Development of a primary care screening algorithm for the early detection of patients at risk of primary antibody deficiency

Marianne A Messelink; Roos M Berbers; Joris M van Montfrans; Pauline M Ellerbroek; André Gladiator; Paco M J Welsing; Helen Leavis

doi:10.1186/s13223-023-00790-7

Development of a primary care screening algorithm for the early detection of patients at risk of primary antibody deficiency

Allergy Asthma Clin Immunol. 2023 May 27;19(1):44. doi: 10.1186/s13223-023-00790-7.

Authors

Marianne A Messelink¹, Roos M Berbers², Joris M van Montfrans², Pauline M Ellerbroek², André Gladiator³, Paco M J Welsing², Helen Leavis²

Affiliations

¹ Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands. M.A.Messelink@umcutrecht.nl.
² Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.
³ Takeda Pharmaceuticals International AG, Thurgauerstrasse 130, 8152, Glattpark-Opfikon, Zurich, Switzerland.

Abstract

Background: Primary antibody deficiencies (PAD) are characterized by a heterogeneous clinical presentation and low prevalence, contributing to a median diagnostic delay of 3-10 years. This increases the risk of morbidity and mortality from undiagnosed PAD, which may be prevented with adequate therapy. To reduce the diagnostic delay of PAD, we developed a screening algorithm using primary care electronic health record (EHR) data to identify patients at risk of PAD. This screening algorithm can be used as an aid to notify general practitioners when further laboratory evaluation of immunoglobulins should be considered, thereby facilitating a timely diagnosis of PAD.

Methods: Candidate components for the algorithm were based on a broad range of presenting signs and symptoms of PAD that are available in primary care EHRs. The decision on inclusion and weight of the components in the algorithm was based on the prevalence of these components among PAD patients and control groups, as well as clinical rationale.

Results: We analyzed the primary care EHRs of 30 PAD patients, 26 primary care immunodeficiency patients and 58,223 control patients. The median diagnostic delay of PAD patients was 9.5 years. Several candidate components showed a clear difference in prevalence between PAD patients and controls, most notably the mean number of antibiotic prescriptions in the 4 years prior to diagnosis (5.14 vs. 0.48). The final algorithm included antibiotic prescriptions, diagnostic codes for respiratory tract and other infections, gastro-intestinal complaints, auto-immune symptoms, malignancies and lymphoproliferative symptoms, as well as laboratory values and visits to the general practitioner.

Conclusions: In this study, we developed a screening algorithm based on a broad range of presenting signs and symptoms of PAD, which is suitable to implement in primary care. It has the potential to considerably reduce diagnostic delay in PAD, and will be validated in a prospective study. Trial registration The consecutive prospective study is registered at clinicaltrials.gov under NCT05310604.

Keywords: Algorithm; Diagnostic delay; Electronic health record; Primary antibody deficiency; Primary care; Screening.

Associated data

ClinicalTrials.gov/NCT05310604