MétaCan
Menu
← Back to cohort
Record W3216903068 · doi:10.26502/jsr.10020186

Analysis of Corticosteroids in Immune Checkpoint Inhibitors (ICI) Induced Myocarditis- A Systematic Review of 352 Screened Articles

2021· review· en· W3216903068 on OpenAlexaffabout
Mona Sheikh, Saumil Patel, Shavy Nagpal, Zeynep Yukselen, Samina Zahid, Vivek Jha, Diana Sánchez, Sima Marzban, Odalys Frontela

Bibliographic record

VenueJournal of Surgery and Research · 2021
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineMyocarditisAdverse effectLymphomaGuidelineNivolumabMelanomaOncologyInternal medicineRenal cell carcinomaIpilimumabLung cancerCancerImmunologyImmunotherapyCancer researchPathology

Abstract

fetched live from OpenAlex

Introduction Immune Checkpoint Inhibitors (ICI) are used as a single agent or as a combination therapies for early or late-stage malignancies. The common malignancies that ICI targets include the following: melanoma, lung cancer, renal cell carcinoma, and hematological malignancies such as Hodgkin’s lymphoma. ICI use is associated with many immune-related adverse events, and ICI-induced myocarditis is one of the rare and most severe AE with a high mortality rate. There are no consensus evidence-based treatment guideline; the expert recommendation is to use high-dose steroids. We aim in this review to assess the effectiveness of steroids in treating ICI-induced myocarditis. Methods We searched the following database Pubmed, Scopus, Cinahl, and Google Scholar, using the following keywords: ICI-induced myocarditis, treatment, steroid. We included articles in the English language, case reports, case series, and published in the last five years. Results 352 articles were screened using PRISMA guidelines. After excluding the articles that were duplicate, irrelevant, and did not meet inclusion criteria, 35 articles with a total number of 50 patients were included. All patients treated with ICI either as a single or combination regimen. The onset of symptoms post initiation varied from one day to a year. 46 out of the 50 cases received high doses of Intravenous steroids as a loading dose followed by an oral or intravenous maintenance dose. Out of 50 patients 14 patients (28%) died but 34 (68%) patients survived, and 2 (4%) patients data were not available. The mean age of the patients was 66.31 ± 14.071 (range 23-88 years), 29 were male (58%), 21 were female (42%). Most of the cases were from the USA (42%), followed by Australia (20%), Japan (14%), Germany, France, and China (4%), Switzerland, Canada, and Spain (2%), and for (6%) cases. A total of 23 patients had cardiovascular comorbidities (46%), which were HTN (14 patients, 60.87%), hyperlipidemia (5 patients, 21.73%), and less than 1% of patients had myocardial ischemia, congestive heart failure, atrial fibrillation, and peripheral vascular disease. While 26 patients (52%) had normal basal cardiac status. Conclusion Our results showed that high doses of steroids were effective in controlling cardiac myocyte inflammation and mortality by 28%. Race was not included in the analysis as it was not reported. More in depth studies are needed to provide a broader representation of steroids in myocarditis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.008
Bibliometrics0.0190.021
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.191
GPT teacher head0.427
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Surgery and Research→Same topicCancer Immunotherapy and Biomarkers→French-language works237,207→