Artificial intelligence tools for phenotyping patients with anaphylaxis

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Abstract

BACKGROUND: Anaphylaxis is the most severe manifestation of immediate systemic hypersensitivity reactions. In recent years, there has been an increase in the number of reported cases of anaphylaxis. Given the variety of clinical manifestations behind the diagnosis of “anaphylaxis”, one of the most urgent issues at present is the task of phenotyping and endotyping of patients with this life-threatening pathology as part of implementation of the individual approach, both during treatment in the acute period and diagnosis in the period of recovery.

AIM: To find the main clinical phenotypes of patients with immediate hypersensitivity reactions, which will further allow stratifying patients by risk groups bringing therapeutic and diagnostic algorithms to the modern level of personalized medicine.

MATERIALS AND METHODS: The study was conducted based on retrospective stepwise analysis of patient medical record data from 2019 to 2022. To identify relatively homogeneous groups of patients based on clinical, agglomerative clustering was performed on 56 variables followed by 2 principal component extraction using the dimensionality reduction method with t-distributed stochastic neighbor embedding. Agglomerative clustering divided patients into 4 major clinical phenotypes, and each patient was assigned a corresponding phenotype.

RESULTS: Based on this analysis, 4 phenotypes of patients with severe immediate-type hypersensitivity reactions were identified.

CONCLUSION: A new classification based on the use of phenotypes, endotypes and biomarkers is currently being developed to broaden our understanding of anaphylactic reactions. Given the limitations of the study (patients were not subjected to additional examinations in the current study), it is not possible to reliably identify endophenotypic differences in different clusters. Additional studies are needed to identify the correlation between anaphylactic reaction phenotypes and its pathophysiological mechanisms of development.

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About the authors

Ekaterina A. Nikitina

Moscow City Hospital 52; The First Sechenov Moscow State Medical University (Sechenov University)

Author for correspondence.
Email: katrin88866@gmail.com
ORCID iD: 0000-0002-0865-8355
SPIN-code: 3507-9106
Россия, Moscow; Moscow

Alexander D. Dushkin

Moscow City Hospital 52; National Medical Research Center for High Medical Technologies — Central Military Clinical Hospital named after A.A. Vishnevsky

Email: alex@drdushkin.ru
ORCID iD: 0000-0002-8013-5276
SPIN-code: 3857-0010
Россия, Moscow; Moscow

Marina S. Lebedkina

Moscow City Hospital 52

Email: marina.ivanova0808@yandex.ru
ORCID iD: 0000-0002-9545-4720
SPIN-code: 1857-8154
Россия, Moscow

Olga A. Mukhina

Moscow City Hospital 52

Email: mukhina.o.a@gmail.com
ORCID iD: 0000-0002-3794-4991
SPIN-code: 7721-1941
Россия, Moscow

Tatyana S. Kruglova

Moscow City Hospital 52

Email: surckova.t@yandex.ru
ORCID iD: 0000-0002-4949-9178
SPIN-code: 2884-5000
Россия, Moscow

Sergey S. Andreev

Moscow City Hospital 52

Email: nerowolf@mail.ru
ORCID iD: 0000-0002-9147-4636
SPIN-code: 4372-7358
Россия, Moscow

Asel Yu. Nurtazina

The First Sechenov Moscow State Medical University (Sechenov University)

Email: asel26nurtazina@mail.ru
ORCID iD: 0000-0002-2337-3307
SPIN-code: 5028-4695

MD, Cand. Sci. (Medicine), Assistant Professor

Россия, Moscow

Ekaterina S. Leonova

The First Sechenov Moscow State Medical University (Sechenov University)

Email: Beesoohappy@yacat.ru
ORCID iD: 0009-0009-1547-2426
Россия, Moscow

Polina A. Volkova

The First Sechenov Moscow State Medical University (Sechenov University)

Email: polynech1902@gmail.com
ORCID iD: 0009-0003-4990-8861
Россия, Moscow

Alexander N. Pampura

Morozov Children’s City Clinical Hospital; N.I. Pirogov Russian National Research Medical University

Email: apampura1@mail.ru
ORCID iD: 0000-0001-5039-8473
SPIN-code: 9722-7961

MD, Dr. Sci. (Medicine), Professor

Россия, Moscow; Moscow

Alexander V. Karaulov

The First Sechenov Moscow State Medical University (Sechenov University); LIFT Center LLC

Email: drkaraulov@mail.ru
ORCID iD: 0000-0002-1930-5424
SPIN-code: 4122-5565

MD, Dr. Sci. (Medicine), Professor, Academician of the Russian Academy of Sciences

Россия, Moscow; Moscow

Mariana A. Lysenko

N.I. Pirogov Russian National Research Medical University

Email: lysenkiMA@zdrav.mos.ru
ORCID iD: 0000-0001-6010-7975
SPIN-code: 3887-6250

MD, Dr. Sci. (Medicine), Professor

Россия, Moscow

Darya S. Fomina

Moscow City Hospital 52; The First Sechenov Moscow State Medical University (Sechenov University); Astana Medical University

Email: daria_fomina@mail.ru
ORCID iD: 0000-0002-5083-6637
SPIN-code: 3023-4538

MD, Cand. Sci. (Medicine), Assistant Professor

Россия, Moscow; Moscow; Astana, Republic of Kazakhstan

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2. Fig. 1. Agglomerative clustering by 2 components. Visualization of clusters obtained using the t-distribution method, which converts a high-dimensional data space into a two-dimensional one for visualization. In the image, each point corresponds to a different sample, and the color coding indicates belonging to a specific cluster.

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