Optimizing the use of point of care testing devices for screening patients

Elie Sarraf, Donald M. Mathews, Mitchell H. Tsai, Andrew J. Goodwin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Point of Care Testing (POCT) devices are regularly used to improve clinical workflows in the hospital setting despite generally having inferior performance when compared to standardized laboratory analyzers. We describe a method to improve the efficacy of using a POCT device as a screening test when the laboratory values occur over a continuum and applied this methodology to the process of International Normalized Ratios (INR) screening on day of surgery. Following IRB approval, laboratory INR values on the day of surgery were extracted from the University of Vermont Medical Center operating room’s electronic health record. Two separate theoretical POCT device values were simulated from the performance characterized by two prior publications (Jacobson and Hur). The sensitivities and specificities of the two theoretical devices were calculated over a range of values, in order detect an INR value greater or equal than 1.5 and 1.8. Subsequently, the percentage of the population with an INR value over each threshold was also calculated. Laboratory data from March 2008 to December 2016 were collected, and 9320 discrete INR values were compiled ranging from 0.8 to > 20. Two POCT devices were simulated using that dataset. The sensitivities and specificities over a range of values were determined, and the optimal cutoff values were identified for each device separately. Calculating the sensitivities and specificities over a range of values can optimize the clinical efficacy of a POCT device. By optimizing the use of POCT devices, hospitals may be able to improve clinical processes and reduce costs.

Original languageEnglish (US)
Pages (from-to)411-419
Number of pages9
JournalJournal of Clinical Monitoring and Computing
Volume34
Issue number3
DOIs
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Critical Care and Intensive Care Medicine
  • Anesthesiology and Pain Medicine

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