Automating the Detection of IV Fluid Contamination Using Unsupervised Machine Learning.
Background Intravenous (IV) fluid contamination is a common cause of preanalytical error that can delay or misguide treatment decisions, leading to patient harm. Current approaches for detecting contamination rely on delta checks, which require a prior result, or manual technologist intervention, wh...
| Publicado en: | Clinical Chemistry Vol. 70; no. 2; pp. 444 - 453 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175465682&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175465682 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Feb2024 vid: 70 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 175465682 10.1093/clinchem/hvad207 175465682 ppf: 444 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automating the Detection of IV Fluid Contamination Using Unsupervised Machine Learning. aug: au: Spies, Nicholas C Hubler, Zita Azimi, Vahid Zhang, Ray Jackups, Ronald Gronowski, Ann M Farnsworth, Christopher W Zaydman, Mark A affil: Department of Pathology, Washington University in St.Louis School of Medicine , St. Louis, MO , United States sug: ab: Background Intravenous (IV) fluid contamination is a common cause of preanalytical error that can delay or misguide treatment decisions, leading to patient harm. Current approaches for detecting contamination rely on delta checks, which require a prior result, or manual technologist intervention, which is inefficient and vulnerable to human error. Supervised machine learning may provide a means to detect contamination, but its implementation is hindered by its reliance on expert-labeled training data. An automated approach that is accurate, reproducible, and practical is needed. Methods A total of 25 747 291 basic metabolic panel (BMP) results from 312 721 patients were obtained from the laboratory information system (LIS). A Uniform Manifold Approximation and Projection (UMAP) model was trained and tested using a combination of real patient data and simulated IV fluid contamination. To provide an objective metric for classification, an "enrichment score" was derived and its performance assessed. Our current workflow was compared to UMAP predictions using expert chart review. Results UMAP embeddings from real patient results demonstrated outliers suspicious for IV fluid contamination when compared with the simulated contamination's embeddings. At a flag rate of 3 per 1000 results, the positive predictive value (PPV) was adjudicated to be 0.78 from 100 consecutive positive predictions. Of these, 58 were previously undetected by our current clinical workflows, with 49 BMPs displaying a total of 56 critical results. Conclusions Accurate and automatable detection of IV fluid contamination in BMP results is achievable without curating expertly labeled training data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|