Validation of the surprise question in gynecologic oncology: A one-question screen to promote palliative care integration and advance care planning

The Surprise Question and clinician-predicted prognosis: systematic review and meta-analysis

BACKGROUND

The Surprise Question, 'Would you be surprised if this person died within the next year?' is a simple tool that can be used by clinicians to identify people within the last year of life. This review aimed to determine the accuracy of this assessment, across different healthcare settings, specialties, follow-up periods and respondents.

METHODS

Searches were conducted of Medline, Embase, AMED, PubMed and the Cochrane Central Register of Controlled Trials, from inception until 01 January 2024. Studies were included if they reported original data on the ability of the Surprise Question to predict survival. For each study (including subgroups), sensitivity, specificity, positive and negative predictive values and accuracy were determined.

RESULTS

Our dataset comprised 56 distinct cohorts, including 68 829 patients. In a pooled analysis, the sensitivity of the Surprise Question was 0.69 ((0.64 to 0.74) I2=97.2%), specificity 0.69 ((0.63 to 0.74) I2=99.7%), positive predictive value 0.40 ((0.35 to 0.45) I2=99.4%), negative predictive value 0.89 ((0.87 to 0.91) I2=99.7%) and accuracy 0.71 ((0.68 to 0.75) I2=99.3%). The prompt performed best in populations with high event rates, shorter timeframes and when posed to more experienced respondents.

CONCLUSIONS

The Surprise Question demonstrated modest accuracy with considerable heterogeneity across the population to which it was applied and to whom it was posed. Prospective studies should test whether the prompt can facilitate timely access to palliative care services, as originally envisioned.

PROSPERO REGISTRATION NUMBER

CRD32022298236.

Randomized Comparison of Electronic Health Record Alert Types in Eliciting Responses about Prognosis in Gynecologic Oncology Patients

OBJECTIVES

 To compare the ability of different electronic health record alert types to elicit responses from users caring for cancer patients benefiting from goals of care (GOC) conversations.

METHODS

 A validated question asking if the user would be surprised by the patient's 6-month mortality was built as an Epic BestPractice Advisory (BPA) alert in three versions-(1) Required on Open chart (pop-up BPA), (2) Required on Close chart (navigator BPA), and (3) Optional Persistent (Storyboard BPA)-randomized using patient medical record number. Meaningful responses were defined as "Yes" or "No," rather than deferral. Data were extracted over 6 months.

RESULTS

 Alerts appeared for 685 patients during 1,786 outpatient encounters. Measuring encounters where a meaningful response was elicited, rates were highest for Required on Open (94.8% of encounters), compared with Required on Close (90.1%) and Optional Persistent (19.7%) (p < 0.001). Measuring individual alerts to which responses were given, they were most likely meaningful with Optional Persistent (98.3% of responses) and least likely with Required on Open (68.0%) (p < 0.001). Responses of "No," suggesting poor prognosis and prompting GOC, were more likely with Optional Persistent (13.6%) and Required on Open (10.3%) than with Required on Close (7.0%) (p = 0.028).

CONCLUSION

 Required alerts had response rates almost five times higher than optional alerts. Timing of alerts affects rates of meaningful responses and possibly the response itself. The alert with the most meaningful responses was also associated with the most interruptions and deferral responses. Considering tradeoffs in these metrics is important in designing clinical decision support to maximize success.

Would you be surprised? Prospective multicenter study of the Surprise Question as a screening tool to predict mortality in trauma patients

BACKGROUND

The Surprise Question (SQ) ("Would I be surprised if the patient died within the next year?") is a validated tool used to identify patients with limited life expectancy. Because it may have potential to expedite palliative care interventions per American College of Surgeons Trauma Quality Improvement Program Palliative Care Best Practices Guidelines, we sought to determine if trauma team members could use the SQ to accurately predict 1-year mortality in trauma patients.

METHODS

A multicenter, prospective, cohort study collected data (August 2020 to February 2021) on trauma team members' responses to the SQ at 24 hours from admission. One-year mortality was obtained via social security death index records. Positive/negative predictive values and accuracy were calculated overall, by provider role and by patient age.

RESULTS

Ten Level I/II centers enrolled 1,172 patients (87.9% blunt). The median age was 57 years (interquartile range, 36-74 years), and the median Injury Severity Score was 10 (interquartile range, 5-14 years). Overall 1-year mortality was 13.3%. Positive predictive value was low (30.5%) regardless of role. Mortality prediction minimally improved as age increased (positive predictive value highest between 65 and 74 years old, 34.5%) but consistently trended to overprediction of death, even in younger patients.

CONCLUSION

Trauma team members' ability to forecast 1-year mortality using the SQ at 24 hours appears limited perhaps because of overestimation of injury effects, preinjury conditions, and/or team bias. This has implications for the Trauma Quality Improvement Program Guidelines and suggests that more research is needed to determine the optimal time to screen trauma patients with the SQ.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Incorporation of Palliative Care in Gynecologic Oncology

PURPOSE OF REVIEW

This review serves to provide clarity on the nature, scope, and benefits of early palliative care integration into the management of patients with gynecologic malignancies.

RECENT FINDINGS

There is increased recognition that timely referral to palliative care improves quality of life for patients and their families by providing goal-concordant care that reduces physical and emotional suffering and limits futile and aggressive measures at the end of life. Palliative care services rendered throughout the continuum of illness ultimately increase engagement with hospice services and drive down health expenditures. Despite these myriad benefits, misconceptions remain, and barriers to and disparities in access to these services persist and warrant continued attention. Palliative care should be offered to all patients with advanced gynecologic cancers early in the course of their disease to maximize benefit to patients and their families.

Guidelines for goals of care discussions in patients with gynecologic cancer

This article represents a distillation of literature to provide guidance for goals of care discussions with patients who have gynecologic malignancies. As clinicians who provide surgical care, chemotherapy, and targeted therapeutics, gynecologic oncology clinicians are uniquely positioned to form longitudinal relationships with patients that can enable patient-centered decision making. In this review, we describe optimal timing, components, and best practices for goals of care discussions in gynecologic oncology.

Validation of the Surprise Question and the Development of a Multivariable Model

CONTEXT

The Surprise Question (SQ) (would you be surprised if this patient died within a year?) is a prognostic variable explored in chronic illnesses. Validation is limited to sensitivity, specificity, and predictive values.

OBJECTIVES

Our objective is to validate the SQ in cancer patients and develop a predictive model with additional variables.

METHODS

A prospective cohort study of adult (age>18) cancer patients seen between October 1, 2019, through March 31, 2021, undergoing systemic therapies had the SQ completed by oncologists prior to each change in systemic therapy. The primary outcome was survival for one year. Secondary outcomes were predictions of survival at three, six, and nine months. Patients were grouped into negative SQ (not surprised) and positive SQ (surprised). Sensitivity, specificity, predictive values, and likelihood ratios (LR) were calculated for the SQ. Additional prognostic variables were age, gender, cancer stage, line of therapy, Charleson Comorbid Index (CCI), palliative care consultation (prior to, after the SQ, or not at all), and healthcare utilization (outpatient, inpatient, and emergency department (ED). Logistic regression and receiver operating characteristics (ROC) were used for discrimination and modeling. Akaike information criterion (AIC) was used to compare the model fit as each predictor.

RESULTS

1366 patients had 1 SQ; 784 died within a year. The SQ predicted survival at one year (P = 0.008), with a positive LR of 1.459 (95%CI 1.316-1.602) and a c-statistic of 0.565 (95%CI 0.530-0.600). Additional variables increased the c-statistic to 0.648 (95% CI 0.608-0.686). The total model best predicted survival at three months, c-statistic of 0.663 (95% CI 0.616-0.706). However, the total model c-statistic remained <0.70.

CONCLUSIONS

The SQ, as a single factor, poorly predicts survival and should not be used to alter therapies. Adding additional objective variables improved prognostication, but further refinement and external validation are needed.

The prognostic value of the 12-, 6-, 3- and 1-month 'Surprise Question' in cancer patients: A prospective cohort study in three hospitals

OBJECTIVE

This prospective study aimed to evaluate the performance of the 'Surprise Question' (SQ) 'Would I be surprised if this patient died in the next 12 months?' in predicting survival of 12, 6, 3 and 1 month(s), respectively, in hospitalised patients with cancer.

METHODS

In three hospitals, physicians were asked to answer SQs for 12/6/3/1 month(s) for inpatients with cancer. Sensitivity, specificity, positive and negative predictive values were calculated.

RESULTS

A total of 783 patients were included, of whom 51% died in the 12-month period after inclusion. Sensitivity of the SQ predicting death within 12 months was 0.79, specificity was 0.66, the positive predictive value was 0.71 and the negative predictive value was 0.75. When the SQ concerned a shorter survival period, sensitivities and positive predictive values decreased, whereas specificities and negative predictive values increased. In multivariable logistic regression analysis, the SQ was significantly associated with mortality (OR 3.93, 95% CI 2.70-5.71, p < 0.01).

CONCLUSIONS

The 12-month SQ predicts death in patients with cancer admitted to the hospital reasonably well. Shortening the timeframe decreases sensitivities and increases specificities. The four surprise questions may help to identify patients for whom palliative care is indicated.

The utility of the surprise question: A useful tool for identifying patients nearing the last phase of life? A systematic review and meta-analysis

BACKGROUND

The surprise question is widely used to identify patients nearing the last phase of life. Potential differences in accuracy between timeframe, patient subgroups and type of healthcare professionals answering the surprise question have been suggested. Recent studies might give new insights.

AIM

To determine the accuracy of the surprise question in predicting death, differentiating by timeframe, patient subgroup and by type of healthcare professional.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Electronic databases PubMed, Embase, Cochrane Library, Scopus, Web of Science and CINAHL were searched from inception till 22nd January 2021. Studies were eligible if they used the surprise question prospectively and assessed mortality. Sensitivity, specificity, negative predictive value, positive predictive value and c-statistic were calculated.

RESULTS

Fifty-nine studies met the inclusion criteria, including 88.268 assessments. The meta-analysis resulted in an estimated sensitivity of 71.4% (95% CI [66.3-76.4]) and specificity of 74.0% (95% CI [69.3-78.6]). The negative predictive value varied from 98.0% (95% CI [97.7-98.3]) to 88.6% (95% CI [87.1-90.0]) with a mortality rate of 5% and 25% respectively. The positive predictive value varied from 12.6% (95% CI [11.0-14.2]) with a mortality rate of 5% to 47.8% (95% CI [44.2-51.3]) with a mortality rate of 25%. Seven studies provided detailed information on different healthcare professionals answering the surprise question.

CONCLUSION

We found overall reasonable test characteristics for the surprise question. Additionally, this study showed notable differences in performance within patient subgroups. However, we did not find an indication of notable differences between timeframe and healthcare professionals.

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

Importance

To date, oncologist and model prognostic performance have been assessed independently and mostly retrospectively; however, how model prognostic performance compares with oncologist prognostic performance prospectively remains unknown.

Objective

To compare oncologist performance with a model in predicting 3-month mortality for patients with metastatic solid tumors in an outpatient setting.

Design, Setting, and Participants

This prognostic study evaluated prospective predictions for a cohort of patients with metastatic solid tumors seen in outpatient oncology clinics at a National Cancer Institute-designated cancer center and associated satellites between December 6, 2019, and August 6, 2021. Oncologists (57 physicians and 17 advanced practice clinicians) answered a 3-month surprise question (3MSQ) within clinical pathways. A model was trained with electronic health record data from January 1, 2013, to April 24, 2019, to identify patients at high risk of 3-month mortality and deployed silently in October 2019. Analysis was limited to oncologist prognostications with a model prediction within the preceding 30 days.

Exposures

Three-month surprise question and gradient-boosting binary classifier.

Main Outcomes and Measures

The primary outcome was performance comparison between oncologists and the model to predict 3-month mortality. The primary performance metric was the positive predictive value (PPV) at the sensitivity achieved by the medical oncologists with their 3MSQ answers.

Results

A total of 74 oncologists answered 3099 3MSQs for 2041 patients with advanced cancer (median age, 62.6 [range, 18-96] years; 1271 women [62.3%]). In this cohort with a 15% prevalence of 3-month mortality and 30% sensitivity for both oncologists and the model, the PPV of oncologists was 34.8% (95% CI, 30.1%-39.5%) and the PPV of the model was 60.0% (95% CI, 53.6%-66.3%). Area under the receiver operating characteristic curve for the model was 81.2% (95% CI, 79.1%-83.3%). The model significantly outperformed the oncologists in short-term mortality.

Conclusions and Relevance

In this prognostic study, the model outperformed oncologists overall and within the breast and gastrointestinal cancer cohorts in predicting 3-month mortality for patients with advanced cancer. These findings suggest that further studies may be useful to examine how model predictions could improve oncologists' prognostic confidence and patient-centered goal-concordant care at the end of life.

Advance Care Planning: Having Goals-of-Care Conversations in Oncology Nursing

Patients with advanced-stage cancer face serious life-limiting illness while receiving palliative treatment, such as chemotherapy, surgery, or radiation therapy. Advance care planning and goals-of-care conversations are important to have with patients with curable or incurable cancer. Oncology nurses can play an important role by having the knowledge and skills required to communicate with patients and families about advance care planning and goals of care during acute and outpatient care. Patient decision tools and aids include guides for advance care planning, goals of care, and related patient resources for acquiring knowledge and skills.

The Value of the Surprise Question to Predict One-Year Mortality in Idiopathic Pulmonary Fibrosis: A Prospective Cohort Study

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive fatal disease with a heterogeneous disease course. Timely initiation of palliative care is often lacking. The surprise question "Would you be surprised if this patient died within the next year?" is increasingly used as a clinical prognostic tool in chronic diseases but has never been evaluated in IPF.

OBJECTIVE

We aimed to evaluate the predictive value of the surprise question for 1-year mortality in IPF.

METHODS

In this prospective cohort study, clinicians answered the surprise question for each included patient. Clinical parameters and mortality data were collected. The sensitivity, specificity, accuracy, negative, and positive predictive value of the surprise question with regard to 1-year mortality were calculated. Multivariable logistic regression analysis was performed to evaluate which factors were associated with mortality. In addition, discriminative performance of the surprise question was assessed using the C-statistic.

RESULTS

In total, 140 patients were included. One-year all-cause mortality was 20% (n = 28). Clinicians identified patients with a survival of <1 year with a sensitivity of 68%, a specificity of 82%, an accuracy of 79%, a positive predictive value of 49%, and a negative predictive value of 91%. The surprise question significantly predicted 1-year mortality in a multivariable model (OR 3.69; 95% CI 1.24-11.02; p = 0.019). The C-statistic of the surprise question to predict mortality was 0.75 (95% CI 0.66-0.85).

CONCLUSIONS

The answer on the surprise question can accurately predict 1-year mortality in IPF. Hence, this simple tool may enable timely focus on palliative care for patients with IPF.