Predictors of fatal outcome in patients hospitalized for graft pyelonephritis

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Objective. Analysis of clinical and laboratory predictors of mortality in patients hospitalized for graft pyelonephritis (GP) to improve risk stratification and optimize treatment tactics.

Material and methods. A single-center retrospective study was conducted, which included 501 patients with GP. Demographic, clinical, laboratory and instrumental data, as well as outcomes, were assessed. Logistic regression was used to identify predictors of mortality.

Results. Overall mortality in the study cohort was 5.19% (26 cases). Statistically significant predictors of death were urosepsis (OR=79.97), non-functioning transplant (OR=5.92), presence of lower respiratory tract infection (OR=7.48), blood system pathology (OR=10.27) and cardiovascular diseases (OR=2.52). Based on the risk factors, a prognostic model of the death risk was constructed. The developed model demonstrated high accuracy (AUC=0.96, 95% CI 0.93–0.99), with a sensitivity of 95 ,7% and specificity of 91.2%.

Conclusion. The identified predictors highlight the need for careful monitoring and early intervention in patients hospitalized for graft pyelonephritis, especially in the presence of urosepsis, non-functioning graft and concomitant infectious complications. The prognostic model provides risk stratification for patients, which may improve clinical outcomes.

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作者简介

Sergey Andreev

City Clinical Hospital No. 52 of Moscow Healthcare Department

编辑信件的主要联系方式.
Email: nerowolf@mail.ru
ORCID iD: 0000-0002-9147-4636

Head of the Department of Clinical Pharmacology

俄罗斯联邦, Moscow

Ruslan Trushkin

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: uro52@mail.ru
ORCID iD: 0000-0002-3108-0539

Dr.Sci. (Med.), Head of the Urology Department

俄罗斯联邦, Moscow

Teymur Isaev

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: dr.isaev@mail.ru
ORCID iD: 0000-0003-3462-8616

Dr.Sci. (Med.), Urologist, Urology Department

俄罗斯联邦, Moscow

Lyudmila Artyukhina

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: arlyu-1404@yandex.ru
ORCID iD: 0000-0003-3353-1636

Cand.Sci.(Med.), Head of the 1st Nephrology Department (Transplanted Kidney Pathology Department)

俄罗斯联邦, Moscow

Nadiya Frolova

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: nadiya.frolova@yandex.ru
ORCID iD: 0000-0002-6086-5220

Cand.Sci.(Med.), Deputy Chief Physician for Nephrological Care

俄罗斯联邦, Moscow

Nadezhda Ilyukhina

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: gkb52@zdrav.mos.ru

Clinical Pharmacologist at the Department of Clinical Pharmacology

俄罗斯联邦, Moscow

Polina Narusova

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: gkb52@zdrav.mos.ru
ORCID iD: 0000-0002-2960-5236

Clinical Pharmacologist at the Department of Clinical Pharmacology

俄罗斯联邦, Moscow

Sherzod Abdullaev

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: abdullaevsp@gmail.com
ORCID iD: 0000-0001-9001-1499

Cand.Svi.(Biol.), Project Analyst

俄罗斯联邦, Moscow

Aleksandr Dushkin

City Clinical Hospital No. 52 of Moscow Healthcare Department

Email: alex@drdushkin.ru
ORCID iD: 0000-0002-8013-5276

Cand.Sci.(Med.), Project Analyst

俄罗斯联邦, Moscow

Maryana Lysenko

City Clinical Hospital No. 52 of Moscow Healthcare Department; Pirogov RNRMU (Pirogov University)

Email: gkb52@zdrav.mos.ru
ORCID iD: 0000-0001-6010-7975

Dr.Sci. (Med.), Professor, Chief Physician, Professor at the Department of General Therapy, Faculty of Continuous Professional Education

俄罗斯联邦, Moscow; Moscow

Marina Zhuravleva

Scientific Center for Expertise of Medical Products; Sechenov First Moscow State Medical University (Sechenov University)

Email: clinpharm23@mail.ru
ORCID iD: 0000-0002-9198-8661

Dr.Sci. (Med.), Professor, Chief External Expert in Clinical Pharmacology, Deputy Director of the Center for Clinical Pharmacology, Professor at the Department of Clinical Pharmacology and Propaedeutics of Internal Diseases

俄罗斯联邦, Moscow; Moscow

参考

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2. Fig. 1. ROC-AUC graph of the logistic predictive model of fatal outcome

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3. Fig. 2. OR values ​​of identified predictors of fatal outcomes

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