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Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP score
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  • Published: 26 March 2026

Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP score

  • Younghee Kim1 na1,
  • Se-Jin Ahn2 na1,
  • Nam-Jun Cho1,
  • Inyong Jeong2,
  • Bomi Choi1,
  • Dong-Jin Lee1,
  • Samuel Park1,
  • Eun Young Lee1,
  • Hwamin Lee2 &
  • …
  • Hyo-Wook Gil1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Kidney diseases
  • Mathematics and computing
  • Toxicology

Abstract

Acute pesticide poisoning frequently leads to acute kidney injury (AKI), which is strongly associated with increased mortality. However, predictive research in this area remains limited, and criteria for AKI detection in patients with pesticide poisoning are not well-defined. This study aimed to evaluate the Kidney Disease: Improving Global Outcomes (KDIGO) criteria and develop a model for early AKI prediction in patients with pesticide poisoning. This retrospective study analyzed 877 patients presenting with acute pesticide poisoning between 2015 and 2020. AKI was defined using KDIGO criteria, considering serum creatinine, urine output, and renal replacement therapy initiation. Six machine learning models with four feature selection methods were compared using fivefold cross-validation, stratified by pesticide category. The final model, Prediction of acute Kidney Injury in Pesticide intoxication (PKIP), was established. KDIGO-defined AKI was significantly associated with mortality, with AKI patients showing a 16.6% mortality compared to 4.7% in non-AKI patients. The PKIP model, incorporating 14 features selected via the Least Absolute Shrinkage and Selection Operator, demonstrated fair discrimination [AUROC 0.720 (95% CI: 0.692–0.747), AUPRC 0.513 (95% CI: 0.464–0.563)]. Furthermore, the model showed prognostic utility for mortality prediction [AUROC 0.839 (95% CI: 0.767–0.910), AUPRC 0.421 (95% CI: 0.246–0.595)]. At the predefined cutoff value of 0.420, the model achieved a sensitivity of 39.0% and a specificity of 89.7%. Risk stratification based on PKIP probabilities showed significant differences in outcomes between groups. The high-risk group demonstrated significantly higher risks of AKI occurrence, progression to higher AKI stages, and mortality compared to the low-risk group. PKIP exhibited superior risk stratification for both AKI and mortality prediction compared to the APACHE II score. This study validates the use of KDIGO criteria for AKI detection in pesticide poisoning and introduces the PKIP model as a tool demonstrating moderate discrimination for early AKI prediction and risk stratification. The web-based PKIP tool can serve as a practical instrument for clinical decision-making for patients with pesticide poisoning. Future research should focus on external validation of the PKIP model and assessment of its impact on patient outcomes in diverse clinical settings.

Trial registration: Retrospectively registered.

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Data availability

The data that support the findings of this study contain potentially identifiable patient information and are not publicly available due to institutional and ethical restrictions, which prohibit public sharing of individual-level data even in de-identified form. Nevertheless, the data from this study can be acquired upon reasonable request from the corresponding author and subject to approval by the institutional review board and the participating institution.

Abbreviations

ABG:

Arterial blood gas

AG:

Anion gap

AKI:

Acute kidney injury

ALP:

Alkaline phosphatase

AUROC:

Area under the precision-recall curve

AUROC:

Area under the receiver operating characteristic curve

BMI:

Body mass index

CAT:

CatBoost

CI:

Confidence interval

CKD:

Chronic kidney disease

EHR:

Electronic health records

GCS:

Glasgow Coma Scale

Hb:

Hemoglobin

HR:

Hazard ratio

ICU:

Intensive care unit

KDIGO:

Kidney Disease: Improving Global Outcomes

LASSO:

Least absolute shrinkage and selection operator

PKIP:

Prediction of acute Kidney Injury in Pesticide intoxication

RBC:

Red blood cell

sCr:

Serum creatinine

UO:

Urine output

WBC:

White blood cell

PKIP:

Prediction of acute Kidney Injury in Pesticide intoxication

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Funding

This study is supported by Soonchunhyang University Research Fund and the Korea Institute for Advancement of Technology (KIAT) grant, funded by the Korean Government (MOTIE) (P0023675, HRD Program for Industrial Innovation).

Author information

Author notes

  1. Younghee Kim and Se-Jin Ahn contributed equally to this study as co-first authors.

Authors and Affiliations

  1. Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-Gil, Dongnam-Gu, Cheonan, 31151, Republic of Korea

    Younghee Kim, Nam-Jun Cho, Bomi Choi, Dong-Jin Lee, Samuel Park, Eun Young Lee & Hyo-Wook Gil

  2. Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea

    Se-Jin Ahn, Inyong Jeong & Hwamin Lee

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Contributions

Younghee Kim: Conceptualization, design, investigation, writing—original draft. Se-Jin Ahn: Investigation, statistical analysis, writing—original draft. Nam-Jun Cho: Administrative, technical, or material support. Inyong Jeong: Investigation, statistical analysis, administrative, technical, or material support, writing—original draft. Bomi Choi: Administrative, technical, or material support. Dong-Jin Lee: Administrative, technical, or material support. Samel Park: Administrative, technical, or material support. Eun Young Lee: Administrative, technical, or material support. Hwamin Lee: Funding acquisition, administrative, technical, or material support, supervision. Hyo-Wook Gil: Conceptualization, design, supervision, funding acquisition, writing—original draft, critical review. All authors: Data acquisition, analysis, interpretation, critical review of the mnuscript for important intellectual content.

Corresponding author

Correspondence to Hyo-Wook Gil.

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The authors declare that they have no conflicts of interest.

Ethical approval

The present study was reviewed and approved by the Soonchunhyang University Cheonan Hospital’s Investigational Review Board (IRB number: 2020–02-016). The requirement for informed consent was waived because of the retrospective design of the study. This study was conducted in accordance with the Declaration of Helsinki.

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Kim, Y., Ahn, SJ., Cho, NJ. et al. Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP score. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41334-4

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  • Received: 06 March 2025

  • Accepted: 19 February 2026

  • Published: 26 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-41334-4

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Keywords

  • Pesticides
  • Poisoning
  • Acute kidney injury
  • Mortality
  • Decision support techniques
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Acute kidney injury prevention and management

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