Evidence map›Paper›PMID 38469459›Full record

ArticleFrontiers in medicine2023

An early warning model for predicting major adverse kidney events within 30 days in sepsis patients.

Xiaoyuan Yu, Qi Xin, Yun Hao, Jin Zhang, Tiantian Ma

Abstract read
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Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiaoyuan YuDepartment of Hematology, The Affiliated Hospital of Northwest University, Xi'an No. 3 Hospital, Shaanxi, Xi'an, China.
Qi XinDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Yun HaoDepartment of Nephrology, Yuequn Yuan District, The First Hospital of Jilin University, Changchun, Jilin, China.
Jin ZhangDepartment of Hematology, The Affiliated Hospital of Northwest University, Xi'an No. 3 Hospital, Shaanxi, Xi'an, China.
Tiantian MaDepartment of Hematology, The Affiliated Hospital of Northwest University, Xi'an No. 3 Hospital, Shaanxi, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In sepsis patients, kidney damage is among the most dangerous complications, with a high mortality rate. In addition, major adverse kidney events within 30 days (MAKE30) served as a comprehensive and unbiased clinical outcome measure for sepsis patients due to the recent shift toward targeting patient-centered renal outcomes in clinical research. However, the underlying predictive model for the prediction of MAKE30 in sepsis patients has not been reported in any study. Methods: A cohort of 2,849 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database was selected and subsequently allocated into a training set ( Results: The construction of the nomogram was based on several independent predictors (AUC above 0.6), including age, respiratory rate (RR), PaO2, lactate, and blood urea nitrogen (BUN). The predictive model demonstrated satisfactory discrimination for MAKE30, with an AUC of 0.740, 0.753, and 0.821 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the simple prediction model exhibited superior predictive value compared to the SOFA model in both the training (AUC = 0.710) and validation (AUC = 0.692) cohorts. The nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Additionally, the predictive model exhibited excellent accuracy in forecasting 30-day mortality (AUC = 0.737), PRD (AUC = 0.639), and new RRT (AUC = 0.846) within the training dataset. Additionally, the model displayed predictive power for 30-day mortality (AUC = 0.765), PRD (AUC = 0.667), and new RRT (AUC = 0.783) in the validation set. Conclusion: The proposed nomogram holds the potential to estimate the risk of MAKE30 promptly and efficiently in sepsis patients within the initial 24 h of admission, thereby equipping healthcare professionals with valuable insights to facilitate personalized interventions.

Indexed as

early warningmajor adverse kidney events within 30 daysMIMIC IVnomogram modelsepsis

Identifiers

PMID38469459
PMCPMC10925638

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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 OpenQuestion graph.