ABSTRACT
Background The COVID-19 pandemic has claimed over 6.4 million lives globally. Finding effective medications to reduce mortality in hospitalized COVID-19 patients remains critical. No previous study has been published on the effects of statin use in a majority African American COVID-19 patient population. Objective This study aims to assess the relationship between in-hospital statin use and mortality in this population. Methods A retrospective chart review of patients diagnosed with COVID-19 from March 2020 to June 2020 admitted to the Phoebe Putney Health System in Albany, Georgia, an early epicenter of the COVID-19 pandemic, was conducted. The outcomes of 735 hospitalized COVID-19 positive patients from over 40 counties in Georgia were analyzed. The primary outcome of interest was all-cause mortality, with secondary outcomes of interest of ICU care, length of ICU stay, need for mechanical ventilator, duration of intubation, and need for dialysis. Multivariate logistic regression and Cox proportional hazards analysis were conducted to examine the effect of in-hospital statin use and mortality. Results 186 of 735 total patients were prescribed statins in-hospital. 83.8% were African American. Multivariate logistic regression found in-hospital statin use was not significantly associated with the primary outcome – all-cause mortality (p=0.23). Similar findings were seen in need for ICU care, length of ICU stay, need for mechanical ventilator, duration of intubation, and need for dialysis (p>0.05). Additionally, results from a Cox proportional hazards model found in-hospital statin use was not associated with survival time. Sensitivity analysis conducted on only African American patients validated that in-hospital statin use was not associated with all-cause mortality in these patients. Of note, immunosuppression and severe disease presentation were associated with a six-fold increase in risk of mortality and the largest decreases in survival time. Conclusion It is possible statins have no mortality benefit for this patient population, but further research beyond this association study would need to be conducted to determine this conclusively. From this study, the best clinical recommendation would be to continue statins for COVID-19 patients with pre-hospital statin use and to launch a randomized clinical trial to definitively determine the efficacy of statins in the treatment of hospitalized COVID-19 patients.
ABSTRACT
Physicians often make diagnosis and treatment decisions based on incomplete data. That is why we practice medicine. We use accumulated knowledge and prior experience, individual and collective, to restore form and function of our patients to return them to normalcy with continued, durable homeostasis. However, due to the complex nature, our diagnosis may be wrong and our treatments ineffective or even harmful. The history of medicine and surgery is replete with such examples from snake oil and bloodletting to Halstedian radical mastectomy. Without knowing the governing dynamics, the cause-effect relationship is often obscure. Prospective, blinded, placebo-controlled trials provide the highest level of evidence, like the COVID vaccine trials, to determine if a treatment works. However, trials cannot explain in detail how and why a treatment works, or why it does not. Variabilities and uncertainty abound and require the correct mathematical methods to tease out the signal from the noise, causality from association. Collectively, statistics is the science of uncertainty and the extraction of reliable, useful information from raw data. The objectives of this review are to provide craniofacial surgeons with a primer in descriptive statistics: how to design investigations, collect, prepare, present, and interpret clinical data. Since large datasets at regional and national depositories represent powerful and valuable resources, and that their proper use requires a working knowledge in epidemiology, we included sections on incidence, prevalence, sensitivity, and specificity regarding diagnosis, treatments, and testing.
ABSTRACT
Background: The first case of COVID-19 was reported in Wuhan, China in December 2019. The disease has spread to 210 countries and has been labelled as a pandemic by the World Health Organization (WHO). Modelling, evaluating, and predicting the rate of disease transmission is crucial in understanding optimal methods for prevention and control. Our aim is to assess the impact of interstate and foreign travel and public health interventions implemented by the United States government in response to the COVID-19 pandemic. Methods: A disjoint mutually exclusive compartmental model was developed to study transmission dynamics of the novel coronavirus. A system of nonlinear differential equations was formulated and the basic reproduction number R 0 was computed. Stability of the model was evaluated at the equilibrium points. Optimal controls were applied in the form of travel restrictions and quarantine. Numerical simulations were conducted. Results: Analysis shows that the model is locally asymptomatically stable, at endemic and foreigners free equilibrium points. Without any mitigation measures, infectivity and subsequent hospitalization of the population increased. When interstate and foreign travel was restricted and the population placed under quarantine, the probability of exposure and subsequent infection decreased significantly; furthermore, the recovery rate increased substantially. Conclusion: Interstate and foreign travel restrictions, in addition to quarantine, are necessary in effectively controlling the pandemic. The United States has controlled COVID-19 spread by implementing quarantine and restricting foreign travel. The government can further strengthen restrictions and reduce spread within the nation more effectively by implementing restrictions on interstate travel.