Nooreena Yusop, Samsiah Mat, Ruslinda Mustafar, Muhammad Ishamuddin Ismail
{"title":"Fuzzy logic nursing tool for early acute kidney injury detection in surgical patients.","authors":"Nooreena Yusop, Samsiah Mat, Ruslinda Mustafar, Muhammad Ishamuddin Ismail","doi":"10.3389/fneph.2025.1624880","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute Kidney Injury (AKI) is a common yet preventable complication among surgical patients, contributing to increased morbidity, prolonged hospital stays, and higher healthcare costs. Early detection is critical; however, the absence of a standardized nursing-led risk assessment tool for AKI limits proactive intervention in clinical practice.</p><p><strong>Objective: </strong>This study aimed to develop and evaluate the Nursing Risk Assessment for Acute Kidney Injury tool, integrating the Fuzzy Logic Model (FLM) to enhance interpretive accuracy and improve nursing-led AKI risk detection and decision-making.</p><p><strong>Methods: </strong>A Design and Development Research (DDR) framework was employed in three phases. Phase 1 involved a needs analysis using a focus group discussion to explore the necessity of AKI assessment among surgical nurses. Phase 2 focused on tool development through expert consensus (surgeon, nephrologist, nursing academician, and experienced nurse) and evidence synthesis via a systematic literature review. In Phase 3, the Nursing Risk Assessment-AKI tool was evaluated through a quasi-experimental design at Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, involving 75 surgical nurses assessing 200 patients.</p><p><strong>Results: </strong>Post-intervention analysis indicated increased nursing confidence, with 95.7% expressing positive perception of tool use. The FLM-supported tool demonstrated a predictive accuracy of 81.3%; however, the potential for false positives or negatives remains, especially given the single-center context. Fuzzy logic stratified patients into risk groups: at risk (33.5%), borderline (20.5%), and no risk (46.0%). ANOVA analysis revealed significant differences (p < 0.05) between AKI risk and factors such as age, gender, comorbidities, clinical/laboratory parameters, surgery types, and nephrotoxic agent usage.</p><p><strong>Conclusion: </strong>While initial findings support the usability and clinical feasibility of the NURA-AKI tool, further multicenter validation is needed. The tool is designed to complement nurse judgment, promoting early AKI detection and structured risk communication in surgical care without replacing clinical autonomy.</p>","PeriodicalId":73091,"journal":{"name":"Frontiers in nephrology","volume":"5 ","pages":"1624880"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12420310/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in nephrology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fneph.2025.1624880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Acute Kidney Injury (AKI) is a common yet preventable complication among surgical patients, contributing to increased morbidity, prolonged hospital stays, and higher healthcare costs. Early detection is critical; however, the absence of a standardized nursing-led risk assessment tool for AKI limits proactive intervention in clinical practice.
Objective: This study aimed to develop and evaluate the Nursing Risk Assessment for Acute Kidney Injury tool, integrating the Fuzzy Logic Model (FLM) to enhance interpretive accuracy and improve nursing-led AKI risk detection and decision-making.
Methods: A Design and Development Research (DDR) framework was employed in three phases. Phase 1 involved a needs analysis using a focus group discussion to explore the necessity of AKI assessment among surgical nurses. Phase 2 focused on tool development through expert consensus (surgeon, nephrologist, nursing academician, and experienced nurse) and evidence synthesis via a systematic literature review. In Phase 3, the Nursing Risk Assessment-AKI tool was evaluated through a quasi-experimental design at Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, involving 75 surgical nurses assessing 200 patients.
Results: Post-intervention analysis indicated increased nursing confidence, with 95.7% expressing positive perception of tool use. The FLM-supported tool demonstrated a predictive accuracy of 81.3%; however, the potential for false positives or negatives remains, especially given the single-center context. Fuzzy logic stratified patients into risk groups: at risk (33.5%), borderline (20.5%), and no risk (46.0%). ANOVA analysis revealed significant differences (p < 0.05) between AKI risk and factors such as age, gender, comorbidities, clinical/laboratory parameters, surgery types, and nephrotoxic agent usage.
Conclusion: While initial findings support the usability and clinical feasibility of the NURA-AKI tool, further multicenter validation is needed. The tool is designed to complement nurse judgment, promoting early AKI detection and structured risk communication in surgical care without replacing clinical autonomy.