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Development of a primary care screening algorithm for the early detection of patients at risk of primary antibody
Marianne A Messelink1, Roos M Berbers2, Joris M van Montfrans2
1Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands. M.A.Messelink@umcutrecht.nl.
Insights
A new screening algorithm for primary antibody deficiencies (PAD) uses electronic health records to identify at-risk patients. This tool aims to reduce diagnostic delays, improving patient outcomes and preventing serious complications from undiagnosed PAD.
Area of Science:
- Immunology
- Primary Care Medicine
- Health Informatics
Background:
- Primary antibody deficiencies (PAD) present heterogeneously with low prevalence, leading to a median diagnostic delay of 3-10 years.
- Delayed diagnosis of PAD increases morbidity and mortality risks, highlighting the need for timely intervention.
- Early detection of PAD is crucial for initiating effective therapy and preventing adverse health outcomes.
Purpose of the Study:
- To develop a screening algorithm utilizing primary care electronic health record (EHR) data to identify patients at risk for PAD.
- To reduce the significant diagnostic delay associated with primary antibody deficiencies.
- To provide general practitioners with a tool to prompt further laboratory evaluation for immunoglobulins.
Main Methods:
- Candidate components for the algorithm were selected based on presenting signs and symptoms of PAD found in primary care EHRs.
- Algorithm components were weighted based on their prevalence in PAD patients versus control groups and clinical rationale.
- Analysis included EHR data from 30 PAD patients, 26 other immunodeficiency patients, and 58,223 controls.
Main Results:
- The median diagnostic delay for PAD patients in the study was 9.5 years.
- Key indicators differentiating PAD patients from controls included the number of antibiotic prescriptions in the 4 years prior to diagnosis (5.14 vs. 0.48).
- The final algorithm incorporated antibiotic prescriptions, infection codes, gastro-intestinal complaints, autoimmune symptoms, malignancies, lymphoproliferative disorders, lab values, and GP visits.
Conclusions:
- A screening algorithm for PAD, based on EHR data, has been developed and is suitable for primary care implementation.
- This algorithm has the potential to significantly decrease the diagnostic delay for primary antibody deficiencies.
- The algorithm will undergo validation in a prospective study (ClinicalTrials.gov: NCT05310604).
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.
