Subphenotypes of acute kidney injury in adults

[Artificial intelligence and acute kidney injury]

Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in a relevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of a threatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.

Endothelium-related biomarkers enhanced prediction of kidney support therapy in critically ill patients with non-oliguric acute kidney injury

Acute kidney injury (AKI) is a common condition in hospitalized patients who often requires kidney support therapy (KST). However, predicting the need for KST in critically ill patients remains challenging. This study aimed to analyze endothelium-related biomarkers as predictors of KST need in critically ill patients with stage 2 AKI. A prospective observational study was conducted on 127 adult ICU patients with stage 2 AKI by serum creatinine only. Endothelium-related biomarkers, including vascular cell adhesion protein-1 (VCAM-1), angiopoietin (AGPT) 1 and 2, and syndecan-1, were measured. Clinical parameters and outcomes were recorded. Logistic regression models, receiver operating characteristic (ROC) curves, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used for analysis. Among the patients, 22 (17.2%) required KST within 72 h. AGPT2 and syndecan-1 levels were significantly greater in patients who progressed to the KST. Multivariate analysis revealed that AGPT2 and syndecan-1 were independently associated with the need for KST. The area under the ROC curve (AUC-ROC) for AGPT2 and syndecan-1 performed better than did the constructed clinical model in predicting KST. The combination of AGPT2 and syndecan-1 improved the discrimination capacity of predicting KST beyond that of the clinical model alone. Additionally, this combination improved the classification accuracy of the NRI and IDI. AGPT2 and syndecan-1 demonstrated predictive value for the need for KST in critically ill patients with stage 2 AKI. The combination of AGPT2 and syndecan-1 alone enhanced the predictive capacity of predicting KST beyond clinical variables alone. These findings may contribute to the early identification of patients who will benefit from KST and aid in the management of AKI in critically ill patients.

Meaning and Management of Perioperative Oliguria

Perioperative oliguria is an alarm signal. The initial assessment includes closer patient monitoring, evaluation of volemic status, risk-benefit of fluid challenge or furosemide stress test, and investigation of possible perioperative complications.

Optimum Care of AKI Survivors Not Requiring Dialysis after Discharge: An AKINow Recovery Workgroup Report

AKI survivors experience gaps in care that contribute to worse outcomes, experience, and cost.Challenges to optimal care include issues with information transfer, education, collaborative care, and use of digital health tools.Research is needed to study these challenges and inform optimal use of diagnostic and therapeutic interventions to promote recovery AKI affects one in five hospitalized patients and is associated with poor short-term and long-term clinical and patient-centered outcomes. Among those who survive to discharge, significant gaps in documentation, education, communication, and follow-up have been observed. The American Society of Nephrology established the AKINow taskforce to address these gaps and improve AKI care. The AKINow Recovery workgroup convened two focus groups, one each focused on dialysis-independent and dialysis-requiring AKI, to summarize the key considerations, challenges, and opportunities in the care of AKI survivors. This article highlights the discussion surrounding care of AKI survivors discharged without the need for dialysis. On May 3, 2022, 48 patients and multidisciplinary clinicians from diverse settings were gathered virtually. The agenda included a patient testimonial, plenary sessions, facilitated small group discussions, and debriefing. Core challenges and opportunities for AKI care identified were in the domains of transitions of care, education, collaborative care delivery, diagnostic and therapeutic interventions, and digital health applications. Integrated multispecialty care delivery was identified as one of the greatest challenges to AKI survivor care. Adequate templates for communication and documentation; education of patients, care partners, and clinicians about AKI; and a well-coordinated multidisciplinary posthospital follow-up plan form the basis for a successful care transition at hospital discharge. The AKINow Recovery workgroup concluded that advancements in evidence-based, patient-centered care of AKI survivors are needed to improve health outcomes, care quality, and patient and provider experience. Tools are being developed by the AKINow Recovery workgroup for use at the hospital discharge to facilitate care continuity.

Identification of subphenotypes in critically ill thrombocytopenic patients with different responses to therapeutic interventions: a retrospective study

Introduction

The causes of thrombocytopenia (TP) in critically ill patients are numerous and heterogeneous. Currently, subphenotype identification is a popular approach to address this problem. Therefore, this study aimed to identify subphenotypes that respond differently to therapeutic interventions in patients with TP using routine clinical data and to improve individualized management of TP.

Methods

This retrospective study included patients with TP admitted to the intensive care unit (ICU) of Dongyang People's Hospital during 2010-2020. Subphenotypes were identified using latent profile analysis of 15 clinical variables. The Kaplan-Meier method was used to assess the risk of 30-day mortality for different subphenotypes. Multifactorial Cox regression analysis was used to analyze the relationship between therapeutic interventions and in-hospital mortality for different subphenotypes.

Results

This study included a total of 1,666 participants. Four subphenotypes were identified by latent profile analysis, with subphenotype 1 being the most abundant and having a low mortality rate. Subphenotype 2 was characterized by respiratory dysfunction, subphenotype 3 by renal insufficiency, and subphenotype 4 by shock-like features. Kaplan-Meier analysis revealed that the four subphenotypes had different in-30-day mortality rates. The multivariate Cox regression analysis indicated a significant interaction between platelet transfusion and subphenotype, with more platelet transfusion associated with a decreased risk of in-hospital mortality in subphenotype 3 [hazard ratio (HR): 0.66, 95% confidence interval (CI): 0.46-0.94]. In addition, there was a significant interaction between fluid intake and subphenotype, with a higher fluid intake being associated with a decreased risk of in-hospital mortality for subphenotype 3 (HR: 0.94, 95% CI: 0.89-0.99 per 1 l increase in fluid intake) and an increased risk of in-hospital mortality for high fluid intake in subphenotypes 1 (HR: 1.10, 95% CI: 1.03-1.18 per 1 l increase in fluid intake) and 2 (HR: 1.19, 95% CI: 1.08-1.32 per 1 l increase in fluid intake).

Conclusion

Four subphenotypes of TP in critically ill patients with different clinical characteristics and outcomes and differential responses to therapeutic interventions were identified using routine clinical data. These findings can help improve the identification of different subphenotypes in patients with TP for better individualized treatment of patients in the ICU.

Risk Classification and Subphenotyping of Acute Kidney Injury: Concepts and Methodologies

Acute kidney injury (AKI) is a complex syndrome with a paucity of therapeutic development. One aspect that could explain the lack of implementation science in the AKI field is the vast heterogeneity of the AKI syndrome, which hinders precise therapeutic applications for specific AKI subpopulations. In this context, there is a consensual focus of the scientific community toward the development and validation of tools to better subphenotype AKI and therefore facilitate precision medicine approaches. The subphenotyping of AKI requires the use of specific methodologies suitable for interrogation of multimodal data inputs from different sources such as electronic health records, organ support devices, and/or biospecimens and tissues. Over the past years, the surge of artificial intelligence applied to health care has yielded novel machine learning methodologies for data acquisition, harmonization, and interrogation that can assist with subphenotyping of AKI. However, one should recognize that although risk classification and subphenotyping of AKI is critically important, testing their potential applications is even more important to promote implementation science. For example, risk-classification should support actionable interventions that could ameliorate or prevent the occurrence of the outcome being predicted. Furthermore, subphenotyping could be applied to predict therapeutic responses to support enrichment and adaptive platforms for pragmatic clinical trials.