Leveraging Centralized Health System Data Management and Large Language Model-Based Data Preprocessing to Identify Predictors for Radiation Therapy Interruption.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
130 patients treated with radiotherapy at the University of Tennessee Medical Center in Knoxville.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSION] Automated data preprocessing permitted efficient identification of treatment course length, marital status, disease site, Medicaid coverage, and socially vulnerable locations as significant factors associated with RTI. These findings underscore the need for data-driven risk assessment and intervention strategies to maintain cancer treatment quality at scale.
[PURPOSE] Unplanned treatment interruptions represent an important care quality shortfall for patients undergoing cancer radiotherapy.
- 95% CI 1.24 to 11.66
APA
Kumsa FA, Brett CL, et al. (2025). Leveraging Centralized Health System Data Management and Large Language Model-Based Data Preprocessing to Identify Predictors for Radiation Therapy Interruption.. JCO clinical cancer informatics, 9, e2500218. https://doi.org/10.1200/CCI-25-00218
MLA
Kumsa FA, et al.. "Leveraging Centralized Health System Data Management and Large Language Model-Based Data Preprocessing to Identify Predictors for Radiation Therapy Interruption.." JCO clinical cancer informatics, vol. 9, 2025, pp. e2500218.
PMID
41150999 ↗
Abstract 한글 요약
[PURPOSE] Unplanned treatment interruptions represent an important care quality shortfall for patients undergoing cancer radiotherapy. This study aimed to evaluate use of a centralized electronic health record warehouse and large language model-based data preprocessing to facilitate identification of risk factors for radiation therapy interruptions (RTI).
[METHODS] We analyzed demographic, behavioral, clinical, and neighborhood-level data for 2,130 patients treated with radiotherapy at the University of Tennessee Medical Center in Knoxville. Treatment interruptions were measured as missed days, adjusted for weekends and holidays. Multinomial logistic regression was used to identify factors associated with moderate (2-4 days) and severe (≥5 days) RTI.
[RESULTS] Moderate RTI occurred in 15.8% of patients, while 7.7% experienced severe RTI. Moderate delays were associated with genitourinary cancer (adjusted odds ratio (AOR), 3.81; 95% CI, 1.24 to 11.66), prostate cancer (AOR, 2.44; 95% CI, 1.34 to 4.46), and Medicaid coverage (AOR, 2.22; 95% CI, 1.32 to 3.73). Severe RTI was associated with marital status (AOR for divorced or separated patients, 1.86; 95% CI, 1.18 to 2.94), head and neck cancer (AOR, 2.31; 95% CI, 1.10 to 4.87), gynecologic cancer (AOR, 2.97; 95% CI, 1.30 to 6.79), Medicaid insurance (AOR, 3.43; 95% CI, 1.77 to 6.64), daily dose of ≤225 cGy (AOR, 2.55; 95% CI, 1.21 to 5.37), and a total dose of ≥6,000 cGy (AOR, 2.30; 95% CI, 1.09 to 4.88). Severe interruptions were also significantly associated with high neighborhood social vulnerability (AOR, 2.60; 95% CI, 1.32 to 5.09).
[CONCLUSION] Automated data preprocessing permitted efficient identification of treatment course length, marital status, disease site, Medicaid coverage, and socially vulnerable locations as significant factors associated with RTI. These findings underscore the need for data-driven risk assessment and intervention strategies to maintain cancer treatment quality at scale.
[METHODS] We analyzed demographic, behavioral, clinical, and neighborhood-level data for 2,130 patients treated with radiotherapy at the University of Tennessee Medical Center in Knoxville. Treatment interruptions were measured as missed days, adjusted for weekends and holidays. Multinomial logistic regression was used to identify factors associated with moderate (2-4 days) and severe (≥5 days) RTI.
[RESULTS] Moderate RTI occurred in 15.8% of patients, while 7.7% experienced severe RTI. Moderate delays were associated with genitourinary cancer (adjusted odds ratio (AOR), 3.81; 95% CI, 1.24 to 11.66), prostate cancer (AOR, 2.44; 95% CI, 1.34 to 4.46), and Medicaid coverage (AOR, 2.22; 95% CI, 1.32 to 3.73). Severe RTI was associated with marital status (AOR for divorced or separated patients, 1.86; 95% CI, 1.18 to 2.94), head and neck cancer (AOR, 2.31; 95% CI, 1.10 to 4.87), gynecologic cancer (AOR, 2.97; 95% CI, 1.30 to 6.79), Medicaid insurance (AOR, 3.43; 95% CI, 1.77 to 6.64), daily dose of ≤225 cGy (AOR, 2.55; 95% CI, 1.21 to 5.37), and a total dose of ≥6,000 cGy (AOR, 2.30; 95% CI, 1.09 to 4.88). Severe interruptions were also significantly associated with high neighborhood social vulnerability (AOR, 2.60; 95% CI, 1.32 to 5.09).
[CONCLUSION] Automated data preprocessing permitted efficient identification of treatment course length, marital status, disease site, Medicaid coverage, and socially vulnerable locations as significant factors associated with RTI. These findings underscore the need for data-driven risk assessment and intervention strategies to maintain cancer treatment quality at scale.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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