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Attend this session at the
2019 ASCO Annual Meeting!


Session: Health Services Research, Clinical Informatics, and Quality of Care

Type: Poster Session

Time: Saturday June 1, 1:15 PM to 4:15 PM

Location: Hall A

Development of an artificial intelligence model to predict survival at specific time intervals for lung cancer patients.

Sub-category:
Clinical Informatics

Category:
Health Services Research, Clinical Informatics, and Quality of Care

Meeting:
2019 ASCO Annual Meeting

Abstract No:
6556

Poster Board Number:
Poster Session (Board #247)

Citation:
J Clin Oncol 37, 2019 (suppl; abstr 6556)

Author(s): Smita Agrawal, Vivek Vaidya, Prajwal Chandrashekaraiah, Hemant Kulkarni, Li Chen, Karl Rudeen, Babu Narayanan, Orr Inbar, Brigham Hyde; Concerto HealthAI, Bengaluru, India; Concerto Health AI, Bengaluru, India; Concerto HealthAI, Bangalore, India; Concerto Health AI, Banglore , India; Concerto HealthAI, Boston, MA; Concerto Health AI, New York, NY; Concerto Health AI, Ra'anana, Israel

Abstract Disclosures

Abstract:

Background: Survival prediction models for lung cancer patients could help guide their care and therapy decisions. The objectives of this study were to predict probability of survival beyond 90, 180 and 360 days from any point in a lung cancer patient’s journey. Methods: We developed a Gradient Boosting model (XGBoost) using data from 55k lung cancer patients in the ASCO CancerLinQ database that used 3958 unique variables including Dx and Rx codes, biomarkers, surgeries and lab tests from ≤1 year prior to the prediction point, which was chosen at random for each patient. We used 40% data for training, 25% for hyper-parameter tuning, 20% for testing and 15% for holdout validation. Death date available in the Electronic Health Record was cross checked by linkage to death registries. Results: The model was validated on the holdout set of 8,468 patients. The Area Under the Curve (AUC) for the model was 0.79. The precision and recall for predicting survival beyond the three time points were between 0.7-0.8 and 0.8-0.9 respectively (see table). This compares favourably to other lung cancer survival models created using different machine learning techniques (Jochems 2017, Dekker 2009). A Cox-PH model created using the top 20 variables also had a significantly lower performance (see table). Analysis of input variables yielded distinctive patterns for patient subgroups and time points. Tumor status, medications, lab values and functional status were found to be significant in patient sub cohorts. Conclusions: An AI model to predict survival of lung cancer patients built using a large real world dataset yielded high accuracy. This general model can further be used to predict survival of sub cohorts stratified by variables such as stage or various treatment effects. Such a model could be useful for assessing patient risk and treatment options, evaluating cost and quality of care or determining clinical trial eligibility.

PrecisionRecallF1 scoreSupportAUCCox PH AUC
Death in 90D0.610.440.5122350.790.69
Alive after 90D0.820.90.8662330.790.69
Death in 180D0.690.530.631510.790.7
Alive after 180D0.760.860.853170.790.7
Death in 360D0.740.670.740730.790.71
Alive after 360D0.720.780.7543950.790.71

 
Other Abstracts in this Sub-Category:

 

1. A predictive model for survival in non-small cell lung cancer (NSCLC) based on electronic health record (EHR) and tumor sequencing data at the Department of Veterans Affairs (VA).

Meeting: 2019 ASCO Annual Meeting Abstract No: 109 First Author: Nathanael Fillmore
Category: Health Services Research, Clinical Informatics, and Quality of Care - Clinical Informatics

 

2. A blinded evaluation of a clinical decision-support system at a regional cancer care center.

Meeting: 2019 ASCO Annual Meeting Abstract No: 6553 First Author: Suthida Suwanvecho
Category: Health Services Research, Clinical Informatics, and Quality of Care - Clinical Informatics

 

3. A framework for building a clinically relevant risk model.

Meeting: 2019 ASCO Annual Meeting Abstract No: 6554 First Author: Robert Michael Daly
Category: Health Services Research, Clinical Informatics, and Quality of Care - Clinical Informatics

 

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