ABSTRACT
COVID-19 showed different characteristics and many cases showed clinical manifestations that could not be attributed to other conditions. We present a 22-year-old female had an uneventful recovery from COVID-19 and after that, she developed a cytokine storm and a worsening clinical condition two days after dental root canal therapy.
Subject(s)
COVID-19ABSTRACT
Background Corona Virus Disease 2019 (COVID-19) presentation resembles common flu or can be more severe; it can result in hospitalization with significant morbidity and/or mortality. We made an attempt to develop a predictive model and a scoring system to improve the diagnostic efficiency for COVID-19 mortality via analysis of clinical features and laboratory data on admission. Methods We retrospectively enrolled 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were extracted from the medical records and analyzed using multiple logistic regression analysis. Results A novel mortality risk score (COVID-19 BURDEN) was calculated, incorporating risk factors from this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (>16.2s), diastolic blood pressure (≤75 mmHg), BUN (>23 mg/dL), and raised LDH (>731 U/L) are the features comprising the scoring system. The patients are triaged to the groups of low- (score <4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting non-response to medical therapy with scores of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusion Using this scoring system in COVID-19 patients, the severity of the disease will be determined in the early stages of the disease, which will help to reduce hospital care costs and improve its quality and outcome.