A data-driven architecture using natural language processing to improve phenotyping efficiency and accelerate genetic diagnoses of rare disorders.

Effective genetic diagnosis requires the correlation of genetic variant data with detailed phenotypic information. However, manual encoding of clinical data into machine-readable forms is laborious and subject to observer bias. Natural language processing (NLP) of electronic health records has great potential to enhance reproducibility at scale but suffers from idiosyncrasies in physician notes and other medical records. We developed methods to optimize NLP outputs for automated diagnosis. We filtered NLP-extracted Human Phenotype Ontology (HPO) terms to more closely resemble manually extracted terms and identified filter parameters across a three-dimensional space for optimal gene prioritization. We then developed a tiered pipeline that reduces manual effort by prioritizing smaller subsets of genes to consider for genetic diagnosis. Our filtering pipeline enabled NLP-based extraction of HPO terms to serve as a sufficient replacement for manual extraction in 92% of prospectively evaluated cases. In 75% of cases, the correct causal gene was ranked higher with our applied filters than without any filters. We describe a framework that can maximize the utility of NLP-based phenotype extraction for gene prioritization and diagnosis. The framework is implemented within a cloud-based modular architecture that can be deployed across health and research institutions.

Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation.

By informing timely targeted treatments, rapid whole-genome sequencing can improve the outcomes of seriously ill children with genetic diseases, particularly infants in neonatal and pediatric intensive care units (ICUs). The need for highly qualified professionals to decipher results, however, precludes widespread implementation. We describe a platform for population-scale, provisional diagnosis of genetic diseases with automated phenotyping and interpretation. Genome sequencing was expedited by bead-based genome library preparation directly from blood samples and sequencing of paired 100-nt reads in 15.5 hours. Clinical natural language processing (CNLP) automatically extracted children's deep phenomes from electronic health records with 80% precision and 93% recall. In 101 children with 105 genetic diseases, a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of those diseases, compared with a match of 0.9 phenotypic features used in manual interpretation. We automated provisional diagnosis by combining the ranking of the similarity of a patient's CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all of the patient's genomic variants. Automated, retrospective diagnoses concurred well with expert manual interpretation (97% recall and 99% precision in 95 children with 97 genetic diseases). Prospectively, our platform correctly diagnosed three of seven seriously ill ICU infants (100% precision and recall) with a mean time saving of 22:19 hours. In each case, the diagnosis affected treatment. Genome sequencing with automated phenotyping and interpretation in a median of 20:10 hours may increase adoption in ICUs and, thereby, timely implementation of precise treatments.

The role of calculated non-caeruloplasmin-bound copper in Wilson's disease.

Background US and European guidelines suggest the use of calculated non-caeruloplasmin-bound copper (free copper index) for the diagnosis and management of Wilson's Disease. However, there is concern that the required analytical measurements of caeruloplasmin and copper may not be sufficiently robust at the concentrations usually found. Methods Aliquots of six plasma specimens were sent to laboratories participating in the UK National External Quality Assessment Scheme for copper and caeruloplasmin. The variability of these two reported measurements and the calculated non-caeruloplasmin-bound copper concentrations were compared. The variability of caeruloplasmin reference ranges quoted by laboratories was also investigated. Results No laboratories use the required enzymatic methods in the calculation of non-caeruloplasmin-bound copper. The interlaboratory variations in caeruloplasmin concentrations and calculated non-caeruloplasmin-bound copper concentrations were very considerable so making clinical interpretation unreliable. Wide differences in the caeruloplasmin reference ranges used were also found. Conclusions Such variations of the calculated non-caeruloplasmin-bound copper concentrations and the predominant use of immunological methods for measuring caeruloplasmin preclude a clinical role for this calculated value in the investigation of Wilson's disease.

The risk of copper deficiency in patients prescribed zinc supplements.

AIMS: In high doses zinc may cause copper deficiency, a diagnosis that is often missed resulting in anaemia, neutropenia and irreversible neurological symptoms. The aim of this study was to assess if zinc deficiency is erroneously diagnosed by misinterpretation of plasma zinc concentrations and whether copper deficiency is induced in patients prescribed zinc. METHODS: Casenotes of 70 patients prescribed zinc were scrutinised. Plasma concentrations of zinc, copper, C reactive protein and albumin were recorded from the laboratory database. RESULTS: 62% of patients were prescribed zinc at doses sufficient to cause copper deficiency. In 48% of the patients, plasma zinc concentrations were low as a probable result of hypoalbuminaemia or the systemic inflammatory response rather than deficiency. Awareness of copper deficiency was lacking; it was only documented as a possible side effect in one patient and plasma copper was measured in only two patients prescribed zinc. 9% of patients developed unexplained anaemia and 7% developed neurological symptoms typical of copper deficiency. CONCLUSIONS: Zinc deficiency is frequently misdiagnosed on the basis of low plasma zinc concentrations. The potential risk of copper deficiency developing in patients prescribed high doses of zinc is apparently infrequently considered. It is probable that a significant minority of patients prescribed with high doses of zinc develop iatrogenic copper deficiency.