Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer: Comparing Five Machine Learning Approaches
- PMID: 36379004
- DOI: 10.1200/CCI.22.00054
Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer: Comparing Five Machine Learning Approaches
Abstract
Purpose: Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration.
Materials and methods: Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated.
Results: The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network-based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death.
Conclusion: This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study.
Similar articles
-
Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?Clin Orthop Relat Res. 2020 Jul;478(7):0-1618. doi: 10.1097/CORR.0000000000001251. Clin Orthop Relat Res. 2020. PMID: 32282466 Free PMC article.
-
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.Med Phys. 2018 Jul;45(7):3449-3459. doi: 10.1002/mp.12967. Epub 2018 Jun 13. Med Phys. 2018. PMID: 29763967 Free PMC article.
-
An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning.Comput Intell Neurosci. 2022 May 6;2022:2220527. doi: 10.1155/2022/2220527. eCollection 2022. Comput Intell Neurosci. 2022. PMID: 35571720 Free PMC article.
-
Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data.Lancet Digit Health. 2021 Apr;3(4):e231-e240. doi: 10.1016/S2589-7500(21)00018-2. Lancet Digit Health. 2021. PMID: 33766287
-
Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach.ESC Heart Fail. 2021 Aug;8(4):2837-2845. doi: 10.1002/ehf2.13358. Epub 2021 Jun 3. ESC Heart Fail. 2021. PMID: 34080784 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical