Abstract 5212<\/a>). The researchers suggested that the findings show that there is a need for greater monitoring within this time window to prevent fatalities.\u00a0<\/p>\n\u201cOur analysis indicates that the first month of a patient receiving ICI therapy is the crucial period for\u00a0determining patients\u2019 risk of myocarditis fatality. If a patent on ICIs develops myocarditis in those first 30\u00a0days, that\u2019s a flashing warning light,\u201d said presenting author\u00a0Hassan Mohammed\u00a0Abushukair, MD, Postdoctoral Researcher\u00a0at the Oklahoma University Stephenson Cancer Center.\u00a0\u201cThis gives clinicians an actionable timeframe for\u00a0determining whom ICI therapy may be dangerous for.\u201d<\/p>\n
The study also characterized incidences and risk factors for triple-M overlap syndrome (TMOS), a dangerous combination of myocarditis, myositis, and myasthenia gravis.\u00a0<\/p>\n
Study Methods\u00a0<\/p>\n
Researchers gathered cases of ICI-related myocarditis, myositis, and myasthenia gravis\u00a0(n = 4,950) in patients with cancer from the World Health Organization (WHO) Vigibase pharmacovigilance database.\u00a0<\/p>\n
The cases were separated into seven group: myocarditis alone, myositis alone, myasthenia gravis alone, myocarditis plus myositis, myocarditis plus myasthenia gravis, myositis plus myasthenia gravis, and\u00a0TMOS.\u00a0\u00a0<\/p>\n
\u201cIn this study, we report the largest clinical series of this rare fatal syndrome, aimed at delineating clinical features, fatality predictors, and temporal trends,\u201d the study authors wrote in their abstract.\u00a0<\/p>\n
The researchers developed a machine-learning model using the XGBoost algorithm to build a subset of patients with ICI-related myocarditis (n = 858) for prediction of myocarditis fatality. The cohort was divided into an 80:20 data split for training and testing the model. They also used an external real-world data set of patients with ICI-related myocarditis (n = 28) for independent validation of the model.\u00a0<\/p>\n
\u201cTMOS and the conditions that compose it can easily cause fatalities for the subset of ICI-treated patients\u00a0who develop these side effects. But clinicians need to know who may be at the greatest risk for fatal\u00a0outcomes, and we do not yet have that level of understanding,\u201d said Dr.\u00a0Abushukair. \u201cOur analysis aimed to\u00a0identify how we can more systematically approach risk stratification for patients who may develop fatal\u00a0cardiac and autoimmune side effects from ICI treatment.\u201d<\/p>\n
Key Findings\u00a0<\/p>\n
Of the overall cases gathered from the WHO Vigibase,\u00a0there were 2,641 cases of ICI-related myocarditis, including 1,911 (72.4%) that represented myocarditis alone and 730 cases (27.6%) that included myocarditis with\u00a0myositis and\/or myasthenia gravis; the cohort included 207 patients (7.8%) with TMOS.\u00a0<\/p>\n
TMOS occurred most often in patients with melanoma (35.8%) and was more likely to occur in men (64.7% vs 52.9%; P\u00a0= .0049) who were\u00a0treated with ICI doublets (25.1% vs 20.4%;\u00a0P\u00a0= .0030), as compared with myocarditis alone.\u00a0Hepatitis occurred most often with TMOS (14.5%).\u00a0<\/p>\n
Myocarditis fatalities were more common in patients who developed TMOS (38%) vs myocarditis alone (21.2%), myocarditis and myositis (22.5%), or myocarditis and myasthenia gravis (25.7%).\u00a0<\/p>\n
Myocarditis alone started later after beginning ICI (median, 60.8 days) than myocarditis plus myositis (27 days), myocarditis and myasthenia gravis (27 days) and TMOS (26 days;\u00a0P\u00a0< .05).\u00a0<\/p>\n
When myocarditis occurred within the first month of ICI initiation, it was independently associated with a greater risk for myocarditis fatality, with adjustments made for age, treatment regimen, cancer type, and co-reactions (odds ratio [OR] for \u2264 1 vs 1\u20133 months = 0.41; 95% confidence interval [CI] = 0.22\u20130.73; P\u00a0= .0036; OR for \u2264 1 vs 3\u201312 months = 0.44; 95% CI = 0.21\u20130.86; P\u00a0= .0212).\u00a0<\/p>\n
The myocarditis fatality machine-learning model demonstrated an area under the curve of 0.79 on the training data set, 0.75 on the internal testing set, and 0.85 on the external validation set. The top features for predicting myocarditis fatalities were early-onset myocarditis and cardiorespiratory co-reactions.<\/p>\n
\u201cI believe the model we\u2019re developing is a great illustration of how even simple clinical data analysis can\u00a0be used to address fatality in cancer treatment. Ultimately, we envision a helpful bedside tool to rule out\u00a0high-risk fatality from TMOS and its constituent conditions,\u201d\u00a0said Dr. Abushukair. \u201cWith a greater understanding of the\u00a0risks that these ICI side effects pose, clinicians and patients alike can be more attuned to which\u00a0symptoms to be on the lookout for. Our hope is that this will create a safer paradigm for ICI treatment.\u201d\u00a0<\/p>\n
DISCLOSURES: Dr.\u00a0Abushukair reported no potential conflicts of interest. For full disclosures of the other study authors, visit abstractsonline.com<\/a>.\u00a0<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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