Impact of Vaccination, Prior Infection and Therapy on Omicron Infection and Mortality.

BACKGROUND: Understanding the immunity against omicron infection and severe outcomes conferred by Covid-19 vaccination, prior SARS-CoV-2 infection, and monoclonal antibody therapy will inform intervention strategies. METHODS: We considered 295,691 patients who were tested for SARS-CoV-2 at Cleveland Clinic between October 1, 2021 and January 31, 2022. We used logistic regression to investigate the association of vaccination and prior infection with the risk of SARS-CoV-2 infection and used Cox regression to investigate the association of vaccination, prior infection and monoclonal antibody therapy with the risks of intensive care unit (ICU) stay and death. RESULTS: Vaccination and prior infection were less effective against omicron than delta infection but provided strong protection against ICU admission and death. Boosting greatly increased vaccine effectiveness against omicron infection and severe outcomes, though the effectiveness waned rapidly over time. Monoclonal antibody therapy considerably reduced the risks of ICU admission and death. Finally, the relatively low mortality of the omicron variant was due to both the reduced lethality of this variant and the increased population immunity acquired from booster vaccination and previous infection. CONCLUSIONS: Booster vaccination and prior SARS-CoV-2 infection provide strong protection against ICU admission and death from omicron infection. Monoclonal antibody therapy is also beneficial.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine.

Background: Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM). Methods: Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified. Results: Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FCij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients. Conclusion: The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log2fold change (log2FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

Critical Care Among Disadvantaged Minority Groups Made Equitable: Trends Throughout the COVID-19 Pandemic.

BACKGROUND: US racial and ethnic minorities have well-established elevated rates of comorbidities, which, compounded with healthcare access inequity, often lead to worse health outcomes. In the current COVID-19 pandemic, it is important to understand existing disparities in minority groups' critical care outcomes and mechanisms behind these-topics that have yet to be well-explored. OBJECTIVE: Assess for disparities in racial and ethnic minority groups' COVID-19 critical care outcomes. DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 2125 adult patients who tested positive for COVID-19 via RT-PCR between March and December 2020 and required ICU admission at the Cleveland Clinic Hospital Systems were included. MAIN MEASURES: Primary outcomes were mortality and hospital length of stay. Cohort-wide analysis and subgroup analyses by pandemic wave were performed. Multivariable logistic regression models were built to study the associations between mortality and covariates. KEY RESULTS: While crude mortality was increased in White as compared to Black patients (37.5% vs. 30.5%, respectively; p = 0.002), no significant differences were appraised after adjustment or across pandemic waves. Although median hospital length of stay was comparable between these groups, ICU stay was significantly different (4.4 vs. 3.4, p = 0.003). Mortality and median hospital and ICU length of stay did not differ significantly between Hispanic and non-Hispanic patients. Neither race nor ethnicity was associated with mortality due to COVID-19, although APACHE score, CKD, malignant neoplasms, antibiotic use, vasopressor requirement, and age were. CONCLUSIONS: We found no significant differences in mortality or hospital length of stay between different races and ethnicities. In a pandemic-influenced critical care setting that operated outside conditions of ICU strain and implemented standardized protocol enabling equitable resource distribution, disparities in outcomes often seen among racial and ethnic minority groups were successfully mitigated.

Safety and Efficacy of Paxlovid Against Omicron Variants of Coronavirus Disease 2019 in Elderly Patients.

INTRODUCTION: Elderly patients are the most affected and vulnerable to COVID-19 and effective therapeutic interventions are urgently required. We clarified the safety and efficacy of Paxlovid in the treatment of elderly patients with coronavirus disease 2019 (COVID-19). METHODS: Patients aged over 60 years and with mild to moderate COVID-19 were admitted to the Zhongshan Hospital MinHang MeiLong Branch, Fudan University and received either Paxlovid treatment or only conventional therapy, between April 1 and May 31, 2022. Viral shedding time, duration of hospital stay, disease progression, and adverse events were analyzed, and multivariate Cox regression analysis was performed to detect the independent high-risk factors for COVID-19 progression in the patients. RESULTS: A total of 163 (82 and 81 in the treatment and control groups, respectively) patients had a median age of 82 (71-89) years, and 89.0% had at least one concomitant disease. The duration of hospitalization reduced from 15 to 13 days, and viral shedding time reduced from 20 to 16.5 days after Paxlovid treatment. The differences of these two variables between the groups were significant (p < 0.01). Moreover, no serious adverse events or obvious changes in laboratory test results were observed in patients treated with Paxlovid. One patient (1.2%) treated with Paxlovid experienced rebound 56 days after negative measurement. Multivariate analysis showed that Paxlovid therapy, age, hemoglobin, and nucleic acid Ct values at admission were independent risk factors for hospitalization within 14 days, and the differences were significant (p < 0.01). CONCLUSION: The use of Paxlovid in elderly patients may promote recovery from COVID-19 and reduce the viral load without adverse events. CLINICAL TRIAL REGISTRATION: www. CLINICALTRIALS: gov , ID: ChiCTR2200066990.

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era.

"Re-visits and drug renewal" is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of "Re-visits and drug renewal" actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era.

Risk factors associated with development of coinfection in critically Ill patients with COVID-19.

BACKGROUND: At outset of the coronavirus disease 2019 (COVID-19) pandemic, the significance of bacterial and fungal coinfections in individuals with COVID-19 was unknown. Initial reports indicated that the prevalence of coinfection in the general population was low, but there was uncertainty regarding the risk of coinfection in critically ill patients. METHODS: Nine hundred critically ill adult patients with COVID-19 infection were enrolled in this observational case-control study. Patients with a coinfection (case) and patients without a coinfection (control) were compared using univariate and multivariable analyses. A subgroup analysis was performed on patients with coinfection, dividing them into early (infection within 7 days) and late (infection after 7 days) infection groups. RESULTS: Two hundred and thirty-three patients (25.9%) had a bacterial or fungal coinfection. Vasopressor use (P<0.001) and severity of illness (higher Acute Physiology and Chronic Health Evaluation III score, P=0.009) were risk factors for the development of a coinfection. Patients with coinfection had higher mortality and length of stay. Vasopressor and corticosteroid use and central line and foley catheter placement were risk factors for late infection (>7 days). There were high rates of drug-resistant infections. CONCLUSIONS: Critically ill patients with COVID-19 are at risk for both community-acquired and hospital-acquired infections throughout their hospitalization for COVID-19. It is important to consider the development of a coinfection in clinically worsening critically ill patients with COVID-19 and consider the likelihood of drug-resistance when choosing an empiric regimen.

D-DIMER-GUIDED ANTICOAGULATION PROPHYLAXIS THERAPY IN COVID-19 IS SAFE AND PREVENTS THROMBOSIS

A high-intensity thrombosis prophylaxis protocol based on D-dimer and weight was implemented across the healthcare system on 04/2020. These data suggest this protocol is a safe and effective thrombosis prophylaxis strategy for critically ill ICU patients with COVID-19. [Extracted from the article] Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.

Mental health prediction is one of the most essential parts of reducing the probability of serious mental illness. Meanwhile, mental health prediction can provide a theoretical basis for public health department to work out psychological intervention plans for medical workers. The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. Besides, we use stepwise logistic regression, binary bat algorithm, hybrid improved dragonfly algorithm and the proposed prediction model to predict mental health of medical workers. The results show that the prediction accuracy of the proposed model is 92.55%, which is better than the existing algorithms. This method can be used to predict mental health of global medical worker. In addition, the method proposed in this paper can also play a role in the appropriate work plan for medical worker.

Predicting the Incident Cases of Emerging Infectious Disease Using a Bayesian Probability Model - China, February 2020.

WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?: The exact number of incident cases of emerging infectious diseases on a daily basis is of great importance to the disease control and prevention, but it is not directly available from the current surveillance system in time. WHAT IS ADDED BY THIS REPORT?: In this study, a Bayesian statistical method was proposed to estimate the posterior parameters of the gamma probability distribution of the lag time between the onset date and the reporting time based on the surveillance data. And then the posterior parameters and corresponding cumulative gamma probability distribution were used to predict the actual number of new incident cases and the number of unreported cases per day. The proposed method was used for predicting COVID-19 incident cases from February 5 to February 26, 2020. The final results show that Bayesian probability model predictions based on data reported by February 28, 2020 are very close to those actually reported a month later. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE?: This research provides a Bayesian statistical approach for early estimation of the actual number of cases of incidence based on surveillance data, which is of great value in the prevention and control practice of epidemics.

A selective sweep in the Spike gene has driven SARS-CoV-2 human adaptation.

The coronavirus disease 2019 (COVID-19) pandemic underscores the need to better understand animal-to-human transmission of coronaviruses and adaptive evolution within new hosts. We scanned more than 182,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes for selective sweep signatures and found a distinct footprint of positive selection located around a non-synonymous change (A1114G; T372A) within the spike protein receptor-binding domain (RBD), predicted to remove glycosylation and increase binding to human ACE2 (hACE2), the cellular receptor. This change is present in all human SARS-CoV-2 sequences but not in closely related viruses from bats and pangolins. As predicted, T372A RBD bound hACE2 with higher affinity in experimental binding assays. We engineered the reversion mutant (A372T) and found that A372 (wild-type [WT]-SARS-CoV-2) enhanced replication in human lung cells relative to its putative ancestral variant (T372), an effect that was 20 times greater than the well-known D614G mutation. Our findings suggest that this mutation likely contributed to SARS-CoV-2 emergence from animal reservoirs or enabled sustained human-to-human transmission.

A High Neutrophil-Lymphocyte Ratio Is Associated With Increased Morbidity and Mortality in Patients With Coronavirus Disease 2019.

OBJECTIVES: The neutrophil-lymphocyte ratio is an inexpensive and simple inflammatory marker. A higher ratio, indicative of an acute hyperinflammatory response or diminished overall physiologic health status, has been associated with poor prognoses. This study aimed to evaluate the prognostic potential of admission neutrophil-lymphocyte ratio in patients admitted to the medical ICU with coronavirus disease 2019. DESIGN: Retrospective review of prospectively collected data. SETTING: Medical ICU from a large medical center. PATIENTS: 2,071 consecutive patients admitted to the medical ICU with laboratory-confirmed severe acute respiratory syndrome coronavirus-2 between March 15, 2020, and December 30, 2020, were grouped by neutrophil-lymphocyte ratio above or below the median (7.45) at the time of hospital admission. INTERVENTIONS: Complete blood count with differential at the time of hospital admission. MEASUREMENTS AND MAIN RESULTS: A neutrophil-lymphocyte ratio above 7.45 at the time of hospital admission was associated with increased need for mechanical ventilation (45.8% vs 38.0%, p < 0.0001), vasopressor therapy (55.6% vs 48.2%, p = 0.001), and decreased survival through 180 days (54.8% vs 67.0%, p < 0.0001). Patients with a high neutrophil-lymphocyte ratio exhibited a 1.32 (95% CI, 1.14-1.54) times greater risk of mortality than those with a low neutrophil-lymphocyte ratio. CONCLUSIONS: The neutrophil-lymphocyte ratio at the time of hospital admission is an independent risk factor for morbidity and mortality. This prognostic indicator may assist clinicians appropriately identify patients at heightened risk for a severe disease course and tailor treatment accordingly.

A Multicenter Evaluation of Survival After In-Hospital Cardiac Arrest in Coronavirus Disease 2019 Patients.

IMPORTANCE: In-hospital cardiac arrest survival among coronavirus disease 2019 patients has been reported to range from 0% to 12%. These numbers are significantly lower than reported prepandemic in-hospital cardiac arrest survival rates of approximately 20-25% in the United States for non-coronavirus disease 2019 patients. OBJECTIVE: To assess the incidence of in-hospital cardiac arrest survival of coronavirus disease 2019 patients. DESIGN: A retrospective cohort study of adult patients with coronavirus disease 2019 subsequently found to have in-hospital cardiac arrest and underwent cardiopulmonary resuscitation (cardiopulmonary resuscitation). SETTING: Multiple hospitals of the Cleveland Clinic Health System. PATIENTS: All adult patients (age ≥ 18 yr) admitted to Cleveland Clinic Health System with a diagnosis of coronavirus disease 2019 who experienced in-hospital cardiac arrest requiring cardiopulmonary resuscitation. MEASUREMENTS AND MAIN RESULTS: From March 01, 2020 to October 15, 2020, 3,555 patients with coronavirus disease 2019 were hospitalized; 1,372 were admitted to the ICU; 58 patients had in-hospital cardiac arrest. Median age of this cohort was 66.5 years (interquartile range, 55.0-76.0 yr). Patients were predominantly male (62.5%), White (53.4%), with a median body mass index of 29.7 (interquartile range, 25.8-34.6). Most in-hospital cardiac arrests were in critical care environments (ICU), 51 of 58 (87.9%); seven of 58 (12.1%) were on ward locations. Thirty-four of 58 patients (58.6%) were on mechanical ventilation prior to in-hospital cardiac arrest with a median duration of mechanical ventilation of 9 days (interquartile range, 2-18 d). Twenty-four of 58 patients (44%) were on vasopressors prior to arrest. Initial arrest rhythm was pulseless electrical activity at (63.8%), asystole (29.3%), and pulseless ventricular tachycardia/fibrillation (6.9%). Of the 58 patients, 35 (60.3%) attained return of spontaneous circulation, and 13 of 58 (22.4%) were discharged alive. CONCLUSIONS: We report a 22% survival to discharge after in-hospital cardiac arrest in coronavirus disease 2019 patients, a survival rate similar to before the coronavirus disease 2019 pandemic.

Multicenter Study of Temporal Changes and Prognostic Value of a CT Visual Severity Score in Hospitalized Patients With Coronavirus Disease (COVID-19).

BACKGROUND. Chest CT findings have the potential to guide treatment of hospitalized patients with coronavirus disease (COVID-19). OBJECTIVE. The purpose of this study was to assess a CT visual severity score in hospitalized patients with COVID-19, with attention to temporal changes in the score and the role of the score in a model for predicting in-hospital complications. METHODS. This retrospective study included 161 inpatients with COVID-19 from three hospitals in China who underwent serial chest CT scans during hospitalization. CT examinations were evaluated using a visual severity scoring system. The temporal pattern of the CT visual severity score across serial CT examinations during hospitalization was characterized using a generalized spline regression model. A prognostic model to predict major complications, including in-hospital mortality, was created using the CT visual severity score and clinical variables. External model validation was evaluated by two independent radiologists in a cohort of 135 patients from a different hospital. RESULTS. The cohort included 91 survivors with nonsevere disease, 55 survivors with severe disease, and 15 patients who died during hospitalization. Median CT visual lung severity score in the first week of hospitalization was 2.0 in survivors with non-severe disease, 4.0 in survivors with severe disease, and 11.0 in nonsurvivors. CT visual severity score peaked approximately 9 and 12 days after symptom onset in survivors with nonsevere and severe disease, respectively, and progressively decreased in subsequent hospitalization weeks in both groups. In the prognostic model, in-hospital complications were independently associated with a severe CT score (odds ratio [OR], 31.28), moderate CT score (OR, 5.86), age (OR, 1.09 per 1-year increase), and lymphocyte count (OR, 0.03 per 1 × 109/L increase). In the validation cohort, the two readers achieved C-index values of 0.92-0.95, accuracy of 85.2-86.7%, sensitivity of 70.7-75.6%, and specificity of 91.4-91.5% for predicting in-hospital complications. CONCLUSION. A CT visual severity score is associated with clinical disease severity and evolves in a characteristic fashion during hospitalization for COVID-19. A prognostic model based on the CT visual severity score and clinical variables shows strong performance in predicting in-hospital complications. CLINICAL IMPACT. The prognostic model using the CT visual severity score may help identify patients at highest risk of poor outcomes and guide early intervention.

Phenotypes and Subphenotypes of Patients With COVID-19: A Latent Class Modeling Analysis.

BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? STUDY DESIGN AND METHODS: We included adult patients (≥ 18 years) positive for laboratory-confirmed SARS-CoV-2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. RESULTS: We analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91). INTERPRETATION: Hypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

The Benefits of Self-Transcendence: Examining the Role of Values on Mental Health Among Adolescents Across Regions in China.

As one of the foundations of existential positive psychology, self-transcendence can bring positive intrapersonal and interpersonal outcomes, especially in the COVID-19 era in which people are suffering huge mental stress. Based on Schwartz's theory of human basic values, the current study combines variable-centered and person-centered approaches to examine the relationships between adolescents' values and mental health across two regions in China. The results generally showed that (1) both self-enhancement and conservation values were positively correlated with depression and loneliness, while both self-transcendence and openness to change values negatively correlated with depression and loneliness. The results also showed that (2) there were four value clusters (i.e., self-focus, other-focus, anxiety-free, undifferentiated), and, compared to adolescents in the self-focus and undifferentiated values cluster, all adolescents in the anxiety-free values cluster reported lower depression and loneliness, while all adolescents in the other-focus values cluster reported higher depression and loneliness. The differences between the two regional groups only emerged in depression. Specifically, adolescents in Shanghai have higher levels of depression than adolescents in Qingdao. This study provides some evidence for the new science of self-transcendence among adolescents and also sheds light on how we may improve the level of mental health during the COVID-19 era.

Public Health Interventions' Effect on Hospital Use in Patients With COVID-19: Comparative Study.

BACKGROUND: Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE: We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS: This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS: The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio's transmission rates declined before the state's "stay at home" order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS: The series of public health interventions in three areas were temporally associated with the burden of COVID-19-attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

Does inpatient hyperglycemia predict a worse outcome in COVID-19 intensive care unit patients?

BACKGROUND: We undertook this study to evaluate the association between hyperglycemia and outcomes in patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU). METHODS: We conducted a multicenter retrospective study involving all adults with COVID-19 admitted to the ICU between March and May 2020. Patients were divided into normoglycemic (average blood glucose <140 mg/dL) and hyperglycemic (average blood glucose ≥140 mg/dL) groups. Outcomes such as mortality, need and duration of mechanical ventilation, and length of hospital and ICU stays were measured. RESULTS: Among 495 patients, 58.4% were male with a median age of 68 years (interquartile range [IQR]: 58.00-77.00), and baseline average blood glucose was 186.6 (SD ± 130.8). Preexisting diabetes was present in 35.8% of the studied cohort. Combined ICU and hospital mortality rates were 23.8%; mortality and mechanical ventilation rates were significantly higher in the hyperglycemic group with 31.4% vs 16.6% (P = .001) and 50.0% vs 37.2% (P = .004), respectively. Age above 60 years (hazard ratio [HR] 3.21; 95% CI 1.78, 5.78) and hyperglycemia (HR 1.79; 95% CI 1.14, 2.82) were the only significant predictors of in-hospital mortality. Increased risk for hyperglycemia was found in patients with steroid use (odds ratio [OR] 1.521; 95% CI 1.054, 2.194), triglycerides ≥150 mg/dL (OR 1.62; 95% CI 1.109, 2.379), and African American race (OR 0.79; 95% CI 0.65, 0.95). CONCLUSIONS: Hyperglycemia in patients with COVID-19 is significantly associated with a prolonged ICU length of stay, higher need of mechanical ventilation, and increased risk of mortality in the critical care setting. Tighter blood glucose control (≤140 mg/dL) might improve outcomes in COVID-19 critically ill patients; evidence from ongoing clinical trials is needed.

Tocilizumab in Coronavirus Disease 2019-Related Critical Illness: A Propensity Matched Analysis.

The primary objective was to evaluate ICU mortality at 28 days in patients with severe hypoxemic respiratory failure due to coronavirus disease 2019 infection who received tocilizumab. The secondary objectives were to evaluate ICU-, hospital-, mechanical ventilation-, and vasopressor-free days at day 28 and development of secondary infections. DESIGN: Retrospective, observational, multicenter, cohort study between March 15, 2020, and May 31, 2020. Using propensity score matching based on ICU admission source, C-reactive protein, Sequential Organ Failure Assessment score, vasopressor use, age, race, weight, and mechanical ventilation, patients who received tocilizumab were matched to patients who did not receive tocilizumab. SETTING: Ten hospitals within the Cleveland Clinic Enterprise. PATIENTS: Adult patients admitted to a medical, surgical, neurosciences, or mixed ICU with severe acute respiratory syndrome coronavirus 2 infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Four-hundred forty-four patients were included: 342 patients (77%) did not receive tocilizumab and 102 patients (23%) received tocilizumab. Of those, 82 patients in each arm were matched. Before matching, patients who received tocilizumab had higher Sequential Organ Failure Assessment scores (6.1 ± 3.4 vs 4.7 ± 3.6), higher C-reactive protein (21.0 ± 10.2 vs 13.7 ± 9.6 mg/dL), higher frequency of intubation, vasopressor requirement, and paralytics. After matching, characteristics were more balanced and over 85% of patients required mechanical ventilation. ICU mortality was lower in tocilizumab group (23.2% vs 37.8%; risk difference, -15%; 95% CI, -29% to -1%), with more ICU-, hospital-, and vasoactive-free days at day 28 compared with those who did not receive tocilizumab. There was no difference in mechanical ventilation-free days at day 28 or development of secondary infections. CONCLUSIONS: Tocilizumab use was associated with a significant decrease in ICU mortality in critically ill coronavirus disease 2019 patients with severe hypoxemic respiratory failure. Future randomized controlled trials limited to tocilizumab administration in critically ill coronavirus disease 2019 patients, with severe hypoxemic respiratory failure, are needed to support these findings.

Clinical characteristics and outcomes of critically Ill patients with COVID-19 in Northeast Ohio: low mortality and length of stay.

BACKGROUND: Published coronavirus disease 2019 (COVID-19) reports suggest higher mortality with increasing age and comorbidities. Our study describes the clinical characteristics and outcomes for all intensive care unit (ICU) patients admitted across the Cleveland Clinic enterprise, a 10-hospital health care system in Northeast Ohio, serving more than 2.7 million people. METHODS: We analyzed the quality data registry for clinical characteristics and outcomes of all COVID-19-confirmed ICU admissions. Differences in outcomes from other health care systems and published cohorts from other parts of the world were delineated. RESULTS: Across our health care system, 495 COVID-19 patients were admitted from March 15 to June 1, 2020. Mean patient age was 67.3 years, 206 (41.6%) were females, and 289 (58.4%) were males. Mean Acute Physiology Score was 45.3, and mean Acute Physiology and Chronic Health Evaluation III score was 60.5. In total, 215 patients (43.3%) were intubated for a mean duration of 9.2 days. Mean ICU and hospital length of stay were 7.4 and 13.9 days, respectively, while mean ICU and hospital mortality rates were 18.4% and 23.8%. CONCLUSIONS: Our health care system cohort is the fourth largest to be reported. Lower ICU and hospital mortality and length of stay were seen compared to most other published reports. Better preparedness and state-level control of the surge in COVID-19 infections are likely the reasons for these better outcomes. Future research is needed to further delineate differences in mortality and length of stay across health care systems and over time.