A clinical risk prediction model to identify patients with hepatorenal syndrome at hospital admission

Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases

Hepatorenal syndrome (HRS) is a key contributor to poor prognosis in liver cirrhosis. This study aims to leverage the database to build a predictive model for early identification of high-risk patients. From two sizable public databases, we retrieved pertinent information about the cirrhosis patients' therapies, comorbidities, laboratory results, and demographics. Patients from the eICU database served as a test set for external validation, while patients from the MIMIC database were divided into training and validation groups. Variables were screened using LASSO regression, Extreme Gradient Boosting (XG Boost), and Random Forest (RF). Core risk factors were determined from the intersection of the three methods. A predictive model was constructed using multivariable logistic regression and visualized via a nomogram. Model performance was assessed using ROC curves, decision curve analysis (DCA), clinical impact curves (CIC), and calibration curves. Eight critical variables associated with HRS were identified using machine learning methods. The final predictive model, based on five key variables-spontaneous bacterial peritonitis, red blood cell count, creatinine, activated partial thromboplastin time, and total bilirubin-showed excellent discrimination, with AUCs of 0.832 (95% CI 0.8069-0.8563) in the training set and 0.8415 (95% CI 0.8042-0.8789) in the validation set. The AUC in the external test set was 0.8212 (95% CI 0.7784-0.864). By integrating the MIMIC-IV database and machine learning algorithms, we developed an effective predictive model for HRS in liver cirrhosis patients, providing a robust tool for early clinical intervention.

Role of Serum Cystatin C as a Diagnostic Tool for Renal Function in Cirrhotic Patients

Background:

Assessment of cystatin C levels could be valuable in the early detection of renal dysfunction because they increase faster than the creatinine levels as the GFR decreases. The aim of this work was to evaluate serum cystatin C as a diagnostic tool for renal dysfunction in cirrhotic patients with and without hepatorenal syndrome (HRS).

Methods:

This case-control study was conducted on 60 patients from the Tropical Medicine Department of Tanta University Hospitals and 10 people served as healthy control volunteers. Serum cystatin C was measured in the three groups.

Results:

A significant difference was observed among the three groups as cystatin C was higher in patients with HRS compared to the cirrhotic group and healthy controls.

Conclusion:

Serum cystatin C is a good predictor for hepatorenal syndrome with a good correlation with serum creatinine, blood urea, GFR, and creatinine clearance.

Development and validation of a prognostic model for patients with hepatorenal syndrome: A retrospective cohort study

BACKGROUND

Hepatorenal syndrome (HRS) is a severe complication of cirrhosis with high mortality, which necessitates accurate clinical decision. However, studies on prognostic factors and scoring systems to predict overall survival of HRS are not enough. Meanwhile, a multicenter cohort study with a long span of time could be more convincing.

AIM

To develop a novel and effective prognostic model for patients with HRS and clarify new prognostic factors.

METHODS

We retrospectively enrolled 1667 patients from four hospitals, and 371 eligible patients were finally analyzed to develop and validate a novel prognostic model for patients with HRS. Characteristics were compared between survivors and non-survivors, and potential prognostic factors were selected according to the impact on 28-d mortality. Accuracy in predicting 28-d mortality was compared between the novel and other scoring systems, including Model for End-Stage Liver Disease (MELD), Chronic Liver Failure-Sequential Organ Failure Assessment (CLIF-SOFA), and Chinese Group on the Study of Severe Hepatitis B-Acute-on-Chronic Liver Failure (COSSH-ACLF).

RESULTS

Five prognostic factors, comprised of gender, international normalized ratio, mean corpuscular hemoglobin concentration, neutrophil percentage, and stage, were integrated into a new score, GIMNS; stage is a binary variable defined by the number of failed organs. GIMNS was positively correlated with MELD, CLIF-SOFA, and COSSH-ACLF. Additionally, it had better accuracy [area under the receiver operating characteristic curve (AUROC): 0.830] than MELD (AUROC: 0.759), CLIF-SOFA (AUROC: 0.767), and COSSH-ACLF (AUROC: 0.759) in the derivation cohort (P < 0.05). It performed better than MELD and CLIF-SOFA in the validation cohort (P < 0.050) and had a higher AUROC than COSSH-ACLF (P = 0.122).

CONCLUSION

We have developed a new scoring system, GIMNS, to predict 28-d mortality of HRS patients. Mean corpuscular hemoglobin concentration and stage were first proposed and found to be related to the mortality of HRS. Additionally, the GIMNS score showed better accuracy than MELD and CLIF-SOFA, and the AUROC was higher than that of COSSH-ACLF.

Identification of acute kidney injury subphenotypes

PURPOSE OF REVIEW

AKI is a complex clinical syndrome with many causes and there is a broad range of clinical presentations that vary according to duration, severity and context. Established consensus definitions of AKI are nonspecific and limited to kidney function. This reduces treatment options to generic approaches rather than individualized, cause-based strategies that have limited both understanding and management of AKI.

RECENT FINDINGS

The context and the temporal phase of kidney injury are critical features in the course of AKI and critical to timing-relevant intervention. These features are missing in generic definitions and terms used to describe AKI. Subphenotypes of AKI can be identified from novel damage biomarkers, from functional changes including creatinine trajectories, from the duration of change and from associated clinical characteristics and comorbidities. Subphenotype parameters can be combined in risk scores, or by association strategies ranging from a simple function-damage matrix to complex methods, such as machine learning. Examples of such strategies are reviewed along with tentative proposals for a revised nomenclature to facilitate description of AKI subphenotypes.

SUMMARY

Appropriate intervention requires refinement of the nomenclature of AKI to identify subphenotypes that facilitate correctly timed and selectively targeted intervention.