Usefulness of the Surprise Question on an Inpatient Oncology Service

Singh, Sarguni; Rodríguez, Adrián; Lee, Darrell; Min, Sung‐Joon; Fischer, Stacy

doi:10.1177/1049909118777990

Cited by 5 publications

(11 citation statements)

References 17 publications

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“…Of note, our institution had initiated efforts to increase palliative care consultations, including automatic consult triggers associated with the 30-day SQ+; however, consult orders can be removed and our results indicate palliative care consultation rates remained low. Our findings are consistent with other studies showing that the SQ is not effective in increasing referrals to palliative care for cancer patients, 43 or for a heterogeneous population of ED patients. 24 The overall low occurrence of palliative care consultations, goals-of-care conversations, and AD completion are consistent with prior studies of COPD 10,11 and other diseases.…”

Section: Discussionsupporting

confidence: 93%

How Well Does the Surprise Question Predict 1-year Mortality for Patients Admitted with COPD?

et al. 2021

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BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often receive burdensome care at end-of-life (EOL) and infrequently complete advance care planning (ACP). The surprise question (SQ) is a prognostic tool that may facilitate ACP. OBJECTIVE: To assess how well the SQ predicts mortality and prompts ACP for COPD patients. DESIGN: Retrospective cohort study. SUBJECTS: Patients admitted to the hospital for an acute exacerbation of COPD between July 2015 and September 2018. MAIN MEASURES: Emergency department (ED) and inpatient clinicians answered, "Would you be surprised if this patient died in the next 30 days (ED)/one year (inpatient)?" The primary outcome measure was the accuracy of the SQ in predicting 30-day and 1-year mortality. The secondary outcome was the correlation between SQ and ACP (palliative care consultation, documented goals-ofcare conversation, change in code status, or completion of ACP document). KEY RESULTS: The 30-day SQ had a high specificity but low sensitivity for predicting 30-day mortality: sensitivity 12%, specificity 95%, PPV 11%, and NPV 96%. The 1-year SQ demonstrated better accuracy for predicting 1-year mortality: sensitivity 47%, specificity 75%, PPV 35%, and NPV 83%. After multivariable adjustment for age, sex, and prior 6-month admissions, 1-year SQ+ responses were associated with greater odds of 1-year mortality (OR 2.38, 95% CI 1.39-4.08) versus SQ-. One-year SQ+ patients were more likely to have a goals-of-care conversation (25% vs. 11%, p < 0.01) and complete an advance directive or POLST (46% vs. 23%, p < 0.01). After multivariable adjustment, SQ+ responses to the 1-year SQ were associated with greater odds of ACP receipt (OR 2.67,. CONCLUSIONS: The 1-year surprise question may be an effective component of prognostication and advance care planning for COPD patients in the inpatient setting.

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Section: Discussionsupporting

confidence: 93%

How Well Does the Surprise Question Predict 1-year Mortality for Patients Admitted with COPD?

et al. 2021

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“…The SQ has been used most commonly with a 1-year time frame but also with time frames between 1 week and 6 months with varying performance. 7 , 9 , 10 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 The SQ has performed better in oncology populations compared with heart failure, kidney failure, and all diagnoses examined in other studies, 7 , 9 , 10 albeit modestly, and further research in this area is needed.…”

Section: Introductionmentioning

confidence: 85%

Prospective Comparison of Medical Oncologists and a Machine Learning Model to Predict 3-Month Mortality in Patients With Metastatic Solid Tumors

et al. 2022

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Key Points Question How do oncologists and a machine learning model compare in predicting 3-month mortality for patients with advanced solid tumors? Findings In this prognostic study, the machine learning model significantly outperformed 74 oncologists in predicting 3-month mortality for 2041 patients with metastatic solid tumors overall and in gastrointestinal and breast cancer subpopulations. Findings were not significant in genitourinary, lung, and rare cancer groups. Meaning The results of this study suggest the potential for a machine learning model trained with electronic health record data to support oncologists in prognostication and clinical decision-making to improve end-of-life care.

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“…Singh et al (26) investigated the use of the surprise question as part of daily multidisciplinary team rounds on an inpatient oncology service (including patients with a gynecologic malignancy) and the impact on PC utilization. PC use was defined as referral for inpatient PC consultation, outpatient PC clinic, community PC services, and/or hospice (26). Data on PC use was collected in the 3 months preceding the study period, during the study period, and 3 months post-study period (26).…”

Section: Surprise Questionmentioning

confidence: 99%

“…PC use was defined as referral for inpatient PC consultation, outpatient PC clinic, community PC services, and/or hospice (26). Data on PC use was collected in the 3 months preceding the study period, during the study period, and 3 months post-study period (26). Response to the surprise question was elicited by the hospital medicine physician or advance practice provider (APP), PC provider, inpatient oncologist, primary oncologist, and the patient's bedside nurse; the answer was categorized as either 'yes' or 'no' (26).…”

Section: Surprise Questionmentioning

confidence: 99%

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Palliative care screening tools in the gynecologic oncology population: a narrative review

Vetter¹

2022

Ann Palliat Med

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Background and Objective: Patients with gynecologic malignancies experience high symptom burden associated with both their disease and sequelae of treatment, and are at risk for aggressive (and often futile) care at end-of-life. Early palliative care (PC) involvement is essential to improving quality of life in this population of patients through symptom management and advanced care planning discussions. The purpose of this review article is to discuss current PC screening tools used to trigger PC consultations for patients with gynecologic malignancies. PC screening tools are reviewed across both inpatient and outpatient clinical settings.Methods: A literature search was conducted utilizing PubMed ® and Excerpta Medica database (EMBASE ® ).Search terms included various combinations of palliative care, gynecologic oncology, gynecologic cancer, gynecologic malignancy, female genital tract cancer, trigger, consult, referral, and screen.

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Usefulness of the Surprise Question on an Inpatient Oncology Service

Cited by 5 publications

References 17 publications

How Well Does the Surprise Question Predict 1-year Mortality for Patients Admitted with COPD?

How Well Does the Surprise Question Predict 1-year Mortality for Patients Admitted with COPD?

Prospective Comparison of Medical Oncologists and a Machine Learning Model to Predict 3-Month Mortality in Patients With Metastatic Solid Tumors

Palliative care screening tools in the gynecologic oncology population: a narrative review

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