Development of a high-throughput SARS-CoV-2 antibody testing pathway using dried blood spot specimens.

BACKGROUND: Serological assays for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have roles in seroepidemiology, convalescent plasma-testing, antibody durability and vaccine studies. Currently, SARS-CoV-2 serology is performed using serum/plasma collected by venepuncture. Dried blood spot (DBS) testing offers significant advantages as it is minimally invasive, avoids venepuncture with specimens being mailed to the laboratory. METHODS: A pathway utilizing a newborn screening laboratory infrastructure was developed using an enzyme-linked immunosorbent assay to detect IgG antibodies against the receptor-binding domain of the SARS-CoV-2 spike protein in DBS specimens. Paired plasma and DBS specimens from SARS-CoV-2 antibody-positive and -negative subjects and polymerase chain reaction positive subjects were tested. DBS specimen stability, effect of blood volume and punch location were also evaluated. RESULTS: DBS specimens from antibody-negative (n = 85) and -positive (n = 35) subjects and polymerase chain reaction positive subjects (n = 11) had a mean (SD; range) optical density (OD) of 0.14 (0.046; 0.03-0.27), 0.98 (0.41; 0.31-1.64) and 1.12 (0.37; 0.49-1.54), respectively. An action value OD >0.28 correctly assigned all cases. The weighted Deming regression for comparison of the DBS and the plasma assay yielded: y = 0.004041 + 1.005x, r = 0.991, Sy/x 0.171, n = 82. Extraction efficiency of antibodies from DBS specimens was >99%. DBS specimens were stable for at least 28 days at ambient room temperature and humidity. CONCLUSIONS: SARS-CoV-2 IgG receptor-binding domain antibodies can be reliably detected in DBS specimens. DBS serological testing offers lower costs than either point of care or serum/plasma assays that require patient travel, phlebotomy and hospital/clinic resources; the development of a DBS assay may be particularly important for resource poor settings.

Multifaceted quality improvement initiative to decrease pediatric asthma readmissions.

BACKGROUND: Asthma is the most common chronic disease of childhood and a leading cause of hospitalization in children. A primary goal of asthma control is prevention of hospitalizations. A hospital admission is the single strongest predictor of future hospital admissions for asthma. The 30-day asthma readmission rate at our institution was significantly higher than that of other hospitals in the Children's Hospital Association. As a result, a multifaceted quality improvement project was undertaken with the goal of reducing the 30-day inpatient asthma readmission rate by 50% within two years. METHODS: Analysis of our institution's readmission patterns, value stream mapping of asthma admission, discharge, and follow-up processes, literature review, and examination of comparable successful programs around the United States were all utilized to identify potential targets for intervention. Interventions were implemented in a stepwise manner, and included increasing inhaler availability after discharge, modifying asthma education strategies, and providing in-home post-discharge follow-up. The primary outcome was a running 12-month average 30-day inpatient readmission rate. Secondary outcomes included process measures for individual interventions. RESULTS: From a peak of 7.98% in January 2013, a steady decline to 1.65% was observed by July 2014, which represented a 79.3% reduction in 30-day readmissions. CONCLUSION: A significant decrease in hospital readmissions for pediatric asthma is possible, through comprehensive, multidisciplinary quality improvement that spans the continuum of care.

Information-gathering patterns associated with higher rates of diagnostic error.

Diagnostic errors are an important source of medical errors. Problematic information-gathering is a common cause of diagnostic errors among physicians and medical students. The objectives of this study were to (1) determine if medical students' information-gathering patterns formed clusters of similar strategies, and if so (2) to calculate the percentage of incorrect diagnoses in each cluster. A total of 141 2nd year medical students completed a computer case simulation. Each student's information-gathering pattern included the sequence of history, physical examination, and ancillary testing items chosen from a predefined list. We analyzed the patterns using an artificial neural network and compared percentages of incorrect diagnoses among clusters of information-gathering patterns. We input patterns into a 35 x 35 self organizing map. The network trained for 10,000 epochs. The number of students at each neuron formed a surface that was statistically smoothed into clusters. Each student was assigned to one cluster, the cluster that contributed the largest value to the smoothed function at the student's location in the grid. Seven clusters were identified. Percentage of incorrect diagnoses differed significantly among clusters (Range 0-42%, Chi (2) = 13.62, P = .034). Distance of each cluster from the worst performing cluster was used to rank clusters. This rank was compared to rank determined by percentage incorrect. We found a high positive correlation (Spearman Correlation = .893, P = .007). Clusters closest to the worst performing cluster had the highest percentages of incorrect diagnoses. Patterns of information-gathering were distinct and had different rates of diagnostic error.