A good preoperative immune prognostic index is precits a better prognosis for locally advanced rectal cancer patients with ypTNM stage II who underwent radical resection after neoadjuvant chemoradiotherapy

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.

The importance of periprostatic fat tissue thickness measured by preoperative multiparametric magnetic resonance imaging in upstage prediction after robot-assisted radical prostatectomy

PURPOSE

We analyzed the surgical results of patients who were treated and followed up for prostate cancer in our clinic to predict the relationship between periprostatic adipose tissue and patients with and without pathologically upstaged disease.

MATERIALS AND METHODS

The study included patients who had undergone robot-assisted radical prostatectomy and preoperative multiparametric prostate magnetic resonance imaging between 18 February 2019 and 1 April 2022. The patients were divided into two groups, and the surgical and transrectal ultrasound-guided biopsy pathology results were compared according to tumor grade and distribution in 124 patients who met the selection criteria. We analyzed the relationships between upgrading/upstaging and periprostatic adipose tissue thickness (PPATT) and subcutaneous adipose tissue thickness (SATT) as measured in magnetic resonance imaging.

RESULTS

The median PPATT was 4.03 mm, whereas the median SATT was 36.4 mm. Upgrading was detected in 45 patients (36.3%), and upstaging was detected in 42 patients (33.9%). A receiver operating characteristic regression analysis revealed that a PPATT >3 mm was a predictive factor for upstaging after radical prostatectomy (area under curve=0.623, 95% confidence interval [CI] 0.519-0.727, p=0.025). Multivariate logistic regression analyses revealed that prostate specific antigen density ≥0.15 ng/mL/cm3 (odds ratio [OR] 5.054, 95% CI 2.008-12.724, p=0.001), International Society of Urological Pathology grade ≥4 (OR 9.369, 95% CI 2.109-21.626, p=0.003) and higher PPATT (OR 1.358, 95% CI 1.081-1.707, p=0.009) were independent risk factors for upstaging after radical prostatectomy.

CONCLUSIONS

We believe that the PPATT may be a predictive indicator for upstaging after robot-assisted laparoscopic radical prostatectomy.