Appl Clin Inform 2024; 15(03): 489-500
DOI: 10.1055/s-0044-1787185
Research Article

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

Adrianna Z. Herskovits*
1   Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Tiffanny Newman*
2   Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Kevin Nicholas*
2   Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Cesar F. Colorado-Jimenez
1   Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Claire E. Perry
2   Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Alisa Valentino
1   Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Isaac Wagner
2   Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Barbara Egan
3   Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Dmitriy Gorenshteyn
4   Thirty Madison, New York, New York, United States
Andrew J. Vickers
5   Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States
Melissa S. Pessin
6   Department of Pathology, University of Chicago, Chicago, Illinois, United States
› Author Affiliations
Funding Supported in part by the National Institutes of Health/National Cancer Institute Cancer Center Support grant P30 CA008748.


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.

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.

Supplementary Material

Publication History

Received: 15 December 2023

Accepted: 29 April 2024

Article published online:
26 June 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

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