Personalized medicine for chronic, complex diseases: chronic obstructive pulmonary disease as an example.

Chronic, complex diseases represent the majority of healthcare utilization and spending in the USA today. Despite this, therapeutics that account for the heterogeneity of these diseases are lacking, begging for more personalized approaches. Improving our understanding of disease phenotypes through retrospective trials of electronic health record data will enable us to better categorize patients. Increased usage of next-generation sequencing will further our understanding of the genetic variants involved in chronic disease. Utilization of data warehousing will be necessary in order to securely handle, integrate and analyze the large sets of data produced with these methods. Finally, increased use of clinical decision support will enable the return of clinically actionable results that physicians can use to apply these personalized approaches.

[1]  Paul J Catalano,et al.  Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer. , 2015, Journal of pain and symptom management.

[2]  Paul A. Harris,et al.  Secondary use of clinical data: The Vanderbilt approach , 2014, J. Biomed. Informatics.

[3]  Daniel J. Vreeman,et al.  The long road to semantic interoperability in support of public health: Experiences from two states , 2014, J. Biomed. Informatics.

[4]  Aziz Sheikh,et al.  Computer decision support systems for asthma: a systematic review , 2014, npj Primary Care Respiratory Medicine.

[5]  E. Silverman,et al.  Chronic Obstructive Pulmonary Disease Genetics: A Review of the Past and a Look Into the Future. , 2014, Chronic obstructive pulmonary diseases.

[6]  Courtney Crim,et al.  Should we view chronic obstructive pulmonary disease differently after ECLIPSE? A clinical perspective from the study team. , 2014, American journal of respiratory and critical care medicine.

[7]  D. Price,et al.  Opportunities to diagnose chronic obstructive pulmonary disease in routine care in the UK: a retrospective study of a clinical cohort. , 2014, The Lancet. Respiratory medicine.

[8]  Fabrício F. Costa.  Big data in biomedicine. , 2014, Drug discovery today.

[9]  J. Pacheco,et al.  Accuracy of Phenotyping Chronic Rhinosinusitis in the Electronic Health Record , 2014, American journal of rhinology & allergy.

[10]  Kari Stefansson,et al.  Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes , 2014, Nature Genetics.

[11]  Stephanie A. Santorico,et al.  Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema , 2014, Thorax.

[12]  Benjamin M. Smith,et al.  Genome-wide study of percent emphysema on computed tomography in the general population. The Multi-Ethnic Study of Atherosclerosis Lung/SNP Health Association Resource Study. , 2014, American journal of respiratory and critical care medicine.

[13]  E. Silverman,et al.  Clarification of the risk of chronic obstructive pulmonary disease in α1-antitrypsin deficiency PiMZ heterozygotes. , 2014, American journal of respiratory and critical care medicine.

[14]  M. Dugas,et al.  A European inventory of common electronic health record data elements for clinical trial feasibility , 2014, Trials.

[15]  Tanya M. Teslovich,et al.  Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci , 2014, PLoS genetics.

[16]  Gerard Tromp,et al.  Mechanistic Phenotypes: An Aggregative Phenotyping Strategy to Identify Disease Mechanisms Using GWAS Data , 2013, PloS one.

[17]  Keith Marsolo,et al.  EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children , 2013, Front. Genet..

[18]  Ianita Zlateva,et al.  Using electronic health records data to identify patients with chronic pain in a primary care setting. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[19]  D Kalra,et al.  Electronic health records: new opportunities for clinical research , 2013, Journal of internal medicine.

[20]  N. Laird,et al.  Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers. , 2013, American journal of respiratory and critical care medicine.

[21]  M. Alda,et al.  The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases , 2013, PloS one.

[22]  G. De,et al.  Development and validation of a claims-based prediction model for COPD severity. , 2013, Respiratory medicine.

[23]  R. DeMatteo,et al.  Targeted therapy for cancer: the gastrointestinal stromal tumor model. , 2013, Surgical oncology clinics of North America.

[24]  Serena G. Liao,et al.  Considerations and pitfalls in phenotyping and reclassification of chronic obstructive pulmonary disease. , 2013, Translational research : the journal of laboratory and clinical medicine.

[25]  R. Wilson,et al.  The Next-Generation Sequencing Revolution and Its Impact on Genomics , 2013, Cell.

[26]  Rainu Kaushal,et al.  Health information exchange system usage patterns in three communities: Practice sites, users, patients, and data , 2013, Int. J. Medical Informatics.

[27]  Julia Adler-Milstein,et al.  Healthcare's "big data" challenge. , 2013, The American journal of managed care.

[28]  A. LaCroix,et al.  Selective oestrogen receptor modulators in prevention of breast cancer: an updated meta-analysis of individual participant data , 2013, The Lancet.

[29]  D. Belletti,et al.  Results of the CAPPS: COPD – Assessment of Practice in Primary Care Study , 2013, Current medical research and opinion.

[30]  Justin Starren,et al.  Crossing the omic chasm: a time for omic ancillary systems. , 2013, JAMA.

[31]  M. Vitacca,et al.  COPD management in primary care: is an educational plan for GPs useful? , 2013, Multidisciplinary Respiratory Medicine.

[32]  L. Emens,et al.  HER2-directed therapy for metastatic breast cancer. , 2013, Oncology.

[33]  I. Hall,et al.  Stratified medicine: drugs meet genetics , 2013, European Respiratory Review.

[34]  M. Wjst,et al.  Genome-wide association studies in asthma: what they really told us about pathogenesis , 2013, Current opinion in allergy and clinical immunology.

[35]  E. Silverman,et al.  Developing New Drug Treatments in the Era of Network Medicine , 2013, Clinical pharmacology and therapeutics.

[36]  Alvar Agusti,et al.  The COPD control panel: towards personalised medicine in COPD , 2012, Thorax.

[37]  S. Shapiro,et al.  Emerging genetics of COPD , 2012, EMBO molecular medicine.

[38]  Marylyn D. Ritchie,et al.  The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era , 2012, Human Genetics.

[39]  L. Wain,et al.  What can genetics tell us about the cause of fixed airflow obstruction? , 2012, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[40]  C. Strange,et al.  The Prevalence of Alpha-1 Antitrypsin Deficiency Among Patients Found to Have Airflow Obstruction , 2012, COPD.

[41]  D. Woodwell,et al.  Physician adoption of electronic health record systems: United States, 2011. , 2012, NCHS data brief.

[42]  Peter N. Robinson,et al.  Deep phenotyping for precision medicine , 2012, Human mutation.

[43]  A. Stojadinovic,et al.  Clinical decision support systems: Potential with pitfalls , 2012, Journal of surgical oncology.

[44]  Edwin K Silverman,et al.  Identification of a chronic obstructive pulmonary disease genetic determinant that regulates HHIP. , 2012, Human molecular genetics.

[45]  L. Edwards,et al.  A genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13. , 2012, Human molecular genetics.

[46]  L. Hood,et al.  Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine , 2012, Journal of internal medicine.

[47]  Greg Gibson,et al.  Rare and common variants: twenty arguments , 2012, Nature Reviews Genetics.

[48]  P. Visscher,et al.  Five years of GWAS discovery. , 2012, American journal of human genetics.

[49]  T. Rosemann,et al.  Management of chronic obstructive pulmonary disease in Swiss primary care: room for improvement. , 2012, Quality in primary care.

[50]  Dana C Crawford,et al.  Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality , 2011, Genetic epidemiology.

[51]  Christian Gieger,et al.  Genome-wide association and large scale follow-up identifies 16 new loci influencing lung function , 2011, Nature Genetics.

[52]  Raymond K. Auerbach,et al.  The real cost of sequencing: higher than you think! , 2011, Genome Biology.

[53]  A. Hauschild,et al.  Improved survival with vemurafenib in melanoma with BRAF V600E mutation. , 2011, The New England journal of medicine.

[54]  Edwin K Silverman,et al.  SOX5 is a candidate gene for chronic obstructive pulmonary disease susceptibility and is necessary for lung development. , 2011, American journal of respiratory and critical care medicine.

[55]  David M. Evans,et al.  A Comprehensive Evaluation of Potential Lung Function Associated Genes in the SpiroMeta General Population Sample , 2011, PloS one.

[56]  C. Chute,et al.  Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium , 2011, Science Translational Medicine.

[57]  Rongling Li,et al.  Quality Control Procedures for Genome‐Wide Association Studies , 2011, Current protocols in human genetics.

[58]  Edwin K Silverman,et al.  Genome-wide association study identifies BICD1 as a susceptibility gene for emphysema. , 2011, American journal of respiratory and critical care medicine.

[59]  E. Regan,et al.  Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.

[60]  Edwin K Silverman,et al.  Chronic obstructive pulmonary disease phenotypes: the future of COPD. , 2010, American journal of respiratory and critical care medicine.

[61]  D. Blumenthal,et al.  The "meaningful use" regulation for electronic health records. , 2010, The New England journal of medicine.

[62]  Inês Barroso,et al.  Genome-wide association study identifies five loci associated with lung function , 2010, Nature Genetics.

[63]  Melissa A. Basford,et al.  Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. , 2010, American journal of human genetics.

[64]  Marylyn D. Ritchie,et al.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations , 2010, Bioinform..

[65]  L. Becquemont.  HLA: a pharmacogenomics success story. , 2010, Pharmacogenomics.

[66]  G. Hartvigsen,et al.  Secondary Use of EHR: Data Quality Issues and Informatics Opportunities , 2010, Summit on translational bioinformatics.

[67]  D. Caillaud,et al.  Clinical COPD phenotypes: a novel approach using principal component and cluster analyses , 2010, European Respiratory Journal.

[68]  K. Shianna,et al.  A Genome-Wide Association Study in Chronic Obstructive Pulmonary Disease (COPD): Identification of Two Major Susceptibility Loci , 2009, PLoS genetics.

[69]  K. R. Chapman,et al.  Epidemiology and costs of chronic obstructive pulmonary disease , 2006, European Respiratory Journal.

[70]  B. Shastry,et al.  Pharmacogenetics and the concept of individualized medicine , 2006, The Pharmacogenomics Journal.

[71]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[72]  H. Kerstjens.  The GOLD classification has not advanced understanding of COPD. , 2004, American journal of respiratory and critical care medicine.

[73]  S. Gabriel,et al.  EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy , 2004, Science.

[74]  D Karlsson,et al.  Medical decision-support systems and the concept of context , 2004, Medical informatics and the Internet in medicine.

[75]  M. King,et al.  Breast and Ovarian Cancer Risks Due to Inherited Mutations in BRCA1 and BRCA2 , 2003, Science.

[76]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[77]  Langreth,et al.  New era of personalized medicine: targeting drugs for each unique genetic profile , 1999, The oncologist.

[78]  D. Levy,et al.  Segregation analysis of pulmonary function among families in the Framingham Study. , 1998, American journal of respiratory and critical care medicine.

[79]  Yue Chen,et al.  Segregation analysis of two lung function indices in a random sample of young families: The humboldt family study , 1996, Genetic epidemiology.

[80]  E. Hoffman,et al.  The Evolving Genome Project: current and future impact. , 1994, American journal of human genetics.

[81]  M V Olson,et al.  The human genome project. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[82]  R. Waterston,et al.  The human genome project. Prospects and implications for clinical medicine. , 1991, JAMA.

[83]  M. King,et al.  Linkage of early-onset familial breast cancer to chromosome 17q21. , 1990, Science.

[84]  F. Speizer,et al.  Assessment of genetic and nongenetic influences on pulmonary function. A twin study. , 2015, The American review of respiratory disease.

[85]  J. Jeppsson,et al.  Properties of isolated human alpha1-antitrypsins of Pi types M, S and Z. , 1978, European journal of biochemistry.

[86]  R. K. Larson,et al.  Genetic and environmental determinants of chronic obstructive pulmonary disease. , 1970, Annals of internal medicine.

[87]  C. Laurell.  Electrophoretic Microheterogeneity of Serum α1-Antitrypsin , 1965 .

[88]  A. Bearn,et al.  Hereditary Deficiency of Serum α1-Antitrypsin , 1964, Science.