ArticleScientific reports2026
Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP score.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.