Objectives: Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. Materials and Methods: Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. Results: The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the

Background Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. Methods The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. Results HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. Conclusions The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

Background Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. Objective The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. Methods Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. Results Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. Conclusions The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Objective To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents. Design Multinational network cohort study. Setting Hospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea. Participants 303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020. Main outcome measures Prescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19. Results Of the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from <5 (<2%) patients in China to 2165 (85.1%) in Spain), azithromycin (from 15 (4.9%) in China to 1473 (57.9%) in Spain), combined lopinavir and ritonavir (from 156 (<2%) in the VA-OMOP US to 2,652 (34.9%) in South Korea and 1285 (50.5%) in Spain), and umifenovir (0% in the US, South Korea, and Spain and 238 (78.3%) in China). Use of adjunctive drugs varied greatly, with the five most used treatments being enoxaparin, fluoroquinolones, ceftriaxone, vitamin D, and corticosteroids. Hydroxychloroquine use increased rapidly from March to April 2020 but declined steeply in May to June and remained low for the rest of the year. The use of dexamethasone and corticosteroids increased steadily during 2020. Conclusions Multiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.

Background: Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity. Methods: Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively. Findings: A predictive HNC-LL (Hypertension, Neutrophil count, C-reactive protein, Lymphocyte count, Lactate dehydrogenase) score was established using multivariate logistic regression analysis. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC]=0.861, 95% confidence interval [CI]: 0.800À0.922; P<0.001); Honghu internal validation cohort (AUC=0.871, 95% CI: 0.769À0.972; P<0.001); and Nanchang external validation cohort (AUC=0.826, 95% CI: 0.746À0.907; P<0.001) and outperformed other models, including CURB-65 (confusion, uraemia, respiratory rate, BP, age 65 years) score model, MuLBSTA (multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) score model, and neutrophil-to-lymphocyte ratio model. The clinical significance of HNC-LL in accurately predicting the risk of future development of severe COVID-19 was confirmed. Interpretation: We developed an accurate tool for predicting disease severity among COVID-19 patients. This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions.

Objectives: Three hundred million people living with rare diseases worldwide are disproportionately deprived of in-time diagnosis and treatment compared with other patients. This review provides an overview of global policies that optimize development, licensing, pricing, and reimbursement of orphan drugs.Methods: Pharmaceutical legislation and policies related to access and regulation of orphan drugs were examined from 194 World Health Organization member countries and 6 areas. Orphan drug policies (ODPs) were identified through internet search, emails to national pharmacovigilance centers, and systematic academic literature search. Texts from selected publications were extracted for content analysis.Results: One hundred seventy-two drug regulation documents and 77 academic publications from 162 countries/areas were included. Ninety-two of 200 countries/areas (46.0%) had documentation on ODPs. Thirty-four subthemes from content analysis were categorized into 6 policy themes, namely, orphan drug designation, marketing authorization, safety and efficacy requirements, price regulation, incentives that encourage market availability, and incentives that encourage research and development. Countries/areas with ODPs were statistically wealthier (gross national income per capita = $10 875 vs $3950, P , .001). Country/area income was also positively correlated with the scope of the respective ODP (correlation coefficient = 0.57, P , .001).Conclusions: Globally, the number of countries with an ODP has grown rapidly since 2013. Nevertheless, disparities in geographical distribution and income levels affect the establishment of ODPs. Furthermore, identified policy gaps in price regulation, incentives that encourage market availability, and incentives that encourage research and development should be addressed to improve access to available and affordable orphan drugs.

Data sharing statement: Analyses were performed locally in compliance with all applicable data privacy laws. Although the underlying data is not readily available to be shared, authors contributing to this paper have direct access to the data sources used in this study. All results (e.g. aggregate statistics, not presented at a patient-level with redactions for minimum cell count) are available for public inquiry. These results are inclusive of site-identifiers by contributing data sources to enable interrogation of each contributing site. All analytic code and result sets are made available at: https://github.com/ohdsi-studies/Covid19CharacterizationCharybdis

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