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Is the Surprise Question Still Clinically Valid?

A doctor in Houston TX contemplates data and prognostication.

The surprise question is, “Would I be surprised if this patient died in the next twelve months?” or whatever time frame the clinician is considering. It was introduced fifteen years ago as a simple screening tool to better identify patients who might benefit from hospice or other palliative care.1 The idea was that the wording would help clinicians mitigate the theoretical effects of optimism and reduce the tendency to overestimate prognosis. For instance, speaking in very general terms, White et al. found that clinicians tend to overestimate survival by a factor of two.2

Pursuant with several studies suggesting the utility of the surprise question, it won widespread adoption, including incorporation into the Gold Standards Framework and the Supportive and Palliative Care Indicators Tool. However, some authors have begun calling the surprise question into question.3,4 In short, their contention is that the surprise question has insufficient sensitivity for a screening tool. Among those raising this concern are James Downar MDCM and Russell Goldman MD MPH.4 In their meta-analysis, they conclude that the surprise question demonstrates a sensitivity of 67% and a specificity of 80.2%. In later commentary published by the Journal of the American Medical Association, Downar, Pete Wigier PhD, and Peter Tanuseputro, MD PhD, argue a preference for big-data approaches that incorporate more variables than “traditional hardware” could computate.3 They offer the mHOMR5 tool and RESPECT6 tool as potentially more accurate alternatives.

With the counterargument, Rafael Romo PhD PHN and Joanne Lynne MD write in support of the surprise question.7 They argue that the surprise question is still a good tool for its intended purpose: identifying patients who need closer attention, especially in terms of more formal prognostication efforts, monitoring the prognosis, and evaluating the patient’s palliative care needs. To be practical, screening tools need to be broadly applicable, lower cost, and more accurate than the alternatives. Perhaps most importantly, screening tools should integrate seamlessly into the existing workflow.

Interestingly, the sensitivity observed by Downer et al. is higher than the sensitivity reported in an earlier review published in Cancer Care.8 Nevertheless, the earlier review found the surprise question to have superior accuracy compared to temporal prognostication. Furthermore, the review found the screening tool to have greater predictive value than type of cancer, age, cancer stage, or time since diagnosis.

For patients in advanced disease states, the ability of the clinical team to predict survival time proves highly important to clinical decision-making and to allowing a patient to play an active role in that decision-making. This proves especially true when it comes to deciding when and if a patient should be informed about the existence of hospice services. As with many referred-to medical services, the general public does not have a prevalent understanding of the purpose, benefits, and qualifications of hospice.9,10 As such, whether a doctor initiates patient education about hospice proves the strongest predictor of whether a patient will receive hospice.10,11 Among the subset of patients with a prognosis of six months or less, the benefits of hospice include family satisfaction and symptom control greater than that achieved in other care settings;12 greater quality of life;13 improved bereavement status and improved survival among widowed spouses;14 survival improved for patients by days to months;15-19 and Medicare benefits paying 100% for hospice services, diagnosis-related medication, supplies, and equipment. Additionally, better hospice utilization is associated with lower costs of care and lower rehospitalization rates.20 Experts recommend that doctors introduce advance care planning subjects when the prognosis is one year, so patients will have ample time to reflect on their options.21 If a doctor makes a recommendation for hospice admission months later, it won’t be the first time families are hearing about it.


Whether clinicians rely on the surprise question, more formal prognostic tools, or a combination of approaches, identifying patients who may benefit from palliative or hospice care is only the first step. Avatar Home Health & Hospice works with physicians and families throughout Greater Houston and The Woodlands to help patients receive the appropriate level of care at home, including palliative care and hospice services. With bilingual clinical staff, 24/7 nursing availability, and a referral team that typically responds within 30 minutes during regular weekday hours, Avatar helps make the transition from clinical recognition to supportive care as timely and seamless as possible.

 

References

  1. Moss AH, Ganjoo J, Sharma S, et al. Utility of the “surprise” question to identify dialysis patients with high mortality. Clin J Am Soc Nephrol. 2008; 3: 1379-84.
  2. White N, Reid F, Harris A, Harries P, Stone P. A systematic review of predictions of survival in palliative care: how accurate are clinicians and who are the experts?. PloS One. 2016 Aug 25;11(8):e0161407.
  3. Downar J, Wegier P, Tanuseputro P. Early Identification of People Who Would Benefit From a Palliative Approach—Moving From Surprise to Routine. JAMA Network Open. 2019 Sep 4; 2 (9): e1911146.
  4. Downar J, Goldman R, Pinto R, Englesakis M, Adhikari NK. The “surprise question” for predicting death in seriously ill patients: a systematic review and meta-analysis. CMAJ. 2017 Apr 3;189 (13): E484-93.
  5. Wegier P, Koo E, Ansari S, et al. mHOMR: a feasibility study of an automated system for identifying inpatients having an elevated risk of 1-year mortality. BMJ Qual Saf. 2019; bmjqs-2018-009285.
  6. Hsu AT, Manuel DG, Taljaard M, et al. Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT). BMJ Open. 2016;6(12): e013666.
  7. Romo RD, Lynn J. The utility and value of the “surprise question” for patients with serious illness. CMAJ: Canadian Medical Association Journal. 2017 Aug 21;189(33):E1072.
  8. Hui D. Prognostication of survival in patients with advanced cancer: predicting the unpredictable?. Cancer Control. 2015 Oct;22(4):489-97.
  9. Schockett E, Teno J, Miller S, Stuart B. Late referral to hospice and bereaved family member perception of quality of end-of-life care. J Pain Symptom Manage. 2005; 30: 400-407.
  10. Thomas J, O’Leary J, Fried T. Understanding their options: determinants of hospice discussion for older persons with advanced illness. J Gen Intern Med. 2009; 24(8): 923-8.
  11. Obermeyer Z, Powers B, Makar M, et al. Physician characteristics strongly predict patient enrollment in hospice. Health Affairs. 2015; 34 (6): 993-1000.
  12. Teno J, et al. Family perspectives on end-of-life care at the last place of care. JAMA. 2004; 291: 88-93.
  13. Baohui Z, Nilsson M, Prigerson H. Factors important to patient’s quality of life at the end of life. Arch Intern Med. 2012 Aug; 172 (15): 1133-1142.
  14. Christakis N, Iwashyna T. The health impact of health care on families: a matched cohort study of hospice use by decedents and mortality outcomes in surviving, widowed spouses. Soc Sci Med. 2003; 57: 465-75.
  15. Hamano J, Yamaguchi T, Maeda I, et al. Multicenter cohort study on the survival time of cancer patients dying at home or in a hospital: Does place matter? Cancer. 2016 May; 122 (9): 1453-60.
  16. Saito A, Landrum M, Neville B, et al. Hospice care and survival among elderly patients with lung cancer. J Palliat Med. 2011; 14 (8): 929-939.
  17. Keyser E, Reed B, Lowery W, et al. Hospice enrollment for terminally ill patients with gynecologic malignancies: impact on outcomes and interventions. Gynecol Oncol. 2010: 118 (3): 274-7.
  18. Connor S, Pyenson B, Fitch K, et al. Comparing hospice and nonhospice patient survival among patients who die within a three-year window. J Pain Symptom Manage. 2007 Mar; 33(3): 238-46.
  19. Christakis N, Iwashyna T, Zhang J. Care after the onset of serious illness: a novel claims-based dataset exploiting substantial cross-set linkages to study end-of-life care. J Palliat Med. 2002; 5: 515-529.
  20. Clough J, Strawbridge L, LeBlanc T, et al. Association of practice-level hospital use with end-of-life out-comes, readmission, and weekend hospitalization among Medicare beneficiaries with cancer. Journal of Oncology Practice. 2016 Aug 16; JOPR013102.
  21. Isaac M, Curtis J. Improving quality of life for patients with terminal respiratory disease. Expert Rev Resp Med. 2009; 3 (6): 597-605.
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