The response of total testing process in clinical laboratory medicine to COVID-19pandemic.

INTRODUCTION: Following a pandemic, laboratory medicine is vulnerable to laboratory errors due to the stressful and high workloads. We aimed to examine how laboratory errors may arise from factors, e.g., flexible working order, staff displacement, changes in the number of tests, and samples will reflect on the total test process (TTP) during the pandemic period. MATERIALS AND METHODS: In 12 months, 6 months before and during the pandemic, laboratory errors were assessed via quality indicators (QIs) related to TTP phases. QIs were grouped as pre-, intra- and postanalytical. The results of QIs were expressed in defect percentages and sigma, evaluated with 3 levels of performance quality: 25th, 50th and 75th percentile values. RESULTS: When the pre- and during pandemic periods were compared, the sigma value of the samples not received was significantly lower in pre-pandemic group than during pandemic group (4.7σ vs. 5.4σ, P = 0.003). The sigma values of samples transported inappropriately and haemolysed samples were significantly higher in pre-pandemic period than during pandemic (5.0σ vs. 4.9σ, 4.3σ vs. 4.1σ; P = 0.046 and P = 0.044, respectively). Sigma value of tests with inappropriate IQC performances was lower during pandemic compared to the pre-pandemic period (3.3σ vs. 3.2σ, P = 0.081). Sigma value of the reports delivered outside the specified time was higher during pandemic than pre-pandemic period (3.0σ vs. 3.1σ, P = 0.030). CONCLUSION: In all TTP phases, some quality indicators improved while others regressed during the pandemic period. It was observed that preanalytical phase was affected more by the pandemic.

The association of nursing home quality ratings and spread of COVID-19.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has severely affected nursing home residents. Given the continued high incidence of COVID-19, and the likelihood that new variants and other infectious agents may cause future outbreaks, we sought to understand the relationship of nursing home quality ratings and measures of COVID-19 outbreak severity and persistence. DESIGN: We analyzed nursing home facility-level data on COVID-19 cases and deaths, county-level COVID-19 rates, and nursing home data from the Centers for Medicare & Medicaid Services (CMS), including ratings from the CMS Nursing Home Five-Star Quality Rating System. We used regression analysis to examine the association between star ratings and cumulative COVID-19 incidence and mortality as well as persistent high resident incidence. SETTING: All nursing homes in the CMS COVID-19 Nursing Home Dataset reporting data that passed quality assurance checks for at least 20 weeks and that were included in the January 2021 Nursing Home Care Compare update. PARTICIPANTS: Residents of the included nursing homes. MEASUREMENTS: Cumulative resident COVID-19 incidence and mortality through January 10, 2021; number of weeks with weekly resident incidence of COVID-19 in the top decile nationally. RESULTS: As of January 10, 2021, nearly all nursing homes (93.6%) had reported at least one case of COVID-19 among their residents, more than three-quarters (76.9%) had reported at least one resident death, and most (83.5%) had experienced at least 1 week in the top decile of weekly incidence. In analyses adjusted for facility and county-level characteristics, we found generally consistent relationships between higher nursing home quality ratings and lower COVID-19 incidence and mortality, as well as with fewer high-incidence weeks. CONCLUSION: Nursing home quality ratings are associated with COVID-19 incidence, mortality, and persistence. Nursing homes receiving five-star ratings, for overall quality as well as for each domain, had lower COVID-19 rates among their residents.

Demographics matter: the potentially disproportionate effect of COVID-19 on hospital ratings.

OBJECTIVE: To identify how features of the community in which a hospital serves differentially relate to its patients' experiences based on the quality of that hospital. DESIGN: A Finite Mixture Model (FMM) is used to uncover a mix of two latent groups of hospitals that differ in quality. In the FMM, a multinomial logistic equation relates hospital-level factors to the odds of being in either group. And a multiple linear regression relates the characteristics of communities served by hospitals to the patients' expected ratings of their experiences at hospitals in each group. Thus, this association potentially varies with hospital quality. The analysis was conducted via Stata. SETTING: Hospital Ratings are measured by Hospital Compare using the HCAHPS survey, a patient satisfaction survey required by the Centers for Medicare and Medicaid Services (CMS) for hospitals in the United States. Participants: 2,816 Medicare-certified acute care hospitals across all US states.

Impact of a prolonged COVID-19 lockdown on patterns of admission, mortality and performance indicators in a cardiovascular intensive care unit.

BACKGROUND: The effects of an early and prolonged lockdown during the coronavirus disease 2019 (COVID-19) pandemic on cardiovascular intensive care units (CICUs) are not well established. OBJECTIVES: This study analyses patterns of admission, mortality and performance indicators in a CICU before and during the Argentine lockdown in the COVID-19 pandemic. METHODS: This is a retrospective observational cross-sectional study of all consecutive patients aged 18 years or more admitted to the cardiac intensive care unit at a high-volume reference hospital in Buenos Aires, Argentina, comparing hospitalization rates, primary causes of admission, inpatient utilization indicators, pharmacy supplies' expenditures and in-hospital mortality between 5 March and 31 July 2020, with two corresponding control periods in 2019 and 2018. RESULTS: We included 722 female patients [mean age of 61.6 (SD 15.5) years; 237 (32.8%)]. Overall hospitalizations dropped 53.2% (95%CI: 45.3, 61.0%), from 295.5 patients/year over the periods 2018/2019 to 137 patients in 2020. Cardiovascular disease-related admissions dropped 59.9%, while admission for non-cardiac causes doubled its prevalence from 9.6% over the periods 2018/2019 to 22.6% in the study period (P < 0.001).In the period 2020, the bed occupancy rate fell from 82.2% to 77.4%, and the bed turnover rate dropped 50% from 7.88 to 3.91 monthly discharges/bed. The average length of stay doubled from 3.26 to 6.75 days, and the turnover interval increased from 3.8 to 8.39 days in 2020.Pharmacy supplies' expenditures per discharge increased 134% along with a rise in antibiotics usage from 6.5 to 11.4 vials/ampoules per discharge (P < 0.02).Overall mortality increased from 7% (n = 41) to 13.9% (n = 19) (P = 0.008) at the expense of non-cardiac-related admissions (3.6-19.4%, P = 0.01). CONCLUSIONS: This study found a significant reduction in overall and cardiovascular disease-related causes of admission to the cardiac intensive care unit, worse performance indicators and increased in-hospital mortality along the first 5 months of the early and long-lasting COVID-19 lockdown in Argentina. These results highlight the need to foster public awareness concerning the risks of avoiding hospital attendance. Moreover, health systems should follow strict screening protocols to prevent potential biases in the admission of patients with critical conditions unrelated to the COVID-19 pandemic.