Wearing N95 masks decreases the odor discrimination ability of healthcare workers: a self-controlled before-after study.

Objective: During the coronavirus disease 2019 (COVID-19) pandemic, the N95 mask is an essential piece of protective equipment for healthcare workers. However, the N95 mask may inhibit air exchange and odor penetration. Our study aimed to determine whether the use of N95 masks affects the odor discrimination ability of healthcare workers. Methods: In our study, all the participants were asked to complete three olfactory tests. Each test involved 12 different odors. The participants completed the test while wearing an N95 mask, a surgical mask, and no mask. The score for each olfactory test was documented. Results: The olfactory test score was significantly lower when the participants wore N95 masks than when they did not wear a mask (7 vs. 10, p < 0.01). The score was also lower when the participants wore N95 masks than surgical masks (7 vs. 8, p < 0.01). Conclusion: Wearing N95 masks decreases the odor discrimination ability of healthcare workers. Therefore, we suggest that healthcare workers seek other clues when diagnosing disease with a characteristic odor.

Wearing N95 masks decreases the odor discrimination ability of healthcare workers: a self-controlled before-after study

Objective During the coronavirus disease 2019 (COVID-19) pandemic, the N95 mask is an essential piece of protective equipment for healthcare workers. However, the N95 mask may inhibit air exchange and odor penetration. Our study aimed to determine whether the use of N95 masks affects the odor discrimination ability of healthcare workers. Methods In our study, all the participants were asked to complete three olfactory tests. Each test involved 12 different odors. The participants completed the test while wearing an N95 mask, a surgical mask, and no mask. The score for each olfactory test was documented. Results The olfactory test score was significantly lower when the participants wore N95 masks than when they did not wear a mask (7 vs. 10, p < 0.01). The score was also lower when the participants wore N95 masks than surgical masks (7 vs. 8, p < 0.01). Conclusion Wearing N95 masks decreases the odor discrimination ability of healthcare workers. Therefore, we suggest that healthcare workers seek other clues when diagnosing disease with a characteristic odor.

Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China

Background Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China. Methods Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization. Results Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in predicting infection level, while AUC=0.8436 and Recall (Weighted avg) = 0.8486 in predicting mortality ratio. This study also identifies two sets of essential clinical features. One is C-reactive protein (CRP) or high sensitivity C-reactive protein (hs-CRP) and the other is chest tightness, age, and pleural effusion. Conclusions Two homogeneous ensemble models are proposed to predict infection levels and mortality of COVID-19 patients in China. New findings of clinical features for benefiting the machine learning models are reported. The evaluation of an actual dataset collected from January 3 to March 8, 2020 demonstrates the effectiveness of the models by comparing them with state-of-the-art models in prediction.

Investigation and precautions of SARS-CoV-2 infection among healthcare workers in Emergency Center

Objective: To analyze the causes of SARS-CoV-2 nosocomial infection among healthcare workers (HCWs) and explore the effective precaution strategies in Emergency Center.

Arbidol is associated with increased in-hospital mortality among 109 patients with severe COVID-19: A multicenter, retrospective study.

BACKGROUND: The antiviral therapy has been considered as an ordinary intervention for COVID-19 patients. However, the effectiveness of antiviral therapy is uncertain. This study was designed to determine the association between the antiviral therapy and in-hospital mortality among severe COVID-19 patients. METHODS: This study enrolled severe COVID-19 patients admitted to four designated hospitals in Wuhan, China. The use of antiviral treatments, demographics, laboratory variables, co-morbidities, complications, and other treatments were compared between survival and fatal cases. The association between antiviral agents and in-hospital mortality were analyzed. RESULTS: In total, 109 severe COVID-19 patients (mean age 65.43) were enrolled for analysis, among which, 61 (56.0%) patients were discharged alive, and 48 (44.0%) died during hospitalization. We found no association between lopinavir/ritonavir (LPV/r) treatment and the in-hospital mortality (odds ratio (OR) = 0.195, 95% confidence interval (CI) = 0.023-1.679). Besides, ribavirin (OR = 0.738, 95% CI = 0.344-1.582), oseltamivir (OR = 0.765, 95% CI = 0.349-1.636), and interferon-alpha (IFN-α) (OR = 0.371, 95% CI = 0.112-1.236) were not associated with the in-hospital mortality. However, arbidol monotherapy (OR = 5.027, 95% CI = 1.795-14.074) or the combination of arbidol and oseltamivir (OR = 5.900, 95% CI = 1.190-29.247) was associated with an increased in-hospital mortality. In addition, the multiple logistic regression identified a significant association between the use of arbidol and the in-hospital mortality (adjusted OR = 4.195, 95% CI = 1.221-14.408). CONCLUSIONS: Our findings indicated that LPV/r, IFN-α, ribavirin, or oseltamivir have no beneficial effects on the prognosis of severe COVID-19 patients, whereas the use of arbidol is associated with increased in-hospital mortality.

Predicting Progression of COVID-19 Infection to Prioritize Medical Resource Allocation: A Novel Triage Model Based on Patient Characteristics and Symptoms at Presentation.

Background: The COVID-19 global pandemic has posed unprecedented challenges to health care systems all over the world. The speed of the viral spread results in a tsunami of patients, which begs for a reliable screening tool using readily available data to predict disease progression. Methods: Multicenter retrospective cohort study was performed to develop and validate a triage model. Patient demographic and non-laboratory clinical data were recorded. Using only the data from Zhongnan Hospital, step-wise multivariable logistic regression was performed, and a prognostic nomogram was constructed based on the independent variables identifies. The discrimination and calibration of the model were validated. External independent validation was performed to further address the utility of this model using data from Jinyintan Hospital. Results: A total of 716 confirmed COVID-19 cases from Zhongnan Hospital were included for model construction. Men, increased age, fever, hypertension, cardio-cerebrovascular disease, dyspnea, cough, and myalgia are independent risk factors for disease progression. External independent validation was carried out in a cohort with 201 cases from Jinyintan Hospital. The area under the curve (AUC) was 0.787 (95% confidence interval [CI]: 0.747-0.827) in the training group and 0.704 (95% CI: 0.632-0.777) in the validation group. Conclusions: We developed a novel triage model based on basic and clinical data. Our model could be used as a pragmatic screening aid to allow for cost efficient screening to be carried out such as over the phone, which may reduce disease propagation through limiting unnecessary contact. This may help allocation of limited medical resources.

Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study.

BACKGROUND: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. METHODS: Laboratory-confirmed COVID-19 and influenza patients between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. RESULTS: Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 7:3, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI: 0.81, 0.87), respectively. CONCLUSION: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens.

Prevalence and Characteristics of Hypoxic Hepatitis in COVID-19 Patients in the Intensive Care Unit: A First Retrospective Study.

Purpose: Coronavirus disease 2019 (COVID-19) has been associated with acute liver injury in reports worldwide. But no studies to date have described hypoxic hepatitis (HH) in patients with COVID-19. We aim to identify the prevalence of and possible mechanisms of HH in COVID-19 patients in the Intensive Care Unit (ICU). Methods: This retrospective study was conducted on 51 patients with confirmed SARS-CoV-2 infection in the ICU at Zhongnan Hospital of Wuhan University from December 21, 2019, to March 11, 2020. Information on clinical features of enrolled patients was collected for analysis. Results: HH was observed in 5.88% of the ICU patients with SARS-CoV-2 infection. All HH patients were progressing to respiratory failure and peak alanine aminotransferase (ALT) values were 1665, 1414, and 1140 U/L during hospitalization, respectively. All patients with HH died as a result of the deterioration of multiple organ failure (MOF). The dynamic changes of ALT, aspartate transaminase (AST), and total bilirubin (TBIL) levels were more dramatic in HH groups. Levels of TBIL, C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6(IL-6) showed statistically significant elevation in HH cases compared with that in non-HH cases (P < 0.001). Besides, the median survival time of the HH group was significantly shorter than the non-HH group (P < 0.05). Conclusions: In ICU, HH was not a rare condition in patients with severe COVID-19 and has a high mortality. The main causes of HH are respiratory and cardiac failure and may be associated with the immune-mediated inflammatory response. Clinicians should search for any underlying hemodynamic or respiratory instability even in patients with normal ALT levels on admission.

Feasibility of a 5G-Based Robot-Assisted Remote Ultrasound System for Cardiopulmonary Assessment of Patients With Coronavirus Disease 2019.

BACKGROUND: Traditional methods for cardiopulmonary assessment of patients with coronavirus disease 2019 (COVID-19) pose risks to both patients and examiners. This necessitates a remote examination of such patients without sacrificing information quality. RESEARCH QUESTION: The goal of this study was to assess the feasibility of a 5G-based robot-assisted remote ultrasound system in examining patients with COVID-19 and to establish an examination protocol for telerobotic ultrasound scanning. STUDY DESIGN AND METHODS: Twenty-three patients with COVID-19 were included and divided into two groups. Twelve were nonsevere cases, and 11 were severe cases. All patients underwent a 5G-based robot-assisted remote ultrasound system examination of the lungs and heart following an established protocol. Distribution characteristics and morphology of the lung and surrounding tissue lesions, left ventricular ejection fraction, ventricular area ratio, pericardial effusion, and examination-related complications were recorded. Bilateral lung lesions were evaluated by using a lung ultrasound score. RESULTS: The remote ultrasound system successfully and safely performed cardiopulmonary examinations of all patients. Peripheral lung lesions were clearly evaluated. Severe cases of COVID-19 had significantly more diseased regions (median [interquartile range], 6.0 [2.0-11.0] vs 1.0 [0.0-2.8]) and higher lung ultrasound scores (12.0 [4.0-24.0] vs 2.0 [0.0-4.0]) than nonsevere cases of COVID-19 (both, P < .05). One nonsevere case (8.3%; 95% CI, 1.5-35.4) and three severe cases (27.3%; 95% CI, 9.7-56.6) were complicated by pleural effusions. Four severe cases (36.4%; 95% CI, 15.2-64.6) were complicated by pericardial effusions (vs 0% of nonsevere cases, P < .05). No patients had significant examination-related complications. INTERPRETATION: Use of the 5G-based robot-assisted remote ultrasound system is feasible and effectively obtains ultrasound characteristics for cardiopulmonary assessment of patients with COVID-19. By following established protocols and considering medical history, clinical manifestations, and laboratory markers, this system might help to evaluate the severity of COVID-19 remotely.

Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic.

BACKGROUND: The average length of stay (LOS) in the intensive care unit (ICU_ALOS) is a helpful parameter summarizing critical bed occupancy. During the outbreak of a novel virus, estimating early a reliable ICU_ALOS estimate of infected patients is critical to accurately parameterize models examining mitigation and preparedness scenarios. METHODS: Two estimation methods of ICU_ALOS were compared: the average LOS of already discharged patients at the date of estimation (DPE), and a standard parametric method used for analyzing time-to-event data which fits a given distribution to observed data and includes the censored stays of patients still treated in the ICU at the date of estimation (CPE). Methods were compared on a series of all COVID-19 consecutive cases (n = 59) admitted in an ICU devoted to such patients. At the last follow-up date, 99 days after the first admission, all patients but one had been discharged. A simulation study investigated the generalizability of the methods' patterns. CPE and DPE estimates were also compared to COVID-19 estimates reported to date. RESULTS: LOS ≥ 30 days concerned 14 out of the 59 patients (24%), including 8 of the 21 deaths observed. Two months after the first admission, 38 (64%) patients had been discharged, with corresponding DPE and CPE estimates of ICU_ALOS (95% CI) at 13.0 days (10.4-15.6) and 23.1 days (18.1-29.7), respectively. Series' true ICU_ALOS was greater than 21 days, well above reported estimates to date. CONCLUSIONS: Discharges of short stays are more likely observed earlier during the course of an outbreak. Cautious unbiased ICU_ALOS estimates suggest parameterizing a higher burden of ICU bed occupancy than that adopted to date in COVID-19 forecasting models. FUNDING: Support by the National Natural Science Foundation of China (81900097 to Dr. Zhou) and the Emergency Response Project of Hubei Science and Technology Department (2020FCA023 to Pr. Zhao).

Cancer patients in SARS-CoV-2 infection: a single-center experience from Wuhan.

Background: The Coronavirus disease 2019 (COVID-19) global pandemic has posed unprecedented challenges to the health-care systems all over the world. Among the booming literatures about COVID-19, there is yet a paucity of study addressing the association between COVID-19 and cancer, which is a rare comorbidity of COVID-19, as well as consensus for treatment of cancer in this pandemic. Methods: In this retrospective, single-center cohort study, information of all inpatient cases with laboratory-confirmed COVID-19 who had treatment outcome were collected from the designated departments in Zhongnan Hospital of Wuhan University, Wuhan, China on March 10, 2020. Demographic data, clinical information, and treatment outcomes were extracted from electronic medical records. Severe events were defined as admission to intensive care unit (ICU), the use of mechanical ventilation, or death. Result: A total of 716 patients with laboratory-confirmed COVID-19 infection were identified. Among them, a total of 12 cases (1.7%, 95% CI: 0.7%-2.6%) had history of cancer with 4 cases (33%) experienced severe events. Compared with cases without cancer, patients with cancer have higher risks of severe events (33% vs 7.7%, p=0.012) and deaths (25% vs 3.6%, p=0.009). Multivariable logistic regression model showed that cancer was independently associated with increased odds of severe events after adjusting for other risk factors (OR 6.51, 95% CI 1.72-24.64; p=0.006). Among COVID-19 patients with cancer, we found that patients older than 60 years (75%), with other comorbidities (50%), or experiencing anticancer treatment in past month (42.9%) had a numerically higher incidence of severe events. Conclusion: Cancer is a rare comorbidity of patients with COVID-19; however, it cannot be overemphasized due to its poorer outcomes. We propose that personalized treatment recommendation for cancer patients should be addressed during COVID-19 pandemic, along with meticulous personal protective protocols for them to mitigate the risk of SARS-CoV-2 infection.

The association between markers of liver injury and clinical outcomes in patients with COVID-19 in Wuhan.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) is a critical challenge for public health. The effect of COVID-19 on liver injury has not been fully established. AIMS: To evaluate the dynamic changes in liver function and the relationship between liver damage and prognosis in patients with COVID-19. METHODS: Retrospective analysis of clinical data of 675 patients with COVID-19 in Zhongnan Hospital of Wuhan University from January 3 to March 8, 2020. Patients were classified as having normal or abnormal liver function and liver injury. RESULTS: Of 675 patients, 253 (37.5%) had abnormal liver function during hospitalisation, and 52 (7.7%) had liver injury. The dynamic changes of ALT and AST levels were more significant in patients with liver injury and in those who died. AST >3-fold upper limit of normal (ULN) had the highest risk of death and mechanical ventilation. Compared to patients with normal AST levels, mortality and risk of mechanical ventilation significantly increased 19.27-fold (95% confidence interval [CI], 4.89-75.97; P < 0.0001) and 116.72-fold (95% CI, 31.58-431.46; P < 0.0001), respectively, in patients with AST above 3-fold ULN. Increased leucocytes, decreased lymphocytes and female sex were independently associated with liver injury. CONCLUSIONS: The dynamic changes in liver function may have a significant correlation with the severity and prognosis of COVID-19. Increased index of liver injury was closely related to mortality and need for mechanical ventilation. Therefore, these indicators should be closely monitored during hospitalisation.

Wearing a N95 mask increases rescuer's fatigue and decreases chest compression quality in simulated cardiopulmonary resuscitation.

OBJECTIVES: N95 mask is essential for healthcare workers dealing with the coronavirus disease 2019 (COVID-19). However, N95 mask causes discomfort breathing with marked reduction in air exchange. This study was designed to investigate whether the use of N95 mask affects rescuer's fatigue and chest compression quality during cardiopulmonary resuscitation (CPR). METHODS: After a brief review of CPR, each participant performed a 2-minute continuous chest compression on a manikin wearing N95 (N95 group, n = 40) or surgical mask (SM group, n = 40). Compression rate and depth, the proportions of correct compression rate, depth, complete chest recoil and hand position were documented. Participants' fatigue was assessed using Borg score. RESULTS: Significantly lower mean chest compression rate and depth were both achieved in the N95 group than in the SM group (p < 0.05, respectively). In addition, the proportion of correct compression rate (61 ± 19 vs. 75 ± 195, p = 0.0067), depth (67 ± 16 vs. 90 ± 14, p < 0.0001) and complete recoil (91 ± 16 vs. 98 ± 5%, p = 0.0248) were significantly decreased in the N95 group as compared to the SM group. At the end of compression, the Borg score in the N95 group was significantly higher than that in the SM group (p = 0.027). CONCLUSION: Wearing a N95 mask increases rescuer's fatigue and decreases chest compression quality during CPR. Therefore, the exchange of rescuers during CPR should be more frequent than that recommended in current guidelines when N95 masks are applied.