Lee JH, et al. J Korean Med Sci. 2020 Sep;35(40):e360. https://doi.org/10.3346/jkms.2020.35.e360

With trends in population aging an increasing number of older patients are visiting the emergency department (ED). This study aimed to identify the characteristics of ED utilization and risk factors for in-hospital mortality in older patients who visited EDs. This nationwide observational study used National Emergency Department Information System data collected during a 2-year period from January 2016 to December 2017. The characteristics of older patients aged 70 years or older were compared with those of younger patients aged 20 to 69 years. Risk factors associated with in-hospital mortality were analyzed by multivariable logistic regression. A total of 6,596,423 younger patients and 1,737,799 older patients were included. In the medical and nonmedical older patient groups, significantly higher proportions of patients were transferred from another hospital, utilized emergency medical services, had Korean Triage and Acuity Scale scores of 1 and 2, required hospitalization, and required intensive care unit admission in the older patient group than in the younger patient group. ED and post-hospitalization mortality rates increased with age; in particular, older medical patients aged 90 or older had an in-hospital mortality rate of 9%. Older age, male sex, transfer from another hospital, emergency medical service utilization, a high Korean Triage and Acuity Scale score, systolic blood pressure 20/min, heart rate >100/min, body temperature <36°C, and altered mental status were associated with in-hospital mortality. Development of appropriate decision-making algorithms and treatment protocols for high risk older patients visiting the ED might facilitate appropriate allocation of medical resources to optimize outcomes.

Objective To evaluate whether oral ciprofloxacin, levofloxacin, ofloxacin and moxifloxacin increase the risk of ventricular arrhythmia in Korea’s general population. Design Population-based cohort study using administrative claims data on a national scale in Korea. Setting All primary, secondary and tertiary care settings from 1 January 2015 to 31 December 2015. Participants Patients who were prescribed the relevant study medications at outpatient visits. Primary outcome measures Each patient group that was prescribed ciprofloxacin, levofloxacin, ofloxacin or moxifloxacin was compared with the group that was prescribed cefixime to assess the risk of serious ventricular arrhythmia (ventricular tachycardia, fibrillation, flutter and cardiac arrest). Using logistic regression analysis with inverse probability of treatment weighting using the propensity score, OR and 95% CI for serious ventricular arrhythmia were calculated for days 1–7 and 8–14 after the patients commenced antibiotic use. Results During the study period, 4 888 890 patients were prescribed the study medications. They included 1 466 133 ciprofloxacin users, 1 141 961 levofloxacin users, 1 830 786 ofloxacin users, 47 080 moxifloxacin users and 402 930 cefixime users. Between 1 and 7 days after index date, there was no evidence of increased serious ventricular arrhythmia related to the prescription of ciprofloxacin (OR 0.72; 95% CI 0.49 to 1.06) and levofloxacin (OR 0.92; 95% CI 0.66 to 1.29). Ofloxacin had a 59% reduced risk of serious ventricular arrhythmia compared with cefixime during 1–7 days after prescription. Whereas the OR of serious ventricular arrhythmia after the prescription of moxifloxacin was 1.87 (95% CI 1.15 to 3.11) compared with cefixime during 1–7 days after prescription. Conclusions During 1–7 days after prescription, ciprofloxacin and levofloxacin were not associated with increased risk and ofloxacin showed reduced risk of serious ventricular arrhythmia. Moxifloxacin increased the risk of serious ventricular arrhythmia.

A novel deep-learning algorithm for artificial neural networks (ANNs) was developed and presented in this paper, which is intuitively understandable, simple, efficient, and completely different from the back-propagation method, i.e., randomly selecting weight factors and bias values of an ANN and adjusting their values by small random amounts during the training session where it does not need to calculate the gradients of the training error to adjust weight factors as does the back-propagation method. The algorithm was applied to predict the location of the glottis in airway images obtained using a video airway device. The glottic locations were marked in 1,200 airway images captured using GlideScope® and fiberoptic laryngoscopy. With the randomly selected 1,000 training set data, 84 ANN models were trained using the above algorithm. We sought an ANN model that minimized the average training error for all training set data by reducing the input image resolution. As the resolution was reduced, the average training error decreased to its lowest level at 30×30 pixels. Eventually, the 900-98-49 ANN model was selected as the prediction model for the location of the glottis; it was the model with the lowest training error, i.e., the highest learning rate. The selected prediction model was applied to the remaining 200 test set data to obtain the test accuracy, and we obtained that the accurate prediction and the adjacent prediction rates were 74.5% and 21.5%, respectively. Reducing the input image resolution to an appropriate level could yield better prediction of the glottic location in airway images. This ANN model can help clinicians perform intubation by presenting the predicted location of the glottis.