Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer
Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer
Bertsimas, Dimitris; Dunn, Jack; Pawlowski, Colin; Silberholz, John; Weinstein, Alexander; Zhuo, Ying Daisy; Chen, Eddy; Elfiky, Aymen A.
2018-06-07 00:00:00
Purpose: With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks and overall mortality. Given the unmet need for accurate prognostication with meaningful clinical rationale, we developed a highly interpretable prediction tool to identify patients with high mortality risk before the start of treatment regimens. Methods: We obtained electronic health record data between 2004 and 2014 from a large national cancer center and extracted 401 predictors, including demographics, diagnosis, gene mutations, treatment history, comorbidities, resource utilization, vital signs, and laboratory test results. We built an actionable tool using novel developments in modern machine learning to predict 60-, 90- and 180-day mortality from the start of an anticancer regimen. The model was validated in unseen data against benchmark models. Results: We identified 23,983 patients who initiated 46,646 anticancer treatment lines, with a median survival of 514 days. Our proposed prediction models achieved significantly higher estimation quality in unseen data (area under the curve, 0.83 to 0.86) compared with benchmark models. We identified key predictors of mortality, such as change in weight and albumin levels. The results are presented in an interactive and interpretable tool (www.oncomortality.com). Conclusion: Our fully transparent prediction model was able to distinguish with high precision between highest- and lowest-risk patients. Given the rich data available in electronic health records and advances in machine learning methods, this tool can have significant implications for value-based shared decision making at the point of care and personalized goals-of-care management to catalyze practice reforms.
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pngJCO Clinical Cancer InformaticsWolters Kluwer Healthhttp://www.deepdyve.com/lp/wolters-kluwer-health/applied-informatics-decision-support-tool-for-mortality-predictions-in-itCD3C5Hzl
Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer
Purpose: With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks and overall mortality. Given the unmet need for accurate prognostication with meaningful clinical rationale, we developed a highly interpretable prediction tool to identify patients with high mortality risk before the start of treatment regimens. Methods: We obtained electronic health record data between 2004 and 2014 from a large national cancer center and extracted 401 predictors, including demographics, diagnosis, gene mutations, treatment history, comorbidities, resource utilization, vital signs, and laboratory test results. We built an actionable tool using novel developments in modern machine learning to predict 60-, 90- and 180-day mortality from the start of an anticancer regimen. The model was validated in unseen data against benchmark models. Results: We identified 23,983 patients who initiated 46,646 anticancer treatment lines, with a median survival of 514 days. Our proposed prediction models achieved significantly higher estimation quality in unseen data (area under the curve, 0.83 to 0.86) compared with benchmark models. We identified key predictors of mortality, such as change in weight and albumin levels. The results are presented in an interactive and interpretable tool (www.oncomortality.com). Conclusion: Our fully transparent prediction model was able to distinguish with high precision between highest- and lowest-risk patients. Given the rich data available in electronic health records and advances in machine learning methods, this tool can have significant implications for value-based shared decision making at the point of care and personalized goals-of-care management to catalyze practice reforms.
Journal
JCO Clinical Cancer Informatics
– Wolters Kluwer Health
Published: Jun 7, 2018
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References
A systematic review of physicians' survival predictions in terminally ill cancer patients
Glare P; Virik K; Jones M
Predicting prognosis in patients with advanced cancer
Stone PC; Lund S
Trading treatment toxicity for survival in locally advanced non-small cell lung cancer
Brundage MD; Davidson JR; Mackillop WJ
Preferences for chemotherapy in patients with advanced non-small cell lung cancer: Descriptive study based on scripted interviews
Silvestri G; Pritchard R; Welch HG
Patient willingness to undergo chemotherapy and thoracic radiotherapy for locally advanced non-small cell lung cancer
Hirose T; Yamaoka T; Ohnishi T
American Society of Clinical Oncology identifies five key opportunities to improve care and reduce costs: The top five list for oncology
Schnipper LE; Smith TJ; Raghavan D
Endorsement summary: Cancer measures
National Quality Forum
Associations between palliative chemotherapy and adult cancer patients' end of life care and place of death: Prospective cohort study
Wright AA; Zhang B; Keating NL
Leucovorin and fluorouracil with or without oxaliplatin as first-line treatment in advanced colorectal cancer
de Gramont A; Figer A; Seymour M
Chemotherapy use among Medicare beneficiaries at the end of life
Emanuel EJ; Young-Xu Y; Levinsky NG
Effect of early palliative care on chemotherapy use and end-of-life care in patients with metastatic non-small-cell lung cancer
Greer JA; Pirl WF; Jackson VA
Predicting survival in patients with advanced disease
Glare P; Sinclair C; Downing M
Machine learning applications in cancer prognosis and prediction
Kourou K; Exarchos TP; Exarchos KP
A new palliative prognostic score: A first step for the staging of terminally ill cancer patients
Pirovano M; Maltoni M; Nanni O
Survival prediction of terminally ill cancer patients by clinical symptoms: Development of a simple indicator
Morita T; Tsunoda J; Inoue S
Nomogram for overall survival of patients with progressive metastatic prostate cancer after castration
Smaletz O; Scher HI; Small EJ
Prediction of survival in terminal cancer patients in Taiwan: Constructing a prognostic scale
Chuang RB; Hu WY; Chiu TY
A simple and accurate prediction model to estimate the intrahospital mortality risk of hospitalised cancer patients
Bozcuk H; Koyuncu E; Yildiz M
Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer
Forrest LM; McMillan DC; McArdle CS
Construction of a new, objective prognostic score for terminally ill cancer patients: A multicenter study
Suh SY; Choi YS; Shim JY
Risk classification of cancer survival using ANN with gene expression data from multiple laboratories
Chen YC; Ke WC; Chiu HW
Machine-learning prediction of cancer survival: A retrospective study using electronic administrative records and a cancer registry
Gupta S; Tran T; Luo W
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
Gevaert O; De Smet F; Timmerman D
Development and validation of a continuous measure of patient condition using the electronic medical record
Rothman MJ; Rothman SI; Beals J IV
Development of Imminent Mortality Predictor for Advanced Cancer (IMPAC), a tool to predict short-term mortality in hospitalized patients with advanced cancer
Adelson K; Lee DKK; Velji S
Artificial neural networks improve the accuracy of cancer survival prediction
Burke HB; Goodman PH; Rosen DB
Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data
Kim J; Shin H
Predicting breast cancer survivability: A comparison of three data mining methods
Delen D; Walker G; Kadam A
Optimal classification trees
Bertsimas D; Dunn J
Bertsimas D, Pawlowski C, Zhuo YD
Bertsimas D; Pawlowski C; Zhuo YD
Practice innovation: The need for nimble data platforms to implement precision oncology care
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