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Updated: Feb 25, 2026

An Orthotopic Murine Model of Human Prostate Cancer Metastasis
Published on: September 18, 2013
Risk factors for metastatic prostate cancer: A sentinel event case series
Channing J Paller1, Alexander P Cole2, Alan W Partin3
1Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Background:
Root cause analysis is a technique used to assess systems factors related to "sentinel events"-serious adverse events within healthcare systems. This technique is commonly used to identify factors, which allowed these adverse events to occur, to target areas for improvement and to improve health care delivery systems. We sought to apply this technique to men presenting with metastatic prostate cancer (PCa).
Methods:
We performed an in-depth case series analysis of 15 patients, who presented with metastatic disease at Johns Hopkins Sidney Kimmel Comprehensive Cancer Center using root cause analysis to refine a list of health system factors that lead to late stage presentation in the current era.
Results:
Key factors in late diagnosis of PCa included lack of insurance, lack of routine PSA testing, comorbidities, reticence of patients to follow up actionable PSA, and aggressive disease. Three patients had aggressive disease that would not have been discovered at an early stage in the disease process, despite routine screening. However, analysis of the remaining 12 patients illuminated health system factors led to missing important diagnostic information, which might have led to diagnosis of PCa at a curable stage.
Conclusions:
The cases help highlight the need for systems based approaches to early diagnosis of PCa. A heterogeneous group of barriers to early diagnosis were identified in our series of patients including economic, health systems, and cultural factors. These findings underscore the need for individualized approaches to preventing delayed diagnosis of PCa. While limited by our single-institution scope, this approach provides a model for research and quality improvement initiatives to identify modifiable systems factors impeding appropriate diagnoses of PCa.
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