Joseph C. Presti, Jr., MD, FACS

Kaiser Permanente Northern California

Oakland, California

Joseph C. Presti, Jr., MD, FACS, is an Adjunct Investigator in the Division of Research and a Physician Researcher in the Delivery Science & Applied Research Physician Researcher Program at Kaiser Permanente in Oakland, California. He is also the Regional Leader for Urologic Oncology Surgery with the Kaiser Permanente Medical Group. Dr. Presti specializes in the evaluation and treatment of urologic cancers. His research focuses on refining detection strategies for prostate cancer.

Dr. Presti earned his medical degree from the University of California, Irvine. He then completed an internship and residency in urology from the University of California, San Francisco. Dr. Presti completed a fellowship in Urologic Oncology at the Memorial Sloan-Kettering Cancer Center in Manhattan, New York.

Dr. Presti’s early work investigated biopsy strategies for improving prostate cancer detection. His current work includes developing and evaluating a risk calculator that uses simple clinical variables to predict a patient’s risk of developing an aggressive prostate cancer. Dr. Presti also studies prostate cancer screening and detection, and he is currently working with the Kaiser Permanente Medical Group’s Department of Adult and Family Medicine to refine their shared decision-making program, which aims to maximize benefits and minimize harms from prostate cancer screening. Dr. Presti also serves as the co-clinical lead for the National KP Prostate Cancer Screening Guideline Team.

Talks by Joseph C. Presti, Jr., MD, FACS

Changes in Prostate Cancer Presentation Following the 2012 USPSTF Screening Statement

Joseph C. Presti, Jr., MD, FACS | Oct 2020

Joseph C. Presti, Jr., MD, FACS, urologist at Kaiser Oakland and Regional Leader of Urologic Oncology Surgery for Kaiser Permanente California, reviews data compiled by Kaiser Permanente Northern California in order to discuss how the 2012 United States Preventive Services Task Force (USPSTF) screening statement, which includes the claim that “the harms of screening outweigh the benefits,” has impacted prostate cancer screening practices. Dr. Presti outlines the research process used, which included looking at screening-eligible men and assessing the annual rates of PSA testing, prostate biopsy, cancer incidence, and metastatic disease incidence over the course of a pre-guideline period (2010-2011) and a post-guideline period (2014-2017). The researchers found that although the eligible screening population grew from 404,000 to 524,000, screening rates decreased from 42% to 29%, biopsy rates went from 1.2% to .5%, and prostate cancer detection decreased from 2063 diagnoses to 994 in 2014 but increased to 1528 in 2017. Concerningly, metastatic prostate cancer incidence increased significantly post-statement. Dr. Presti concludes by summarizing the data and discussing the strengths (ability to define a screening-eligible population and a large, diverse sample size) and weaknesses (inability to access family history or look for indolent cancers) of the study.

Kaiser Permanente Prostate Cancer Risk Calculator 1.0

Joseph C. Presti, Jr., MD, FACS | Sep 2020

Joseph C. Presti, Jr., MD, FACS, Regional Leader of Urologic Oncology Surgery at Kaiser Permanente Northern California, discusses the development of Kaiser Permanente’s new prostate cancer risk calculator and its merits. Dr. Presti explains that older risk calculators tend to oversimplify variables like race, are based on outdated systematic biopsy schemes, and are often poorly calibrated due to the sampling frame used. Using TRIPOD guidelines (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) and the LASSO (least absolute shrinkage and selection operator) system of selection, Kaiser Permanente researchers determined that the variables that ought to be included in a prostate cancer prediction model are age, race, PSA, body-mass index, family history, number of prior negative biopsies, digital rectal exam (DRE), and prostate volume. They created 3 different models based on this, with the simplest but least accurate including clinical core variables but no DRE and no prostate volume, the second-most accurate including DRE but no prostate volume, and the most accurate including DRE and prostate volume. Dr. Presti notes that all of these models compare favorably to other risk calculators.