Kinetics of SARS-CoV-2 antibody responses pre-COVID-19 and post-COVID-19 convalescent plasma transfusion in patients with severe respiratory failure: an observational case-control study.

AIMS: While the SARS-CoV-2 pandemic may be contained through vaccination, transfusion of convalescent plasma (CCP) from individuals who recovered from COVID-19 (CCP) is considered an alternative treatment. We investigate if CCP transfusion in patients with severe respiratory failure increases plasma titres of SARS-CoV-2 antibodies and improves clinical outcomes. METHODS: Patients with COVID-19 (n=34) were consented for CCP transfusion and serial blood draws pretransfusion and post-transfusion. Plasma SARS-CoV-2 antireceptor binding domain (RBD) IgG and IgM titres were measured by ELISA serially, and compared with serial plasma titre levels from control patients (n=68). The primary outcome was survival at 30 days, and secondary outcomes were length of ventilator and/or extracorporeal membrane oxygenation (ECMO) support, length of stay (LOS) in the hospital and in the intensive care unit (ICU). Outcomes were compared with matched control patients (n=34). Kinetics of antibodies and clinical outcomes were compared using LOess regression and ORs, respectively. RESULTS: Prior to CCP transfusion, 74% of patients were anti-RBD seropositive for IgG (median 1:3200), and 81% were anti-RBD IgM seropositive (median 1:320), while 16% were seronegative. The kinetics of antibody titres in CCP recipients were similar to controls. CCP recipients presented with similar survival, duration on ventilatory and/or ECMO support, as well as ICU and hospital LOS compared with controls. CONCLUSIONS: CCP transfusion did not increase the kinetics of SARS-CoV2 antibodies and did not result in improved clinical outcomes in patients with COVID-19 with severe respiratory failure, suggesting that CCP may not be indicated in this category of patients.

Clinical evaluation of Sofia Rapid Antigen Assay for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among emergency department to hospital admissions.

OBJECTIVE: To determine the utility of the Sofia SARS rapid antigen fluorescent immunoassay (FIA) to guide hospital-bed placement of patients being admitted through the emergency department (ED). DESIGN: Cross-sectional analysis of a clinical quality improvement study. SETTING: This study was conducted in 2 community hospitals in Maryland from September 21, 2020, to December 3, 2020. In total, 2,887 patients simultaneously received the Sofia SARS rapid antigen FIA and SARS-CoV-2 RT-PCR assays on admission through the ED. METHODS: Rapid antigen results and symptom assessment guided initial patient placement while confirmatory RT-PCR was pending. The sensitivity, specificity, positive predictive values, and negative predictive values of the rapid antigen assay were calculated relative to RT-PCR, overall and separately for symptomatic and asymptomatic patients. Assay sensitivity was compared to RT-PCR cycle threshold (Ct) values. Assay turnaround times were compared. Clinical characteristics of RT-PCR-positive patients and potential exposures from false-negative antigen assays were evaluated. RESULTS: For all patients, overall agreement was 97.9%; sensitivity was 76.6% (95% confidence interval [CI], 71%-82%), and specificity was 99.7% (95% CI, 99%-100%). We detected no differences in performance between asymptomatic and symptomatic individuals. As RT-PCR Ct increased, the sensitivity of the antigen assay decreased. The mean turnaround time for the antigen assay was 1.2 hours (95% CI, 1.0-1.3) and for RT-PCR it was 20.1 hours (95% CI, 18.9-40.3) (P < .001). No transmission from antigen-negative/RT-PCR-positive patients was identified. CONCLUSIONS: Although not a replacement for RT-PCR for detection of all SARS-CoV-2 infections, the Sofia SARS antigen FIA has clinical utility for potential initial timely patient placement.

Chlorhexidine Gluconate Bathing Program to Reduce Health Care-Associated Infections in Both Critically Ill and Non-Critically Ill Patients.

BACKGROUND: Critical care nurses take care of patients with complicated, comorbid, and compromised conditions. These patients are at risk for health care-associated infections, which affect patients' lives and health care systems in various ways. OBJECTIVE: To gauge the impact of routinely bathing patients with 4% chlorhexidine gluconate solution on the incidence of health care-associated infections in a medical-surgical intensive care unit and a postoperative telemetry unit; to outline the framework for a hospital-wide presurgical chlorhexidine gluconate bathing program and share the results. METHODS: A standard bathing protocol using a 4% chlorhexidine gluconate solution was developed. The protocol included time studies, training, monitoring, and surveillance of health care-associated infections. RESULTS: Consistent patient bathing with 4% chlorhexidine gluconate was associated with a 52% reduction in health care-associated infections in a medical-surgical intensive care unit. The same program in a postoperative telemetry unit yielded a 45% reduction in health care-associated infections. CONCLUSION: A comprehensive daily 4% chlorhexidine gluconate bathing program can be implemented with standardized protocols and detailed instructions and can significantly reduce the incidence of health care-associated infections in intensive care unit and non-intensive care unit hospital settings.

Population Decline in COPD Admissions During the COVID-19 Pandemic Associated with Lower Burden of Community Respiratory Viral Infections.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has led to widespread implementation of public health measures, such as stay-at-home orders, social distancing, and masking mandates. In addition to decreasing spread of severe acute respiratory syndrome coronavirus 2, these measures also impact the transmission of seasonal viral pathogens, which are common triggers of chronic obstructive pulmonary disease (COPD) exacerbations. Whether reduced viral prevalence mediates reduction in COPD exacerbation rates is unknown. METHODS: We performed retrospective analysis of data from a large, multicenter health care system to assess admission trends associated with community viral prevalence and with initiation of COVID-19 pandemic control measures. We applied difference-in-differences analysis to compare season-matched weekly frequency of hospital admissions for COPD prior to and after implementation of public health measures for COVID-19. Community viral prevalence was estimated using regional Centers for Disease Control and Prevention test positivity data and correlated to COPD admissions. RESULTS: Data involving 4422 COPD admissions demonstrated a season-matched 53% decline in COPD admissions during the COVID-19 pandemic, which correlated to community viral burden (r = 0.73; 95% confidence interval, 0.67-0.78) and represented a 36% greater decline over admission frequencies observed in other medical conditions less affected by respiratory viral infections (incidence rate ratio 0.64; 95% confidence interval, 0.57-0.71, P < .001). The post-COVID-19 decline in COPD admissions was most pronounced in patients with fewer comorbidities and without recurrent admissions. CONCLUSION: The implementation of public health measures during the COVID-19 pandemic was associated with decreased COPD admissions. These changes are plausibly explained by reduced prevalence of seasonal respiratory viruses.

Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions.

Importance: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores. Objective: To identify the type of score that best predicts hospital readmissions. Design, Setting, and Participants: This prognostic study included 14 062 consecutive adult hospital patients with 16 649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions. Main Outcomes and Measures: The 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score. Results: Of the 10 732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P < .001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P < .001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients. Conclusions and Relevance: Among 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.