ABSTRACT
Purpose: Sepsis is one of the most common causes of death after surgery. Several conventional scoring systems have been developed to predict the outcome of sepsis; however, their predictive power is insufficient. The present study applies explainable machine-learning algorithms to improve the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis. Methods: We performed a retrospective analysis of data from demographic, clinical, and laboratory analyses, including the delta neutrophil index (DNI), WBC and neutrophil counts, and CRP level. Laboratory data were measured before surgery, 12-36 hours after surgery, and 60-84 hours after surgery. The primary study output was the probability of mortality. The areas under the receiver operating characteristic curves (AUCs) of several machine-learning algorithms using the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score (SAPS) 3 models were compared. 'SHapley Additive exPlanations' values were used to indicate the direction of the relationship between a variable and mortality. Results: The CatBoost model yielded the highest AUC (0.933) for mortality compared to SAPS3 and SOFA (0.860 and 0.867, respectively). Increased DNI on day 3, septic shock, use of norepinephrine therapy, and increased international normalized ratio on day 3 had the greatest impact on the model's prediction of mortality. Conclusion: Machine-learning algorithms increase the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.
ABSTRACT
The objectives of this study were to analyze antioxidant activities and identify volatile compounds in mixed berry juice after fermentation by lactic acid bacteria (LAB). Antioxidant activity of the mixed berry juice increased significantly from 209.57±2.93 to 268.30±1.75 µmol TE/g after 24 h of fermentation. After LAB fermentation, 34 volatile compounds were identified. Among them, three compounds-benzoic acid, benzaldehyde, and vitispirane-showed significant changes in their concentrations. Peak areas of benzoic acid and benzaldehyde, which are known to possess antioxidant activities, increased by 64 and 188%, respectively, after fermentation. However, the peak area of vitispirane, which is the most abundant terpene compound in berry juices, decreased by 92% after fermentation.
ABSTRACT
PURPOSE: Competency-based assessment helps to improve performance and to standardize education programs for hospice and palliative care professionals. This paper aims to report the process and results of developing the hospice and palliative care competencies by multidisciplinary experts in Korea. METHODS: The competency development task force team of Korean hospice and palliative care professionals was comprised of seven physicians, four nurses, two social workers, and two clergy. To build consensus regarding competencies, the team performed a two-round Delphi survey. The importance of competency domains was assessed by using a 5-point Likert scale. After the completion of the Delphi survey, final competency domains were decided in a consensus meeting. RESULTS: The competencies were composed of knowledge, skills, and attitudes. The competency domains were identified as the following: 11 domains and 16 subdomains for physicians, 11 domains for nurses, 5 domains and 15 subdomains for social workers, as well as 3 domains and 5 subdomains for spiritual care providers. The high importance domains were different by specialties. Physical care and treatment for physicians, symptom management for nurses, bereavement care for social workers, and communication for spiritual care providers were ranked as highly important. For nurses and spiritual care providers, attitude-related domains were ranked the highest in importance. CONCLUSION: The competencies developed by multidisciplinary professionals are useful to identify the appropriate roles of each hospice and palliative care specialist involved in a team approach to patient care.
