Predicting Direct-Specimen SARS-CoV-2 Assay Performance Using Residual Patient Samples.

BACKGROUND: Diagnostic sensitivities of point-of-care SARS-CoV-2 assays depend on specimen type and population-specific viral loads. Evaluation of these assays require "direct" specimens from paired-swab studies rather than more accessible residual specimens in viral transport media (VTM). METHODS: Residual VTM and limit-of-detection studies were conducted on Abbott ID NOW™ COVID-19, Quidel Sofia 2™ SARS Antigen FIA, and DiaSorin Simplexa™ COVID-19 Direct assays, with cycle threshold (CT) adjustments to approximate direct-specimen testing based on gene-target doubling each PCR cycle. Logistic regression was used to model assay performance by specimen CT. These models were applied to CT distributions of symptomatic and asymptomatic populations presenting to emergency services to predict the percentage of specimens that would be detected by each assay. A 96-sample paired-swab study was conducted to confirm model results. RESULTS: When using direct nasopharyngeal samples and fit with either VTM or limit-of-detection data, percent positivities for ID NOW (symptomatic 94.9%/97.4%; asymptomatic 88.4.0%/89.6%) and Simplexa (symptomatic 97.8%/97.2%; asymptomatic 91.1%/90.8%) were predicted to be similar. Likewise, percent positivities for ID NOW with direct nasal specimens (symptomatic 77.8%; asymptomatic 64.5%) and, fit with VTM data, Sofia 2 with direct nasopharyngeal specimens (symptomatic 76.6%, asymptomatic 60.3%) were similar. The paired-swab study comparing direct nasopharyngeal specimens on ID NOW and nasopharyngeal VTM specimens on Simplexa showed 99% concordance. CONCLUSIONS: Assay performance can be modeled as dependent on viral load, fit using laboratory bench study results, and adjusted to account for direct-specimen testing. When using nasopharyngeal specimens, direct testing on Abbott ID NOW and VTM testing on DiaSorin Simplexa have similar performance.

Emergency department provider in triage: assessing site-specific rationale, operational feasibility, and financial impact.

Emergency department (ED) crowding is recognized as a critical threat to patient safety, while sub-optimal ED patient flow also contributes to reduced patient satisfaction and efficiency of care. Provider in triage (PIT) programs-which typically involve, at a minimum, a physician or advanced practice provider conducting an initial screening exam and potentially initiating treatment and diagnostic testing at the time of triage-are frequently endorsed as a mechanism to reduce ED length of stay (LOS) and therefore mitigate crowding, improve patient satisfaction, and improve ED operational and financial performance. However, the peer-reviewed evidence regarding the impact of PIT programs on measures including ED LOS, wait times, and costs (as variously defined) is mixed. Mechanistically, PIT programs exert their effects by initiating diagnostic work-ups earlier and, sometimes, by equipping triage providers to directly disposition patients. However, depending on local contextual factors-including the co-existence of other front-end interventions and delays in ED throughput not addressed by PIT-we demonstrate how these features may or may not ultimately translate into reduced ED LOS in different settings. Consequently, site-specific analysis of the root causes of excessive ED LOS, along with mechanistic assessment of potential countermeasures, is essential for appropriate deployment and successful design of PIT programs at individual EDs. Additional motivations for implementing PIT programs may include their potential to enhance patient safety, patient satisfaction, and team dynamics. In this conceptual article, we address a gap in the literature by demonstrating the mechanisms underlying PIT program results and providing a framework for ED decision-makers to assess the local rationale for, operational feasibility of, and financial impact of PIT programs.

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods.

BACKGROUND: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. OBJECTIVE: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. METHODS: The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. RESULTS: In non-COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95% CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95% CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale). CONCLUSIONS: The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.