Chemical Emissions from Cured and Uncured 3D-Printed Ventilator Patient Circuit Medical Parts.

Medical shortages during the COVID-19 pandemic saw numerous efforts to 3D print personal protective equipment and treatment supplies. There is, however, little research on the potential biocompatibility of 3D-printed parts using typical polymeric resins as pertaining to volatile organic compounds (VOCs), which have specific relevance for respiratory circuit equipment. Here, we measured VOCs emitted from freshly printed stereolithography (SLA) replacement medical parts using proton transfer reaction mass spectrometry and infrared differential absorption spectroscopy, and particulates using a scanning mobility particle sizer. We observed emission factors for individual VOCs ranging from ∼0.001 to ∼10 ng cm-3 min-1. Emissions were heavily dependent on postprint curing and mildly dependent on the type of SLA resin. Curing reduced the emission of all observed chemicals, and no compounds exceeded the recommended dose of 360 µg/d. VOC emissions steadily decreased for all parts over time, with an average e-folding time scale (time to decrease to 1/e of the starting value) of 2.6 ± 0.9 h.

Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.

Increase in COVID-19 inpatient survival following detection of Thromboembolic and Cytokine storm risk from the point of admission to hospital by a near real time Traffic-light System (TraCe-Tic)

Introduction Our goal was to evaluate if traffic-light driven personalized care for COVID-19 was associated with improved survival in acute hospital settings. Methods Discharge outcomes were evaluated before and after prospective implementation of a real-time dashboard with feedback to ward-based clinicians. Thromboembolism categories were "medium-risk"(D-dimer >1000 ng/mL or CRP >200 mg/L);"high-risk"(D-dimer >3000 ng/mL or CRP >250 mg/L) or "suspected"(D-dimer >5000 ng/mL). Cytokine storm risk was categorized by ferritin. Results 939/1039 COVID-19 positive patients (median age 67 years, 563/939 (60%) male) completed hospital encounters to death or discharge by 21st May 2020. Thromboembolism flag criteria were reached by 568/939 (60.5%), including 238/275 (86.6%) of the patients who died, and 330/664 (49.7%) of the patients who survived to discharge, p <0.0001. Cytokine storm flag criteria were reached by 212 (22.6%) of admissions, including 80/275 (29.1%) of the patients who died, and 132/664 (19.9%) of the patients who survived, p <0.0001. The maximum thromboembolism flag discriminated completed encounter mortality (no flag: 37/371 [9.97%] died;medium-risk: 68/239 [28.5%];high-risk: 105/205 [51.2%];and suspected thromboembolism: 65/124 [52.4%], p <0.0001). Flag criteria were reached by 535 consecutive COVID-19 positive patients whose hospital encounter completed before traffic-light introduction: 173/535 (32.3% [95% confidence intervals 28.0, 36.0]) died. For the 200 consecutive admissions after implementation of real-time traffic light flags, 46/200 (23.0% [95% confidence intervals 17.1, 28.9]) died, p = 0.013. Adjusted for age and sex, the probability of death was 0.33 (95% confidence intervals 0.30, 0.37) before traffic light implementation, 0.22 (0.17, 0.27) after implementation, p <0.001. In subgroup analyses, older patients, males, and patients with hypertension (p ≤0.01), and/or diabetes (p = 0.05) derived the greatest benefit from admission under the traffic light system. Conclusion Personalized early interventions were associated with a 33% reduction in early mortality. We suggest benefit predominantly resulted from early triggers to review/enhance anticoagulation management, without exposing lower-risk patients to potential risks of full anticoagulation therapy.

Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting.

Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust's COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged.

Increase in COVID-19 inpatient survival following detection of Thromboembolic and Cytokine storm risk from the point of admission to hospital by a near real time Traffic-light System (TraCe-Tic).

INTRODUCTION: Our goal was to evaluate if traffic-light driven personalized care for COVID-19 was associated with improved survival in acute hospital settings. METHODS: Discharge outcomes were evaluated before and after prospective implementation of a real-time dashboard with feedback to ward-based clinicians. Thromboembolism categories were "medium-risk" (D-dimer >1000ng/mL or CRP >200mg/L); "high-risk" (D-dimer >3000ng/mL or CRP >250mg/L) or "suspected" (D-dimer >5000ng/mL). Cytokine storm risk was categorized by ferritin. RESULTS: 939/1039 COVID-19 positive patients (median age 67 years, 563/939 (60%) male) completed hospital encounters to death or discharge by 21st May 2020. Thromboembolism flag criteria were reached by 568/939 (60.5%), including 238/275 (86.6%) of the patients who died, and 330/664 (49.7%) of the patients who survived to discharge, p<0.0001. Cytokine storm flag criteria were reached by 212 (22.6%) of admissions, including 80/275 (29.1%) of the patients who died, and 132/664 (19.9%) of the patients who survived, p<0.0001. The maximum thromboembolism flag discriminated completed encounter mortality (no flag: 37/371 [9.97%] died; medium-risk: 68/239 [28.5%]; high-risk: 105/205 [51.2%]; and suspected thromboembolism: 65/124 [52.4%], p<0.0001). Flag criteria were reached by 535 consecutive COVID-19 positive patients whose hospital encounter completed before traffic-light introduction: 173/535 (32.3% [95% confidence intervals 28.0, 36.0]) died. For the 200 consecutive admissions after implementation of real-time traffic light flags, 46/200 (23.0% [95% confidence intervals 17.1, 28.9]) died, p=0.013. Adjusted for age and sex, the probability of death was 0.33 (95% confidence intervals 0.30, 0.37) before traffic light implementation, 0.22 (0.17, 0.27) after implementation, p<0.001. In subgroup analyses, older patients, males, and patients with hypertension (p≤0.01), and/or diabetes (p=0.05) derived the greatest benefit from admission under the traffic light system. CONCLUSION: Personalized early interventions were associated with a 33% reduction in early mortality. We suggest benefit predominantly resulted from early triggers to review/enhance anticoagulation management, without exposing lower-risk patients to potential risks of full anticoagulation therapy.