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. Man Desires to Know the Future!

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

5. 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.

6. 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.

7. RCRI and Frailty
Score
Makary MA J Am Coll Surg 2010; 210:901-908

8. 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

9. Percentage of One or more Postoperative complications
in each risk group
P=0.016

10. 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

11. 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

12. Study Population
• Patients > 75 years of age
• English speaking
• Elective major cancer surgery
• Charts reviewed: from September 1st,
2010 to December 31st, 2011

13. 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)

14. 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

15. 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%

16. Outcome Measures
• Postoperative Delirium
• 30 day Readmission
• 30 day UCC visit
• 30 day Mortality
• 6 month Mortality

17. 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

18. Postop
Delirium
Variables:
CCS (categorical)
IADL
Falls in last 6 mo
P < 0.1

19. 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

20. 6 Months
Mortality
Variables:
CCS
Age
Na
Weight Loss
P< 0.1

21. 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

23. 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.

25. 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.

26. 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.

27. 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.

28. Ann Intern Med. 2013;159(10):667-676. doi:10.7326/0003-4819-159-10-201311190-00005

30. 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