Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission.
Background Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. Methods We collected non-intensive care unit (non-ICU) data fro...
| Publicado en: | Clinical Chemistry Vol. 70; no. 3; pp. 506 - 516 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2024
|
| 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=175938336&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175938336 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Mar2024 vid: 70 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 175938336 10.1093/clinchem/hvae001 175938336 ppf: 506 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission. aug: au: Steinbach, Daniel Ahrens, Paul C Schmidt, Maria Federbusch, Martin Heuft, Lara Lübbert, Christoph Nauck, Matthias Gründling, Matthias Isermann, Berend Gibb, Sebastian Kaiser, Thorsten affil: University Institute for Laboratory Medicine, OWL University Hospital of Bielefeld University , Detmold , Germany sug: ab: Background Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. Methods We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. Results After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857–0.887). External validations show AUROCs of 0.805 (95% CI, 0.787–0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837–0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836–0.877) than PCT alone (0.790; 95% CI, 0.759–0.821; P < 0.001). Conclusions Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|