Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression

Article type: Research Article

Authors: Khajehpiri, Boshra^a | Moghaddam, Hamid Abrishami^a | Forouzanfar, Mohamad^{a; b} | Lashgari, Reza^c | Ramos-Cejudo, Jaime^e | Osorio, Ricardo S.^{d; e} | Ardekani, Babak A.^{d; *} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Machine Vision and Medical Image Processing (MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran | [b] Department of Systems Engineering, École deTechnologie Supérieure, Université du Québec, Montreal, Quebec, Canada | [c] Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran | [d] The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA | [e] Department of Psychiatry, New York University (NYU) Grossman School of Medicine, New York, NY, USA

Correspondence: [*] Correspondence to: Babak A. Ardekani, PhD, Center for Brain Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA. E-mail: ardekani@nki.rfmh.org.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data-base (https://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:Evaluating the risk of Alzheimer’s disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. Objective:The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. Methods:We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. Results:In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. Conclusion:Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

Keywords: Alzheimer’s disease, brain, hippocampal atrophy, machine learning, magnetic resonance imaging, mild cognitive impairment, proportional hazards model, survival analysis, Xgboost

DOI: 10.3233/JAD-215266

Journal: Journal of Alzheimer's Disease, vol. 85, no. 2, pp. 837-850, 2022