Evidence map›Paper›PMID 42343250›Full record

ArticleBMC geriatrics2026

An interpretable prediction model for severe sepsis-associated acute kidney injury in older ICU patients with sepsis: a prospective multicenter cohort study.

Wenliang Ma, Jingyi Wang, Jin Zhang, Xiaoxia Guo, Wenxiong Li, Hao Wang, Zhigang Chang, Yuefu Wang, Na Cui

Abstract readMulticenter Study
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wenliang MaDepartment of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.
Jingyi WangDepartment of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.
Jin ZhangDepartment of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.
Xiaoxia GuoDepartment of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.
Wenxiong LiDepartment of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.
Hao WangDepartment of Critical Care Medicine, Beijing Jishitan Hospital, Beijing, China.
Zhigang ChangDepartment of Critical Care Medicine, Beijing Hospital, Beijing, China.
Yuefu WangSurgical Intensive Care Unit, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Na CuiDepartment of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China. cyyy_cn@163.com.

Funding

Beijing Shijingshan District Key Specialties Development and Health Promotion Project No. JSWP2026-07Clinical Research Enhancement Project of Beijing Chaoyang Hospital No. CYTS2025A03Wu Jieping Medical Foundation No. 320.6750.2024-22-6
6 · The paper itself

Abstract

backgroundSevere sepsis-associated acute kidney injury (severe SA-AKI) is clinically important in older adults with sepsis, but early identification remains challenging. This study aimed to characterize clinical and immuno-inflammatory features of severe SA-AKI and develop an interpretable prediction model for older ICU patients with sepsis.

methodsThis prospective multicenter cohort study enrolled older adults with sepsis admitted to five tertiary ICUs between June 2023 and December 2025. Patients were temporally assigned to a training cohort and an internal temporal validation cohort. Severe SA-AKI was defined as KDIGO acute kidney injury stage 2 or 3 within 7 days after sepsis time zero or during ICU stay. Candidate predictors collected within 24 h after time zero were evaluated in the training cohort, and a logistic regression nomogram was developed and validated.

resultsAmong 1,236 included patients, the minimum age was 65 years, and the mean age was 74.6 ± 7.5 years; 258 patients developed severe SA-AKI. The final 7-variable model included non-renal SOFA score, chronic kidney disease, log(Lactate + 1), log(IL-6 + 1), NK-cell percentage, CD4 + T-cell percentage, and C3. The model achieved AUCs of 0.917 and 0.904 in the training and validation cohorts, respectively. Brier scores were 0.086 and 0.096. Severe SA-AKI incidence increased across low-, intermediate-, and high-risk groups: 0.5%, 13.0%, and 77.5%.

conclusionsA parsimonious model integrating clinical and immuno-inflammatory variables may support early severe SA-AKI risk stratification in older ICU patients with sepsis. External validation is warranted before broader clinical implementation.

Indexed as

Acute Kidney InjuryIntensive Care UnitsSepsisAgedAged, 80 and overCohort StudiesFemaleHumansMaleNomogramsProspective StudiesAcute kidney injuryCritical careImmuno-inflammatory biomarkersNomogramOlder adultsRisk predictionSepsisSepsis-associated acute kidney injury

Identifiers

PMID42343250
PMCPMC13397684

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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 Socratic graph.