Michael E O'Callaghan1,2,3,4, Zumin Shi5, Tina Kopsaftis6,7, Kim Moretti7,8,9,10,11,12. 1. Urology Unit, SA Health, Repatriation General Hospital, Daws Road, Daw Park, Adelaide, SA, 5041, Australia. michael.ocallaghan@health.sa.gov.au. 2. South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, SA, Australia. michael.ocallaghan@health.sa.gov.au. 3. Discipline of Medicine, Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, SA, Australia. michael.ocallaghan@health.sa.gov.au. 4. Flinders Centre for Innovation in Cancer, Flinders University of South Australia, Bedford Park, Adelaide, SA, Australia. michael.ocallaghan@health.sa.gov.au. 5. Population Research and Outcome Studies, School of Medicine, University of Adelaide, Adelaide, SA, Australia. 6. Urology Unit, SA Health, Repatriation General Hospital, Daws Road, Daw Park, Adelaide, SA, 5041, Australia. 7. South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, SA, Australia. 8. Discipline of Medicine, Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, SA, Australia. 9. Department of Surgery, Flinders University of South Australia, Bedford Park, Adelaide, SA, Australia. 10. Discipline of Surgery, University of Adelaide, Adelaide, SA, Australia. 11. School of Population Health, University of South Australia, Adelaide, SA, Australia. 12. Urology Unit, SA Health, The Queen Elizabeth Hospital, Adelaide, SA, Australia.
Abstract
OBJECTIVES: To examine the survival effect of treatment delays from the time of confirmed diagnosis of prostate cancer to first treatment in an Australian population. METHODS: Three thousand one hundred and forty patients were identified from the South Australian Prostate Cancer Clinical Outcomes Collaborative database for analysis. Selected patients had dates recorded for both diagnosis and treatment. We examined the effect of treatment delay (the time from diagnosis to date of first treatment) on survival using Cox and competing risks regression and compared quartiles of delay across the cohort. Adjustment was made for age, PSA levels, treatment modality and Gleason score. Outcomes included overall survival (OS) and prostate cancer-specific mortality (PCSM). RESULTS: Quartiles of delay were as follows (days)-Q1: 35, Q2: 86, Q3: 138.0, Q4: 264. Shorter delays were associated with hormonal treatment, high Gleason score and high PSA values. Measuring PCSM with Q2 as reference, age-adjusted associations were-Q1: sHR 4.37 (2.75-6.94), Q3: sHR 1.29 (0.73-2.28), Q4: sHR 1.55 (0.91-2.63). After additional adjustment for treatment type, Gleason score and PSA, Q1 remained at increased risk [sHR 2.46 (1.10-5.54)]. A similar trend was observed for OS. In analysis stratified by Gleason score, delays were not significantly associated with OS. CONCLUSIONS: Factors associated with shorter delay in treatment include high Gleason score, high PSA and hormonal treatment. After adjustment for these variables, increased delays were not associated with OS or PCSM in this cohort. The nonlinear association of delay with risk may explain conflicting reports in the literature.
OBJECTIVES: To examine the survival effect of treatment delays from the time of confirmed diagnosis of prostate cancer to first treatment in an Australian population. METHODS: Three thousand one hundred and forty patients were identified from the South Australian Prostate Cancer Clinical Outcomes Collaborative database for analysis. Selected patients had dates recorded for both diagnosis and treatment. We examined the effect of treatment delay (the time from diagnosis to date of first treatment) on survival using Cox and competing risks regression and compared quartiles of delay across the cohort. Adjustment was made for age, PSA levels, treatment modality and Gleason score. Outcomes included overall survival (OS) and prostate cancer-specific mortality (PCSM). RESULTS: Quartiles of delay were as follows (days)-Q1: 35, Q2: 86, Q3: 138.0, Q4: 264. Shorter delays were associated with hormonal treatment, high Gleason score and high PSA values. Measuring PCSM with Q2 as reference, age-adjusted associations were-Q1: sHR 4.37 (2.75-6.94), Q3: sHR 1.29 (0.73-2.28), Q4: sHR 1.55 (0.91-2.63). After additional adjustment for treatment type, Gleason score and PSA, Q1 remained at increased risk [sHR 2.46 (1.10-5.54)]. A similar trend was observed for OS. In analysis stratified by Gleason score, delays were not significantly associated with OS. CONCLUSIONS: Factors associated with shorter delay in treatment include high Gleason score, high PSA and hormonal treatment. After adjustment for these variables, increased delays were not associated with OS or PCSM in this cohort. The nonlinear association of delay with risk may explain conflicting reports in the literature.
Entities:
Keywords:
Biochemical recurrence; Delay; Health services research; Prostate cancer
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