Risk factors for renal failure and short-term prognosis in patients with spontaneous intracerebral haemorrhage complicated by acute kidney injury

Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis

BACKGROUND

Sepsis-associated acute kidney injury (SA-AKI) is a critical condition with significant clinical implications, yet there is a need for a predictive model that can reliably assess the risk of its development. This study is undertaken to bridge a gap in healthcare by creating a predictive model for SA-AKI with the goal of empowering healthcare providers with a tool that can revolutionize patient care and ultimately lead to improved outcomes.

METHODS

A cohort of 615 patients afflicted with sepsis, who were admitted to the intensive care unit, underwent random stratification into 2 groups: a training set (n = 435) and a validation set (n = 180). Subsequently, a multivariate logistic regression model, imbued with nonzero coefficients via LASSO regression, was meticulously devised for the prognostication of SA-AKI. This model was thoughtfully rendered in the form of a nomogram. The salience of individual risk factors was assessed and ranked employing Shapley Additive Interpretation (SHAP). Recursive partition analysis was performed to stratify the risk of patients with sepsis.

RESULTS

Among the panoply of clinical variables examined, hypertension, diabetes mellitus, C-reactive protein, procalcitonin (PCT), activated partial thromboplastin time, and platelet count emerged as robust and independent determinants of SA-AKI. The receiver operating characteristic curve analysis for SA-AKI risk discrimination in both the training set and validation set yielded an area under the curve estimates of 0.843 (95% CI: 0.805 to 0.882) and 0.834 (95% CI: 0.775 to 0.893), respectively. Notably, PCT exhibited the most conspicuous influence on the model's predictive capacity. Furthermore, statistically significant disparities were observed in the incidence of SA-AKI and the 28-day survival rate across high-risk, medium-risk, and low-risk cohorts (P < .05).

CONCLUSION

The composite predictive model, amalgamating the quintet of SA-AKI predictors, holds significant promise in facilitating the identification of high-risk patient subsets.

Systemic immune-inflammation index may predict the acute kidney injury and prognosis in patients with spontaneous cerebral hemorrhage undergoing craniotomy: a single-center retrospective study

BACKGROUND

The systemic immune-inflammation index (SII) is an emerging prognostic marker of cancer. We aimed to explore the predictive ability of the SII on acute kidney injury (AKI) and prognosis in patients with spontaneous cerebral hemorrhage (SCH) who underwent craniotomy.

METHODS

Patients with SCH who underwent craniotomy between 2014 and 2021 were enrolled in this study. The epidemiology and predictive factors for AKI after SCH were analyzed. The prognostic factors for clinical outcomes in patients with SCH and AKI were further investigated. The prognostic factors were then analyzed using a logistic regression model and a receiver operating characteristic curve.

RESULTS

In total, 305 patients were enrolled in this study. Of these, 129 (42.3%) patients presented with AKI, and 176 (57.7%) patients were unremarkable. The SII (odds ratio [OR], 1.261; 95% confidence interval [CI], 1.036-1.553; P = 0.020) values and serum uric acid levels (OR, 1.004; 95% CI, 1.001-1.007; P = 0.005) were significant predictors of AKI after SCH craniotomy. The SII cutoff value was 1794.43 (area under the curve [AUC], 0.669; 95% CI, 0.608-0.730; P < 0.001; sensitivity, 65.9%; specificity, 65.1%). Of the patients with AKI, 95 and 34 achieved poor and good outcomes, respectively. SII values (OR, 2.667; 95% CI, 1.167-6.095; P = 0.020), systemic inflammation response index values (OR, 1.529; 95% CI, 1.064-2.198; P = 0.022), and Glasgow Coma Scale (GCS) scores on admission (OR, 0.593; 95% CI, 0.437-0.805; P = 0.001) were significant in the multivariate logistic regression analysis. The cutoff SII value was 2053.51 (AUC, 0.886; 95% CI, 0.827-0.946; P < 0.001; sensitivity, 78.9%; specificity, 88.2%).

CONCLUSIONS

The SII may predict AKI in patients with SCH who underwent craniotomy and may also predict the short-term prognosis of these patients.

Nomogram Prediction Model of Serum Chloride and Sodium Ions on the Risk of Acute Kidney Injury in Critically Ill Patients

Purpose

This study aims to investigate the effect of serum chloride and sodium ions on AKI occurrence in ICU patients, and further constructs a prediction model containing these factors to explore the predictive value of these ions in AKI.

Methods

The clinical information of patients admitted to ICU of Beijing Friendship Hospital Affiliated to Capital Medical University was collected for retrospective analysis. Logistic regression analysis was used to analyzing the influencing factors. A nomogram for predicting AKI risk was constructed with R software and validated by repeated sampling. Afterwards, the effectiveness and accuracy of the model were tested and evaluated.

Results

A total of 446 cases met the requirements of this study, of which 178 developed AKI during their stay in ICU, with an incidence rate of 39.9%. Hypernatremia, heart failure, sepsis, APACHE II score, and initial creatinine value and BE value at ICU admission before the diagnosis of AKI were identified as independent risk factors for developing AKI during ICU stay. These predictors were incorporated into the nomogram of AKI risk in critically ill patients, which was constructed by using R software. Receiver operating characteristic curve analysis was further used and showed that the area under the curve of the model was 0.7934 (95% CI 0.742–0.8447), indicating that the model had an ideal value. Finally, further evaluated its clinical effectiveness. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model owned a certain clinical effectiveness.

Conclusion

The nomogram based on hypernatremia, heart failure, sepsis, APACHE II score, and initial creatinine and BE value in ICU can predict the individualized risk of AKI with satisfactory distinguishability and accuracy.