Unrelieved pain remains a significant challenge during the postoperative period. A staggering 25% of surgical patients, or about 15 million U.S. surgical patients annually, experience severe acute pain (defined as pain e 7 out of 10 on a Verbal Rating Scale) in the immediate postoperative period. Severe post-surgical pain is associated with serious post-surgical complications and is the most common cause of delayed hospital discharge and unanticipated readmission. In fact, at least $1 billion annually in health care costs is attributed to direct spending on uncontrolled post-surgical pain. Furthermore, poorly managed acute post-surgical pain has far-reaching and long-term consequences as it has been associated with the development of chronic pain. A systematically constructed predictive model and clinical risk score for pain, like those developed for other conditions with multi-factorial causes such as postoperative nausea and vomiting, cardiovascular diseases, and cancer, could afford clinicians the opportunity to adopt a more proactive and personalized approach to post-surgical pain management and preclude patients from unduly suffering both now and in the future. This study will use multivariable analysis to evaluate clinical risk factors for severe post-surgical pain, and then develop and validate a clinical risk score that will allow clinicians to closely monitor and provide tailored prophylactic pain management to high- risk patients.
In Specific Aim 1 we will identify the key independent predictors and develop a risk score for severe post-surgical pain by performing a series of data analysis on pain data prospectively collected from 1,700 adult ambulatory surgery patients from 12 US medical centers.
In Specific Aim 2 we will validate the risk score by evaluating its discriminating power to predict severe post-surgical pain when applied to perioperative data from 1) 456 ambulatory surgery patients and 2) 2,000 adult surgical in-patients. This study will provide clinicians with a practical tool to stratify patients according their risk profile for severe post-surgical pain and facilitate personalized pain management accordingly. It will also provide insight into key clinical risk factors for severe pain and pinpoint those that can be avoided or modified by the clinician. Furthermore, this R03 proposal will enable the development and testing of novel treatments for preventing and treating severe post-surgical pain and is designed to build a foundation for a series clinically relevant and paradigm-shifting R01 grant proposals.

Public Health Relevance

Unrelieved severe post-surgical pain affects a staggering 25% of U.S. surgical patients, causes unnecessary patient suffering, costs the U.S. healthcare system close to $1 billion annually, and may contribute to the development of chronic post-surgical pain. We will develop a clinically applicable predictive model for severe post-surgical pain that will facilitate early identification of high-risk patients for closer monitoring and afford clinicians the opportunity to adopt a more proactive and personalized approach to post-surgical pain management. Insights gained from this study on acute post-surgical pain will allow clinicians to address modifiable risk factors and direct the development of novel pain therapies towards better, more individualized patient care.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Small Research Grants (R03)
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Nursing and Related Clinical Sciences Study Section (NRCS)
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Marden, Susan F
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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