Background and objectives: Hydroxychloroquine is widely used in patients with rheumatoid arthritis. However, large-scale studies examining the long-term effects of hydroxychloroquine on the development of kidney disease in patients with rheumatoid arthritis are lacking. We aimed to assess the long-term association of hydroxychloroquine use with the risk of developing CKD in this population. Design, setting, participants, & measurements: We conducted an observational cohort study for patients with newly diagnosed rheumatoid arthritis who were enrolled prospectively in Taiwan’s National Health Insurance Research Database between January 1, 2000 and December 31, 2013. We used multivariable Cox proportional hazard regression to analyze the association of hydroxychloroquine use with incident CKD. Results: A total of 2619 patients, including 1212 hydroxychloroquine users and 1407 hydroxychloroquine nonusers, were analyzed. Incident CKD was reported in 48 of 1212 hydroxychloroquine users and 121 of 1407 hydroxychloroquine nonusers. The incidence rate of CKD was lower in hydroxychloroquine users than in hydroxychloroquine nonusers (10.3 versus 13.8 per 1000 person-years). After multivariable adjustment, hydroxychloroquine users still had a lower risk of incident CKD (adjusted hazard ratio, 0.64; 95% confidence interval, 0.45 to 0.90; P=0.01) than hydroxychloroquine nonusers. The lower risk of subsequent CKD development was dose dependent and consistent across subgroup analyses. Conclusions: Hydroxychloroquine use in patients with newly diagnosed rheumatoid arthritis is associated with a significantly lower risk of incident CKD compared with in nonusers.

Objective We compared the incidence and risk of chronic kidney disease (CKD) between subjects with new-onset migraine and matched controls without migraine in this large-scale retrospective cohort study. Design Population-based cohort study. Setting 8880 subjects with migraine and 503 070 subjects without migraine were enrolled between January 1, 2000 and December 31, 2013, all diagnosed to be without kidney disease. All the participants were registered in the National Health Insurance Research Database. Participants Finally, data from 7156 subjects with migraine and 7156 propensity-score-matched control subjects were analysed. Primary outcome measure We used Cox proportional hazards regression to estimate adjusted HRs for incident CKD; subgroup analyses were performed to assess the interactive effects of migraine with demographics, comorbidities and long-term medications. Results The incidence of CKD was higher in the migraine group than in the control group. The risk of developing CKD was significantly higher in subjects with migraine than without migraine (P=0.031). Subjects with migraine aged <65 years (age 40–64 (adjusted HR (aHR) 1.35; 95% CI 1.05 to 1.73); age <40 (aHR 1.55; 95% CI 1.02 to 2.36)), with ≥1 comorbid diseases (1–2 diseases (aHR 1.30; 95% CI 1.01 to 1.68); ≥3 diseases (aHR 1.45; 95% CI 1.01 to 2.07)), and not receiving anti-migraine agents (aHR 1.26; 95% CI 1.04 to 1.54) were at a higher risk of developing CKD compared with the control subjects. The interaction between migraine and comorbidities was not significant; age, male gender and long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) were independent risk factors for CKD in subjects with migraine. Conclusion Migraine may be an independent risk factor for CKD. Young subjects with migraine, and those with comorbid conditions or without medical control, are likely to be at higher risk for CKD. Ageing, male sex and NSAIDs tend to have an association with CKD in subjects with migraine.

Anemia is associated with high mortality and poor prognosis after acute coronary syndrome (ACS). Increased red cell distribution width (RDW) is a strong independent predictor for adverse outcomes in ACS. The common underlying mechanism for anemia and increased RDW value is iron deficiency. It is not clear whether serum iron deficiency without anemia affects left ventricular (LV) performance after primary angioplasty for acute myocardial infarction (AMI). We investigated the prognostic value of serum iron concentration on LV ejection fraction (EF) at 6 months and its relationship to thrombolysis in myocardial infarction (TIMI) risk score in post MI patients. We recruited 55 patients who were scheduled to undergo primary coronary balloon angioplasty after AMI and 54 age- and sex-matched volunteers. Serum iron concentration and interleukin-6 levels were measured before primary angioplasty. LVEF was measured by echocardiography at baseline and after 6 months. TIMI risk score was calculated for risk stratification. Serum iron concentration was significantly lower in those in whom LVEF had not improved ≥10% from baseline (52.7±24.1 versus 80.8±50.8 µg/dl, P = 0.016) regardless of hemoglobin level, and was significantly lower in the AMI group than in the control group (62.5±37.7 versus 103.0±38.1 µg/dl, P<0.001). Trend analysis revealed that serum iron concentration decreased as TIMI risk score increased (P = 0.002). In addition, lower serum iron concentrations were associated with higher levels of inflammatory markers. Multiple linear regression showed that baseline serum iron concentration can predict LV systolic function 6 months after primary angioplasty for AMI even after adjusting for traditional prognostic factors. Hypoferremia is not only a marker of inflammation but also a potential prognostic factor for LV systolic function after revascularization therapy for AMI, and may be a novel biomarker for therapeutic intervention.

Background: The renin-angiotensin system (RAS) has significant influences on heart and renal disease progression. Angiotensin converting enzyme (ACE) and angiotensin converting enzyme II (ACE2) are major peptidases of RAS components and play counteracting functions through angiotensin II (Ang II)/ATIR and angiotensin-(1–7) (Ang-(1–7))/Mas axis, respectively. Methods: There were 360 uremic patients on regular hemodialysis (HD) treatment (inclusive of 119 HD patients with cardiovascular diseases (CVD) and 241 HD patients without CVD and 50 healthy subjects were enrolled in this study. Plasma ACE, ACE2, Ang II and Ang-(1–7) levels of the HD patients were determined. Results: We compared pre-HD levels of plasma ACE, ACE2, Ang II and Ang-(1–7) in the HD patients with and without CVD to those of the controls. The HD patients, particularly those with CVD, showed a significant increase in the levels of ACE and Ang II, whereas ACE2 and Ang-(1–7) levels were lower than those in the healthy controls. Therefore, imbalanced ACE/ACE2 was observed in the HD patients with CVD. In the course of a single HD session, the plasma ACE, ACE/ACE2 and Ang II levels in the HD patients with CVD were increased from pre-HD to post-HD. On the contrary, ACE2 levels were decreased after the HD session. These changes were not detected in the HD patients without CVD. Conclusions: Pathogenically imbalanced circulating ACE/ACE2 was detected in the HD patients, particularly those with CVD. HD session could increase ACE/Ang II/AT1R axis and decrease ACE2/Ang-(1–7)/Mas axis activity in the circulation of HD patients with CVD.

Background: Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. Objective: We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. Methods: Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. Results: Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. Conclusions: Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.