Evidence map›Paper›PMID 34508179›Full record

ArticleNPJ precision oncology2021

Real-world data analyses unveiled the immune-related adverse effects of immune checkpoint inhibitors across cancer types.

Feicheng Wang, Shihao Yang, Nathan Palmer, Kathe Fox, Isaac S Kohane, Katherine P Liao, Kun-Hsing Yu, S C Kou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
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  7. The role of tumor types in immune-related adverse events.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Feicheng WangDepartment of Statistics, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-9268-554X
Shihao YangDepartment of Statistics, Harvard University, Cambridge, MA, USA.
Nathan PalmerDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Kathe FoxDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Isaac S KohaneDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Katherine P LiaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Kun-Hsing Yu *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Kun-Hsing_Yu@hms.harvard.edu.ORCID http://orcid.org/0000-0001-9892-8218
S C Kou *Department of Statistics, Harvard University, Cambridge, MA, USA. kou@stat.harvard.edu.ORCID http://orcid.org/0000-0002-1774-3316
Harvard University · USBrigham and Women's Hospital · USGeorgia Institute of Technology · US

Funding

Robust, Generalizable, and Fair Machine Learning Models for BiomedicineR35GM142879 · NIGMS · HARVARD MEDICAL SCHOOL · PI YU, KUN-HSING · 2021 to 2025
$2.4M
Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence ApproachR01HL174679 · NHLBI · HARVARD MEDICAL SCHOOL · PI Kun-Hsing Yu · 2024 to 2026
$2.3M
Harvard Medical School Blavatnik Center for Computational Biomedicine AwardHarvard University Harvard Data Science FellowshipNHLBI NIH HHS R01 HL174679NIGMS NIH HHS R35 GM142879U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM142879
6 · The paper itself

Abstract

Immune checkpoint inhibitors have demonstrated significant survival benefits in treating many types of cancers. However, their immune-related adverse events (irAEs) have not been systematically evaluated across cancer types in large-scale real-world populations. To address this gap, we conducted real-world data analyses using nationwide insurance claims data with 85.97 million enrollees across 8 years. We identified a significantly increased risk of developing irAEs among patients receiving immunotherapy agents in all seven cancer types commonly treated with immune checkpoint inhibitors. By six months after treatment initialization, those receiving immunotherapy were 1.50-4.00 times (95% CI, lower bound from 1.15 to 2.16, upper bound from 1.69 to 20.36) more likely to develop irAEs in the first 6 months of treatment, compared to matched chemotherapy or targeted therapy groups, with a total of 92,858 patients. The risk of developing irAEs among patients using nivolumab is higher compared to those using pembrolizumab. These results confirmed the need for clinicians to assess irAEs among cancer patients undergoing immunotherapy as part of management. Our methods are extensible to characterizing the effectiveness and adverse effects of novel treatments in large populations in an efficient and economical fashion.

Identifiers

PMID34508179
PMCPMC8433190
OpenAlexW3198241928

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.