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.

Objective To evaluate the clinical performance of checklist for early recognition and treatment of acute illness (CERTAIN) on patients in the intensive care unit (ICU). Methods A prospective observational study was performed. 100 patients (age > 18 years old, the length of ICU stay > 72 hours) admitted to ICU of the Second People's Hospital of Lu'an from January to July in 2018 were enrolled. By convenience sampling methods, 50 patients admitted to the hospital from January to April in 2018 were selected as the control group. Standard ward inspection was given to the control group by three senior-level and intermediate-level doctors blinded from the research plan; at the end of March 2018, these three doctors were trained with the CERTAIN checklist and certified by the Mayo Clinic distance learning training. Fifty patients enrolled from March to July 2018 received medical rounds using CERTAIN (observation group). The CERTAIN checklist contained 20 items that cover the range of daily critical ward rounds, which need clinicians to quantify each item. The data included the length of ICU stay, central venous catheter (CVC) indwelling time, catheter indwelling time, duration of mechanical ventilation, drug use rate, ICU mortality, and incidence of adverse events were collected and compared between the two groups. The independent factors affecting ICU death were analyzed by log-rank univariate analysis and Cox regression multivariate analysis. Results Compared with control group, the length of ICU stay (days: 8.68±4.84 vs. 13.64±9.37), catheter indwelling time (days: 8.16±5.29 vs. 13.32±9.31), duration of mechanical ventilation (days: 3.46±4.14 vs. 6.62±9.57) in observation group were significantly decreased, insulin use rate (34.0% vs. 56.0%) and ICU mortality (2.0% vs. 14.0%) were significantly decreased, with statistically significant differences (all P < 0.05). Besides, the use of CERTAIN can significantly improve the efficiency of the ward inspection. The ward inspection time was shortened from (8.00±0.45) minutes to (5.00±0.33) minutes by using the CERTAIN checklist (t = 9.312, P < 0.01). Survival analysis showed that CERTAIN application could reduce ICU mortality (χ2= 3.898, P = 0.048), but the use of CERTAIN was not an independent factor for reducing ICU mortality [odds ratios (OR) = 1.001, P = 0.922]. Conclusions CERTAIN application has a significant effect on critical patients. It is suggested to spread in ICU of China.

Anemia/epidemiology* ; Humans ; Intensive Care Units