PURPOSE: The purpose of this study was to investigate partnerships with nurses, social support and readiness for discharge among mothers of premature infants in the neonatal intensive care unit (NICU) and to examine the factors associated with readiness for discharge.METHODS: A survey was conducted among 85 mothers of premature infants hospitalized in a NICU in Seoul, Korea. The collected data were analyzed with descriptive statistics, the t-test, one-way analysis of variance, Pearson' correlation coefficients, and multiple linear regression using SPSS version 25.0.RESULTS: The results of the regression analysis showed that partnerships with nurses (β=.32, p=.011) and parenting experience (β=.32, p=.001) were significantly associated with readiness for discharge.CONCLUSION: To improve the readiness for discharge among mothers of premature infants, developing strategies to strengthen their partnership with nurses and to provide family-centered care will be needed.
Humans ; Infant, Newborn ; Infant, Premature ; Intensive Care Units, Neonatal ; Intensive Care, Neonatal ; Korea ; Linear Models ; Mothers ; Parenting ; Parents ; Patient Discharge ; Seoul

Purpose: This study aimed to investigate the incidence rate, types and risk factors for delirium in patients admitted to the surgical Step-Down Unit (SDU). Methods: This study was a retrospective study of the electronic medical records of patients admitted to a surgical SDU from February 2020 to July 2020. The delirium assessment was conducted using the short Confusion Assessment Method (a tool that allows quick and easy screening of delirium) and the incidence and risk factors for delirium were determined. Results: Among a total of 227 patients in the study, the incidence rate of delirium was 35 cases (16.7%). It predominantly occurred on the first day (n = 16, 45.7%) and the second day of admission (n = 15, 42.8%). The peak occurrence of delirium was between 14:00 and 22:00 (n = 30, 85.7%). The most common type of delirium was hypoactive (n = 19, 54.3%). The results of multiple logistic regression analysis indicated that the factors influencing the occurrence of delirium in the surgical step down unit (SDU) were age, hypertension, stroke, white blood cell count, and the use of restraints. Conclusion Considering the characteristics of high-risk groups for delirium in the surgical SDU, it is necessary to establish nursing practice guidelines to minimize delirium.

Background: Most studies of systematic drug repositioning have used drug-oriented data such as chemical structures, gene expression patterns, and adverse effect profiles. As it is often difficult to prove repositioning candidates’ effectiveness in real-world clinical settings, we used patient-centered real-world data for screening repositioning candidate drugs for multiple diseases simultaneously, especially for diabetic complications. Methods: Using the National Health Insurance Service-National Sample Cohort (2002 to 2013), we analyzed claims data of 43,048 patients with type 2 diabetes mellitus (age ≥40 years). To find repositioning candidate disease-drug pairs, a nested case-control study was used for 29 pairs of diabetic complications and the drugs that met our criteria. To validate this study design, we conducted an external validation for a selected candidate pair using electronic health records. Results: We found 24 repositioning candidate disease-drug pairs. In the external validation study for the candidate pair cerebral infarction and glycopyrrolate, we found that glycopyrrolate was associated with decreased risk of cerebral infarction (hazard ratio, 0.10; 95% confidence interval, 0.02 to 0.44). Conclusion To reduce risks of diabetic complications, it would be possible to consider these candidate drugs instead of other drugs, given the same indications. Moreover, this methodology could be applied to diseases other than diabetes to discover their repositioning candidates, thereby offering a new approach to drug repositioning.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

OBJECTIVE: It has been reported that untreated sleep-disordered breathing (SDB) deteriorates over time, however this remains contentious. The aim of the present study is to evaluate the clinical course of SDB in middle-aged and older SDB patients, and to identify how relevant factors contribute to the change in SDB severity. METHODS: Baseline and follow-up polysomnographic data of 56 untreated SDB patients (mean age, 61.2±5.71) were obtained retrospectively and the mean interval was 62.4±22.0 months. Subgroup analysis was performed based on the baseline severity, and the factors associated with the course of SDB were analyzed. RESULTS: At the baseline, 13 subjects were simple snorers, 15 had mild to moderate SDB, and 28 were severe SDB patients. While there was no significant change in apnea-hypopnea index (AHI) as a whole, subgroup analysis showed decrease of AHI in severe SDB patients (43.9±10.6 to 35.6±20.0, p=0.009). The change in supine time percent and baseline AHI were associated with the change in AHI (β=0.387, p=0.003; β=-0.272, p=0.037). CONCLUSION: Untreated SDB did not deteriorate over time with modest improvement in severe SDB. A proportion of severe SDB patients might expect decrease in SDB severity irrespective of changes in sleep position or body weight.
Aged* ; Body Weight ; Follow-Up Studies ; Humans ; Retrospective Studies ; Sleep Apnea Syndromes*

Purpose: In patients with epidermal growth factor receptor (EGFR)-mutant non–small cell lung cancer (NSCLC), EGFR tyrosine kinase inhibitors (TKIs) improve response rate and survival. However, most patients eventually develop resistance. This study aimed to identify the role of CD73 in EGFR-mutant NSCLC and explore whether CD73 inhibition may serve as a therapeutic strategy in NSCLC patients with acquired resistance to EGFR-TKIs. Materials and Methods: We evaluated the prognostic role of CD73 expression in EGFR-mutant NSCLC using tumor samples from a single institution. We silenced CD73 in EGFR-TKI–resistant cell lines using short hairpin RNA (shRNA) targeting CD73 and also transfected a vector alone as a negative control. Using these cell lines, cell proliferation and viability assays, immunoblot assays, cell cycle analysis, colony-forming assays, flow cytometry, and apoptosis analysis were performed. Results: High expression of CD73 was associated with shorter survival in patients with metastatic EGFR-mutant NSCLC treated with first-generation EGFR-TKI. CD73 inhibition synergistically inhibited cell viability with first-generation EGFR-TKI treatment compared with the negative control. When CD73 inhibition and EGFR-TKI treatment were combined, G0/G1 cell cycle arrest was induced through the regulation of p21 and cyclin D1. In addition, the apoptosis rate was increased in CD73 shRNA-transfected cells treated with EGFR-TKI. Conclusion High expression of CD73 adversely affects the survival of patients with EGFR-mutant NSCLC. The study demonstrated that inhibiting CD73 in EGFR-TKI–resistant cell lines resulted in increased apoptosis and cell cycle arrest, which overcame the acquired resistance to first-generation EGFR-TKIs. Further research is needed to determine whether blocking CD73 plays a therapeutic role in EGFR-TKI–resistant patients with EGFR-mutant NSCLC.

Since sarcomatoid carcinoma in the common bile duct (CBD) is rarely reported, the clinical course and prognosis after surgery are unclear. We report a case of a patient who died within 1 month after surgery due to rapid tumor progression. A 65-year-old woman had abdominal pain with jaundice. She was diagnosed with CBD cancer and underwent pancreatoduodenectomy. Pathologic examination revealed sarcomatoid carcinoma. There was no postoperative complication, but multiple liver metastasis was diagnosed on computed tomography at 7 days after surgery. Also, the patient complained of abdominal pain and had jaundice with elevated liver enzyme on the 14th postoperative day. Her general condition was getting worse and she died of hepatic failure 23 days after surgery. We report a case of sarcomatoid carcinoma of the CBD that progressed very rapidly. Further research and case reports are needed to establish proper diagnostic and treatment tools.
Abdominal Pain ; Aged ; Carcinosarcoma ; Common Bile Duct ; Female ; Humans ; Jaundice ; Liver ; Liver Failure ; Neoplasm Metastasis ; Pancreaticoduodenectomy ; Postoperative Complications ; Prognosis