Evidence map›Paper›PMID 39396996›Full record

ArticleOrphanet journal of rare diseases2024

A machine learning algorithm for the detection of paroxysmal nocturnal haemoglobinuria (PNH) in UK primary care electronic health records.

Amanda Worker, Hadley Mahon, Jack Sams, Freya Boardman-Pretty, Elena Marchini, Rand Dubis, Alan Warren, Jez Stockdale, Jyothika Kumar, Elizabeth Varones and 4 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Orphanet journal of rare diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Amanda WorkerMendelian, The Trampery, Old Street, London, UK. amanda@mendelian.co.ORCID 0000-0003-3103-867X
Hadley MahonMendelian, The Trampery, Old Street, London, UK.
Jack SamsMendelian, The Trampery, Old Street, London, UK.
Freya Boardman-PrettyMendelian, The Trampery, Old Street, London, UK.
Elena MarchiniMendelian, The Trampery, Old Street, London, UK.
Rand DubisMendelian, The Trampery, Old Street, London, UK.
Alan WarrenMendelian, The Trampery, Old Street, London, UK.
Jez StockdaleMendelian, The Trampery, Old Street, London, UK.
Jyothika KumarMendelian, The Trampery, Old Street, London, UK.
Elizabeth VaronesMendelian, The Trampery, Old Street, London, UK.
Daniel OllerenshawMendelian, The Trampery, Old Street, London, UK.
Calum GrantMendelian, The Trampery, Old Street, London, UK.
Peter FishMendelian, The Trampery, Old Street, London, UK.
Richard J KellySt. James's University Hospital, Leeds, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundParoxysmal Nocturnal Haemoglobinuria (PNH) is an ultra-rare, acquired disorder that is challenging to diagnose due to varied symptoms, heterogeneous patient presentations, and lack of awareness among healthcare professionals. This leads to frequent misdiagnosis and delays in diagnosis. This study evaluated the feasibility of a machine learning model to identify undiagnosed PNH patients using structured electronic health records.

methodsThe study used data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. PNH patients were identified by the presence, and control patients by the absence of a PNH diagnosis code in their records. Clinical features (symptoms, diagnoses, healthcare utilisation) from 131 patients in the PNH group and 593,838 patients in the control group, were inputted to a tree-based XGBoost machine learning model to classify patients as either "positive" or "negative" for PNH suspicion. The algorithm was finalised after additional exclusions and inclusions applied. Performance was assessed using positive predictive value (PPV), recall and specificity. As the sample used to develop the algorithm was not representative of the true population prevalence, PPV was additionally adjusted to reflect performance in the wider population.

resultsOf all the patients in the PNH group, 27% were classified as positive (recall). 99.99% of the control group were classified as negative (specificity). Of all the patients classified as positive, 60.4% had a diagnosis of PNH in their record (PPV). The PPV adjusted for the population prevalence of PNH was 19.59 suggesting nearly 1 in 5 patients flagged may warrant further PNH investigation. The key clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari syndrome.

conclusionThis is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed.

Indexed as

AlgorithmsElectronic Health RecordsHemoglobinuria, ParoxysmalMachine LearningPrimary Health CareAdultAgedFemaleHumansMaleMiddle AgedUnited KingdomYoung AdultMachine learningParoxysmal Nocturnal Haemoglobinuria (PNH)Primary careRare diseaseUK electronic health records

Identifiers

PMID39396996
PMCPMC11479535

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.