Machine Learning in Glomerular Diseases: Promise for Precision Medicine

Related Article, p. 300 Related Article, p. 300 In the past decade, there have been tremendous strides in both the large-scale generation of phenotypic data with the advent of electronic health record (EHR) systems and the availability of novel computational tools and resources to analyze this highly correlated unstructured “noisy data.” These advanced statistical methods, tools, and resources are collectively named as “machine learning.” Machine learning, which itself is a subset of a broader universe of computational learning of artificial intelligence (AI), is now embedded in many aspects of our daily life, including social media, e-commerce, and various other applications. Although health care is lagging behind compared with other fields, machine learning and AI are predicted to revolutionize and disrupt health care processes, including biomedical research and health care delivery.1Topol E.J. High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019; 25: 44-56Crossref PubMed Scopus (1754) Google Scholar Though it has not reached its full potential, AI has already started to make an impact on the medical field. A good example is the utility of a subset of AI, referred to as “deep learning,” in which algorithms are built, mimicking the structure of a neural network, to automatically select features and discern patterns in data. Deep learning has been extensively used for image processing and lesion recognition in radiology and pathology.2Esteva A. Kuprel B. Novoa R.A. et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118Crossref PubMed Scopus (54) Google Scholar, 3Mayo R.C. Leung J. Artificial intelligence and deep learning - radiology’s next frontier?.Clin Imaging. 2018; 49: 87-88Abstract Full Text Full Text PDF PubMed Scopus (69) Google Scholar Advanced analytics linked to big data have been transforming clinical medicine, especially in the fields of oncology, pathology, and radiology, in which precision medicine approaches are already applied in the clinical sphere.4Jang Y. Choi T. Kim J. et al.An integrated clinical and genomic information system for cancer precision medicine.BMC Med Genom. 2018; 11: 34Crossref PubMed Scopus (9) Google Scholar However, nephrology has lagged behind other medical disciplines in the implementation of machine learning and other advanced analytics, in part because patients with kidney disease have tremendous variability in disease progression, outcomes, and response to therapy. Even so, because the kidneys regulate many key homeostatic processes, including electrolyte and fluid levels, acid-base balance, and hormonal production, and kidney-related consequences manifest perturbations in routinely measured blood and urine chemistry results, the use of these tools and techniques to more precisely phenotype patients and predict outcomes from large-scale EHR and other “omics” data has a perfect application in nephrology.5Saez-Rodriguez J. Rinschen M.M. Floege J. Kramann R. Big science and big data in nephrology.Kidney Int. 2019; 95: 1326-1337Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar This is why the present effort by Chen et al,6Chen T Li X. Li Y. et al.Prediction and risk stratification of kidney outcomes in IgA nephropathy.Am J Kidney Dis. 2019; 74: 300-309Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar in the current issue of AJKD, comes at an opportune time. Immunoglobulin A nephropathy (IgAN) is one of the most common primary kidney diseases in the world and especially in Asia.7Li L.-S. Liu Z.-H. Epidemiologic data of renal diseases from a single unit in China: analysis based on 13,519 renal biopsies.Kidney Int. 2004; 66: 920-923Abstract Full Text Full Text PDF PubMed Scopus (380) Google Scholar In addition to the wide range of phenotypic presentations seen with IgAN, there is variability in progression and outcomes. Although previous prediction models for IgAN have used standard modeling with a small number of predefined variables,8Berthoux F. Mohey H. Laurent B. Mariat C. Afiani A. Thibaudin L. Predicting the risk for dialysis or death in IgA nephropathy.J Am Soc Nephrol. 2011; 22: 752-761Crossref PubMed Scopus (249) Google Scholar, 9Barbour S.J. Espino-Hernandez G. Reich H.N. et al.The MEST score provides earlier risk prediction in lgA nephropathy.Kidney Int. 2016; 89: 167-175Abstract Full Text Full Text PDF PubMed Scopus (152) Google Scholar this was one of the first to use a supervised machine learning approach with a larger number of variables to predict outcomes. The authors show that a machine learning approach using an eXtreme Gradient Boosting (XGBoost) model, which generates a series of iteratively constructed decision points with each tree based on the previous one, performs well in predicting kidney outcomes in IgAN.6Chen T Li X. Li Y. et al.Prediction and risk stratification of kidney outcomes in IgA nephropathy.Am J Kidney Dis. 2019; 74: 300-309Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar This is a method that is part of the suite of supervised machine learning including other algorithms, such as random forest and support vector machine. The model itself was constructed from 36 clinical and pathology features (with imputation when variables had missing data) and created derived features from the existing features. This algorithm had a C statistic of 0.84 for the prediction of end-stage kidney disease or 50% reduction in estimated glomerular filtration rate within 5 years after the biopsy diagnosis. Using the most important variables identified from this model, Chen et al then constructed a validated simplified scoring scale model that is made available online. Although the study had limitations, including not validating in a prospective cohort and using data from only the Chinese population, thus limiting generalizability, this still represents meaningful progress in the application of machine learning to primary glomerular diseases in nephrology to further the precision medicine movement. The efforts by Chen et al represent a first step in advanced prediction for glomerulonephritis through machine learning. The widespread availability of EHR systems has sometimes proved cumbersome for physicians, with increasing time spent on documenting and billing, leading to dissatisfaction. However, being the primary mode of patient interaction and engagement, EHR systems also give opportunities for providing actionable clinical decision support linked to machine-learned risk stratification at the appropriate time during the clinical encounter.10Sitapati A. Kim H. Berkovich B. et al.Integrated precision medicine: the role of electronic health records in delivering personalized treatment.Wiley Interdiscip Rev Syst Biol Med. 2017; 9https://doi.org/10.1002/wsbm.1378Crossref PubMed Scopus (28) Google Scholar Adding this “intelligence layer” to routine clinical practice may deepen the clinician-patient encounter by allowing for more time spent on management and counseling rather than aggregating and inputting data. Supervised machine learning approaches, such as those used in the article by Chen et al, are only the tip of the iceberg when considering the use of AI in nephrology. The utilities of these tools in furthering nephrology research and practice are myriad, from automated image processing, to risk prediction in acute and chronic kidney disease, to subtyping disease to better understand distinct pathophysiologic processes, to repurposing medications approved for other indications. Table 1 briefly reviews the potential use cases for different AI approaches in nephrology with select examples from other disciplines and subspecialties. This is not meant to be an exhaustive list but rather to demonstrate how these methodologies are currently being used and how research/clinical care may benefit from them in the future.Table 1Different Machine Learning Approaches and Their Potential UtilityMethodPotential ApplicationSelected Examples From Other DisciplinesSelected Examples From NephrologyPotential Future ApplicationsSupervised machine learning-Risk prediction and stratification of clinical outcomes-Readmissions in heart failure13Mahajan S.M. Heidenreich P. Abbott B. Newton A. Ward D. Predictive models for identifying risk of readmission after index hospitalization for heart failure: a systematic review.Eur J Cardiovasc Nurs. 2018; 17: 675-689Crossref PubMed Scopus (35) Google Scholar-Prediction of septic shock14Henry K.E. Hager D.N. Pronovost P.J. Saria S. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med.http://stm.sciencemag.org/content/7/299/299ra122Date accessed: March 30, 2019Google Scholar-Prediction of outcomes in AKI15Huang C. Murugiah K. Mahajan S. et al.Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: a retrospective cohort study.PLoS Med. 2018; 15e1002703Crossref PubMed Scopus (59) Google Scholar-Prediction of outcomes in T2DM and individuals with APOL1 risk variants16Nadkarni GN, Fleming F, McCullough JR, et al. Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record data. Preprint at https://www.biorxiv.org/content/10.1101/587774v1. Accessed April 1, 2019.Google Scholar-Personalized machine learning–derived models for risk stratification-Addition of risk scores to guide individualized clinical pathways for improving outcomesSemi-supervised and unsupervised clustering-Defining disease subtypes-Subtyping in T2DM17Li L. Cheng W.-Y. Glicksberg B.S. et al.Identification of type 2 diabetes subgroups through topological analysis of patient similarity.Sci Transl Med. 2015; 7: 311ra174Crossref PubMed Scopus (331) Google Scholar-Subtyping Parkinson disease18Zhang X. Chou J. Liang J. et al.Data-driven subtyping of Parkinson’s disease using longitudinal clinical records: a cohort study.Sci Rep. 2019; 9: 797Crossref PubMed Scopus (65) Google Scholar-Archetype analysis of outcomes in kidney transplantation19Aubert O. Higgins S. Bouatou Y. et al.Archetype analysis identifies distinct profiles in renal transplant recipients with transplant glomerulopathy associated with allograft survival.J Am Soc Nephrol. 2019; 30: 625-639Crossref PubMed Scopus (35) Google Scholar-Subtyping various kidney diseases using integrated clinical, EHR, and -omics data.Supervised learning-Drug repurposing-Drug repurposing for Ebola virus20Schuler J. Hudson M.L. Schwartz D. Samudrala R. A systematic review of computational drug discovery, development, and repurposing for Ebola virus disease treatment.Molecules. 2017; 22https://doi.org/10.3390/molecules22101777Crossref PubMed Scopus (19) Google Scholar-Informatics approaches for drug repurposing for allograft fibrosis21Li L. Greene I. Readhead B. et al.Novel therapeutics identification for fibrosis in renal allograft using integrative informatics approach.Sci Rep. 2017; 7: 39487Crossref PubMed Scopus (20) Google Scholar-Drug repurposing for various glomerular and nonglomerular kidney diseasesDeep learning-Pattern identification and “learning” on massive data sets-Lesion identification in dermatology2Esteva A. Kuprel B. Novoa R.A. et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118Crossref PubMed Scopus (54) Google Scholar-Image identification in radiology3Mayo R.C. Leung J. Artificial intelligence and deep learning - radiology’s next frontier?.Clin Imaging. 2018; 49: 87-88Abstract Full Text Full Text PDF PubMed Scopus (69) Google Scholar-Automated segmentation of MR images in polycystic kidney disease22Kline T.L. Korfiatis P. Edwards M.E. et al.Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys.J Digit Imaging. 2017; 30: 442-448Crossref PubMed Scopus (84) Google Scholar-Biopsy image processing and renal outcomes23Kolachalama V.B. Singh P. Lin C.Q. et al.Association of pathological fibrosis with renal survival using deep neural networks.Kidney Int Rep. 2018; 3: 464-475Abstract Full Text Full Text PDF PubMed Scopus (82) Google Scholar-Automated processing for kidney biopsy images and imaging studies-Discovery of unknown risk factors and correlates of kidney disease progressionAbbreviations: AKI, acute kidney injury; APOL1, apolipoprotein L1; EHR, electronic health record; MR, magnetic resonance; T2DM, type 2 diabetes mellitus. Open table in a new tab Abbreviations: AKI, acute kidney injury; APOL1, apolipoprotein L1; EHR, electronic health record; MR, magnetic resonance; T2DM, type 2 diabetes mellitus. It would be remiss not to point out several pitfalls that the nephrology community should be cognizant of during this AI revolution. First, as in other areas, indiscriminate use of algorithms may exacerbate underlying disparities, especially if the algorithms capture biases in the data on which they are trained. This has proved to be an issue in other areas and may be a particular problem in nephrology, in which there are severe racial/ethnic disparities due to genetic and socioeconomic differences.11Choi A.I. Rodriguez R.A. Bacchetti P. Bertenthal D. Hernandez G.T. O’Hare A.M. White/black racial differences in risk of end-stage renal disease and death.Am J Med. 2009; 122: 672-678Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar Particularly if the algorithms are trained on narrowly representative data, and even if trained on representative real-world data, clinicians and health systems should be vigilant that socioeconomic biases present in the data are not perpetuated further. Second, most of these approaches have only undergone in silico validation and training, so the importance of prospective validation and testing the impact using randomized clinical trials cannot be emphasized enough. Finally, and perhaps most importantly, in this era of algorithms, subject matter and domain knowledge will become more essential rather than less essential.12Goldstein B.A. Carlson D. Bhavsar N.A. Subject matter knowledge in the age of big data and machine learning.JAMA Netw Open. 2018; 1e181568Crossref PubMed Scopus (7) Google Scholar It will be important to have a deep understanding of the disease itself, as well as the relationships being modeled, along with robust validation and mechanistic follow-up of promising findings so that the resulting discoveries are not erroneous. Despite these limitations, we are entering an era when AI will be a part of medical practice. It behooves us as physicians and researchers to play an active part in this coming age and to collaborate with and lead multidisciplinary teams furthering these new models in research and clinical practice. If we play a role in supplementing our clinical capabilities using these approaches, AI could become worthy of the moniker “augmented intelligence” in the near future and may transform outcomes for the most vulnerable patients. Girish N. Nadkarni, MD, MPH, Kumardeep Chaudhary, PhD, and Steven G. Coca, DO, MS. None. Drs Nadkarni and Coca receive financial compensation as consultants and advisory board members for and own equity in and are scientific cofounders of RenalytixAI, Inc. Dr Coca has received consulting fees from Goldfinch Bio, CHF Solutions, Quark Biopharma, Janssen Pharmaceuticals, and Takeda Pharmaceuticals in the past 3 years. Dr Nadkarni has received operational funding from Goldfinch Bio and consulting fees from BioVie Inc and GLG Consulting in the past 3 years. Drs Nadkarni and Coca are former members of the advisory board of PulseData, received consulting fees for their services, and continue to hold equity interests in PulseData. The remaining author declares that he has no relevant financial interests. Received April 1, 2019, in response to an invitation from the journal. Accepted April 11, 2019, after editorial review by an Associate Editor and a Deputy Editor. Prediction and Risk Stratification of Kidney Outcomes in IgA NephropathyAmerican Journal of Kidney DiseasesVol. 74Issue 3PreviewImmunoglobulin A nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes and stratifying risk are important for clinical decision making and designing future clinical trials. Full-Text PDF