A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department

BMC Emergency Medicine

Research

Wouter W. van Solinge¹  Saskia Haitjema¹  Imo E. Hoefer¹  Michael S. A. Niemantsverdriet²  Karin A. H. Kaasjager³  Titus A. P. de Hond³  Jan Jelrik Oosterheert⁴  Domenico Bellomo⁵ 

[1] Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Room Number G03.551, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands;Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Room Number G03.551, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands;SkylineDx, Rotterdam, The Netherlands;Department of Internal Medicine and Acute Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands;Department of Internal Medicine, Infectious Diseases, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands;SkylineDx, Rotterdam, The Netherlands;

关键词: Sepsis;    Electronic health records;    Emergency department;    Machine learning;    Endpoint adjudication;   

DOI  :  10.1186/s12873-022-00764-9

 received in 2022-08-01, accepted in 2022-12-06,  发布年份 2022

来源: Springer

【 摘 要 】

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these “silver” labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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