Predictive Patient Care: Survival model to prevent Medication Non-Adherence
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This document describes a study that used survival analysis techniques like the Kaplan-Meier estimator and Cox proportional hazards model to develop a predictive model for medication non-adherence using a large French healthcare database. The model analyzed factors like medical history, treatments, and healthcare utilization to estimate patient survival curves and risk scores for remaining on tamoxifen treatment for breast cancer. The results validated using these statistical techniques to predict medication abandonment risk and identified the most influential variables.
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Predictive Patient Care: Survival model to prevent Medication Non-Adherence
- 1. Predictive Patient Care : Survival Model to Prevent Medication Non-Adherence D. Kanoun, Clinique Pasteur T. Janssoone, P. Rinder, P. Hornus, Sêmeia
- 2. • Cox proportional hazards model • Survival analysis • Iterative combination • The studied Database: SNIIRAM • Kaplan Meier estimator 2 Plan Kaplan Meier estimator Cox proportional hazards model SNIIRAM 0 200 400 600 800 1000 0.00.20.40.60.81.0 Survival curves given the risk score Number of days Score < 0.8 0.8 ... score < 1.3 Score ... 1.3
- 3. 3 . The SNIIRAM Database: records of reimbursement data of the French health system. . Focus on women’s breast cancer: our cohort is composed of 50% of women, diagnosed with breast cancer, having purchased Tamoxifen between 2013 and 2015. . Raw data are processed to phase that allows the reconstruction of the patient’s care paths. The Database SNIIRAM
- 4. 0 200 400 600 800 1000 02468 Hazard function Number of days 1/10000 . Kaplan-Meier Estimator: (or product-limit estimator ) probability of surviving in a given length of time while considering time in many small intervals. . Hazard function can be derivated from Kaplan-Meier estimator: Roughly characterizes the « instantaneous probability » of a drug drop-out at time t 4 Survival Analysis
- 5. 5 . Cox regression: estimates the effect of each characteristic to the study phenomena as an Hazard Ratio (HR) HR = 1: no effect; HR < 1: reduction in hazard; HR > 1: increase in hazard Proportional hazard model Variable HR CMU-C 1.47 ACS 1.52 Time since ALD status 1.08 Number of medical consultation 0.99 Psychiatric illness 1.19 Recent hospitalization for malignant neoplasms of breast 0.68 Previous treatment: Tamoxifen 2.49 Previous treatment: radiotherapy 0.49 Previous treatment: chemotherapy 0.42
- 6. . Insight of Cox regression into Kaplan-Meier estimator: Use of information about the patient to compute more accurate survival functions For example: 1. Compute a risk score at the beginning of a phase 2. Survival function according to the previous treatment 1. Compute a risk score at the beginning of a phase 6 Combination 0 200 400 600 800 1000 0.00.20.40.60.81.0 Survival curves given the risk score Number of days Score < 0.8 0.8 ... score < 1.3 Score ... 1.3 0 200 400 600 800 1000 0.00.20.40.60.81.0 Survival curves given the previous treatment Number of days Chemotherapy Radiotherapy None Tamoxifen Other hormonotherapy
- 7. 7 • Validate the possibility to estimate the risk of abandonment during a phase of treatment • Insights about the most significant variables and their weights. • Limitations of the statistical models due to strong hypothesis • Use in an application for patient support • Test other approaches (deep learning, reinforcement learning, sequence mining, …) • Provide a meta-analysis of these tools on different pathologies Conclusion & Future work
- 8. Predictive Patient Care : Survival Model to Prevent Medication Non-Adherence D. Kanoun, Clinique Pasteur T. Janssoone, P. Rinder, P. Hornus, Sêmeia