{"id":401,"date":"2022-09-20T19:58:50","date_gmt":"2022-09-20T19:58:50","guid":{"rendered":"https:\/\/tickle.utk.edu\/ise\/?p=401"},"modified":"2024-12-10T17:08:06","modified_gmt":"2024-12-10T17:08:06","slug":"sepsis-detection-algorithm-wins-first-place","status":"publish","type":"post","link":"https:\/\/tickle.utk.edu\/ise\/sepsis-detection-algorithm-wins-first-place\/","title":{"rendered":"Sepsis Detection Algorithm Wins First Place"},"content":{"rendered":"<p>An alarm at a patient\u2019s bedside is supposed to spur immediate, life-saving action. But when pre-emptive alarms are constantly sounding off, they blend into the background.<\/p>\n<p>\u201cSometimes as you walk down the hall of an intensive care unit (ICU), there are dozens of alarms coming at you,\u201d said ISE Associate Professor <a href=\"https:\/\/tickle.utk.edu\/ise\/faculty\/anahita-khojandi\/\">Anahita Khojandi<\/a>. \u201cThey may be so prevalent that your brain starts to tune them out.\u201d<\/p>\n<p>Medical workers experiencing this \u201calarm fatigue\u201d may not recognize when a patient is truly in need of attention until it\u2019s too late. For sepsis, when the body overreacts to an infection and starts destroying its own tissues, the mortality rate increases by 8% for every hour that treatment is delayed.<\/p>\n<p>\u201cIt\u2019s all about detecting it early, early, early,\u201d Khojandi emphasized, \u201cbut you need to balance that with the issue of alarm fatigue.\u201d<\/p>\n<p>Current state-of-the-art, machine learning-based sepsis detection algorithms sound the alarm when a patient\u2019s heart rate, blood pressure, and other readings mimic what has been seen in former patients who developed sepsis. The problem is that many of these readings fluctuate, momentarily reaching the algorithms\u2019 danger threshold even in patients who never develop sepsis.<\/p>\n<p>Now the ISE team, which includes Khojandi, Professor <a href=\"https:\/\/tickle.utk.edu\/ise\/faculty\/xueping-li\/\">Xueping Li<\/a>, and their former student <a href=\"https:\/\/directory.statler.wvu.edu\/faculty-staff-directory\/zeyu-liu\">Zeyu Liu<\/a>, has produced a revolutionary program that monitors the patient\u2019s physiological variables in real time and develops an informed, changeable \u201cbelief\u201d about a patient\u2019s true but \u201chidden\u201d sepsis status.<\/p>\n<p>\u201cOur model begins with the belief that the patient does not have sepsis,\u201d said Khojandi. \u201cAs the bedside monitors gather more data, that belief may change.\u201d<\/p>\n<p>By partnering with clinicians at the <a href=\"https:\/\/uthsc.edu\/\">UT Health Science Center<\/a> and <a href=\"https:\/\/www.med.emory.edu\/\">Emory University<\/a>, the team has been able to train and test the new model using patient bedside data of unparalleled detail and precision.<\/p>\n<p>\u201cOnly a handful of centers in the US collect this kind of data,\u201d Khojandi said. \u201cI cannot emphasize enough the importance of working with clinicians and the importance of the data they have been collecting.\u201d<\/p>\n<p>The developed program reduces false sepsis alarms by up to 28%, decreasing alarm fatigue and restricting the much-needed care to patients who really need it. This remarkable improvement has earned them the First Place <a href=\"https:\/\/connect.informs.org\/computing\/awards\/greenberg-research-award\">Harvey J. Greenberg Research Award<\/a> from The Institute for Operations Research and the Management Sciences (INFORMS).<\/p>\n<p>The award will officially be conferred at the 2022 INFORMS Annual Meeting this October.<\/p>\n<h2 class=\"wp-block-heading h4 orange-mark\"><strong>Contact<\/strong><\/h2>\n<p>Izzie Gall (865-974-7203,\u00a0<a href=\"mailto:egall4@utk.edu\">egall4@utk.edu<\/a>)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Anahita Khojandi\u2019s team has developed an algorithm that leads to improved sepsis detection.<\/p>\n","protected":false},"author":36,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3,4],"tags":[9,161,163,12,162],"class_list":["post-401","post","type-post","status-publish","format-standard","hentry","category-full-time-adjunct-faculty","category-research","tag-anahita-khojandi","tag-sepsis-detection","tag-ut-health-science-center","tag-xueping-li","tag-zeyu-liu"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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