TY - JOUR
T1 - A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department
AU - Niemantsverdriet, Michael
AU - de Hond, Titus
AU - Höfer, IE
AU - van Solinge, W.W.
AU - Bellomo, Domenico
AU - Oosterheert, JJ
AU - Kaasjager, Karin
AU - Haitjema, Saskia
N1 - Funding Information:
M.S.A. Niemantsverdriet is supported by a PhD fellowship from SkylineDx, Rotterdam.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12/23
Y1 - 2022/12/23
N2 - 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.
AB - 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.
KW - Electronic health records
KW - Emergency department
KW - Endpoint adjudication
KW - Machine learning
KW - Sepsis
UR - http://www.scopus.com/inward/record.url?scp=85144520144&partnerID=8YFLogxK
U2 - 10.1186/s12873-022-00764-9
DO - 10.1186/s12873-022-00764-9
M3 - Article
C2 - 36550392
AN - SCOPUS:85144520144
SN - 1471-227X
VL - 22
SP - 1
EP - 10
JO - BMC Emergency Medicine
JF - BMC Emergency Medicine
IS - 1
M1 - 208
ER -