key: cord-0803581-6ydyzcw4
authors: Joshi, Rohan P.; Pejaver, Vikas; Hammarlund, Noah E.; Sung, Heungsup; Lee, Seong Kyu; Furmanchuk, Al’ona; Lee, Hye-Young; Scott, Gregory; Gombar, Saurabh; Shah, Nigam; Shen, Sam; Nassiri, Anna; Schneider, Daniel; Ahmad, Faraz S.; Liebovitz, David; Kho, Abel; Mooney, Sean; Pinsky, Benjamin A.; Banaei, Niaz
title: A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results
date: 2020-06-10
journal: J Clin Virol
DOI: 10.1016/j.jcv.2020.104502
sha: 65a64c2542951cf39dd0f17842f0355a30e76a37
doc_id: 803581
cord_uid: 6ydyzcw4

BACKGROUND: Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate. OBJECTIVE: To prediction tool based on complete blood count components and patient sex could predict SARS-CoV-2 PCR positivity. STUDY DESIGN: A retrospective case-control design for collection of data and a logistic regression prediction model was used. Participants were emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing. 33 confirmed SARS-CoV-2 PCR positive and 357 negative patients at Stanford Health Care used for model training. Validation cohorts consisted of emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California (41 PCR positive, 495 PCR negative), Seattle, Washington (40 PCR positive, 306 PCR negative), Chicago, Illinois (245 PCR positive, 1015 PCR negative), and South Korea (9 PCR positive, 236 PCR negative). RESULTS: A decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78%, an optimized sensitivity of 93%, and generalizability to other emergency department populations. By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60% of patients, this tool would allow a 33% increase in properly allocated resources. CONCLUSIONS: A prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge.

Results: A decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78%, an optimized sensitivity of 93%, and generalizability to other emergency department populations. J o u r n a l P r e -p r o o f By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60% of patients, this tool would allow a 33% increase in properly allocated resources.

A prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge.

Keywords: COVID-19; SARS-CoV-2; Rapid Testing; Machine Learning; Prediction Tool Health care systems worldwide are struggling to meet the demands of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic 1 . Weeks after local transmission was first recognized in the US, laboratory testing for COVID-19, the disease caused by SARS-CoV-2, was limited by assay complexity and reagent shortages. Limited testing and long turnaround times led to misutilization of resources resulting in strained health systems in high burden regions. While testing capacity is improving, alternative testing approaches and specimen sources are needed to handle COVID-19 surges, particularly in low-resource health systems. The aim of this study was to develop a decision support tool that integrates readily available routine lab values to predict negative SARS-CoV-2 results in patients presenting to the emergency department (ED). Use of this tool could reserve confirmatory SARS-CoV-2 testing and health system resources such as personal protective equipment and isolation rooms for those patients more likely to have COVID-19 ( Figure 1) .

A number of studies reported associations between certain non-SARS-CoV-2 test results and COVID-19 disease 2,3 . We therefore collected complete blood count (CBC) data from 3/1/2020 to 3/20/2020 ordered within 24 hours of a SARS-CoV-2 PCR 4 (based off of the WHO assay) J o u r n a l P r e -p r o o f order, which resulted in a dataset of 33 confirmed SARS-CoV-2 PCR positive and 357 PCR negative ED patients at Stanford Health Care. CBC were generally concurrently ordered on ED patients with an Emergency Severity Index 5 (ESI) 1-3 and 50% of ED patients with SARS-CoV-2 PCR tests had an accompanying CBC. ED patients with both CBC and SARS-CoV-2 PCR tests were therefore of older age (median 59 years old) compared to ED patients with SARS-CoV-2 PCR tests alone (median 37 years old), with a similar sex ratio and rate of SARS-CoV-2 PCR positivity. CBC orders were placed before positive SARS-CoV-2 PCR tests resulted and 83% of patients had CBC orders placed within 4 hours of the PCR order.

For training, we selected 3 CBC components, absolute neutrophil count (ANC), absolute lymphocyte count (ALC), and hematocrit (HCT), based on a univariate analysis suggesting association with positive SARS-CoV-2 PCR, evidence of ANC and ALC association with disease severity in the literature 2,3 , and a low Pearson correlation of these features to each other. We included male sex as a feature in our model since it was associated with positive SARS-CoV-2 PCR status independent of hematocrit. In cross-validation within the training set, this manual variable selection method performed comparably to other model-based variable selection methods (e.g. recursive feature elimination, L1-penalization, and L2-penalization).

Using ANC, ALC, hematocrit, and patient sex, we trained an L2-regularized logistic regression model 6 . ANC and ALC were negative predictors while male sex and HCT were positive predictors of SARS-CoV-2 PCR positivity. Using a receiver operating characteristic curve, we chose a test probability threshold to optimize for >50% specificity and >80% sensitivity.

Validation sets consisted of data that were not seen by the model during training from emergency department patients who received SARS-CoV-2 PCR testing and concurrent CBC.

In testing on a validation set from patients presenting to Stanford Health Care from 3/21/2020 to 4/7/2020 (confirmed SARS-CoV-2 PCR positive, n=41; negative, n=495), the decision support tool showed a diagnostic C-statistic of 0.78 (Figure 2A) . We examined the tradeoff between J o u r n a l P r e -p r o o f negative predictive value (NPV) and specificity using our model and found high NPV was maintained across a range of specificities, with a specificity-weighted average NPV of 98%.

Using the operating threshold defined using the training set, the model accurately ruled out SARS-CoV-2 in 40% of total test patients with an NPV of 99% and sensitivity of 93%. The data that support the findings of this study are available on request from the corresponding author NB. The data are not publicly available due to them containing information that could compromise research participant privacy.

Code Availability:

Code for prediction using the model is available upon request from the corresponding author NB. In addition, a web interface has been made available at http://web.stanford.edu/~gscott2/cgibin/CovidTool/. 

Fair Allocation of Scarce Medical Resources in the Time of Covid-19

Clinical Characteristics of Coronavirus Disease 2019 in China

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Sample Pooling as a Strategy to Detect Community Transmission of SARS-CoV-2

The emergency severity index triage algorithm version 2 is reliable and valid

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

Rates of Co-infection Between SARS-CoV-2 and Other Respiratory Pathogens

We thank Ethan Steinberg for review of data curation and the prediction model. NB conceived and supervised the study. All authors read and approved the manuscript.

The authors declare no competing interests.