Detection of Falsely Elevated Point-of-Care Potassium Results Due to Hemolysis Using Predictive Analytics
| Autor: | Thomas J. Gniadek, Robert Benirschke |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Point-of-Care Systems
Machine learning computer.software_genre Logistic regression Hemolysis Machine Learning medicine Humans Basic metabolic panel False Positive Reactions Point of care Hematologic Tests medicine.diagnostic_test business.industry General Medicine Predictive analytics medicine.disease Point-of-Care Testing Potassium Artificial intelligence Core laboratory business computer Test panel Test data |
| Zdroj: | American journal of clinical pathology. 154(2) |
| ISSN: | 1943-7722 |
| Popis: | Objectives Preanalytical factors, such as hemolysis, affect many components of a test panel. Machine learning can be used to recognize these patterns, alerting clinicians and laboratories to potentially erroneous results. In particular, machine learning might identify which cases of elevated potassium from a point-of-care (POC) basic metabolic panel are likely erroneous. Methods Plasma potassium concentrations were compared between POC and core laboratory basic metabolic panels to identify falsely elevated POC results. A logistic regression model was created using these labels and the other analytes on the POC panel. Results This model has high predictive power in classifying POC potassium as falsely elevated or not (area under the curve of 0.995 when applied to the test data set). A rule-in and rule-out approach further improves the model’s applicability with a positive predictive value of around 90% and a negative predictive value near 100%. Conclusions Machine learning has the potential to detect laboratory errors based on the recognition of patterns in commonly requested multianalyte panels. This could be used to alert providers at the POC that a result is suspicious or used to monitor the quality of POC results. |
| Databáze: | OpenAIRE |
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