Defining a comprehensive verotype using electronic health records for personalized medicine

Abstract

The burgeoning adoption of electronic health records (EHR) introduces a golden opportunity for studying individual manifestations of myriad diseases, which is called 'EHR phenotyping'. In this paper, we break down this concept by: relating it to phenotype definitions from Johannsen; comparing it to cohort identification and disease subtyping; introducing a new concept called 'verotype' (Latin: vere = true, actually) to represent the 'true' population of similar patients for treatment purposes through the integration of genotype, phenotype, and disease subtype (eg, specific glucose value pattern in patients with diabetes) information; analyzing the value of the 'verotype' concept for personalized medicine; and outlining the potential for using network-based approaches to reverse engineer clinical disease subtypes.

Keywords: Electronic Health Records; Genetics; Genotype; Phenotype.

Figure 1

Factors that influence ‘phenotype’ identification in genetic and clinical data. Various factors introduce phenotypic variance in the traditional genetics model and the clinical data model (that utilizes EHR). Places where EHR can be utilized to assess each factor are highlighted in light orange. Thicker arrows show the main path for factors. EHR, electronic health record; VE, variance due to environment; VG, variance due to genetics; VHD, variance due to healthcare documentation; VHP, variance due to healthcare process. We include ‘well-controlled’, ‘stable’ and ‘critical’ condition as examples of patient status. For disease status, we include ‘early (eg, stage i)’, and ‘advanced (eg, stage iii)’ as examples. A patient may have a disease status indicating that their breast cancer is ‘advanced or stage iii’. If that same patient is later admitted to the hospital due to a car accident and is in a ‘critical condition’ then their patient status would be ‘critical’ while their disease status would remain unaffected (advanced breast cancer still present). The loop at the top of disease status indicates that a disease's status can affect the status of a second disease. For example, if a patient has advanced diabetes then their status for a second disease—retinopathy—could be affected.

Figure 2

A semantic network illustrating the relationship between genotype, phenotype, clinical disease subtype, biotype and verotype. Places where electronic health records can be utilized are highlighted in light orange.

Figure 3

The expression of markers for electronic health record (EHR) entities can be used to enable clinical disease subtyping. The highly diverse types of ‘markers’ stored in the EHR, for example, laboratory test, medication, specialist visits, can be utilized to reverse engineer clinical disease subtypes using a flexible ‘expression’ approach based on the type of marker. Each pattern indicates the most likely patient state based on the marker's expression level. HbA1C, hemoglobin A1C.