Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients

Abstract

Objectives While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk.

Methods This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.

Results Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model's estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.

Conclusion The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.

Keywords

mortality risk - model - oncology - prognosis - end-of-life - laboratory values

Protection of Human and Animal Subjects

The study was reviewed by the institutional review board of Memorial Sloan Kettering Cancer Center and is in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects.

^* Shared first authorship.

Publikationsverlauf

Eingereicht: 15. Dezember 2023

Angenommen: 29. April 2024

Artikel online veröffentlicht:
26. Juni 2024

• References

• 1 Knutzen KE, Sacks OA, Brody-Bizar OC. et al. Actual and missed opportunities for end-of-life care discussions with oncology patients: a qualitative study. JAMA Netw Open 2021; 4 (06) e2113193
• 2 Dow LA, Matsuyama RK, Ramakrishnan V. et al. Paradoxes in advance care planning: the complex relationship of oncology patients, their physicians, and advance medical directives. J Clin Oncol 2010; 28 (02) 299-304
• 3 Lambden J, Zhang B, Friedlander R, Prigerson HG. Accuracy of oncologists' life-expectancy estimates recalled by their advanced cancer patients: correlates and outcomes. J Palliat Med 2016; 19 (12) 1296-1303
• 4 Deelen J, Kettunen J, Fischer K. et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat Commun 2019; 10 (01) 3346
• 5 Christakis NA, Lamont EB. Extent and determinants of error in physicians' prognoses in terminally ill patients: prospective cohort study. West J Med 2000; 172 (05) 310-313
• 6 Hui D, Park M, Liu D. et al. Clinician prediction of survival versus the Palliative Prognostic Score: which approach is more accurate?. Eur J Cancer 2016; 64: 89-95
• 7 Cheon S, Agarwal A, Popovic M. et al. The accuracy of clinicians' predictions of survival in advanced cancer: a review. Ann Palliat Med 2016; 5 (01) 22-29
• 8 Glare P, Virik K, Jones M. et al. A systematic review of physicians' survival predictions in terminally ill cancer patients. BMJ 2003; 327 (7408) 195-198
• 9 Chu C, Anderson R, White N, Stone P. Prognosticating for adult patients with advanced incurable cancer: a needed oncologist skill. Curr Treat Options Oncol 2020; 21 (01) 5
• 10 Hui D, Kilgore K, Nguyen L. et al. The accuracy of probabilistic versus temporal clinician prediction of survival for patients with advanced cancer: a preliminary report. Oncologist 2011; 16 (11) 1642-1648
• 11 Liao L, Mark DB. Clinical prediction models: are we building better mousetraps?. J Am Coll Cardiol 2003; 42 (05) 851-853
• 12 Gupta S, Tran T, Luo W. et al. Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry. BMJ Open 2014; 4 (03) e004007
• 13 Kawai N, Yuasa N. Laboratory prognostic score for predicting 30-day mortality in terminally ill cancer patients. Nagoya J Med Sci 2018; 80 (04) 571-582
• 14 Manz CR, Chen J, Liu M. et al. Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer. JAMA Oncol 2020; 6 (11) 1723-1730
• 15 Parikh RB, Manz C, Chivers C. et al. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA Netw Open 2019; 2 (10) e1915997
• 16 Temple MW, Lehmann CU, Fabbri D. Natural language processing for cohort discovery in a discharge prediction model for the neonatal ICU. Appl Clin Inform 2016; 7 (01) 101-115
• 17 Chopannejad S, Sadoughi F, Bagherzadeh R, Shekarchi S. Predicting major adverse cardiovascular events in acute coronary syndrome: a scoping review of machine learning approaches. Appl Clin Inform 2022; 13 (03) 720-740
• 18 Farion KJ, Wilk S, Michalowski W, O'Sullivan D, Sayyad-Shirabad J. Comparing predictions made by a prediction model, clinical score, and physicians: pediatric asthma exacerbations in the emergency department. Appl Clin Inform 2013; 4 (03) 376-391
• 19 DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44 (03) 837-845
• 20 Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26 (06) 565-574
• 21 Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 2016; 352: i6
• 22 Selby D, Chakraborty A, Lilien T, Stacey E, Zhang L, Myers J. Clinician accuracy when estimating survival duration: the role of the patient's performance status and time-based prognostic categories. J Pain Symptom Manage 2011; 42 (04) 578-588
• 23 Viganò A, Dorgan M, Bruera E, Suarez-Almazor ME. The relative accuracy of the clinical estimation of the duration of life for patients with end of life cancer. Cancer 1999; 86 (01) 170-176
• 24 Bertsimas D, Dunn J, Pawlowski C. et al. Applied informatics decision support tool for mortality predictions in patients with cancer. JCO Clin Cancer Inform 2018; 2: 1-11
• 25 Manz CR, Parikh RB, Small DS. et al. Effect of integrating machine learning mortality estimates with behavioral nudges to clinicians on serious illness conversations among patients with cancer: a stepped-wedge cluster randomized clinical trial. JAMA Oncol 2020; 6 (12) e204759
• 26 Oo TH. et al. Improved palliative care practices through machine-learning prediction of 90-day risk of mortality following hospitalization. NEJM Catal 2023; 4 (01) 1-20
• 27 Wright AA, Zhang B, Ray A. et al. Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA 2008; 300 (14) 1665-1673
• 28 Zhang B, Wright AA, Huskamp HA. et al. Health care costs in the last week of life: associations with end-of-life conversations. Arch Intern Med 2009; 169 (05) 480-488
• 29 Cronin S, Li A, Bai YQ. et al. How do respondents of primary care surveys compare to typical users of primary care? A comparison of two surveys. BMC Prim Care 2023; 24 (01) 80
• 30 Wong ST, Hogg W, Burge F, Johnston S, French I, Blackman S. Using the CollaboraKTion framework to report on primary care practice recruitment and data collection: costs and successes in a cross-sectional practice-based survey in British Columbia, Ontario, and Nova Scotia, Canada. BMC Fam Pract 2018; 19 (01) 87
• 31 Hendra R, Hill A. Rethinking response rates: new evidence of little relationship between survey response rates and nonresponse bias. Eval Rev 2019; 43 (05) 307-330
• 32 Cheng L, DeJesus AY, Rodriguez MA. Using laboratory test results at hospital admission to predict short-term survival in critically ill patients with metastatic or advanced cancer. J Pain Symptom Manage 2017; 53 (04) 720-727
• 33 Owusuaa C, van der Padt-Pruijsten A, Drooger JC. et al. Development of a clinical prediction model for 1-year mortality in patients with advanced cancer. JAMA Netw Open 2022; 5 (11) e2244350
• 34 Zhu Z, Li L, Ye Z. et al. Prognostic value of routine laboratory variables in prediction of breast cancer recurrence. Sci Rep 2017; 7 (01) 8135
• 35 Curtin D, Dahly DL, van Smeden M. et al. Predicting 1-year mortality in older hospitalized patients: external validation of the HOMR model. J Am Geriatr Soc 2019; 67 (07) 1478-1483
• 36 van Walraven C, Forster AJ. The HOMR-Now! Model Accurately Predicts 1-Year Death Risk for Hospitalized Patients on Admission. Am J Med 2017; 130 (08) 991.e9-991.e16
• 37 van Walraven C, McAlister FA, Bakal JA, Hawken S, Donzé J. External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission. CMAJ 2015; 187 (10) 725-733
• 38 Wegier P, Kurahashi A, Saunders S. et al. mHOMR: a prospective observational study of an automated mortality prediction model to identify patients with unmet palliative needs. BMJ Support Palliat Care 2021; 14 (e1): e969-e975
• 39 Davis M, Vanenkevort E, Young A. et al. Validation of the surprise question and the development of a multivariable model. J Pain Symptom Manage 2023; 65 (05) 456-464
• 40 Moss AH, Lunney JR, Culp S. et al. Prognostic significance of the “surprise” question in cancer patients. J Palliat Med 2010; 13 (07) 837-840
• 41 Aquino YSJ, Carter SM, Houssami N. et al. Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives. J Med Ethics 2023; (e-pub ahead of print)
• 42 Medicare.gov. 2022 [cited 2022 12/23/2022]; Accessed May 13, 2024 at: https://www.medicare.gov/coverage/hospice-care