Predict future-make-decision

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Predict future-make-decision

  1. 1. Should Understanding of Future Influence Clinical Decision Making Today? Sung Wu Sun, MD, FACP Geriatrics Service Utilizing Prediction Models in day-to-day Practice
  2. 2. Man Desires to Know the Future!
  3. 3. What Do Men without Devine Communications Channel Do? • Gathered and Analyzed DATA to predict Future • Since biblical times, governments have held censuses to gather huge datasets • Supercomputers and BIG DATA
  4. 4. Derivation and Prospective Validation of a Simple Index for Prediction of Cardiac Risk of Major Noncardiac Surgery by Thomas H. Lee, Edward R. Marcantonio, Carol M. Mangione, Eric J. Thomas, Carisi A. Polanczyk, E. Francis Cook, David J. Sugarbaker, Magruder C. Donaldson, Robert Poss, Kalon K. L. Ho, Lynn E. Ludwig, Alex Pedan, and Lee Goldman Circulation Volume 100(10):1043-1049 September 7, 1999 Copyright © American Heart Association, Inc. All rights reserved.
  5. 5. Bars represent rate of major cardiac complications in entire patient population (both derivation and validation cohorts combined) for patients in Revised Cardiac Risk Index classes according to type of procedure performed. Lee T et al. Circulation 1999;100:1043-1049 Copyright © American Heart Association, Inc. All rights reserved.
  6. 6. RCRI and Frailty Score Makary MA J Am Coll Surg 2010; 210:901-908
  7. 7. Preoperative Geriatric Assessment Function (ADL, IADL, falls) Poly-pharmacy Chronic Comorbidity Nutrition Cognitive Function (Mini-Cog) Psycho-social assessment Robinson, TN Ann Surg 2009;250: 449-455
  8. 8. Percentage of One or more Postoperative complications in each risk group P=0.016
  9. 9. AUC 95% CI P-value Risk Calculator 0.638 0.520 - 0.755 0.026 Geriatric Score 0.700 0.581 – 0.817 < 0.001 Geriatric preoperative assessment (green) vs. Standard preoperative assessment (blue) Robinson TN Ann Surg 2009;250: 449-455 Robinson TN J Am Coll Surg (2011) 213:37 Robinson TN, et al. Am J Surg (2011) 202:511
  10. 10. Interdisciplinary Preoperative Geriatric Assessment: Standard of Care • History and physical examination • Functional evaluation (ADLs, IADLs and falls in the last six months) • Cognitive evaluation (Mini-Cog) • Number of medications • Charlson Co-morbidity Score (CCS) • Nutritional status; weight loss in the last 6 months, albumin, BMI • Psychosocial issues; social support, depression
  11. 11. Study Population • Patients > 75 years of age • English speaking • Elective major cancer surgery • Charts reviewed: from September 1st, 2010 to December 31st, 2011
  12. 12. Patient Characteristics • N= 420 patients • Age: Median = 80 (66-98) • Gender: 191 male (45.5%) • BMI: Median = 26 (11.6 – 53.4) • CCS: Median = 3 (0 – 12)
  13. 13. Procedure Disciplines Number of cases Hepato-Pancreatobiliary 87 Colorectal 73 Head and Neck 57 Urology 53 Gynecology 45 Thoracic 29 Neurosurgery 26 Gastric and Mixed Tumor 24 Interventional Radiology 5 Others 20 N=420
  14. 14. Geriatric Assessment • MiniCog score <4: 30.5% • No Social Support: 33 (7.9 %) • Falls in last 6 months: 84 (20.3%) • Dependent of ADL: 101 (24.3%) • Dependent of IADL: 92 (22.2%) • Number of Medications: 6 (0 - 25) • Weight loss > 10 lbs: 46.9%
  15. 15. Outcome Measures • Postoperative Delirium • 30 day Readmission • 30 day UCC visit • 30 day Mortality • 6 month Mortality
  16. 16. Geriatric markers associated with Postoperative Delirium: multivariate Variable OR (95% CI) P-value CCS > 3 1.827 (1.059-3.150) 0.03 Falls 1.741 (0.975-3.110) 0.061 IADL dependency 2.022 (1.153-3.547) 0.014
  17. 17. Postop Delirium Variables: CCS (categorical) IADL Falls in last 6 mo P < 0.1
  18. 18. Geriatric markers associated with 6 month mortality: multivariate Variable OR (95% CI) P-value Age (increase 10) 4.222 (1.820 – 9.793) <0.001 Na (decrease 10) 4.525 (1.73-11.765) 0.002 Weight loss 10 lbs 2.858 (1.293-6.316) 0.01 CCS (increase 1) 1.311 (1.101-1.561) 0.002
  19. 19. 6 Months Mortality Variables: CCS Age Na Weight Loss P< 0.1
  20. 20. Ability of (A) risk score versus (B) physician-rated Karnofsky performance status (KPS) to predict chemotherapy toxicity. Hurria A et al. JCO 2011;29:3457-3465 ©2011 by American Society of Clinical Oncology
  21. 21. Copyright © 2014 American Medical Association. All rights reserved. From: Cancer Screening in Elderly Patients: A Framework for Individualized Decision Making JAMA. 2001;285(21):2750-2756. doi:10.1001/jama.285.21.2750 Data from the Life Tables of the United States.
  22. 22. The impact of functional status on life expectancy in older persons. Keeler E1, Guralnik JM, Tian H, Wallace RB, Reuben DB. J Gerontol A Biol Sci Med Sci. 2010 Jul;65(7):727-33. doi: 10.1093/gerona/glq029. Epub 2010 Apr 2.
  23. 23. Date of download: 4/29/2014 From: Comorbidity-Adjusted Life Expectancy: A New Tool to Inform Recommendations for Optimal Screening Strategies Copyright © American College of Physicians. All rights reserved.
  24. 24. Date of download: 4/29/2014 From: Comorbidity-Adjusted Life Expectancy: A New Tool to Inform Recommendations for Optimal Screening Strategies Ann Intern Med. 2013;159(10):667-676. doi:10.7326/0003-4819-159-10-201311190-00005 Copyright © American College of Physicians. All rights reserved.
  25. 25. Ann Intern Med. 2013;159(10):667-676. doi:10.7326/0003-4819-159-10-201311190-00005
  26. 26. Future Directions • Watson type of supercomputer to analyze data generated in traditional medicine • BIG DATA? – Wearable technology – Imbedded sensors – Genetic data – Molecular imaging – Functional imaging

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