A comparison of TRECs and flow cytometry for naive T cell quantification

Assessment of thymic output by measurement of naive T cells is carried out routinely in clinical diagnostic laboratories, predominantly using flow cytometry with a suitable panel of antibodies. Naive T cell measurements can also be made using molecular analyses to quantify T cell receptor excision circle (TRECs) levels in sorted cells from peripheral blood. In this study we have compared TRECs levels retrospectively with CD45RA+ CD27+ T cells and also with CD45RA+ CD31+ T cells in 134 patient samples at diagnosis or during follow-up. Both panels provide naive T cell measurements that have a strongly positive correlation with TRECs numbers and are suitable for use with enumerating naive T cell levels in a clinical laboratory

Screening of Neonatal UK Dried Blood Spots Using a Duplex SMN1 Screening Assay

Spinal muscular atrophy (SMA) is an autosomal inherited neuromuscular genetic disease caused, in 95% of cases, by homozygous deletions involving the SMN1 gene exon 7. It remains the leading cause of death in children under 2 years of age. New treatments have been developed and adopted for use in many countries, including the UK. Success of these treatments depends on early diagnosis and intervention in newborn babies, and many countries have implemented a newborn screening (NBS) or pilot NBS program to detect SMN1 exon 7 deletions on dried blood spots. In the UK, there is no current NBS program for SMA, and no pilot studies have commenced. For consideration of adoption of NBS for a new condition, numerous criteria must be satisfied, including critical assessment of a working methodology. This study uses a commercially available real-time PCR assay to simultaneously detect two different DNA segments (SMN1 exon 7 and control gene RPP30) using DNA extracted from a dried blood spot. This study was carried out in a routine clinical laboratory to determine the specificity, sensitivity, and feasibility of SMA screening in a UK NBS lab setting. Just under 5000 normal DBSs were used alongside 43 known SMA positive DBSs. Study results demonstrate that NBS for SMA using real-time PCR is feasible within the current UK NBS Laboratory infrastructure using the proposed algorithm

An Outcome-Weighted Network Model for Characterizing Collaboration

<div><p>Shared patient encounters form the basis of collaborative relationships, which are crucial to the success of complex and interdisciplinary teamwork in healthcare. Quantifying the strength of these relationships using shared risk-adjusted patient outcomes provides insight into interactions that occur between healthcare providers. We developed the <i>Shared Positive Outcome Ratio</i> (SPOR), a novel parameter that quantifies the concentration of positive outcomes between a pair of healthcare providers over a set of shared patient encounters. We constructed a collaboration network using hospital emergency department patient data from electronic health records (EHRs) over a three-year period. Based on an outcome indicating patient satisfaction, we used this network to assess pairwise collaboration and evaluate the SPOR. By comparing this network of 574 providers and 5,615 relationships to a set of networks based on randomized outcomes, we identified 295 (5.2%) pairwise collaborations having significantly higher patient satisfaction rates. Our results show extreme high- and low-scoring relationships over a set of shared patient encounters and quantify high variability in collaboration between providers. We identified 29 top performers in terms of patient satisfaction. Providers in the high-scoring group had both a greater average number of associated encounters and a higher percentage of total encounters with positive outcomes than those in the low-scoring group, implying that more experienced individuals may be able to collaborate more successfully. Our study shows that a healthcare collaboration network can be structurally evaluated to characterize the collaborative interactions that occur between healthcare providers in a hospital setting.</p></div

The collaboration evaluation strategy used in this study.

<p>The collaboration evaluation strategy used in this study.</p

A simple example of the graph data model showing five actions performed by four providers during two encounters.

<p>(A) Providers 1, 2, and 3 each performed one activity during encounter 1, while provider 4 performed two activities during encounter 2. A hyperedge was used to represent an instance of activity during an encounter. Notice that both Provider 3 and Provider 4 performed a “Nursing Assessment” activity during different encounters. (B) Without hyperedges between the provider and the encounter nodes, it would not be possible to determine, for example, which provider performed the “Nursing Assessment" during encounter 2.</p

An example provider collaboration network showing 21 providers and 21 SPOR relationships.

<p>Properties associated with the highlighted edge (yellow) including the SPOR coefficient, the number of shared patient encounters between the two providers (num_collabs), and an indication of the significance of the SPOR coefficient (p-value) are shown in the bottom left. The proximity of nodes to each other is based on the SPOR coefficient, with high-scoring relationships being shorter in length than low-scoring relationships.</p

Statistics for five provider collaboration networks based on an increasing number of shared patients required for a collaborative interaction.

<p>Statistics for five provider collaboration networks based on an increasing number of shared patients required for a collaborative interaction.</p