Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

Gao, Tianyun; Nong, Zhiqiang; Luo, Yuzhen; Mo, Manqiu; Chen, Zhaoyan; Yang, Zhenhua; Pan, Ling

doi:10.1080/0886022x.2024.2316267

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…The primary goal is to learn a mapping function that can accurately predict the output labels for new, unseen data. In the context of SA-AKI, supervised learning can be utilized to predict the risk of AKI in sepsis patients [16,[23][24][25][26][27][28][29][30][31]55]. Researchers can gather historical patient data, including clinical parameters such as vital signs, laboratory results, and patient demographics, as well as information about whether AKI developed during their hospital stay.…”

Section: Supervised Learningmentioning

confidence: 99%

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

Cheungpasitporn,

Thongprayoon,

Kashani

2024

Kidney Res Clin Pract

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Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

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Section: Supervised Learningmentioning

confidence: 99%

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

Cheungpasitporn,

Thongprayoon,

Kashani

2024

Kidney Res Clin Pract

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Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

Lin,

Zheng

et al. 2024

Renal Failure

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In this study, we aimed to develop and validate a nomogram to predicting the risk of sepsis-associated acute kidney injury (SA-AKI) in patients admitted to emergency departments (EDs). We randomly divided a retrospective dataset of 391 patients with sepsis into a 294-person training cohort and a 97-person validation cohort, and developed three predictive models using multivariate logistic regression analysis and clinical insight. No difference was observed between the three models using the DeLong test and Model 3 was selected as the risk prediction model based on the principle of least inclusion indicators. The use of vasopressor drugs, patient age, platelet count, procalcitonin, and D-dimer levels were included. The training and validation cohorts had a consistency index of 0.832 and 0.866, respectively, indicating high accuracy and stability in predicting SA-AKI risk. The area under the receiver operating characteristic curve was 0.832, showing excellent discrimination. The calibration curves for the training and validation cohorts showed excellent calibration. The decision curve and clinical impact curve analyses showed that the net clinical benefit of using the nomogram was greatest over a probability threshold of 0.05–0.90. In addition, the model showed moderate validity in predicting the 30-day survival and the incidence of major adverse renal events within 30 days. The nomogram developed for SA-AKI risk assessment in patients in EDs showed good discriminability and clinical utility. It can provide a theoretical basis for emergency physicians to prevent SA-AKI.

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RNN with attentional feedback predicts early acute kidney injury

Liu,

Zhang,

Ding

et al. 2024

2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)

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Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

Cited by 8 publications

References 40 publications

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

RNN with attentional feedback predicts early acute kidney injury

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