"The surprise questions" using variable time frames in hospitalized patients with advanced cancer

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.

Are Surprise Questions and Probabilistic Questions by Nurses Useful in Home Palliative Care? A Prospective Study

Background: Surprise questions (SQs) are used as screening tools in palliative care. Probabilistic questions (PQs) are more accurate than temporal predictions. However, no study has examined the usefulness of SQs and PQs assessed by nurses. Objectives: To examine the accuracy of nurses' SQ and PQ assessments in patients with advanced cancer receiving home palliative care. Design: A prospective single-center cohort study. Setting/Subjects: Adult patients with advanced cancer who received palliative care at home in South Korea between 2019 and 2020. Measurements: Palliative care specialized nurses were asked the SQ, "Would you be surprised if the patient died in a specific timeframe?" and PQ, "What is the probability that this patient will be alive (0 to 100%) within a specific timeframe?" at the 1-, 2-, 4-, and 6-week timeframes at enrollment. We calculated the sensitivities and specificities of the SQs and PQs. Results: 81 patients were recruited with 47 days of median survival. The sensitivity, specificity, and overall accuracy (OA) of the 1-week SQ were 50.0, 93.2, and 88.9%, respectively. The accuracies for the 1-week PQ were 12.5, 100.0, and 91.3%, respectively. The 6-week SQ showed sensitivity, specificity, and OA of 84.6, 42.9, and 62.9%, respectively; the accuracies for the 6-week PQ were 59.0, 66.7, and 63.0%, respectively.Conclusion: The SQ and PQ showed acceptable accuracy in home palliative care patients. Interestingly, PQ showed higher specificity than SQ at all timeframes. The SQ and PQ assessed by nurses may be useful in providing additional prognostic information for home palliative care.

Should the Surprise Question be Used as a Prognostic Tool for People With Life-limiting Illnesses?

The surprise question screening tool ("Would I be surprised if this person died within the next 12 months?") was initially developed to identify possible palliative care needs. One controversial topic regarding the surprise question is whether it should be used as a prognostic tool (predicting survival) for patients with life-limiting illnesses. In this "Controversies in Palliative Care" article, three groups of expert clinicians independently answered this question. All experts provide an overview of current literature, practical advice, and opportunities for future research. All experts reported on the inconsistency of the prognostic capabilities of the surprise question. Two of the three expert groups felt that the surprise question should not be used as a prognostic tool due to these inconsistencies. The third expert group felt that the surprise question should be used as a prognostic tool, particularly for shorter time frames. The experts all highlighted that the original rationale for the surprise question was to trigger a further conversation about future treatment and a potential shift in the focus of the care, identifying patients who many benefit from specialist palliative care or advance care planning; however, many clinicians find this discussion a difficult one to initiate. The experts agreed that the benefit of the surprise question comes from its simplicity: a one-question tool that requires no specific information about the patient's condition. More research is needed to better support the application of this tool in routine practice, particularly in noncancer populations.

Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO Clinical Practice Guideline

•This ESMO Clinical Practice Guideline provides key recommendations for using prognostic estimates in advanced cancer. •The guideline covers recommendations for patients with cancer and an expected survival of months or less. •An algorithm for use of clinical predictions, prognostic factors and multivariable risk prediction models is presented. •The author group encompasses a multidisciplinary group of experts from different institutions in Europe, USA and Asia. •Recommendations are based on available scientific data and the authors’ collective expert opinion.

Prognostication in palliative radiotherapy—ProPaRT: Accuracy of prognostic scores

Background

Prognostication can be used within a tailored decision-making process to achieve a more personalized approach to the care of patients with cancer. This prospective observational study evaluated the accuracy of the Palliative Prognostic score (PaP score) to predict survival in patients identified by oncologists as candidates for palliative radiotherapy (PRT). We also studied interrater variability for the clinical prediction of survival and PaP scores and assessed the accuracy of the Survival Prediction Score (SPS) and TEACHH score.

Materials and methods

Consecutive patients were enrolled at first access to our Radiotherapy and Palliative Care Outpatient Clinic. The discriminating ability of the prognostic models was assessed using Harrell’s C index, and the corresponding 95% confidence intervals (95% CI) were obtained by bootstrapping.

Results

In total, 255 patients with metastatic cancer were evaluated, and 123 (48.2%) were selected for PRT, all of whom completed treatment without interruption. Then, 10.6% of the irradiated patients who died underwent treatment within the last 30 days of life. The PaP score showed an accuracy of 74.8 (95% CI, 69.5–80.1) for radiation oncologist (RO) and 80.7 (95% CI, 75.9–85.5) for palliative care physician (PCP) in predicting 30-day survival. The accuracy of TEACHH was 76.1 (95% CI, 70.9–81.3) and 64.7 (95% CI, 58.8–70.6) for RO and PCP, respectively, and the accuracy of SPS was 70 (95% CI, 64.4–75.6) and 72.8 (95% CI, 67.3–78.3).

Conclusion

Accurate prognostication can identify candidates for low-fraction PRT during the last days of life who are more likely to complete the planned treatment without interruption.

All the scores showed good discriminating capacity; the PaP had the higher accuracy, especially when used in a multidisciplinary way.

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.

Prognostication of the last days of life: Review article

Accurate prediction of impending death (i.e., last few days of life) is essential for terminally-ill cancer patients and their families. International guidelines state that clinicians should identify patients with impending death, communicate the prognosis with patients and families, help them with their end-of-life decision-making, and provide sufficient symptom palliation. Over the past decade, several national and international studies have been conducted that systematically investigated signs and symptoms of impending death as well as how to communicate such a prognosis effectively with patients and families. In this article, we summarize the current evidence on prognostication and communication regarding the last days of life of patients with cancer, and future directions of clinical research.