Machine learning and COVID-19: a tool for healthcare setting choice by primary care physicians

Background Primary care physicians have a crucial role in determining the appropriate healthcare setting for their confirmed or suspect COVID-19 patients. Machine learning provides science-based tools that can be used for clinical decision-making which have already been applied to the fight against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) both in the therapeutic and the prevention area. The aim of this study was to develop a machine learning-based tool to support primary care physicians to decide between home monitoring and hospitalization for their patients before diagnostic test results are available. Methods A retrospective cohort study with data from a hospital setting was performed. Patients' medical history and clinical, laboratory and radiological findings were collected and the dataset was used to train a predictive model for COVID-19 severity. The patients were divided between confirmed and suspect cases on the basis of the positivity of the nasopharyngeal RT-PCR test results. A splitting algorithm was recursively used to choose the predictor. A decision tree was built. Results A total of 198 subjects were enrolled for the study. Out of them, 28 cases were classified as mild disease, 62 as moderate disease, 64 as severe disease, and 44 as critical disease, according to WHO guidelines. The G2 value was used to determine the contribution of each obtained value to build the decision tree. The tree was, therefore, built choosing values that maximized G2 and LogWorth. SpO2 (cut point = 92%) was chosen for the optimal first split. The correspondence between inputs and outcomes was validated. Conclusions Our tool provides accurate clinical severity prediction for both confirmed and suspect COVID-19 patients. We, therefore, propose its implementation in the everyday life challenges of primary care physicians to support their clinical decision-making in providing appropriate and timely care for their patients. Key messages Primary care physicians have a crucial role in determining the appropriate healthcare setting for their confirmed or suspect COVID-19 patients. We propose a tool that provides an accurate clinical severity prediction for both confirmed and suspect COVID-19 patients to help choosing the appropriate healthcare setting for them.

Legal resposability of vaccinating doctor

The legal responsibility of the vaccinating doctor is one of the central issues in the current setting of the Covid-19 pandemic. The aim of this statement is to outline the profiles of the medical legal liability, with a focus on the figure of the vaccinating physician, in criminal, civil, and disciplinary terms, based on the Italian legislation in force. The vaccinating doctor responds for his work in the field of vaccination in the same way as any other health service should perform (diagnostic, therapeutic, etc.). Helpful in this context is the adoption of the L. 76/2021;it was developed to find a balance between safeguarding the person privacy and greater guarantees for the doctor. This law introduces a criminal shield that can put a limit to litigation, curbing the phenomenon of so-called defensive medicine. The climate of uncertainty and fear of legal repercussions for the doctors, and the constant updating and redefinition of the indications of operability in the vaccination campaigns, underline the need to focus on the knowledge of the responsibilities and the safeguard of the vaccinating doctors. In addition to the regulatory cornerstones, the statement also addresses the issue of informed consent and the role of the occupational doctor as a central figure in the vaccination campaign in the workplace.

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

Healthcare systems strategies to manage pandemics transition phases: a systematic review

Background Several countries facing the COVID-19 pandemic were not prepared to manage it. Public health mitigation strategies, ranging from isolation of infected cases to implementation of national lockdowns, proved their effectiveness for the outbreaks control. However, the adjustment of public health measures is crucial during transition phases to avoid new outbreaks. To address the need for designing evidence-based strategies, we performed a systematic review, identifying healthcare systems approaches, experiences and recommendations used to manage COVID-19 and other epidemics. Methods PubMed, Web of Science, Scopus and Cochrane were searched to retrieve eligible studies of any study design, published in English until April 17th, 2020. Double-blinded screening process was conducted by titles/abstracts and subsequently eligible full-texts were read and pertinent data were extracted. We performed a narrative analysis of each implemented strategy. Results We included a total of 24 articles addressing the public health strategies implemented for respiratory viral infections outbreaks as COVID-19, influenza A H1N1, MERS and SARS. The identified strategies are ascribable to two main categories: healthcare systems management at a national level and healthcare providers strategies at a local level. The key components of the transition strategies regarded the implementation of evidence-based contextual policies, intrahospital management approaches, community healthcare facilities, non-pharmaceutical interventions, enhanced surveillance, workplace preventive measures, mental health interventions and communication plans. Conclusions The identified healthcare systems strategies applied worldwide to face epidemics or pandemics, are a useful knowledge base to inform decision-makers about control measures to be used in the transition phases of COVID-19 and beyond. Key messages Healthcare systems strategies that can be implemented to manage pandemics/epidemics transition phases are a useful knowledge base to inform policy makers about the most effective solutions to adopt. The evidence reporting the healthcare systems management of respiratory viruses epidemics/pandemics, show the lack of a common and shared approach and more evidence-based research is needed.