Dr. Junepill Seok

When managing patients who require repeated venous access, gaining a viable intravenous route has been problematic. To improve the situation, various studies on techniques for venous access have been conducted. The aim of this study is to evaluate the clinical results of complications following totally implanted central venous access port (TICVAP) insertion. A retrospective analysis was conducted on 163 patients, from December 2008 to March 2013. The occurrence of complications was studied in three separate periods of catheter use: the intraoperative period, postoperative period, and period during the treatment. A total of 165 cases of TICVAP insertions involving 156 patients were included in the final analysis. There were 35 complications (21%) overall. Among these, 31 cases of complications (19%) occurred during the treatment period and the other 4 cases were intraoperative and postoperative complications (2%). There were no statistically significant differences in age and gender of the patients between the two groups to be risk factors (p=0.147, p=0.08). Past history of chemotherapy, initial laboratory findings, and the locations of TICVAP insertion also showed no statistical significance as risk factors (p>0.05). Because the majority of complications occurred after port placement and during treatment, meticulous care and management and appropriate education are necessary when using TICVAPs.

18 August 2023 in Medicine

Journal Article

Comparing machine learning and logistic regression for acute kidney injury prediction in trauma patients: A retrospective observational study at a single tertiary medical center

Hanlim Choi

Seheon Kim

Acute kidney injury (AKI) is common in patients with trauma and is associated with poor outcomes. Therefore, early prediction of AKI in patients with trauma is important for risk stratification and the provision of optimal intensive care unit treatment. This study aimed to compare 2 models, machine learning (ML) techniques and logistic regression, in predicting AKI in patients with trauma. We retrospectively reviewed the charts of 400 patients who sustained torso injuries between January 2016 and June 2020. Patients were included if they were aged > 15 years, admitted to the intensive care unit, survived for > 48 hours, had thoracic and/or abdominal injuries, had no end-stage renal disease, and had no missing data. AKI was defined in accordance with the Kidney Disease Improving Global Outcomes definition and staging system. The patients were divided into 2 groups: AKI (n = 78) and non-AKI (n = 322). We divided the original dataset into a training (80%) and a test set (20%), and the logistic regression with stepwise selection and ML (decision tree with hyperparameter optimization using grid search and cross-validation) was used to build a model for predicting AKI. The models established using the training dataset were evaluated using a confusion matrix receiver operating characteristic curve with the test dataset. We included 400 patients with torso injury, of whom 78 (19.5%) progressed to AKI. Age, intestinal injury, cumulative fluid balance within 24 hours, and the use of vasopressors were independent risk factors for AKI in the logistic regression model. In the ML model, vasopressors were the most important feature, followed by cumulative fluid balance within 24 hours and packed red blood cell transfusion within 4 hours. The accuracy score showed no differences between the 2 groups; however, the recall and F1 score were significantly higher in the ML model (.94 vs 56 and.75 vs 64, respectively). The ML model performed better than the logistic regression model in predicting AKI in patients with trauma. ML techniques can aid in risk stratification and the provision of optimal care.