Atezolizumab Induced Myocarditis on a Background of Cardiac Amyloidosis

Gupta, Rahul; Zaid, Syed; Sayed, Amer; Ranchal, Purva; Castillo, Leonardo Z; Patterkine, Rugved; Levine, Avi; Panza, Julio A.

doi:10.1097/mjt.0000000000000968

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Only 0.27%-1.14% of patients receiving ICIs experience this reaction, which is extremely uncommon. In the majority of reported cases, steroid therapy has been used to treat it [14].…”

Section: Atezolizumabmentioning

confidence: 99%

Development and comparison between three type of checkpoint inhibitor drug

Jiang¹

2023

HSET

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In the recent years, many monoclonal antibody drugs related to the immunotherapy is taken by many cancer patients. This passage will give the information about the three common checkpoint inhibitor drugs. Information of each of drug will be divide into three parts including mechanism and structure, efficiency and strength, drawback and side-effect. Then limitation and new improvement of checkpoint inhibitor drugs of all three drugs will be introduced in the last part.

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“…Only 0.27%-1.14% of patients receiving ICIs experience this reaction, which is extremely uncommon. In the majority of reported cases, steroid therapy has been used to treat it [14].…”

Section: Atezolizumabmentioning

confidence: 99%

Development and comparison between three type of checkpoint inhibitor drug

Jiang¹

2023

HSET

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“…Over 150 cases of ICI-related myocarditis have been reported in the post-marking period for the PD-1 inhibitors. There are cases for all of the widely used ICIs, including pembrolizumab [2][3][4][5][6][7][8][9][10][11] nivolumab, [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] the CTLA-4 inhibitor ipilimumab, either alone or in combination with nivolumab, 14,16,18,[31][32][33][34] and the PD-L1 inhibitors atezolizumab 35 and durvalumab. 36,37 A multicenter observational cohort study estimated the prevalence of ICI-related myocarditis to be approximately 1.14%.…”

Section: Introductionmentioning

confidence: 99%

Neural Net Modeling of Checkpoint Inhibitor Related Myocarditis and Steroid Response

Stefanovic¹,

Gomez-Caminero²,

Jacobs³

et al. 2022

CPAA

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Background Serious but rare side effects associated with immunotherapy pose a difficult problem for regulators and practitioners. Immune checkpoint inhibitors (ICIs) have come into widespread use in oncology in recent years and are associated with rare cardiotoxicity, including potentially fatal myocarditis. To date, no comprehensive model of myocarditis progression and outcomes integrating time-series based laboratory and clinical signals has been constructed. In this paper, we describe a time-series neural net (NN) model of ICI-related myocarditis derived using supervised machine learning. Methods We extracted and modeled data from electronic medical records of ICI-treated patients who had an elevation in their troponin. All data collection was performed using an electronic case report form, with approximately 300 variables collected on as many occasions as available, yielding 6000 data elements per patient over their clinical course. Key variables were scored 0–5 and sequential assessments were used to construct the model. The NN model was developed in MatLab and applied to analyze the time course and outcomes of treatments. Results We identified 23 patients who had troponin elevations related to their ICI therapy, 15 of whom had ICI-related myocarditis, while the remaining 8 patients on ICIs had other causes for troponin elevation, such as myocardial infarction. Our model showed that troponin was the most predictive biomarker of myocarditis, in line with prior studies. Our model also identified early and aggressive use of steroid treatment as a major determinant of survival for cases of grade 3 or 4 ICI-related myocarditis. Conclusion Our study shows that a supervised learning NN can be used to model rare events such as ICI-related myocarditis and thus provide clinical insight into drivers of progression and treatment outcomes. These findings direct attention to early detection biomarkers and clinical symptoms as the best means of implementing early and potentially life-saving steroid treatment.

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