protective effect when analyzed in time-dependent models in the whole sample; however, steroids seemed to exert a protective effect in more severe patients. When analyzing the progression to different states of O2 supplementation during hospital stay, mortality was almost exclusively associated with the use of high-flow O2 or CPAP. The analysis of the transition from one state to the other by Cox-Markov models confirmed that age and the severity of lung involvement at admission, along with fever, were relevant factor for mortality or progression.

Potential for Further Mismanagement of Fever During COVID-19 Pandemic: Possible Causes and Impacts

Fever remains an integral part of acute infectious diseases management, especially for those without effective therapeutics, but the widespread myths about "fevers" and the presence of confusing guidelines from different agencies, which have heightened during the coronavirus disease 2019 (COVID-19) pandemic and are open to alternate interpretation, could deny whole populations the benefits of fever. Guidelines suggesting antipyresis for 37.8-39°C fever are concerning as 39°C boosts the protective heat-shock and immune response (humoral, cell-mediated, and nutritional) whereas ≥40°C initiates/enhances the antiviral responses and restricts high-temperature adapted pathogens, e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strains of influenza, and measles. Urgent attention is accordingly needed to address the situation because of the potential public health consequences of the existence of conflicting guidelines in the public domain. We have in this article attempted to restate the benefits of fever in disease resolution, dispel myths, and underline the need for alignment of national treatment guidelines with that of the WHO, to promote appropriate practices and reduce the morbidity and mortality from infectious diseases, such as COVID-19.

Nursing care recommendation for pediatric COVID-19 patients in the hospital setting: A brief scoping review

BACKGROUND

The hospitalization of children during the COVID-19 pandemic has affected their physical and mental health. Pediatric nurses have faced challenges in providing high-quality nursing care for children and their families. However, the pediatric nursing care recommendations for COVID-19 patients in the hospital setting remain unclear. The current scoping review provides recommendations for nursing interventions for pediatric COVID-19 patients in the hospital setting.

METHODS AND FINDINGS

The selected articles containing management and nursing recommendations for COVID-19 that have occurred in pediatric patients ages 0-19 years old. A search strategy was developed and implemented in seven databases. We included peer-reviewed articles that reported observational or interventional studies, as well as policy papers, guides or guidelines, letters and editorials, and web articles. A total of 134 articles and other documents relevant to this review were included. We categorized the results based on The Nursing Intervention Classification (NIC) taxonomy which consists of six domains (e.g., Physiological: Basic); eleven classes (e.g., Nutrition Support); and eighteen intervention themes (e.g., Positioning, Family Presence Facilitation, Family Support, and Discharge Planning).

CONCLUSION

Apart from the intervention of physical problems, there is a need to promote patient- and family-centered care, play therapy, and discharge planning to help children and families cope with their new situation.

Research and Development of a COVID-19 Tracking System in Order to Implement Analytical Tools to Reduce the Infection Risk

The whole world is currently focused on COVID-19, which causes considerable economic and social damage. The disease is spreading rapidly through the population, and the effort to stop the spread is entirely still failing. In our article, we want to contribute to the improvement of the situation. We propose a tracking system that would identify affected people with greater accuracy than medical staff can. The main goal was to design hardware and construct a device that would track anonymous risky contacts in areas with a highly concentrated population, such as schools, hospitals, large social events, and companies. We have chosen a 2.4 GHz proprietary protocol for contact monitoring and mutual communication of individual devices. The 2.4 GHz proprietary protocol has many advantages such as a low price and higher resistance to interference and thus offers benefits. We conducted a pilot experiment to catch bugs in the system. The device is in the form of a bracelet and captures signals from other bracelets worn at a particular location. In case of contact with an infected person, the alarm is activated. This article describes the concept of the tracking system, the design of the devices, initial tests, and plans for future use.

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.