{"id":62963,"date":"2026-04-18T19:02:39","date_gmt":"2026-04-18T17:02:39","guid":{"rendered":"https:\/\/pharma-trend.com\/en\/hss-presents-new-research-leveraging-ai-to-uncover-insights-related-to-pain-risk-and-anesthesia-education-at-asra-annual-meeting\/"},"modified":"2026-04-18T19:02:39","modified_gmt":"2026-04-18T17:02:39","slug":"hss-presents-new-research-leveraging-ai-to-uncover-insights-related-to-pain-risk-and-anesthesia-education-at-asra-annual-meeting","status":"publish","type":"post","link":"https:\/\/pharma-trend.com\/en\/hss-presents-new-research-leveraging-ai-to-uncover-insights-related-to-pain-risk-and-anesthesia-education-at-asra-annual-meeting\/","title":{"rendered":"HSS Presents New Research Leveraging AI to Uncover Insights Related to Pain Risk and Anesthesia Education at ASRA Annual Meeting"},"content":{"rendered":"
\n

PHOENIX–(BUSINESS WIRE)–At this year\u2019s American Society of Regional Anesthesia and Acute Pain Medicine<\/a> (ASRA) annual meeting, investigators at Hospital for Special Surgery<\/a> (HSS) presented significant studies leveraging artificial intelligence (AI) to provide insights into long-term pain risk after surgery and what patients want to know about anesthesia. These insights may ultimately help guide anesthesiologists\u2019 consultations with patients scheduled for surgery.<\/p>\n

<\/a>
<\/a><\/p>\n

\nWhat follows are highlights from these studies:<\/i><\/p>\n

\nHSS Study Uses Machine Learning to Predict Risk of Long-Term Pain After Knee Replacement Surgery<\/b><\/p>\n

\nA new study led by researchers at HSS used a type of AI known as machine learning (ML) to identify key clinical and biological factors that raise a person\u2019s risk of having persistent pain after total knee arthroplasty (TKA). Risk factors included elevated levels of certain inflammatory cytokines (proteins) in the blood, severe preoperative pain, and a longer period of tourniquet use in the operating room.<\/p>\n

\n\u201cThese findings highlight the importance of incorporating biological markers like cytokine levels with patient-specific pain profiles and what the surgeon does during the operation to more accurately predict the risk of long-term pain after surgery,\u201d says Meghan Kirksey, MD, PhD<\/a>, an anesthesiologist at HSS and senior author of the study.<\/p>\n

\nML is a specialized approach that uses algorithms and statistical models to analyze patterns in large amounts of data to learn, predict, and make recommendations.<\/p>\n

\n\u201cMachine learning is allowing us to look at patient and clinician information in new ways,\u201d says Alexandra Sideris, PhD<\/a>, Director of the HSS Pain Prevention Research Center and a coauthor of the study. \u201cIt gives us a multidimensional approach to understanding patients\u2019 pain experience that we didn\u2019t have in our arsenal even five or 10 years ago.\u201d<\/p>\n

\nOne in five people has significant knee pain months after having TKA, also known as total knee replacement surgery. \u201cThe lingering pain greatly affects their daily activities and quality of life, so that\u2019s why it\u2019s an important focus for us,\u201d explains Dr. Sideris.<\/p>\n

\nPersistent postoperative pain (PPP) is typically flagged when a patient has lasting pain at the site of the operation that is above a four on a scale of zero to 10 and severely impacts their activities of daily living three to six months after surgery.<\/p>\n

\nThe researchers used four different ML models to analyze data from a previously published study that collected comprehensive clinical information and blood samples from 160 patients before and after TKA at HSS. The new study identified key predictors associated with PPP beyond risk factors that were already known such as sex (women tend to have a higher risk), preexisting pain, and mental health issues like anxiety and depression.<\/p>\n

\nThe findings showed that having high blood levels of an inflammatory marker called TARC immediately after surgery raises the risk of PPP. \u201cThis molecule hasn\u2019t been extensively studied in pain, but the evidence shows that it was consistently associated with persistent pain six months after surgery across all four ML models we tested,\u201d notes Dr. Kirksey.<\/p>\n

\nOther top predictors of PPP that emerged from the analysis included a higher preoperative pain score at rest, longer tourniquet time (a tourniquet is a device that squeezes the leg to help clear the area of blood flow during surgery), and higher blood levels of other inflammatory cytokines right after surgery.<\/p>\n

\nIn this study, researchers entered 318 clinical and biological characteristics collected from patients in the older study and asked each of the ML models to figure out the most important features associated with pain after TKA. The researchers also evaluated the accuracy of the ML models they used and found that XGBoost was the most informative.<\/p>\n

\n\u201cTo my knowledge, this is the first study that looked at all of this information and tried to make sense of the best ML approach to use,\u201d says Dr. Sideris. \u201cWhat is exciting to us is that there was one feature \u2013 TARC \u2013 that consistently popped up across all four models and it wasn\u2019t on anybody\u2019s radar beforehand\u2026this gives us hope that ML can help us identify with high integrity targets that haven\u2019t been studied before.\u201d<\/p>\n

\nThe researchers note that more research is needed to know if their findings can be used to impact clinical care. \u201cOur goal is to be able to use these tools and data to tailor pain management strategies, prevent long-term complications, and personalize treatment decisions,\u201d says Dr. Kirksey.<\/p>\n

\nHSS AI Analysis Reveals What Patients Are Googling About Local Anesthesia<\/b><\/p>\n

\nA new study by researchers at HSS used AI to systematically evaluate the types of questions that patients are Googling related to regional (local) anesthesia, identify websites that are frequently presented in search results, and assess the quality of the information provided.<\/p>\n

\n\u201cWe knew that patients frequently search for information about anesthesia online, but we wanted to know exactly what they were looking for so that we could proactively address these topics and concerns in our conversations in the clinic and our patient education materials,\u201d says Jashvant Poeran, MD, PhD<\/a>, Director of Research in the Department of Anesthesiology, Critical Care and Pain Management at HSS and lead author of the study.<\/p>\n

\nThe analysis found that most patients\u2019 questions focused on risks, complications, and details surrounding medications, awareness during sedation, nerve block duration, and the recovery process. The study also found that while the overall quality of information accessed during Google searches was accurate, the source of that information was not always clear.<\/p>\n

\nAnesthesiologists see patients in the clinic for a short period to prepare them for surgery and manage expectations.<\/p>\n

\n\u201cThere\u2019s so much information being conveyed during that limited time that sometimes patients forget what to ask, or they don\u2019t even know what they should be asking during the visit,\u201d explains Dr. Poeran. \u201cOur study results will help us anticipate some of their questions and give us a starting point when we sit down with them so it\u2019s not so much of a guessing game.\u201d<\/p>\n

\nThe researchers entered seven search terms into Google Web Search: \u201cregional nerve block,\u201d \u201cregional anesthesia,\u201d \u201cperipheral nerve block,\u201d \u201cpain block,\u201d \u201cneuraxial anesthesia,\u201d \u201cepidural anesthesia,\u201d and \u201cspinal anesthesia.\u201d The top 200 questions in the \u201cPeople Also Ask\u201d section and its associated websites were collected, totaling 1,400 question and website combinations.<\/p>\n

\nThe authors then used AI to categorize themes and assess website quality. They found that most questions pertained to facts around risks and complications, comparisons between different techniques and approaches, technical details, and indications.<\/p>\n

\n\u201cWe were expecting questions around risks and complications, but it was surprising that so many patients were looking at technical details, specifically around sedation,\u201d notes Dr. Poeran. \u201cThey were not always aware that you can be awake for a peripheral nerve block, for example.\u201d<\/p>\n

\nBecause patients\u2019 questions are linked to specific websites, researchers also wanted to know where patients were being referred to and how reliable the information presented was. The AI analysis found that 55% of websites were academic, 19% were government, and 11% were public\/social media sources. Information on government and academic\/hospital websites scored the highest in terms of accuracy, and medical practice websites scored the lowest.<\/p>\n

\nDr. Poeran cautioned that some patients\u2019 questions are nuanced, and online information can bias a person in the wrong direction.<\/p>\n

\n\u201cFor example, if you ask whether regional anesthesia is better than general anesthesia you will be able to get generic information about those approaches online, but you need to talk to your doctor to get a more personalized recommendation based on your specific circumstances,\u201d says Dr. Poeran.<\/p>\n

\nWhile the study revealed important questions, it\u2019s not all-encompassing. \u201cThere will be questions that aren\u2019t captured by this study, but anesthesiologists can use this data to guide their consultations with patients scheduled for surgery, provide more-informative patient education materials, and refer them to the most reliable websites for more information,\u201d notes Dr. Poeran.<\/p>\n

\nHe plans to continue leveraging AI in his future research endeavors.<\/p>\n

\n\u201cBased on the information we gathered, we will update our educational materials with the questions we now know patients ask most often and may even provide it in different languages and reading levels,\u201d says Dr. Poeran. \u201cWe can then use AI to see how that information is perceived and understood by patients, study differences in search terms entered in different languages, etc.\u201d<\/p>\n

\nAbout HSS<\/b><\/p>\n

\nHSS is the world\u2019s leading academic medical center focused on musculoskeletal health. At its core is Hospital for Special Surgery, nationally ranked No. 1 in orthopedics (for the 16th consecutive year), No. 3 in rheumatology by U.S. News & World Report (2024-2025), and the best pediatric orthopedic hospital in NY, NJ and CT by U.S. News & World Report \u201cBest Children\u2019s Hospitals\u201d list (2025-2026). Founded in 1863, the Hospital has the lowest readmission rates in the nation for orthopedics, and among the lowest infection and complication rates. HSS was the first in New York State to receive Magnet Recognition for Excellence in Nursing Service from the American Nurses Credentialing Center five consecutive times. An affiliate of Weill Cornell Medical College, HSS has a main campus in New York City and facilities in New Jersey, Connecticut and in the Long Island and Westchester County regions of New York State, as well as in Florida. In addition to patient care, HSS leads the field in research, innovation and education. The HSS Research Institute comprises 20 laboratories and 300 staff members focused on leading the advancement of musculoskeletal health through prevention of degeneration, tissue repair and tissue regeneration. In addition, more than 200 HSS clinical investigators are working to improve patient outcomes through better ways to prevent, diagnose, and treat orthopedic, rheumatic and musculoskeletal diseases. The HSS Innovation Institute works to realize the potential of new drugs, therapeutics and devices. The HSS Education Institute is a trusted leader in advancing musculoskeletal knowledge and research for physicians, nurses, allied health professionals, academic trainees, and consumers in more than 165 countries. The institution is collaborating with medical centers and other organizations to advance the quality and value of musculoskeletal care and to make world-class HSS care more widely accessible nationally and internationally. www.hss.edu<\/a>.<\/p>\n

Contacts<\/b> <\/p>\n

\nTracy Hickenbottom\/Rachael Rennich
\n
212-606-1197
\n
mediarelations@hss.edu<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

PHOENIX–(BUSINESS WIRE)–At this year\u2019s American Society of Regional Anesthesia and Acute Pain Medicine (ASRA) annual meeting, investigators at Hospital for Special Surgery (HSS) presented significant studies leveraging artificial intelligence (AI) to provide insights into long-term pain risk after surgery and what patients want to know about anesthesia. 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