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Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study
Orly Efros1,2,3, Shelly Soffer2,4, Aya Mudrik5
1National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel orlyefros@gmail.com.
Objectives:
This study aimed to develop and validate a machine-learning (ML) model to predict iron deficiency without anaemia (IDWA) using routinely collected electronic health record (EHR) data. The primary hypothesis was that an ML model could achieve better accuracy in identifying low ferritin levels (<30 ng/mL) in non-anaemic patients compared with traditional methods.
Design:
A retrospective cohort study.
Setting:
Data were derived from secondary and tertiary care facilities within the eight-hospital Mount Sinai Health System, an urban academic health system.
Participants:
The study included 211 486 adult patients (aged ≥18 years) with normal haemoglobin levels (≥130 g/L for men and ≥120 g/L for women) and recorded ferritin measurements.
Primary And Secondary Outcome Measures:
The primary outcome was the prediction of low ferritin levels (<30 ng/mL) using extreme gradient-boosted decision trees, an ML algorithm suited for structured clinical data. Secondary outcomes included subgroup analyses stratified by sex and age to evaluate model performance in different populations.Data from 211 486 Mount Sinai Health System patients with normal haemoglobin levels and ferritin testing were analysed. The model used demographic data, blood count indices and chemistry results to identify low ferritin levels (<30 ng/mL).
Results:
Of the 211 486 patients analysed, 19.56% (n=41 368) of the patients had low ferritin levels. In the low ferritin group, the mean age was 41.28 years with 89.64% females. In contrast, the normal ferritin group had a mean age of 50.14 years with 62.02% females. The model achieved an area under the curve (AUC) of 0.814. At a sensitivity threshold of 70%, the model had a specificity of 75.85%, with a positive predictive value of 37.6% and a negative predictive value of 92.41%. The model outperformed an alternative model based only on complete blood count indices (AUC 0.814 vs 0.741). Subgroup analysis showed that model accuracy varied by sex and age, with lower performance in premenopausal women (AUC 0.736) compared with postmenopausal women (AUC 0.793) and men (AUC of 0.832 in those under 60 years and 0.806 in those aged 60 and above).
Conclusions:
The ML model provides an effective approach to screening for IDWA using readily available EHR data. Implementing this tool in clinical settings may facilitate early diagnosis of IDWA.
