tetano
Editor, Senior Moderator
Clin Infect Dis
. 2020 Aug 12;ciaa1175.
doi: 10.1093/cid/ciaa1175. Online ahead of print.
A SARS-CoV-2 Prediction Model from Standard Laboratory Tests
Vafa Bayat[SUP] 1 [/SUP], Steven Phelps[SUP] 2 [/SUP], Russell Ryono[SUP] 3 [/SUP], Chong Lee[SUP] 4 [/SUP], Hemal Parekh[SUP] 4 [/SUP], Joel Mewton[SUP] 5 [/SUP], Farshid Sedghi[SUP] 6 [/SUP], Payam Etminani[SUP] 6 [/SUP], Mark Holodniy[SUP] 7 8 9 [/SUP]
Affiliations
Abstract
Background: With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.
Methods: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.
Results: In a cohort of 75,991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July, 2020, 7,335 of whom were positive by RT-PCR or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).
Conclusions: While molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.
Keywords: Coronavirus; Machine Learning; Pneumonia; Polymerase Chain Reaction; human; viral.
. 2020 Aug 12;ciaa1175.
doi: 10.1093/cid/ciaa1175. Online ahead of print.
A SARS-CoV-2 Prediction Model from Standard Laboratory Tests
Vafa Bayat[SUP] 1 [/SUP], Steven Phelps[SUP] 2 [/SUP], Russell Ryono[SUP] 3 [/SUP], Chong Lee[SUP] 4 [/SUP], Hemal Parekh[SUP] 4 [/SUP], Joel Mewton[SUP] 5 [/SUP], Farshid Sedghi[SUP] 6 [/SUP], Payam Etminani[SUP] 6 [/SUP], Mark Holodniy[SUP] 7 8 9 [/SUP]
Affiliations
- PMID: 32785701
- DOI: 10.1093/cid/ciaa1175
Abstract
Background: With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.
Methods: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.
Results: In a cohort of 75,991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July, 2020, 7,335 of whom were positive by RT-PCR or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).
Conclusions: While molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.
Keywords: Coronavirus; Machine Learning; Pneumonia; Polymerase Chain Reaction; human; viral.