Use of systemic cancer treatments based on a validated survival prediction model in metastatic cancer.

Authors

null

Mohana Roy

Stanford Cancer Institute, Stanford, CA

Mohana Roy , Michael F. Gensheimer , Daniel Tandel Chang , Surbhi Singhal , Ali Raza Khaki

Organizations

Stanford Cancer Institute, Stanford, CA, Stanford University, Stanford, CA, Stanford Hospital & Clinics, Stanford, CA

Research Funding

No funding received

Background: Use of systemic anti-cancer treatment near the end of life (EOL) is recognized as a low value practice with limited benefit to patients. Machine learning (ML) models that identify patients in close proximity to death can help prospectively assess oncology practice of systemic therapy use. We hypothesized that systemic therapy use would be higher based on predicted survival compared with actual survival. Methods: We calculated prevalence of systemic therapy use based on predicted and actual survival among patients with metastatic cancer at Stanford Healthcare from 2008-2019. Patients were included if they were in the test set of the ML model, had an eligible outpatient oncology clinic visit for which a predicted survival was calculated and were deceased . Median predicted survival was calculated from the ML model at each outpatient oncology visit and treatment was linked to a visit date if within 14 days of each other. Prevalence of systemic therapy was calculated for patients with a predicted or actual survival of < 6 months, 6-12 months, 12-18 months and 18-24 months. The five categories of treatment were: chemotherapy, targeted/antibody, hormone, immunotherapy, and other. Results: A total of 951 deceased patients who received anticancer treatment are included and a total of 21,283 doses of treatment were administered with a mean of 22 doses per patient. The median age at metastatic cancer diagnosis was 58 years, 53% of patients were female and most patients identified as White (55%) or Asian (23%). The most common disease groups were gastrointestinal (21.6%), thoracic (18.6%) and breast (14.9%). Overall, the use of different treatment types did not differ based on either predicted or actual survival (Table). In all the survival groupings, chemotherapy remained the predominant medication type, however with a trend of decreasing use with longer predicted and actual survival. Conclusions: The use of cancer medications and the type of medication given did not change based on predicted or actual survival in a large group of patients with metastatic cancer. There was a trend of decreasing chemotherapy use with longer prognosis. Further investigation into use in time intervals closer to (predicted or actual) death and inclusion of those who did not receive any systemic therapy are underway.

Use of Medication Type By Survival
Total Number of Doses
Chemotherapy
Targeted or Antibody Treatments
Hormone Treatments
Immunotherapy
Other
0-6 months Predicted
2829
69%
19%
7%
3%
2%
0-6 months Actual
5345
64%
20%
6%
7%
3%
6-12 months Predicted
5067
69%
18%
5%
4%
4%
6-12 months Actual
4956
64%
20%
6%
6%
4%
12-18 months Predicted
2895
58%
24%
6%
6%
6%
12-18 months Actual
3489
58%
23%
8%
6%
5%
18-24 months Predicted
1978
55%
24%
8%
5%
8%
18-24 months Actual
2502
54%
24%
9%
6%
7%

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Abstract Details

Meeting

2022 ASCO Annual Meeting

Session Type

Publication Only

Session Title

Care Delivery and Regulatory Policy

Track

Care Delivery and Quality Care

Sub Track

Care Delivery

Citation

J Clin Oncol 40, 2022 (suppl 16; abstr e13515)

DOI

10.1200/JCO.2022.40.16_suppl.e13515

Abstract #

e13515

Abstract Disclosures

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