Tracking longitudinal biomarkers in burn patients with sepsis and acute kidney injury: an unsupervised clustering approach

Kim, Myongjin; Kym, Dohern; Hur, Jun; Park, Jongsoo; Yoon, Jaechul; Cho, Yong Suk; Chun, Wook; Yoon, Dogeon

doi:10.1186/s40001-023-01268-3

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…A distinguishing feature of this study is the utilization of longitudinal data to identify AKI subgroups in burn patients. Unlike cross-sectional studies, longitudinal studies track patients over time, providing a dynamic view of disease progression and potential predictive markers [ 10 ]. This approach is crucial for understanding the temporal patterns of AKI in burn patients, including the significance of creatinine in the diagnosis and prognosis of AKI and its time-series changes for clustering in research.…”

Section: Introductionmentioning

confidence: 99%

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers

Lee,

Kym,

Yoon

et al. 2024

IJMS

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Acute kidney injury (AKI) is a significant complication in burn patients, impacting outcomes substantially. This study explores the heterogeneity of AKI in burn patients by analyzing creatinine time-series data to identify distinct AKI clusters and evaluating routine biomarkers’ predictive values. A retrospective cohort analysis was performed on 2608 adult burn patients admitted to Hangang Sacred Heart Hospital’s Burn Intensive Care Unit (BICU) from July 2010 to December 2022. Patients were divided into four clusters based on creatinine trajectories, ranging from high-risk, severe cases to lower-risk, short-term care cases. Cluster A, characterized by high-risk, severe cases, showed the highest mortality and severity, with significant predictors being PT and TB. Cluster B, representing intermediate recovery cases, highlighted PT and albumin as useful predictors. Cluster C, a low-risk, high-resilience group, demonstrated predictive values for cystatin C and eGFR cys. Cluster D, comprising lower-risk, short-term care patients, indicated the importance of PT and lactate. Key biomarkers, including albumin, prothrombin time (PT), cystatin C, eGFR cys, and total bilirubin (TB), were identified as significant predictors of AKI development, varying across clusters. Diagnostic accuracy was assessed using area under the curve (AUC) metrics, reclassification metrics (NRI and IDI), and decision curve analysis. Cystatin C and eGFR cys consistently provided significant predictive value over creatinine, with AUC values significantly higher (p < 0.05) in each cluster. This study highlights the need for a tailored, biomarker-driven approach to AKI management in burn patients, advocating for the integration of diverse biomarkers in clinical practice to facilitate personalized treatment strategies. Future research should validate these biomarkers prospectively to confirm their clinical utility.

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Section: Introductionmentioning

confidence: 99%

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers

Lee,

Kym,

Yoon

et al. 2024

IJMS

Self Cite

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Long non‐coding RNA MALAT1 triggers ferroptosis via interaction with FUS to enhance ACSF2 mRNA stabilization in septic acute kidney injury

Duan,

Zheng,

Huang

et al. 2024

The Kaohsiung J of Med Scie

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Septic acute kidney injury (AKI) is a fatal disease in the intensive care unit, with ferroptosis playing a crucial role in its pathogenesis. Long non‐coding RNA (LncRNA) metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) has been implicated in septic‐induced AKI inflammation and apoptosis. However, its regulatory role in ferroptosis and underlying mechanisms remain unclear. In vivo and in vitro models of septic AKI were established using cecal ligation and puncture (CLP) and lipopolysaccharide (LPS) challenge, respectively. Serum levels of creatinine (Cr), blood urea nitrogen (BUN), kidney injury molecule‐1 (Kim‐1), neutrophil gelatinase‐associated lipocalin (NGAL), and inflammatory cytokine in kidney tissues were determined by ELISA kits. Histopathological alterations and apoptosis were evaluated by HE staining and TUNEL. Ferroptosis was accessed by measuring MDA, GSH, Fe2+, total and lipid ROS levels, and mitochondrial ultrastructure changes. Target molecular levels were determined using RT‐qPCR, Western blotting, and immunofluorescence. Interactions among MALAT1, acyl‐CoA synthetase family member 2 (ACSF2) and FUS RNA binding protein (FUS) were validated by RIP and RNA‐pull down. MALAT1 level was significantly elevated in both in vivo and in vitro septic AKI models, of which knockdown impeded ferroptosis to alleviate septic AKI. Mechanistically, high MALAT1 expression increased ACSF2 mRNA stability via interaction with FUS. Rescue experiments showed that ACSF2 overexpression partially reversed the ferroptosis inhibition mediated by MALAT1 silencing. MALAT1 induces ferroptosis and exacerbates septic AKI by stabilizing ACSF2 mRNA with the assistance of FUS. These findings provide theoretical evidence for MALAT1 as a potential therapeutic target for septic AKI.

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Unsupervised Clustering in Neurocritical Care: A Systematic Review

Tas,

Rass,

Ianosi

et al. 2024

Neurocrit Care

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Managing patients with acute brain injury in the neurocritical care (NCC) unit has become increasingly complex because of technological advances and increasing information derived from multiple data sources. Diverse data streams necessitate innovative approaches for clinicians to understand interactions between recorded variables. Unsupervised clustering integrates different data streams and could be supportive. Here, we provide a systematic review on the use of unsupervised clustering using NCC data. The primary objective was to provide an overview of clustering applications in NCC studies. As a secondary objective, we discuss considerations for future NCC studies. Databases (Medline, Scopus, Web of Science) were searched for unsupervised clustering in acute brain injury studies including traumatic brain injury (TBI), subarachnoid hemorrhage, intracerebral hemorrhage, acute ischemic stroke, and hypoxic-ischemic brain injury published until March 13th 2024. We performed the systematic review in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. We identified 18 studies that used unsupervised clustering in NCC. Predominantly, studies focused on patients with TBI (12 of 18 studies). Multiple research questions used a variety of resource data, including demographics, clinical- and monitoring data, of which intracranial pressure was most often included (8 of 18 studies). Studies also covered various clustering methods, both traditional methods (e.g., k-means) and advanced methods, which are able to retain the temporal aspect. Finally, unsupervised clustering identified novel phenotypes for clinical outcomes in 9 of 12 studies. Unsupervised clustering can be used to phenotype NCC patients, especially patients with TBI, in diverse disease stages and identify clusters that may be used for prognostication. Despite the need for validation studies, this methodology could help to improve outcome prediction models, diagnostics, and understanding of pathophysiology.Registration number: PROSPERO: CRD4202347097676.

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Tracking longitudinal biomarkers in burn patients with sepsis and acute kidney injury: an unsupervised clustering approach

Cited by 4 publications

References 27 publications

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers

Long non‐coding RNA MALAT1 triggers ferroptosis via interaction with FUS to enhance ACSF2 mRNA stabilization in septic acute kidney injury

Unsupervised Clustering in Neurocritical Care: A Systematic Review

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