Low-Dose Versus High-Dose Tocilizumab in Covid-19 Patients with Hypoxic Respiratory Failure

INTRODUCTION: Tocilizumab has been shown to decrease mortality when used concomitantly with steroids in COVID-19. Tocilizumab dose of 8 mg/kg (max: 800 mg), stemmed from the RECOVERY trial, has been the standard dose for COVID. Due to a drug shortage of tocilizumab, our study seeks to assess whether low dose (400 mg) shows similar benefit compared to high dose for COVID patients concurrently on same median dose of steroids. METHOD(S): This was a retrospective observational study of COVID-19 patients who received tocilizumab in conjunction with steroids. Between March 2020 and August 2021, adult patients with positive COVID-19 PCR, hypoxic respiratory failure defined as FiO2>70%, and received a dose of tocilizumab in conjunction with steroids were included. Patients were excluded if they have died within 24 hours of treatment initiation. Primary outcome was 28-day mortality and secondary outcomes included biomarker improvement and relative risk of infection. Propensity matched analysis between groups was performed. RESULT(S): A total of 407 patients met the study criteria and were analyzed. The low dose and high dose tocilizumab group had 222 and 185 patients respectively. Gender and age were similar between groups and all patients received steroids. The low dose group was significantly more ill at baseline as a higher percentage of patients received vasopressors, were admitted to the ICU and on mechanical ventilation. In the propensity-matched analysis of 56 patients in each group, with a median dose of steroid of 10 mg in both groups showed no difference in 28 day mortality (HR 0.82 [95% CI: 0.41-1.67];p=0.6138). A greater decrease to normalization of CRP (p< 0.0001) and downtrend of ferritin (p=0.503) was observed in the high dose group at day 14. The high dose group trended a higher rate of fungal and viral infections. CONCLUSION(S): Compared to low dose tocilizumab, high dose did not provide additional efficacy and mortality benefit but resulted in uptrend of fungal and viral infections. While a greater decrease in CRP was seen in the high dose group, it did not translate into lower mortality. This study illustrates that low dose tocilizumab can be an alternative to high dose during a drug shortage of tocilizumab without compensating for efficacy and safety, conserving resources for more patients.

Prevalence of propofol infusion syndrome in critically ill patients

INTRODUCTION: Propofol has been widely used in the ICU for sedation and refractory status epilepticus. PRIS is a serious and potentially fatal condition that is characterized by a spectrum of clinical symptoms and abnormalities. Literature suggests that a longer duration of propofol ≥ 48 hours or a dose ≥ 83 mcg/kg/min is associated with a higher risk of PRIS. Many of the critically ill patients in our health system required a larger dose of propofol and prolonged duration of infusion for sedation in the ICU, especially during Covid-19. Delayed treatment of PRIS can lead to death. It is very likely that patients who develop PRIS may often go unrecognized as the manifestations of PRIS can overlap with common ICU conditions. The current prevalence of PRIS is unknown, however, a prospective study has reported a prevalence of 1.1% in critically ill patients. METHODS: Patients were identified by querying the NYU Langone Health COVID clinical data mart from March 2020 till February 2021. The inclusion criteria included patients receiving propofol infusion for ≥ 48 hours or receiving a dose ≥ 60 mcg/kg/min for more than 24 hours. Pregnant patients, children, and patients with rhabdomyolysis prior to the start of infusion were excluded. PRIS was defined by the development of metabolic acidosis and cardiac dysfunction with 2 or more minor criteria (rhabdomyolysis, hypertriglyceridemia, renal failure, and hepatic transaminitis) or developing 3 or more minor criteria. RESULTS: 424 patients were included in our study. Of the 424 patients, 21 patients were found to have developed PRIS. The occurrence of PRIS was observed at the median infusion rate of 36.1 mcg/kg/min and a median duration of infusion of 147 hours. The prevalence of PRIS was found to be 4.9%. CONCLUSIONS: The prevalence of PRIS in our study was found to be 4.9%. The occurrence of PRIS was observed at the median infusion rate of 36.1 mcg/kg/min suggesting that PRIS can be developed at a lower rate of infusion than previously reported. We suggest that patients - especially those receiving a duration ≥ 48 hours and a higher dose of 60 mcg/kg/min - should be monitored for signs and symptoms of PRIS during propofol infusion as it may be underrecognized because PRIS is characterized by multiple clinical manifestations that overlap with critical illness.

A Hybrid Data Mining Method for EEG Analysis in Online Education Enhanced by Background Music

Oitline teaching arc facing drikmatic challenges due to the COVIDI9 pandemic requiring massive online education. Students are experiencing mental and physical isolation during this period. This research :Aims to find an efficient may to discover students emotion status through 1,;El: patient recognition (1'10. Traditional PR !method,' haie been applied eNtensikeh in 1":Ft;recognition including Artificial Neuron Networks iANNi, Support Vector Nlak'hinekSVNI Nearest Xeighhorc (KNN), and so on. In this paper, a association rule -based PR method has been introduced through incorporating clustering and Apriori association rube methods. The experimental results demonst rale that Ute tgdimized rule -based 1":F:t;PR model Can improve real-time recognition eflicienc. Tlic proposed model can Ike used for identifying students cognitive statuses and improve educational perfikrrnunce in (.0\11/19 period

Savable but Lost Lives when ICU Is Overloaded: a Model from 733 Patients in Epicenter Wuhan, China

Coronavirus Disease 2019 (COVID-19) causes a sudden turnover to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted into the ICU will be valuable to optimize the management and assignment of ICU. Retrospective, 733 in-patients diagnosed with COVID-19 at a local hospital (Wuhan, China), as of March 18, 2020. Demographic, clinical and laboratory results were collected and analyzed using machine learning to build a predictive model. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing ICU care yet they did not as Missing-ICU (MI)-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care. Its predictive accuracy was 0.8288, with an AUC of 0.9119. On our cohort of 733 patients, 25 in-patients who have been predicted by our model that they should need ICU, yet they did not enter ICU due to lack of shorting ICU wards. Our analysis had shown that the MI-mortality is 41%, yet the mortality of ICU is 32%, implying that enough bed of ICU in treating patients in critical conditions.

Machine Learning to Predict ICU Admission, ICU Mortality and Survivors' Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

COVID-19 causes burdens to the ICU. Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. This study aims to identify variables and test the accuracy to predict the need for ICU admission, death despite ICU care, and among survivors, length of ICU stay, before patients were admitted to ICU. Retrospective data from 733 in-patients confirmed with COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical and laboratory were collected and analyzed using machine learning to build the predictive models. The built machine learning model can accurately assess ICU admission, length of ICU stay, and mortality in COVID-19 patients toward optimal allocation of ICU resources. The prediction can be done by using the clinical data collected within 1-15 days before the actual ICU admission. Lymphocyte absolute value involved in all prediction tasks with a higher AUC.