Practical Prognostication In the Elderly

Transcription

1 Practical Prognostication In the Elderly Rick Hoffmaster, MD 25th Annual Clinical Update in Geriatric Medicine Pittsburgh Marriott City Center Friday April 7, 2017

2 Disclosures None

3 Objective By the end of this presentation, the audience will have used multiple prognostication tools to estimate the life expectancy of an elderly patient across multiple settings.

4 Why Is Accurate Prognostication Important? We re pretty crummy at it by nature 1 Patients want accurate information, and their involvement in care planning improves outcomes 2 Hospice remains underutilized 3 Over- and undertreatment of older adults remains problematic 4

5 Ms. B

6 Case #1 Ms. B is an 82 year old woman who lives alone in a senior highrise PMH: Mild dementia, moderate non-oxygen dependent COPD, HTN, former smoker Ambulates short distances with a 2-wheeled walker, requires help with shopping, financial matters and appointments but is independent for all self-care

7 Case #1 (cont) Meds: HCTZ, donepezil, aspirin Patient rates her own health as good BMI = 22.1 Exam remarkable only for a somewhat frail woman, lungs are clear, mild cognitive slowing MOCA = 25/30 She has been clinically stable for the past 12 months without hospitalization

8 Case #1 (cont) What s Ms. B s life expectancy? What are the odds that she ll die within 5 years? How about over the next 10 years?

9 eprognosis Free compilation of several validated online prognostic tools Useful for patients in community, hospital, nursing home, hospice/palliative care unit

10 Lee-Schonberg Index for Community Dwelling Adults 5,6 Community dwelling adults age >50 All-cause 5- and 10-year mortality c-statistic (probability that predicting the outcome is better than chance) Lee index: 82% Schonberg index: 75%

11 Case #2 Ms. B does well over the following year, but then contracts bacterial pneumonia Hospitalized for IV antibiotics and steroids Admission albumin=3.6, Cr=1.2 Hospital course is complicated by moderate delirium, decreased oral intake and a prolonged stay She is discharged to SNF as she now requires help with nearly all ADLs

12 Case #2 (cont) How has Ms. B s life expectancy changed? What are the odds that she will die within the next 12 months?

13 Walter Index for Hospitalized Elderly 7 Hospitalized adults age >70 All-cause 1-year mortality c-statistic (probability that predicting the outcome is better than chance): 79%

14 Case #3 6 weeks later Ms. B has modestly recovered but requires long-term institutionalization Moderately dyspneic with activity though can toilet and dress herself Continent of bowel/bladder and has no skin breakdown Spends most of her day sitting and watching TV Eats well and BMI is stable at 21.5 MOCA is now 19/30

15 Case #3 (cont) Now what can Ms. B and her family expect? What are the odds that she will die within the next 6 months?

16 Mitchell and Porock Indices for Institutionalized Elderly 8,9 Nursing home adults +/- dementia (MDS data set) All-cause 6-month mortality c-statistic (probability that predicting the outcome is better than chance): Mitchell Index: 67% Porock Index: 75%

17 Case #4 Ms. B becomes increasingly dyspneic and debilitated over the following year Hospice care initiated Mostly in bed, very little activity, dependent for most care Reduced oral intake Increasing confusion

18 Case #4 (cont) How long can Ms. B expect to live with hospice care?

19 Palliative Performance Scale 10 Community-based and inpatient hospice patients (any age) Median survival in days Discrimination and validation not well assessed

20

21 Disease-Specific Tools

22 Disease-Specific Tools: CHF Seattle Heart Failure Model 11 Excellent validity and generalizability Able to easily compare survival with various potential interventions Somewhat complicated input data

23 Disease-Specific Tools: COPD BODE Index 12 r/bode-index-copd Requires special testing (6 min walk) c-statistic (probability that predicting the outcome is better than chance) BODE: 74% FEV 1 alone: 65%

24 Disease-Specific Tools: CKD Prognostic tools are lacking and data is conflicting Kidney Failure Risk Equation 13 Estimates risk of progression to ESRD at 2- and 5- years Very simple to use and lay-friendly

25 Disease-Specific Tools: Cancer Ask your oncologist Treatment options, and prognostic estimates, are constantly evolving ECOG/KPS/PPS for general sense of life expectancy

26 Disease-Specific Tools: ICU APACHE II Score 14,15 Risk of hospital death after admission to ICU Uses easily available data May not be generalizable to patients with specific medical conditions

27 Summary Prognostication is challenging due to a number of factors Familiarity bias, patient/family/provider emotional barriers, time constraints Patients and families want truthful information to help make informed decisions Disease- and non-disease-specific tools are available and relatively accurate More research focusing on functional recovery rather than simple survival

28 References 1. Christakis NA, Lamont EB. Extent and determinants of error in physicians prognoses in terminally ill patients: prospective cohort study. Western Journal of Medicine. 2000;172(5): Say RE, Thomson R. The importance of patient preferences in treatment decisions challenges for doctors BMJ 2003; 327 : Gozalo P, Plotzke M, Mor V, Miller SC, Teno JM. Changes in Medicare Costs with the Growth of Hospice Care in Nursing Homes. N Engl J Med 2015; 372: Walter LC, Covinsky KE. Cancer Screening in Elderly Patients, A Framework for Individualized Decision Making. JAMA. 2001;285(21): doi: /jama Lee SJ, Lindquist K, Segal MR, Covinsky KE. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA Feb 15;295(7): Schonberg MA, Davis RB, McCarthy EP, and Marcantonio ER. Index to predict 5-year mortality of community dwelling adults aged 65 an older using data from the National Health Interview Survey. J Gen Intern Med. 2009;24(10): Walter LC, Brand RJ, Counsell SR, Palmer RM, Landefeld CS, Fortinsky RH, Covinsky KE. Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization. JAMA 2001;285: Mitchell SL, Miller SC, Teno JM, Kiely DK, Davis RB, Shaffer ML. Prediction of 6-Month Survival of Nursing Home Residents With Advanced Dementia Using ADEPT vs Hospice Eligibility Guidelines. JAMA. 2010;304(17): doi: /jama

29 References 9. Porock D, Parker-Oliver D, Petroski GF, Rantz M. The MDS mortality risk index: The evolution of a method for predicting 6-month mortality in nursing home residents. BMC Research Notes, 2010 July 16:3: Anderson F, Downing GM, Hill J, Casorso L, Lerch N. Palliative Performance scale (PPS): a new tool. J Pall Care 1996 ;12(1): Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, Anand I, Maggioni A, Burton P, Sullivan MD, Pitt B, Poole-Wilson PA, Mann DL, Packer M, The Seattle Heart Failure Model: Prediction of Survival in Heart Failure. Circulation, 113: Celli BR, Cote CG, Marin JM, et. al. The body-mass index, airflow obstruction, dyspnea and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med Mar 4;350(10): Tangri N, Grams ME, Levey AS, Coresh J, Appel LJ, Astor BC, Chodick G, Collins AJ, Djurdjev O, Elley CR, Evans M, Garg AX, Hallan SI, Inker LA, Ito S, Jee SH, Kovesdy CP, Kronenberg F, Heerspink HJL, Marks A, Nadkarni GN, Navaneethan SD, Nelson RG, Titze S, Sarnak MJ, Stengel B, Woodward M, Iseki K, for the CKD Prognosis Consortium. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney FailureA Meta-analysis. JAMA. 2016;315(2): doi: /jama Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med Oct;13(10): Capuzzo M, Valpondi V, Sgarbi A, Bortolazzi S, Pavoni V, Gilli G, Candini G, Gritti G, Alvisi R. Validation of severity scoring systems SAPS II and APACHE II in a single-center population. Intensive Care Med Dec;26(12):