A validation study of the identification of haemophagocytic lymphohistiocytosis in England using population-based health data.
Mark J BishtonPeter StilwellTim R CardPeter LanyonLu BanLucy Ellis-BrookesJessica MansonVasanta NanduriKate EarpLuke FlowerRaj AmarnaniJudith RankinEthan S SenRachel S TattersallColin J CrooksJeanette AstonVeronika SiskovaProf Joe WestMary BythellPublished in: British journal of haematology (2021)
We assessed the validity of coded healthcare data to identify cases of haemophagocytic lymphohistiocytosis (HLH). Hospital Episode Statistics (HES) identified 127 cases within five hospital Trusts 2013-2018 using ICD-10 codes D76.1, D76.2 and D76.3. Hospital records were reviewed to validate diagnoses. Out of 74 patients, 73 were coded D76.1 or D76.2 (positive predictive value 89·0% [95% Confidence Interval {CI} 80·2-94·9%]) with confirmed/probable HLH. For cases considered not HLH, 44/53 were coded D76.3 (negative predictive value 97·8% [95% CI 88·2-99·9%]). D76.1 or D76.2 had 68% sensitivity in detecting HLH compared to an established active case-finding HLH register in Sheffield. Office for National Statistics (ONS) mortality data (2003-2018) identified 698 patients coded D76.1, D76.2 and D76.3 on death certificates. Five hundred and forty-one were coded D76.1 or D76.2 of whom 524 (96·9%) had HLH in the free-text cause of death. Of 157 coded D76.3, 66 (42·0%) had HLH in free text. D76.1 and D76.2 codes reliably identify HLH cases, and provide a lower bound on incidence. Non-concordance between D76.3 and HLH excludes D76.3 as an ascertainment source from HES. Our results suggest electronic healthcare data in England can enable population-wide registration and analysis of HLH for future research.
Keyphrases
- healthcare
- end stage renal disease
- electronic health record
- ejection fraction
- newly diagnosed
- big data
- chronic kidney disease
- public health
- emergency department
- prognostic factors
- mental health
- machine learning
- peritoneal dialysis
- risk factors
- data analysis
- patient reported outcomes
- acute care
- health information
- deep learning
- drug induced