Stratification of Companion Animal Life Stages from Electronic Medical Record Diagnosis Data

Abstract

Aging is a complex, multifactorial process, where different life stages reflect changes in metabolic processes, immune capacities, and genetic/epigenetic repertoires. With accumulating exposure to environmental stresses and deterioration of physiological functions, body systems become more prone to low-grade chronic inflammation and an increasing range of pathologies. We hypothesized that differential susceptibility to diseases across life span reflects phased changes in an organism's physiological capacity that may highlight when interventions may be appropriately used. Furthermore, the number of life stages may vary between species and be impacted by signalment such as breed. We tested this hypothesis using disease diagnoses data from veterinary electronic medical records containing almost 2 million cats and over 4 million dogs. Bi-clustering (on rates of disease diagnoses) and adaptive branch pruning were used to identify age clusters that could be used to define adult life stages. Clustering among diagnoses were then interpreted within the context of each defined life stage. The analyses identified 5 age clusters in cats and 4 age clusters within each of the 4 canine breed size categories used. This study, using population scale data for two species, one with differential size and life expectancies, is the first to our knowledge to use disease diagnosis data to define adult life stages. The life stages presented here are a result of a data-driven approach to age and disease stratification and are intended to support conversations between clinicians and clients about appropriate health care recommendations.

Keywords: Co-morbidity; Epidemiology; Health.

Figure 1.

Diagram illustrating the frequency distribution of breed and gender (FS = female spayed; MN = male neutered) within each breed size category for the dog data set.

Figure 2.

Diagram illustrating results produced by the bi-clustering and adaptive branch-pruning algorithms for cat (using the final values of “diagnosis threshold” and “age deep split”). Cells are colored to show the (scaled) count of diagnoses per pet-year with darker shading indicating higher values. Clusters identified by the adaptive branch-pruning algorithm are separated from neighboring clusters by white lines.

Figure 3.

Diagram illustrating results produced by the bi-clustering and adaptive branch-pruning algorithms for 4 breed-sizes of dog (using the final values of “diagnosis threshold” and “age deep split”), with A–D representing toy, small, medium, and large, respectively. Cells are colored to show (scaled) count of diagnoses per pet-year with darker shading indicating higher values. Clusters identified by the adaptive branch-pruning algorithm are separated from neighboring clusters by white lines.