Real world risk-stratified clinical pathways for cancer care management.

Abstract

e13864 Background: Individuals diagnosed with metastatic and advanced cancers are living substantially longer. This population has prognostic uncertainty, high symptom burden, unpredictable symptom trajectories, adherence challenges, and increased medical costs. Patient-reported outcomes (PROs) can predict symptom burdens, medical expenditures, and prognostics. Calls-to-action emphasize risk-stratified pathways for palliative and supportive cancer care. In response, we used real-time PRO surveillance to risk-stratify patients into low, medium, and high-risk categories. Methods: Multimodal interventions are effective in cancer symptom management. Following national guidelines, medical oncologists, advanced practice providers, mental health therapists, dietitians, and occupational therapists delivered a virtual supportive/palliative care intervention. The SF-12 Vitality Enhanced tool measured physical and mental health. Risk algorithm calculations stratified age/sex matched patients as "same or better", "below," or "well-below" a normal population. "Well-below" indicates more than one standard deviation below normal population. "Below" is 0.5 to 1.0 standard deviations below normal population. Results: Patients were referred from a Utah oncology clinic, other clinics, or self-referred (N=132; mean age 52; 84% female). At baseline, 35% started treatment, 21% were on post-treatment hormonal therapy, and 65% were post-treatment. Baseline Mental Component Scores (MCS) classified 48% high-risk, 25% medium-risk, and 27% low-risk. Physical Component Scores (PCS) classified 35% high-risk, 17% medium-risk, and 48% low-risk. Follow-up MCS scores classified 28% high-risk, 24% medium-risk, and 49% low-risk, with PCS scores at 31% high-risk, 14% medium-risk, and 56% low-risk. Establishing high, medium, and low-risk distributions is a first step in understanding patient burden. MCS scores were then placed on a Y-axis and PCS scores on a X-axis. This matrix provides greater resolution on disease risk stratification that support improved disease management by extending evaluation of health risk into precise clinical pathways. For example, those with high physical and mental health risk represent high complexity care cases. Trend-over time, analyses revealed improvements in mental and physical health across risk categories (Table 1). Conclusions: PROs can be used to establish and follow risk-stratified pathways. With clinically valid risk-stratified algorithms, we can improve management of disease burdens, reduce prognostic uncertainty, mitigate unnecessary medical expenditure, and improve treatment adherence. Stratified risk matrix. PCS PCS PCS Same Better Below Well Below MCS Same/Better 13% 2% 12% Baseline Measurement MCS Below 12% 5% 8% MCS Well Below 22% 10% 15% PCS PCS PCS Same Better Below Well Below MCS Same/Better 25% 6% 18% Follow-up at 12-16 Weeks MCS Below 17% 5% 2% MCS Well Below 14% 3% 11%