ABSTRACT
BACKGROUND: Coronavirus disease 2019 increased the numbers of patients requiring prolonged mechanical ventilation, with a subsequent increase in tracheostomy procedures. Coronavirus disease 2019 patients are high risk for surgical complications. This review examines open surgical and percutaneous tracheostomy complications in coronavirus disease 2019 patients. METHODS: Medline and Embase databases were searched (November 2021), and the abstracts of relevant articles were screened. Data were collected regarding tracheostomy technique and complications. Complication rates were compared between percutaneous and open surgical tracheostomy. RESULTS: Percutaneous tracheostomy was higher risk for bleeding, pneumothorax and false passage. Surgical tracheostomy was higher risk for peri-operative hypoxia. The most common complication for both techniques was post-operative bleeding. CONCLUSION: Coronavirus disease 2019 patients undergoing tracheostomy are at higher risk of bleeding and peri-operative hypoxia than non-coronavirus disease patients. High doses of anti-coagulants may partially explain this. Reasons for higher bleeding risk in percutaneous over open surgical technique remain unclear. Further research is required to determine the causes of differences found and to establish mitigating strategies.
ABSTRACT
The Covid-19 pandemic has obligated healthcare systems to triage patients efficiently in order to utilize resources in the proper manner. Robust, validated clinical prediction tools are lacking that identify patients with coronavirus disease 2019 who are at the highest risk of mortality. A cohort of 661 positive COVID patients admitted to the Loyola hospital from 3/11/2020 to 10/12/2020 was used to validate previously published results with the composite outcome of 'critically ill', defined as invasive ventilation, ICU admission, or in-hospital death. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Fifty-two (52) percent of our cohort had critical illness compared to 8% in the Liang paper (the first predictor model which was developed in China). Our population was older, had significantly higher abnormal chest x-ray, dyspnea, and number of comorbidities compared to their population. In addition, our population had higher values for NLR and LDH and lower direct bilirubin. Using the model coefficients presenting in Liang et al, the AUC was only 0.68 (95% CI: 0.64, 0.72), considerably lower than .88 presented in their publication. LASSO variable selection identified 8 variables (HR, SBP, fever, SpO2<90, LDH, ferritin, D dimer, CRP on admission) for a multivariable model. In the multivariable model: SpO2 <90, fever at admission, and increasing LDH were found to be statistically associated with our outcome. We finally added baseline predictors of age, sex, race, and number of comorbidities to the LASSO predictors which resulted in an AUC of 0.75 (95% CI: 0.71, 0.78). In our study, we found that three predictors (SpO2 <90, fever, and increasing LDH on admission) were selected by the LASSO analysis to construct a predictive nomogram. The application of our model would help clinicians make a prompt decision to optimize patient stratification management. However, this quantitative tool needs to be validated by further large-scale prospective studies.