Background The recent emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a rapid proliferation of serologic assays. However, little is known about their clinical performance. Here, we compared two commercial SARS-CoV-2 IgG assays. Methods 103 specimens from 48 patients with PCR-confirmed SARS-CoV-2 infections and 153 control specimens were analyzed using SARS-CoV-2 serologic assays by Abbott and EUROIMMUN (EI). Duration from symptom onset was determined by medical record review. Diagnostic sensitivity, specificity, and concordance were calculated. Results The Abbott SARS-CoV-2 assay had a diagnostic specificity of 99.4% (95% CI; 96.41–99.98%), and sensitivity of 0.0% (95% CI; 0.00–26.47%) at <3 days post symptom onset, 30.0% (95% CI; 11.89–54.28) at 3–7d, 47.8% (95% CI; 26.82–69.41) at 8–13d and 93.8% (95% CI; 82.80–98.69) at ≥14d. Diagnostic specificity on the EI assay was 94.8% (95% CI; 89.96–97.72) if borderline results were considered positive and 96.7% (95% CI; 92.54–98.93) if borderline results were considered negative. The diagnostic sensitivity was 0.0% (95% CI; 0.00–26.47%) at <3d, 25.0% (95% CI; 8.66–49.10) at 3–7d, 56.5% (95% CI; 34.49–76.81) at 3–7d and 85.4% (95% CI; 72.24–93.93) at ≥14d if borderline results were considered positive. The qualitative concordance between the assays was 0.83 (95% CI; 0.75–0.91). Conclusion The Abbott SARS-CoV-2 assay had fewer false positive and false negative results than the EI assay. However, diagnostic sensitivity was poor in both assays during the first 14 days of symptoms.

Objective. To contrast the safety-related concerns raised by front-line staff about hospital work systems (operational failures) with national patient safety initiatives. Data Sources. Data Collection. Hospitals submitted data on the operational failures identified through managers' interactions with front-line workers. Data were analyzed for type of failure and frequency of occurrence. Recommendations from staff were compared with recommendations from national initiatives. Principal Findings. The two most frequent categories of operational failures, equipment/supplies and facility issues, posed safety risks and diminished staff efficiency, but have not been priorities in national initiatives. Conclusions. Our study suggests an underutilized strategy for improving patient safety and staff efficiency: leveraging front-line staff experiences with work systems to identify and address operational failures. In contrast to the perceived tradeoff between safety and efficiency, fixing operational failures can yield benefits for both. Thus, prioritizing improvement of work systems in general, rather than focusing more narrowly on specific clinical conditions, can increase safety and efficiency of hospitals.

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Differences among and within hospitals suggest that strategies for improving safety climate and patient safety should be tailored for work areas and disciplines.

Aims The efficacy of flash glucose monitoring (flash GM) systems has been demonstrated by improvements in glycaemia; however, during high rates of glucose flux, the performance of continuous glucose monitoring systems was impaired, as detailed in previous studies. This study aimed to determine the performance of the flash GM system during daily‐life glycaemic challenges such as carbohydrate‐rich meals, bolus insulin‐induced glycaemic disturbances and acute physical exercise in individuals with type 1 diabetes. Materials and methods This study comprised four randomized trial visits with alternating pre‐ and post‐exercise bolus insulin doses. Throughout the four 14‐hour inpatient phases, 19 participants received three carbohydrate‐rich meals and performed moderate‐intensity exercise. Venous blood glucose and capillary blood glucose during exercise was compared to interstitial glucose concentrations. Flash GM accuracy was assessed by median absolute relative difference (MARD) (interquartile range [IQR]) using the Bland–Altman method and Clark error grid, as well as according to guidelines for integrated CGM approvals (Class II–510(K)). Results The overall MARD (IQR) during inpatient phases was 14.3% (6.9%–22.8%), during hypoglycaemia (≤3.9 mmol/L) was 31.6% (16.2%–46.8%), during euglycaemia (4.0 mmol/L − 9.9 mmol/L) was 16.0% (8.5%–24.0%) and during hyperglycaemia (≥10 mmol/L) was 9.4% (5.1%–15.7%). Overall Bland–Altman analysis showed a bias (95% LoA) of 1.26 mmol/L (−1.67 to 4.19 mmol/L). The overall MARD during acute exercise was 29.8% (17.5%–39.8%), during hypoglycaemia was 45.1% (35.2%–51.1%), during euglycaemia was 30.7% (18.7%–39.2%) and during hyperglycaemia was 16.3% (10.0%–22.8%). Conclusion Flash GM interstitial glucose readings were not sufficiently accurate within the hypoglycaemic range and during acute exercise and require confirmatory blood glucose measurements.