Evidence for Use of the Surprise Question 1 A L V I N H . M O S S - PDF document

1/23/2020 Improving Care of the Seriously Ill: Evidence for Use of the Surprise Question 1 A L V I N H . M O S S , M D S E CT I O N S O F N E P H R O L O G Y A N D G E R I A TR I CS & P A L L I A TI V E M E D I CI N E D E P A R T M E N T O F M E D I CI N E W E S T V I R G I N I A U N I V E R S I TY M O R G A N T O W N , W V A M O S S @ H S C. W V U . E D U No relevant financial relationships to disclose The Patient Reluctantly Agreed to Dialysis 2  81 yo frail widower c stage 5 CKD from hypertensive nephrosclerosis  Former heavy smoker with oxygen-dependent COPD  Severe PAD with claudication; oxycodone qhs for leg pain  eGFR dropped to 5 ml/ min but not uremic  Serum albumin 2.9 mg/ dL  Nephrologist urged dialysis with daughter’s agreement  Lived alone and valued his independence and freedom  Did not want to be tied down to a dialysis schedule 3X/ week  Started reluctantly to please his daughter  Would you be surprised if this patient died in the next year? 1

1/23/2020 Objectives 3  Explain the value of the surprise question in clinical practice  Describe the surprise question and its role in prognostication, shared decision-making and advance care planning  Review the medical literature on outcomes with use of the surprise question  Suggest how to implement use of the surprise question to improve care of patients with serious illness Definition of Serious Illness 4  one that carries a high risk of death over the course of a year, but cure may remain a possibility  has a strong negative impact on one’s QOL and functioning, independent of its impact on mortality  highly burdensome to a person and his or her family Kelley AS. Defining “Serious Illness.” J Palliat Med 2014; 17: 985. 2

1/23/2020 Thinking about Serious Illness: Markers of Poor Prognosis in ESRD • Age • Comorbidity • Functional impairment • Frailty • Cognitive impairment • Malnutrition PubMed Surprise Question articles per Year 40 3334 35 32 30 # articles 25 19 20 14 15 11 11 10 10 8 10 7 5 4 4 4 4 3 3 4 4 3 4 4 3 3 4 5 1 1 1 1 2 1 1 1 2 2 1 1 2 1 1 1 0 1991 2001 2011 1976 1977 1979 1982 1983 1984 1985 1986 1988 1989 1992 1993 1994 1995 1996 1997 1998 1999 2002 2003 2004 2005 2006 2007 2009 2012 2013 2014 2015 2016 2017 2018 2019 1978 1980 1990 2000 2008 2010 Year 3

1/23/2020 The “Surprise Question” (SQ) 7  Would I be surprised if this patient died in the next year?  “No, I would not be surprised.” Prom pts action  “Yes, I would be surprised.”  Variations  6-months to align with hospice criterion  30 days  prior to discharge  2 years Problem the SQ Is Meant to Address 8  Clinicians are inaccurate at prognostication.  Earlier referral to palliative care improves patient outcomes.  Therefore…  Can we devise a simple, feasible, and effective approach to make identification of patients with a serious illness and a poor prognosis routine and thereby aid shared decision-making and advance care planning discussions? 4

1/23/2020 Understanding the Surprise Question 9  Allows clinicians to exercise skilled intuitive judgment  if the environment is a sufficiently regular to be predictable  if there is an opportunity to learn these regularities through experience (Kahneman D. Thinking, Fast and Slow , 2011)  can be integrated with objective measures to improve accuracy  Is NOT meant to be a tool to predict death  SQ may improve prognostic accuracy because it “allows physicians to think in a new way about their patients.”  The question prompts consideration of whether the patient m ight be dying or is at risk of dying sooner. Use of the Surprise Question 10  Identify patients with a serious illness, those “who have a greatly increased risk of mortality in the coming year.”  A tool, trigger or prompt to identify patients “who might benefit from palliative care.” Moroni M. SQ in advanced cancer patients: A prospective study among GPs. Palliat Med 2014;28:959-964. 5

1/23/2020 Why is this important? 11 Three Key Steps in Innovation Assessment • Identifying the target population • Describing baseline performance • Documenting the components of the evaluation History of the Surprise Question (SQ) 12  1998 Institute for Healthcare Improvement  Collaborative-Improving Care at the End of Life  Franciscan Health System primary care clinics  6-fold increase in appropriate hospice referral  Fewer hospital days  More deaths outside of hospital  More days in hospice  Higher patient and family satisfaction with care Pattison M. J Palliat Med 2001 6

1/23/2020 History of the SQ 13  2001 Perspectives on Care at the End of Life.  Lynn J. JAMA 2001;285:925-932.  Joanne Lynn books and presentations  Moss AH. Dialysis patients. CJASN 2008  Moss AH. Cancer patients. J Palliat Med 2010  Subsequent patient populations studied COPD Hospitalist patients Primary Care Surgery Heart failure Chronic kidney disease Neurology Emergency Department Use of the SQ 14  No differences found among physicians  WVU nephrologists and nurse practitioners equally accurate  Physicians are better at its use than experienced nurses who are better than younger nurses  Da Silva Gane M. Nephron Clin Pract 2013  Higher sensitivity and odds ratios in sicker patients  Advanced cancer  Intensive care 7

1/23/2020 Use of the SQ in Hemodialysis Patients N=147 15  23% “No, I would not be surprised” Variable No Yes P value Age (yrs) 72.5 64.5 .005 CCI-comorbidity 7.1 5.8 .004 Karnofsky-function 69.7 81.6 <.001 Albumin (g/ dL) 3.7 3.9 .046 1-Yr Mortality (%) 29.4 10.6 .032  Odds ratio of dying within 1 year =OR 3.5, P=.01  SQ identified sicker patients Moss AH, et al. CJASN 2008 Lakin JR. J Gen Intern Med , 2019 16  Boston Primary Care Clinics with 1,163 patients Discipline SQ Response Dead Alive Total* Physician No 143 309 452 (31.6%) Yes 37 674 711 (5.2%) Total 180 983 1163 (15.5%) Physicians classified 38.9% as “No” compared to 24.3% for nurses. 8

1/23/2020 Terms to Evaluate SQ Performance 17 SQ response Died Alive Total SQ “No” a True Positive b False Positive a + b SQ “Yes” c False negative d True Negative c + d Total a + c b + d a + b + c+ d • Sensitivity=a/ (a + c) probability of “No” response when patient died • Specificity=d/ (b +d) probability of “Yes” response when patient alive • Accuracy=a + d/ a+b+c+d overall probability that a patient will be correctly classified by the SQ response • Odds ratio=a/ b÷c/ d=a x d/ b x c odds that a patient who is SQ “No” will have died compared to odds that a person who is SQ “Yes” will have died Multivariate Logistic Regression with Outcome of Vital Status at 2 Years 18 Physician unadjusted OR 8.4 for death at 2 years for “No,” AUC=.74; Nurse OR 4.6 with AUC=0.67 9

1/23/2020 19  “… 2-year SQ holds promise for identification of appropriate patients for serious illness conversations in the primary care setting.”  “Future work should focus on pairing it with appropriate analytical tools and  Studying the effect of its use on conducting discussions [goals of care conversations.]” Use of the SQ in Cancer Patients 20  231 stage 4 cancer patients; 54.5% “No”  42 general practitioners  1-yr mortality 45.8%  68.5 vs 16.2% (P<.001) “No” vs “Yes”  OR death within 1 year=11.6 Moroni M et al. Palliat Med 2014 10

1/23/2020 21 • Overall pooled accuracy was 75%. • SQ may detect as many “false positives” as “true positives.” • Not clear whether the SQ is a cost-effective way to identify patients suitable for palliative care. • Combination of SQ with other prognostic measures may be more accurate than the SQ alone. 22 CMAJ 2017 April 3;189:E484‐93. • For 17 cohorts, median incidence of death was 15.1%; AUC=.81 • Pooled results show poor-modest accuracy of SQ-12: 67% sensitivity (95% CI, 55.7-76.7), 80.2% specificity (95% CI, 73.3-85.6%), and 37.1% positive predictive value (95% CI, 30.2-44.6). • Possible that a false positive SQ response would be beneficial; ASCO recommends early palliative care. • Further studies will be needed to determine whether SQ with other clinical indicators improves identification of patients appropriate for palliative care. 11

1/23/2020 Summary Thus Far 23  SQ increases odds of identifying seriously ill patients.  Recent metaanalyses showed that it does not perform well by itself as a predictive tool for death (White, BMC Med 2017;Downar, CMAJ 2017) .  Despite its limitations the SQ has been found to contribute to estimates of mortality over and above other factors such as age and comorbidities (Lakin JAMA Intern Med 2016).  Might incorporating the SQ into an integrated prognostic model improve the accuracy of predictions? Using the SQ in the MICU 24 2017;14(10):1556–1561. PubMed: 28598196 12