ABSTRACT
Coronavirus disease 2019 (COVID-19) has rapidly evolved into a pandemic, causing a global public health crisis. Many frontline healthcare workers providing ear, nose and throat services have been reported to contract COVID-19 at work. Early during the COVID-19 outbreak, several medical professionals in Otolaryngology-Head and Neck Surgery were infected in Wuhan, China. A series of measures were then taken immediately, which successfully halted the spread of the disease. Here we would like to share the lessons we have learned, and our experience to protect our health care workers during the COVID-19 pandemic.
ABSTRACT
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. METHODS: The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses. RESULTS: Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42-16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13-11.25]). CONCLUSION: We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.
ABSTRACT
The practices of head and neck surgical oncologists must evolve to meet the unprecedented needs placed on our health care system by the Coronavirus disease 2019 (COVID-19) pandemic. Guidelines are emerging to help guide the provision of head and neck cancer care, though in practice, it can be challenging to operationalize such recommendations. Head and neck surgeons at Wuhan University faced significant challenges in providing care for their patients. Similar challenges were faced by the University of Toronto during the severe acute respiratory syndrome (SARS) pandemic in 2003. Herein, we outline our combined experience and key practical considerations for maintaining an oncology service in the midst of a pandemic.