Patient Similarity: Emerging Concepts in Systems and Precision Medicine.

Healthcare data generates a huge volume of information in various formats at high velocity with sometimes questionable veracity (Barkhordari and Niamanesh, 2015) (4V). As a result, big data tools such as patient similarity are necessary to facilitate analytics, which reduces costs (Srinivasan and Arunasalam, 2013) and improves healthcare systems (Jee and Kim, 2013). Patient similarity investigates distances between a variety of components of patient data, and determines methods of clustering patients, based on short distances between some of their characteristics. Although patient similarity is in its early stages, ultimately information about diseases, risk factors, lifestyle habits, medication use, co-morbidities, molecular and histopathological information, hospitalizations, or death are compared with laboratory investigations, imaging, and other clinical data assessing medical evidence of human behavior (Figure ​(Figure1).1). Such analytics consist of efficient computational analyses with patient stratification by multiple co-occurrence statistics, based on clinical characteristics. Algorithms create subgroups of patients based on similarities among their electronic avatars. Among electronic avatars found to be similar, subgroups of patients can be evaluated by further stratification guided by individual diagnoses, risk factors, medications, and so on. Because of the multiple networks of subgroups of patients, patient similarity can be considered an application of network medicine, with the output termed “patient similarity networks.” Thus, data mining extracts clinically relevant information hidden in clinical notes and embedded in other areas of the electronic health record (EHR) coupled with International Classification of Disease codes. The result is a systematic individualized analysis of a subset of patients that can improve outcome prediction and help guide management for a particular patient currently being cared for by a clinician (Lee et al., 2015). The communication or output from the algorithms can be used to identify and predict disease correlations and occurrence, and potentially for clinical decision support at the point of care. Patient similarity analytics are not restricted to global findings from large clinical trials consisting of somewhat heterogeneous patient populations (Roque et al., 2011). In this way, patient similarity represents a paradigm shift that introduces disruptive innovation to optimize personalization of patient care. Some promising examples are regarding mental and behavioral disorders (Roque et al., 2011), infectious diseases (Li et al., 2015), cancers (Wu et al., 2005; Teng et al., 2007; Chan et al., 2010, 2015; Klenk et al., 2010; Cho and Przytycka, 2013; Li et al., 2015; Wang, 2015; Bolouri et al., 2016; Wang et al., 2016), endocrine (Li et al., 2015; Wang, 2015), and metabolic diseases (Zhang et al., 2014; Ng et al., 2015). Others involve diseases of the nervous system (Lieberman et al., 2005; Carreiro et al., 2013; Cho and Przytycka, 2013; Qian et al., 2014; Buske et al., 2015a; Li et al., 2015; Bolouri et al., 2016; Wang et al., 2016), eyes (Buske et al., 2015a; Li et al., 2015), skin (Buske et al., 2015a; Li et al., 2015), heart (Wu et al., 2005; Tsymbal et al., 2007; Syed and Guttag, 2011; Buske et al., 2015a; Li et al., 2015; Panahiazar et al., 2015a,b; Wang, 2015; Bjornson et al., 2016), liver (Chan et al., 2015), intestines (Buske et al., 2015a), musculoskeletal system (Buske et al., 2015a), congenital malformations (Buske et al., 2015a), and various other conditions or factors influencing health status (Gotz et al., 2012; Subirats et al., 2012; Ng et al., 2015). Figure 1 The patient similarity analytics loop in systems medicine. Once a query patient is selected, the patient and clinician (e.g., physician or other health professional) in partnership can enter the “patient similarity analytics loop” (step ... Patient similarity in systems medicine Patient similarity is just starting to spread its wings and has the potential to transform Systems Medicine, which is Systems Biology applied to health care. Systems Biology studies the characteristics of cells, tissues, organisms, or other comprehensive biological units as whole systems. Systems Biology seeks to determine how changes in one part of the system can affect the behavior of the whole system, and often focuses on predictive modeling of the system in a perturbed state. Patient similarity analytics could be developed to bring together characteristics of the patient as a whole human system, and compare these to a multitude of similar patients. Accordingly, patient similarity analytics should in the near future incorporate genomics, transcriptomics, proteomics, microbiomics, and other “omics” and diverse components of systems medicine. In addition, simulation of physiology at the level of the molecule, cell, tissue, organ, and organism should be consolidated as a comprehensive similarity feature to give a broader view of interactions among organ systems. Patient similarity analytics could provide predictive models of a patient's outcome in the setting of disease perturbations or diagnoses relevant to the index patient. Making adjustments in the query data that serve as input for the predictive models would allow for assessment of how new diagnoses or therapies could impact the overall behavior and phenotype of the whole patient. Beyond the reasoning above, integrating the majority of these systems medicine tools into patient similarity analytics is potentially the next frontier in Systems Medicine, for at least a few reasons. First, patient similarity analytics embrace a systems view by assessing a myriad of characteristics for hundreds or thousands of patients to produce a meaningful and useful result. Second, patient similarity analytics are analogous to various “omics” that in part compose Systems Biology. Just as transcriptomics refers to generation of messenger RNA expression profiles (Briefing, 1999), one could consider a term similaromics referring to generation or identification of patients similar to an index patient. Similaromics is also akin to phenomics, proteomics, and genomics, among others. Phenomics refers to cataloging the observable characteristics conferred by a gene and proteomics describes the generation of proteins expressed by a cell (Briefing, 1999). One might argue that patient similarity is not quite analogous to genomics, since an individual's genome is thought to be constant throughout their lifetime. However, this is no longer necessarily the case, due to the current progress of genome editing tools. Indeed, patient similarity is analogous to these various omics, all with the potential to change over the lifetime of the individual. Thus, just as a genome is the complement of all DNA within a cell, a similarome is the complement of patients found to be similar to an index patient. Within the similarome, one can further distinguish subgroups of patients that are most similar to an index patient, based on preferentially assigning preeminence to comorbidities or medications of most interest or relevance to the index patient, e.g., during a focused shared decision-making session with a clinician. Similar to genotyping then, which determines the presence or absence of a particular gene feature, simotyping would allocate the presence or absence of a particular similarity feature, for example, a diagnosis of diabetes. In this context then, a similarity-wide association study (SiWAS) has the goal of discovering clusters of patients similar to an index patient and identifying similar features that associate with specific outcomes, such as complications, procedures, hospitalizations, or death. For example, investigating whether in patients most similar to an index patient diabetes is more likely to associate with non-healing leg ulcers, critical limb ischemia, or gangrene leading to limb amputation. Third, patient similarity analytics have the potential to bring together a variety of omics and other systems medicine tools, if we can do so in a way that is effective, accurate, consistent, and computationally efficient (Brown, 2015a). Indeed, several groups have proposed methods of aggregating omics and monitoring these over time for individual patients, and perhaps even using comprehensive patient avatars. Integrating these methods with patient similarity has the potential to launch systems medicine further into a future where medicine is even more precisely individualized. Patient similarity will likely become and persist as a useful tool in systems medicine.