Background. Azithromycin exposure has been reported to increase the risk of QT prolongation and cardiovascular death. However, findings on the association between azithromycin and cardiovascular death are controversial, and azithromycin is still used in actual practice. Additionally, quantitative assessments of risk have not been performed, including the risk of QT prolongation when patients are exposed to azithromycin in a real-world clinical setting. Therefore, in this study, we aimed to evaluate the risk of exposure to azithromycin on QT prolongation in a real-world clinical setting using a 21-year medical history database of a tertiary medical institution. Methods. We analyzed the electrocardiogram results and relevant electronic health records of 402,607 subjects in a tertiary teaching hospital in Korea from 1996 to 2015. To evaluate the risk of QT prolongation of azithromycin, we conducted a case-control analysis using amoxicillin for comparison. Multiple logistic regression analysis was performed to correct for age, sex, accompanying drugs, and disease. Results. The odds ratio (OR) for QT prolongation (QTc>450 ms in male and >460 ms in female) on azithromycin exposure was 1.40 (95% confidence interval [CI], 1.23-1.59), and the OR for severe QT prolongation (QTc>500 ms) was 1.43 (95% CI, 1.13-1.82). On the other hand, the ORs on exposure to amoxicillin were 1.06 (95% CI, 0.97-1.15) and 0.88 (95% CI, 0.70-1.09). In a subgroup analysis, the risk of QT prolongation in patients aged between 60 and 80 years was significantly higher when they are exposed to azithromycin. Conclusions. The risk of QT prolongation was increased when patients, particularly the elderly aged 60-79 years, were exposed to azithromycin. Therefore, clinicians should pay exercise caution using azithromycin or consider using other antibiotics, such as amoxicillin, instead of azithromycin.

The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results. To construct an ADR reference dataset, we extracted known drug–laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug–laboratory event pairs, except known ones, are considered unknown. To detect a known drug–laboratory event pair, three existing algorithms—CERT, CLEAR, and PACE—were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug–laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC). All measures of ML models outperformed those of existing algorithms with sensitivity of 0.593–0.793, specificity of 0.619–0.796, NPV of 0.645–0.727, PPV of 0.680–0.777, F1-measure of 0.629–0.709, and AUROC of 0.737–0.816. Features related to change or distribution of shape were considered important for detecting ADR signals. Improved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.

Objective The present study aimed to determine whether the suicide risk increased after a cancer diagnosis. Design Population-based cohort study. Setting and participants This study incorporated the National Health Insurance Service-National Sample Cohort in South Korea. Of the 975 348 subjects, 39 027 with cancer and 936 321 who were cancer free participated between 2005 and 2013. Primary outcome measure Suicide. Results A total of 110 suicides (82 men, 28 women) were identified among these 39 027 subjects with cancer during a total of 127 184 person-years; among the 936 321 cancer-free subjects, 2163 suicides were reported during a total of 8 222 479 person-years. Cox proportional hazards models were used to compare all-cause and suicide mortalities after cancer diagnosis following adjustment for possible confounding covariates. After adjusting for factors related to suicide, we identified an elevated relative risk of suicide among patients with cancer (HR: 1.480, 95% CI: 1.209 to 1.812). Among men, the relative risk was substantially increased among patients with lip, oral cavity/pharyngeal, colon and rectal, pancreatic and lung cancers when compared with cancer-free subjects; whereas among women, the relative risk was substantially increased among patients with colon and rectal cancers. Conclusion Our study observed an increased risk of suicide among patients with cancer that varied according to the anatomical cancer site, even after accounting for clinical comorbidities and psychiatric illness. Our findings indicate a need for social support and suicide prevention strategies for patients with cancer.

Objectives: The objective of this study was to identify the association between continuity of ambulatory psychiatric care after hospital discharge among psychiatric patients and readmission, mortality and suicide. Design: Nationwide nested case-control study. Settings: South Korea. Participants: Psychiatric inpatients. Interventions: Continuity of psychiatric outpatient care was measured from the time of hospital discharge until readmission or death occurred, using the continuity of care index. Main Outcome Measures: Readmission, all-cause mortality and suicides within 1-year post-discharge. Results: Of 18 702 psychiatric inpatients in the study, 8022 (42.9%) were readmitted, 355 (1.9%) died, and 108 (0.6%) died by suicide within 1 year after discharge. Compared with the psychiatric inpatients with a high continuity-of-care score, a significant increase in the readmission risk within 1 year after discharge was found in those with medium and low continuity of care scores. An increased risk of all-cause mortality within 1 year after hospital discharge was shown in the patients in the low continuity group, relative to those in the high-continuity group. The risk of suicide within 1 year after hospital discharge was higher in those with medium and low continuity of care than those with high continuity of care. Conclusion: The results of this study provide empirical evidence of the importance of continuity of care when designing policies to improve the quality of mental health care, such as increasing patient awareness of the importance of continuity and implementation of policies to promote continuity.

Kim JH, et al. J Korean Med Sci. 2016 Jul;31(7):1020-1026. http://dx.doi.org/10.3346/jkms.2016.31.7.1020

We investigated the association of changes in the frequency of moderate-to-vigorous physical activity (MVPA) and the risks of all-cause and cardiovascular disease (CVD) mortality. This study used the nationally representative National Health Insurance Service-National Sample Cohort database. We included 286,402 individuals aged ≥20 years and estimated changes in the frequency of MVPA over a two-year period. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazard regression models. The HRs (95% CIs) for the risk of all-cause and CVD mortality for an increased frequency of MVPA from physical inactivity compared with continual physical inactivity were 0.82 and 0.68 (0.73–0.92 and 0.51–0.91) for 1–2, 0.72 and 0.48 (0.62–0.84 and 0.31–0.74) for 3–4, and 0.73 and 0.70 (0.63–0.85 and 0.50–0.98) for ≥5 sessions of MVPA/week. The HRs (95% CIs) for the risk of all-cause and CVD mortality were 1.28 and 1.58 (1.07–1.53 and 1.01–2.46), 1.25 and 2.17 (1.01–1.57 and 1.14–4.12), and 1.43 and 1.44 (1.15–1.77 and 0.84–2.47) for changes from 1–2, 3–4, and ≥5 sessions of MVPA/week to physical inactivity, respectively. This study showed the beneficial effect of increasing physical activity, particularly for those who were physically inactive at baseline, as well as the increased risk of all-cause and CVD mortality after adapting a physically inactive lifestyle regardless of their baseline physical activity status.