Utility of the “Surprise” Question to Identify Dialysis Patients with High Mortality

Abstract

Background and objectives: Dialysis patients are increasingly characterized by older age, multiple comorbidities, and shortened life expectancy. This study investigated whether the "surprise" question, "Would I be surprised if this patient died in the next year?" identifies patients who are at high risk for early mortality.Design, setting, participants, & measurements: This prospective cohort study of 147 patients in three hemodialysis dialysis units classified patients into "yes" and "no" groups on the basis of the ؆surprise؆ question response and tracked patient status (alive or dead) at 12 mo. Demographics, Charlson Comorbidity Index score, and Karnofsky Performance Status score were measured.Results: Initially, 34 (23%) patients were classified in the "no" group. Compared with the 113 patients in the "yes" group, the patients in the "no" group were older (72.5 ؎ 12.8 versus 64.5 ؎ 14.9), had a higher comorbidity score (7.1 ؎ 2.3 versus 5.8 ؎ 2.1), and had a lower performance status score (69.7 ؎ 17.1 versus 81.6 ؎ 15.8). At 12 mo, 22 (15%) patients had died; the mortality rate for the "no" group was 29.4% and for the "yes" group was 10.6%. The odds of dying within 1 yr for the patients in the "no" group were 3.5 times higher than for patients in the "yes" group, (odds ratio 3.507, 95% CI 1.356 to 9.067, P ‫؍‬ 0.01).Conclusions: The ؆surprise؆ question is effective in identifying sicker dialysis patients who have a high risk for early mortality and should receive priority for palliative care interventions.

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

“…[33][34][35] More limited literature demonstrates that adding outpatient utilization information and psychosocial factors improve predictive models for acute care utilization. [35][36][37] Prior studies of physician's ability to predict death or acute care utilization results show mixed results; [13][14][15][16][17][18]22 however, our quantitative model, designed to predict physician-defined complexity from increasingly available data, shows promise as an additional tool in identifying and stratifying high-risk patients for population management interventions. Health care delivery systems could replicate our approach in their own context or add proxies for psychosocial complexity that are available in their data repositories to strengthen their risk-stratification approaches.…”

“…Current quantitative methods for identifying the complex patients at highest risk for suboptimal future clinical quality and utilization outcomes rely primarily on diagnosis-based and utilization-based algorithms to predict future utilization. [13][14][15][16][17][18][19][20][21][22][23] These tools miss clinical characteristics that are not present in billing data and may not capture non-clinical contributors to patient complexity.…”

CKD in Elderly Patients Managed without Dialysis

“…The decision to recommend dialysis or a nondialysis pathway was that of the individual nephrologist in conjunction with the patient and his or her family, aiming for a shared decision (13)(14)(15).…”

Transitions at the end of life for older adults – patient, carer and professional perspectives: a mixed-methods study

“…This 'surprise' question is commonly used to identify individuals nearing the end of life, 74,75 in particular where prognosis is complex, as is the case with heart or renal failure, for example. 76 Along with the criteria listed above, participants had to be aware of their heart failure, stroke or lung cancer diagnosis. Our initial aim was to interview 10 patients for each of these three conditions.…”