Prevalence and factors associated with hyponatremia in older adults who visited emergency department

Objectives@#Hyponatremia is prevalent electrolyte disorder and can be fatal in older adults. Evaluative studies on hyponatremia among older adults are scarce, especially targeting for those who visited emergency department (ED). We aimed to estimate the prevalence and to identify risk factors of hyponatremia among elderly patients visiting the ED. @*Methods@#A retrospective chart review was completed including 65 or older patients who visited ED at Seoul National University Bundang Hospital from September to December 2019. Patients with the serum sodium concentration of less than 130mEq/L was defined as a hyponatremia group. Logistic regression analysis was conducted to assess predictive factors for hyponatremia. @*Results@#Of the total 2,445 patients, 155 (6.3%) were confirmed to have hyponatremia at the time of ED visits. Risk factors for hyponatremia identified in logistic regression analysis were thiazides (aOR=2.64, 95% CI 1.66-4.21), opioids (exclude tramadol) (aOR=3.45, 95% CI 1.72-6.94), and desmopressin (aOR=6.98, 95% CI 2.45-19.84). Compared to the use of thiazides alone, it was confirmed that the possibility of hyponatremia was more than quadrupled when proton pump inhibitor (PPI) was used together (aOR=4.08, 95% CI 1.74-9.55). @*Conclusions@#About 6.3% of older adults visiting the ED had hyponatremia. Age, number of medications taken, previous history of hyponatremia, heart failure, cirrhosis, pneumonia, sepsis, prescribed drugs including thiazides, opioids (exclude tramadol), or desmopressin or taking PPI together with thiazides was confirmed to correlate with the risk of hyponatremia.

The Effect of Night Eating Syndrome Tendency on Mood, Sleep, and Alcohol Use in Female Undergraduate Students

OBJECTIVES: The current study aimed to investigate individuals with night eating syndrome tendency in 115 female undergraduate sample based on night eating syndrome criteria, and analyze its association between mood, sleep, and alcohol use. METHODS: Subjects were divided into high and low tendency group of night eating syndrome based on the night eating questionnaire. All participants completed the Hospital Anxiety and Depression Scale, Insomnia Severity Index, Munich Chronotype Questionnaire, and Alcohol-Use Disorders Identification Test. Data was collected at two time points which were 3 months apart. All analyses were conducted using repeated measure ANOVA. RESULTS: Results indicated a significant difference between night eating syndrome tendency groups for anxiety and depression [F(1,113)=12.35, p=0.001 and F(1,113)=9.59, p=0.002, respectively]. Depression also had a significant time effect [F(1,113)=11.15, p=0.001]. Additionally, the high night eating syndrome tendency group had higher levels of insomnia severity [F(1,113)=24.34, p<0.001], eveningness [F(1,113)=15.09, p<0.001], and alcohol use [F(1,113)=6.73, p=0.011], and lower sleep efficiency [F(1,113)=6.30, p=0.014] compared to the low night eating syndrome tendency group. CONCLUSIONS: The high night eating syndrome tendency group had higher negative mood, sleep disturbance, and alcohol use compared to the low night eating syndrome tendency group. In summary, intervention for night eating syndrome may be important to consider in undergraduate students.

A Study on the Factors Related to the Readmission and Ambulatory Visit in an University Hospital: Using Patient Care Information DB / 대한의료정보학회지

To identify the factors related to the readmission and ambulatory visit we analyzed the data in discharge abstract DB(DADB) and outpatient database(OPDB) for 19,983 patients discharged in 1990 from an university hospital(S Hospital) in Seoul. The target patients were limited to those who didn' t have previous episode of discharge in that hospital. Readmission data for 10 years(1990-1999) and ambulatory visit data for 5 years(1995-1999) were analyzed by using x2 test and multiple logistic regression analysis. The main results of this study is as follows. 1) As the number of readmission was increased, readmission rate(RR) was also increased while the average length of stay(ALOS) was decreased. 2) RR was higher in male, transferred from other health care facilities, with consultation, biopsy, ICU care episode during hospitalization. 3) In logistic regression, RR of patients living close to S Hospital hospital was higher than the others wh?n other variables were adjusted. 4) RR of the patients with the diagnosis(Dx) of cancer or cancer related condition was the highest(47.6%), and the consistency rate (CR) of principal Dx group with that of previous admission was also the highest in cancer patients. As the number of readmission was increased the CR of Dx group was also increased. 5) 23.4%(4866) of the target patients had episode of visiting outpatient dispensary(OPD) for between 1995-1999 and the average number of visit was 13.6 times. Patients with the Dx of heart disease showed the highest proportion in ambulatory visit.

Application of Survival Analysis to Data from Discharge Abstract of Medical Record Department: Focused on Readniission / 대한의료정보학회지

Abundant data on patients have been accumulated in hospital since the introduction of the computerized system. Now data mining is required for the survival and growth of hospital. Cases of 19,558 patients were analyzed to investigate factors influencing readmission and repeated admissions, and to estimate probability of readmission with considering covariate effects. Techniques of Kaplan-Meier method, Cox proportional hazards model, and WLW method were applied to the analysis. The conclusions are as follows. The severity of disease, congenital defect and chronicity of disease are influencing readmission or repeated admissions of a patient. Patient s characteristics, such as gender, distance from residence and type of discharge are also related to them. The probability of readmission can be estimated for a patient with variety of conditions for certain period of time. It is suggestive that survival analysis is a good methodology for data mining works on computerized data in hospital. If death certificate data are connected with patients' data, we will be able to get a good data source to medical studies.