Novel biologic therapies have revolutionised the management of severe asthma with more ambitious treatment aims. Here we analyse the definition of clinical remission as a suggested treatment goal and consider the characteristics associated with clinical remission in a large, real-world severe asthma cohort.This was a retrospective analysis of severe asthma patients registered in the UK Severe Asthma Registry (UKSAR) who met strict national access criteria for biologics. Patients had a pre-biologics baseline assessment and annual review. The primary definition of clinical remission applied included Asthma Control Questionnaire (ACQ)-5 <1.5 and no oral corticosteroids for disease control and forced expiratory volume in 1 s above lower limit of normal or no more than 100 mL less than baseline.18.3% of patients achieved the primary definition of remission. The adjusted odds of remission on biologic therapy were 7.44 (95% CI 1.73–31.95)-fold higher in patients with type 2 (T2)-high biomarkers. The adjusted odds of remission were lower in patients who were female , obese  or had ACQ-5 ≥1.5  pre-biologic therapy. The likelihood of remission reduced by 14% (95% CI 0.76–0.97) for every 10-year increase in disease duration. 12–21% of the cohort attained clinical remission depending on the definition applied; most of those who did not achieve remission failed to meet multiple criteria.18.3% of patients achieved the primary definition of clinical remission. Remission was more likely in T2-high biomarker patients with shorter duration of disease and less comorbidity. Further research on the optimum time to commence biologics in severe asthma is required. Analysis−2, shorter disease duration, T2-high biomarkers and lower symptom burdenof a real-world severe asthma registry shows clinical remission rates of 18%; associated pre-biologic characteristics include male sex, never smoking, BMI <30 kg·mhttps://bit.ly/46JLeDb It is estimated that 5–10% of asthmatic patients have “severe asthma”, defined by the European Respiratory Society (ERS)/American Thoracic Society (ATS) as “asthma which requires treatment with Global Initiative for Asthma (GINA) step 4–5 treatment or oral corticosteroids (OCS) for 50% of the last year to prevent it becoming uncontrolled”  .Biologics targeting the type 2 (T2) cytokine pathway have reduced reliance on OCS and revolutionised the management of severe asthma to the extent that there is a need for a defined treatment goal that reflects disease quiescence or stability on treatment . In othe2-agonist (SABA) use should be an important part of the definition of remission [In severe asthma, a modified Delphi exercise identified core components of disease remission based on the definitions in other chronic inflammatory diseases . The autemission .Clinical remission in severe asthma is not the same as spontaneous remission, which can occur in mild asthma, or a “cure”, which usually refers to complete remission from all manifestations of the disease, while off all treatment for a prolonged period of time. Clinical remission has been explored in clinical trial populations and real-world cohorts of patients with severe asthma –13, althThe UK Severe Asthma Registry (UKSAR) is a national database of patients with uncontrolled asthma referred to specialist UK severe asthma centres . Biologisupplementary material), and had baseline assessment at registration and at least one annual review assessment within 9–24 months. The registry has been described elsewhere [supplementary figure S1.This retrospective, observational analysis includes severe asthma patients registered in UKSAR from specialist, severe asthma services in the UK since January 2016. All patients were ≥18 years old, met ERS/ATS criteria for severe asthma, met NICE access criteria for biologics  above lower limit of normal (LLN) or no more than 100 mL less than baseline, pre-biologics FEV1. Sensitivity analyses with different definitions of clinical remission were applied as described in the following Statistical methods section.The primary definition of remission was: at annual review, Asthma Control Questionnaire (ACQ)-5 <1.5, and no OCS for disease control (no OCS bursts for exacerbations in the last 12 months) and no mOCS for disease control , and forced expiratory volume in 1 s  ≥20 ppb are used to describe evidence of T2 inflammation in keeping with GINA guidelines [−2.Where analysis includes T2 biomarker values, blood eosinophil count (BEC) ≥0.15×10idelines . Obesity1 above LLN or no more than 100 mL less than baseline, pre-biologics FEV1 (primary definition); 2) ACQ-5 <1.5, and no OCS for disease control, and FEV1 above LLN or no more than 5% lower than at pre-biologics assessment; 3) ACQ-5 <1.5, and no OCS for disease control, and FEV1 above LLN or no lower than at pre-biologics assessment; 4) ACQ-5 <1.5, and no OCS for disease control; 5) ACQ-5 <1.5, and no OCS for disease control, and no SABA; 6) ACQ-5 <1.5, and no OCS for disease control, and ≤2 puffs of SABA per day; and 7) ACQ-5 ≤0.75, and no OCS for disease control, and FEV1 above LLN or no more than 100 mL less than baseline, pre-biologics FEV1.Sensitivity analyses were undertaken to examine the impact of different definitions of clinical remission: 1) ACQ-5 <1.5, and no OCS for disease control (including no OCS-requiring asthma exacerbations and no daily mOCS for asthma), and FEVStata version 16.0 SE  was used to perform the analyses using a complete-case framework.supplementary figure S1). The baseline demographic and clinical details for the entire cohort are outlined in supplementary tables S1–S3). Of this cohort, 830 had all explanatory variables for the primary definition of remission; their baseline demographics are displayed in supplementary table S4 . This cohort had substantial OCS exposure, a high exacerbation count, T2-high biomarkers and a high symptom burden. Supplementary table S7 outlines changes in outcomes from commencing biologics to 1-year review with significant clinical improvements in exacerbations, acute care utilisation, FEV1 and reported symptom burden.A total of 1111 patients met study inclusion criteria across 14 specialist centres ; p=0.022), those who were slightly older commencing biologics (55 (48–65) versus 51 (41–59) years; p<0.001) and had a shorter duration of asthma (20 (8–32) versus 25 (12–37) years; p=0.008) (FENO high) prior to commencing biologics, with both higher FENO and highest recorded BEC (supplementary tables S4–S6).In total, 18.3% (152/830) (95% CI 15.7–21.1%) of patients met the primary definition for clinical remission after 1 year of biologic therapy. Remission was more common in males  p=0.008) . Remissiversus 2%; p<0.001), higher number of exacerbations (5 (3–8) versus 4 (3–6); p<0.001), emergency department attendances  and hospital admissions . FEV1 % pred was slightly lower in the non-remission cohort, although FEV1/forced vital capacity ratio was no different. Baseline symptom burden was greater (ACQ-5 3.2 (2.2–4.2) versus 2.0 (1.2–3.4); p<0.001), quality of life more impaired and a trend towards increased mOCS use was seen in those who did not achieve remission (supplementary table S8).The non-remission cohort had a greater incidence of depression/anxiety (13% supplementary table S9).There were distinct differences between the group of patients that achieved remission and those that did not; age commencing biologics, obesity, smoking status, composite T2 status, comorbid depression/anxiety and ACQ-5 continue to be significantly different between the cohorts when adjusting for potential confounders including hospital site and time to review (FENO high) was 7.44 times as high as the composite T2-low group  (supplementary table S9).Clinical remission was more likely with a shorter duration of symptoms; every increasing 10 years duration of asthma symptoms before commencing biologics was associated with a 14% decreased odds of achieving remission , such that an individual commencing biologics with over 30 years of asthma symptoms is 49% less likely to achieve remission than someone with a duration of symptoms of less than 10 years . Being older when commencing biologics was associated with increased remission; with every increasing 10 years of age at pre-biologics assessment, the odds of remission increased by 31% . The odds of remission for those who were composite T2-high . Comorbid depression/anxiety  and a higher pre-biologics symptom burden  were associated with a significantly lower odds of remission. Anti-IgE therapy was associated with 81% lower odds of remission than anti-interleukin-5 biologics (note that access criteria and thereby the patient population receiving these therapies are different) (supplementary table S9). Ethnic disparities lose significance when adjusted for hospital site and baseline morbidity  (supplementary table S9). The adjusted probabilities of achieving clinical remission for each covariate are plotted in supplementary figure S2.Females were 36% less likely to achieve remission . The odds of remission were 47% lower in obese compared with non-obese patients  and with every increased unit of BMI ≥30 kg·msupplementary table S10). Most of those who did not meet the criteria for remission failed to meet a number of individual remission criteria  , with ACcriteria .1 (5%), rather than an actual volume reduction (100 mL), in the definition of remission did not affect the rates of remission (18.3% (152/830) (95% CI 15.7–21.1%)). This definition did not change the baseline characteristics associated with remission and non-remission (supplementary table S11 and supplementary figure S3).Using a proportional reduction in FEV1 criteria for remission (FEV1 no lower than baseline) had minimal impact on the remission analysis (17.7% (147/830) (95% CI 15.2–20.4%) achieved remission), and did not substantially change the characteristic differences between the remission and non-remission cohorts (supplementary table S12 and supplementary figure S4).Applying a stricter FEV1 in the remission definition, this definition had no difference in outcomes in ethnicity (p=0.060) (supplementary table S13 and supplementary figure S5).A definition of remission which included only two criteria for remission and did not have any lung function criteria resulted in a slightly higher rate of remission (21.2% (196/925) (95% CI 18.6–24.0%)). Compared with analyses including FEVsupplementary table S14 and supplementary figure S6).Only 13.8% (123/891) (95% CI 11.6–16.2%) of the cohort reached remission using this criterion, of which 75% (667/891) did not meet the SABA criteria, 67% (594/891) did not meet the OCS criteria and 60% (533/891) did not meet the ACQ-5 criteria. In this analysis, compared with the primary definition of remission, the difference in gender, ethnicity, atopic disease and composite T2 biomarker groups was lost between those who met the difference in remission and those who did not (1 ≤100 mL from baseline or >LLN), only 11.9% (99/830) (95% CI 9.8–14.3%) met the definition of remission (supplementary table S16 and supplementary figure S8)When applying a definition of remission which includes more stringent asthma control ; however, only a minority of patients achieved clinical remission.Clinical remission was more commonly achieved in males, never-smokers and non-obese individuals with higher T2 biomarkers. These patients were older at disease onset, with shorter disease duration, lower symptom score and fewer exacerbations at initiation of biologic therapy.Non-remission was more common with earlier asthma onset and longer disease duration prior to commencing biologics, with greater exacerbation burden and resultant OCS exposure. Asthma exacerbations are associated with accelerated lung function decline compared with aged-matched peers , with the effect of multiple exacerbations being cumulative, and we see lung function was lower in the non-remission cohort with a higher exacerbation burden . FurtherThe described clinical and demographic features associated with remission are present at initial assessment and the ROC analysis confirms that together the variables included in the regression model are highly discriminatory for predicting remission; however, no single feature was able to predict remission. Recognition of these clinical and demographic characteristics at baseline will help clinicians identify patients who are more likely to achieve clinical remission, and importantly to inform patient discussion about what can realistically be achieved with biologic treatment.versus 1 (0–3)) after 1 year of biologics; however, despite this substantial impact, many patients failed to reach remission criteria because of ongoing OCS use.The criteria for clinical remission on treatment have been the subject of a series of consensus statements , 9, 36, Another criterion is the “absence of significant symptoms”, which is challenging as severe asthma patients are often highly symptomatic , 37 and 1 measurement is influenced by natural daily variation, bronchodilator medication and natural lung function decline which varies with gender  [1 as a cut-point for the definition of remission does not substantially affect the proportion of patients achieving remission, or significantly change the clinical characteristics of the remission/non-remission groups. Lung function assessment in biologic-treated severe asthma cohorts will require longitudinal assessment over years to draw evidence-based conclusions on what constitutes “lung function stability” and whether the use of age-adjusted lung function equations is sufficient [Lung function criterion is not straightforward to consider longitudinally in severe asthma; FEVy males) , 41. Addfficient .In terms of the final criterion, specifically “patient/clinician agreement on the achievement of remission”, information on this issue was not available in this retrospective analysis. It will be important to explore this prospectively in future studies as shared decision making and agreement is an important aspect of clinical remission in other inflammatory diseases –45.Advantages of this analysis are its size and the “real-world” nature of the cohort. Limitations include the possibility of type 1 error due to testing across multiple variables within the cohort; however, this is a hypothesis-generating study and logistic regression modelling was limited to a smaller number of variables. As with all “real-world” observational studies, there is an inherent possibility that the proportion of patients achieving remission may be overestimated due to regression to the mean, but it would be unethical to have a control arm for comparison . Lastly,In summary, this severe asthma cohort benefited substantially from biologic therapy, although only 18% of patients achieved clinical remission using the proposed definition. The characteristics of those who attain remission and those who do not are described, with the suggestion that remission is achieved in patients with shorter duration of disease, fewer comorbidities and greater T2 inflammation. Further studies are needed to demonstrate that achievement and maintenance of disease remission improve long-term clinical outcomes in severe asthma and specifically whether earlier intervention with effective biologic treatment can improve long-term disease trajectory.10.1183/13993003.00819-2023.Supp1Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.ERJ-00819-2023.SupplementSupplementary material 10.1183/13993003.00819-2023.Shareable1This one-page PDF can be shared freely online.ERJ-00819-2023.ShareableShareable PDF


A high resting heart rate (HR) has been associated with an increased risk of diabetes mellitus. This study explored the association between initial in-hospital HR and glycemic control in patients with acute ischemic stroke (AIS) and diabetes mellitus.We analyzed data from 4,715 patients with AIS and type 2 diabetes mellitus enrolled in the Chang Gung Research Database between January 2010 and September 2018. The study outcome was unfavorable glycemic control, defined as glycated hemoglobin (HbA1c) ≥ 7%. In statistical analyses, the mean initial in-hospital HR was used as both a continuous and categorical variable. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression analysis. The associations between the HR subgroups and HbA1c levels were analyzed using a generalized linear model.p < 0.001).Compared with the reference group (HR < 60 bpm), the adjusted ORs for unfavorable glycemic control were 1.093 (95% CI 0.786–1.519) for an HR of 60–69 bpm, 1.370 (95% CI 0.991–1.892) for an HR of 70–79 bpm, and 1.608 (95% CI 1.145–2.257) for an HR of ≥ 80 bpm. Even after adjusting for possible confounders, the HbA1c levels after admission and discharge among diabetic stroke patients increased significantly in the subgroups with higher HRs (High initial in-hospital HR is associated with unfavorable glycemic control in patients with AIS and diabetes mellitus, particularly in those with an HR of ≥ 80 bpm, compared with those with an HR of < 60 bpm. Diabetes mellitus is a chronic disease with long-term complications, and the development of hyperglycemia is common in individuals with diabetes mellitus after an acute stroke . AlthougA high resting heart rate (HR) is associated with an increased risk of type 2 diabetes mellitus –9 and poThis study evaluated the relationship between mean initial in-hospital HR and glycemic control in patients with AIS and type 2 diabetes mellitus.We conducted this retrospective cohort study by using data from the Chang Gung Research Database , the lar−1.154 × (age)−0.203 × 0.742  [Primary demographic and clinical characteristics were collected, including stroke severity as assessed using the claims-based stroke severity index (SSI). The SSI was then converted to the National Institutes of Health Stroke Scale (NIHSS) score by using the following equation: estimated NIHSS (eNIHSS) = 1.1722 × SSI − 0.7533 . Stroke  female) . Height, female) . Mean sy female) .The study outcome was the status of glycemic control. Favorable glycemic control was defined as mean HbA1c < 7%, and unfavorable glycemic control was defined as mean HbA1c ≥ 7%.t-test or Wilcoxon’s rank sum test for continuous data and the chi-square test for categorical data.Quantitative variables are summarized as mean (standard deviation) or median (interquartile range), depending on the distribution of the data, and categorical variables are presented as number (percentage). Differences in demographic features between the patients with favorable and unfavorable glycemic control were evaluated using the student’s To further evaluate the effect of initial in-hospital HR on the levels of HbA1c and glycemic control, the patients were classified into four subgroups according to mean HR . Multivariable logistic regression analysis was used to evaluate the relationship between HR and unfavorable glycemic control. In statistical analyses, the mean initial in-hospital heart rate was used as both a continuous and categorical variable. In addition to crude odds ratios (ORs), adjusted ORs and 95% confidence intervals (CIs) were calculated using the HR < 60 bpm subgroup as the reference for the analysis. Model 1 included HR, age, and sex, and model 2 included HR, age, sex, stroke severity, body mass index, hypertension, dyslipidemia, atrial fibrillation, coronary artery disease, congestive heart failure, history of cancer before admission, smoking status, levels of total cholesterol, triglyceride, eGFR, and alanine aminotransferase, systolic blood pressure, diastolic blood pressure, and the use of beta blockers and insulin.The influence of the initial in-hospital HR on the levels of HbA1c was assessed using the generalized linear model. We examined the effect of initial in-hospital HR on HbA1c level by adjusting for possible confounders, namely age, sex, stroke severity, body mass index, hypertension, dyslipidemia, atrial fibrillation, coronary artery disease, congestive heart failure, history of cancer before admission, smoking status, levels of total cholesterol, triglyceride, eGFR, and alanine aminotransferase, systolic blood pressure, diastolic blood pressure, and the use of beta blockers and insulin. All analyses were performed using SPSS for Windows, version 22 .A total of 4,715 patients with AIS and type 2 diabetes mellitus were included in this study . The median systolic blood pressure and diastolic blood pressure were 154.00 (interquartile range: 141.42–167.55 mmHg) and 85.13 mmHg (interquartile range: 78.70–92.33 mmHg), respectively, and the median HR was 74.71 bpm (interquartile range: 68.25–80.87 bpm) (Table p for trend < 0.001).Univariate analysis of the data revealed that several clinical factors were significantly associated with glycemic control. These included age, sex, stroke severity, smoking status, HR, use of beta blockers and insulin after discharge, levels of total cholesterol and triglyceride, and history of hypertension, atrial fibrillation, and cancer before admission Table . The levThe results of the multivariable logistic regression analysis for unfavorable glycemic control are presented in Table p < 0.001). These results suggested that a lower mean initial in-hospital HR was associated with better glycemic status.Because a significant relationship was detected between the mean initial in-hospital HR and glycemic control, the associations between the mean initial in-hospital HR subgroups and HbA1c levels after admission as well as mean HbA1c levels after discharge were analyzed using a generalized linear model. Even after the possible confounders were adjusted for, the HbA1c levels after admission and discharge among stroke patients with diabetes increased significantly in the subgroups with higher HRs . In humans at rest, the parasympathetic tone predominates, and therefore, resting HR is lower than the intrinsic rate of the sinoatrial node . An incrAccording to the data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study, a higher HR predicted cardiovascular disease and major adverse cardiovascular events, independent of other risk factors in patients with type 1 diabetes mellitus . A low HBecause of the propensity of beta blockers to cause insulin resistance and impair glycemic control in patients with diabetes mellitus, the influence of beta blockers in glycemic control remains controversial –40. In tMany factors are associated with unfavorable glycemic control –47. In tBecause of the observational nature of the study, this study has several limitations. The association between mean initial in-hospital HR and glycemic control may not imply a cause–effect relationship. Because the vital signs were collected for the first 3 days of hospitalization, patients who were hospitalized for less than 3 days were excluded from the study; therefore, some patients with mild stroke may not have been included. Additionally, because this study was conducted on a population of patients with AIS and diabetes mellitus, our results may not be applicable to patients with other cardiovascular disease with diabetes mellitus.This study addressed the prognostic implications of the initial in-hospital HR on glycemic control. Treatment for augmenting parasympathetic activity and reducing HR, such as with acetylcholinesterase inhibitors, may potentially improve glycemic control and cardiovascular outcomes in patients with AIS and type 2 diabetes mellitus. Further prospective clinical trials should be conducted to determine whether interventions that aim to decrease HR are beneficial for this population.


Established cardiovascular risk assessment tools lack chronic kidney disease–specific clinical factors and may underestimate cardiovascular risk in non–dialysis-dependent chronic kidney disease (CKD) patients.A retrospective analysis of a cohort of patients with stage 3–5 non–dialysis-dependent chronic kidney disease in the Salford Kidney Study  was performed. Multivariable Cox regression models with backward selection and repeated measures joint models were used to evaluate clinical risk factors associated with cardiovascular events , mortality , and need for renal replacement therapy. Models were established using 70% of the cohort and validated on the remaining 30%. Hazard ratios ([95% CIs]) were reported.P = 0.002) and serum albumin reduction of 5 g/L . All-cause mortality occurred in 740 (33.4%) patients, median time to death was 3.8 years; predictors included reduction of estimated glomerular filtration of 5 mL/min/1.73 m2  and increase of phosphate of 0.1 mmol/L , whereas a 10 g/L hemoglobin increase was protective . In 394 (18.0%) patients who received renal replacement therapy, median time to event was 2.3 years; predictors included halving of estimated glomerular filtration rate  and antihypertensive use . Increasing age, albumin reduction, and prior history of diabetes or cardiovascular disease were risk factors for all outcomes except renal replacement therapy.Among 2192 patients, mean follow-up was 5.6 years. Cardiovascular major adverse cardiovascular events occurred in 422 (19.3%) patients; predictors included prior history of diabetes (1.39 [1.13–1.71]; Several chronic kidney disease–specific cardiovascular risk factors were associated with increased mortality and cardiovascular event risk in patients with non–dialysis-dependent chronic kidney disease.The online version contains supplementary material available at 10.1007/s40620-023-01626-8. Chronic kidney disease (CKD) increases the risk of cardiovascular disease (CVD) –3. CardiThe Framingham Risk Equation predicts CV risk using traditional CV risk factors: age, sex, blood pressure, cholesterol, diabetes, and smoking , 8. Howe2 to the Framingham Risk Equation may not improve its predictive ability in patients with CKD [The addition of an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 mwith CKD . Underst2 are eligible to join. Patients are enrolled in the SKS after written informed consent is obtained.The Salford Kidney Study (SKS) is an ongoing (2002–present), prospective study of patients with CKD referred to renal services at Salford Royal National Health Service (NHS) Foundation Trust in northwest England , which s2 recorded in the 12 months preceding their recruitment date were included. Exclusion reasons included receiving dialysis or a kidney transplant or not having recruitment date, sex, or baseline age recorded.Patients in the SKS between October 2002 and December 2016 with NDD-CKD and eGFR > 10 and ≤ 60 mL/min/1.73 mPatients were followed annually from recruitment until death, RRT initiation, or data lock  .Biochemical parameters collected included hemoglobin, hematocrit, ferritin, transferrin saturation, eGFR, creatinine, phosphate, corrected calcium, random glucose, parathyroid hormone, C-reactive protein (CRP), albumin, and urinary protein:creatinine ratio (Table S1). The MDRD  equation was used to calculate eGFR . Anemia Mortality was established using date of death from the NHS Spine database and cause of death from the Office of National Statistics death certificate or Salford electronic patient record. Additional data collection details are provided in Online Resource 1.The primary outcome was first occurrence of CV-MACE, defined as the earliest date of non-fatal myocardial infarction (MI) diagnosis, non-fatal stroke diagnosis, or death due to a CV event (assumed if CV death was indicated in the cause-of-death portion of the dataset).Secondary outcomes were first occurrence of any CV event  or ACM, first occurrence of MI, first occurrence of non-fatal stroke, CVM, ACM, and occurrence of RRT.Baseline demographics and clinical characteristics were summarized descriptively. The sample was bifurcated by random selection: 70% was used to build a predictive model and 30% was used for independent model validation.Time-to-event analyses were performed for each outcome. Kaplan–Meier plots stratified by CKD stage at baseline were generated, then a proportional hazards (PH) model was fitted, and the best-fitting baseline hazard function (assessed using Bayesian information criterion) was adopted.Univariate analyses were performed to determine whether a baseline clinical or laboratory parameter predicted a study outcome. A multivariable PH model was developed including all statistically significant parameters from the univariate analyses and clinically important variables, followed by a backward selection procedure. The associations of risk factors with outcomes were investigated in a joint model  that comMissing event dates and key variables were imputed as described in Online Resource 1.The model was assessed on the validation cohort for positive and negative predictive value using area under receiver operating characteristics curves (AUCs) as summary measures of the model’s accuracy. The joint model was used to predict the cumulative 3-, 5-, and 10-year risk of events in the validation cohort for patients who had an event (to obtain sensitivity) and those who did not have an event (to obtain specificity).2, 57.7% had ferritin levels > 100 µg/L, and 86.5% were not treated with an erythropoiesis-stimulating agent (ESA) . Most patients were male, the mean age at baseline was 64.1 years, mean eGFR was 33.2 mL/min/1.73 mn = 2192), 422 patients (19.3%) experienced a CV-MACE. Mean (SD) time to first CV-MACE was 4.5 (3.4) years (Table S2). The Kaplan–Meier curve for CV-MACE outcomes flattened after 10 years , a 10-year age increase, history of diabetes or CVD, 5 g/L serum albumin decrease, and ESA treatment were significantly associated with higher CV-MACE risk in the joint final model Table .Fig. 1KaOverall, 792 patients (36.1%) experienced a CV event or died; mean (SD) time to any CV event or ACM was 4.2 (3.14) years. Median time to event was 8.3 years and 6.5 years for patients with stage 4 and stage 5 CKD, respectively. Only 2.0% (16 events) of patients experienced CCF (Table S2).The risk for any CV event and/or ACM generally increased with increasing CKD stage Fig. a. After 2 eGFR decrease; 5 g/L serum albumin decrease; 0.1 mmol/L phosphate increase; and doubling the urinary protein:creatinine ratio significantly increased risk for any CV event or ACM in the final joint model , a 10-year age increase; history of diabetes, CVD, cerebrovascular disease, or peripheral vascular disease (PVD); 5 mL/min/1.73 mFifty-five patients (2.5%) experienced MI with a mean (SD) time to event of 3.0 (3.28) years (Table S2). Myocardial infarction risk increased with decreasing renal function .2 eGFR decrease, halving eGFR from baseline, use of anticoagulants or lipid-lowering drugs, and 10 mmHg increase in pulse pressure (Table S5). Renin-angiotensin blockade was associated with reduced MI risk. No multivariate model for time to first MI could be built as few events occurred.Statistically significant predictors of increased MI risk in the univariate analysis included 10-year age increase, advanced CKD stage , history of CVD or diabetes, history of smoking, 5 mL/min/1.73 mFew patients experienced stroke (2.1%), unstable angina (0.6%), coronary revascularization therapy (0.2%), or CCF (1.0%) (Table S2). Mean (SD) times to event were 3.6 (2.91) years for stroke, 2.9 (3.27) years for unstable angina, 3.8 (4.78) years for coronary revascularization therapy, and 4.2 (3.38) years for CCF. Across individual CV events, ~ 18% of patients were censored for progression to RRT (Table S2).In univariate analyses, only a few associations between risk factors and individual CV events were observed, including history of CVD for most individual CV events. Due to the limited numbers of events, multivariate models could not be established for these individual CV events.Three hundred and fifty patients (16.0%) died from a CV event with a mean (SD) time to CVM of 5.0 (3.5) years Table S2). In the final joint model, variables noted to significantly increase CVM risk included 10-year age increase, history of diabetes or CVD, 5 g/L serum albumin decrease, and doubling CRP levels (Table . In the Seven hundred and forty patients (33.8%) experienced ACM; mean (SD) time to ACM was 4.6 3.21) years (Table S2). The Kaplan–Meier survival curve flattened after 7.5 years for patients with stage 5 CKD  received RRT; mean (SD) time to RRT was 3.3 (3.04) years (Table S2). The Kaplan–Meier survival curve flattened for patients with stage 4 or 5 CKD . In the final joint model, halving eGFR, a 10 mmHg increase in systolic blood pressure, and anticoagulant use significantly increased the risk of progression to RRT (Table S6). Female sex and renal diagnoses other than polycystic kidney disease were associated with a significantly decreased risk of progression to RRT.In the post hoc analysis in which MACE was defined to include ACM rather than CVM, 780 patients (35.6%) experienced MACE. Mean (SD) time to event was 4.3 (3.1) years (Table S2). The Kaplan–Meier curve for MACE outcomes flattened after 10 years .2 eGFR decrease; 5 g/L serum albumin decrease; and 0.1 mmol/L phosphate increase were significantly associated with higher risk of MACE in the joint final model (Table S8). Female sex and 10 g/L increase in hemoglobin levels each had a protective effect.Of the variables that were statistically significant in the univariate analysis (Table S7), 10-year age increase; history of diabetes, CVD, cerebrovascular disease, or PVD; 5 mL/min/1.73 mFor the stratified subgroup analyses, 37% of the sample had CRP levels > 5 mg/L; 42% of the sample had ferritin levels < 100 µg/L, 41% between 100 and 300 µg/L, and 17% ≥ 300 µg/L.More than 50% of patients in the CRP > 5 mg/L subgroup and more than 40% in the ferritin ≥ 300 µg/L subgroup experienced MACE (Table S9). For all other subgroups, the percentage of patients experiencing MACE was between 31 and 38%. The hazard ratios were generally comparable across these different subgroups. Log-ferritin by log-CRP interaction had a significant impact on the MACE risk in two subgroups (CRP > 5 mg/L and ferritin ≥ 300 µg/L).n = 422) had a CV-MACE, with a mean follow-up time of 5.6 years (Table S10). Event rates were slightly higher (21%) in patients with non-missing CRP (n/N = 351/1710) or ferritin (366/1767) values. CV-MACE events were observed in 28% and 23% of patients in the CRP > 5 mg/L and ferritin ≥ 300 µg/L subgroups, respectively. Although not all variables reached significance, the hazard ratios were comparable between the CRP and ferritin subgroups for each eGFR decrease of 5 mL/min/1.73 m2, aging, history of diabetes or CVD, decrease in albumin levels, urinary protein:creatinine ratio, and each hemoglobin increase of 10 g/L. Although any ESA treatment was associated with a significantly increased risk of CV-MACE in the total cohort, only ESA dose < 40 µg per 2 weeks was associated with a significantly increased risk of CV-MACE in the CRP > 5 mg/L and ferritin ≥ 300 µg/L subgroups.Approximately 19% of the model-building cohort .In the second sensitivity analysis, urinary protein:creatinine ratio was dropped from the model. The magnitude of the risks and majority of risk factors in this analysis were similar to the primary model (Table S12).The AUCs show the model adequately predicts the cumulative risk for time to first CV-MACE . AUCs across the remaining study endpoints were generally ≥ 0.80.2 decrease in eGFR and doubling of the urinary protein:creatinine ratio was associated with an increased risk of all outcomes except CV-MACE. Doubling CRP levels and antihypertensive use were also associated with increased CVM risk; a history of smoking and doubling parathyroid hormone levels were also associated with ACM. These findings are consistent with results from other studies in NDD-CKD [Patients with NDD-CKD were at an increased risk for CV events. Significantly higher risks of CV-MACE, CVM, and all other individual CV and mortality outcomes were associated with increasing age, a 5 g/L serum albumin decrease, and history of diabetes or CVD. Erythropoiesis-stimulating agent treatment was also associated with significantly higher risks of CV-MACE and CVM, which requires further investigation. Each 5 mL/min/1.73 m NDD-CKD –17.Anticoagulation use was associated with progression to RRT. Although precise causes for this cannot be ascertained, potential explanations are that atrial fibrillation prevalence increases with worsening CKD stage, and vitamin K antagonists  may precipitate anticoagulant nephropathy.After 10 years, patients with stage 4 versus 5 CKD had a worse prognosis for any CV event and/or ACM. This may have been due to low patient numbers at later time points, but is most likely due to competing risks: fewer patients with stage 4 (vs. stage 5) CKD would be expected to progress to RRT in this time frame; therefore, this analysis is more likely to capture patients with stage 4 CKD who die. The Kaplan–Meier curve for time to RRT flattened over time for patients with stage 4 or 5 CKD, perhaps due to a mix of no event and mortality censoring or survivor bias. The Kaplan–Meier curve for CV-MACE outcomes also flattened after 10 years, perhaps due to survivor bias .In our analysis, a 10 g/L increase in hemoglobin levels relative to another patient was associated with decreased risk of any CV event and ACM combined and decreased risk of ACM alone in a population in which most patients (86.5%) were not receiving ESAs. Although we did not capture iron replacement therapy in this analysis, the mean hemoglobin level was 123.6 g/L and mean ferritin level was 183.7 μg/L. Anemia of CKD is associated with increased risk of CVD, impaired activity and work productivity, and significant decreases in patients’ quality of life (QOL) , 18.P < 0.001) and having CV conditions was associated with significantly reduced QOL (P = 0.028) and work productivity (P = 0.032) [Previous studies have reported on CV events or risks of CV events associated with anemia and/or ESAs in patients with NDD-CKD. The FIND-CKD trial found no difference in CV events between patients receiving intravenous ferric carboxymaltose and oral iron . In the = 0.032) .http://ckdpcrisk.org/lowgfrevents/) using a Markov model based on data from cohorts of patients participating in the international Chronic Kidney Disease Prognosis Consortium [The results of our hypothesis-generating research can be used to guide the development of future CKD-specific CV risk equations. The risk factors identified in our analysis of SKS patient data could be used as inputs in future prognostic models. For example, Grams et al. generated a risk calculator for RRT, nonfatal CVD events, and death , which represents real-world clinical practice. The frequency of CCF events was unexpectedly low (2.0%), considering the number of patients with a history of CVD at baseline (34.6%), perhaps due to difficulties in recording this endpoint correctly or flaws in its designation at follow-up.We used a robust multiple imputation method to generate values for covariate data with missing values, but the underlying ‘missing at random’ assumption is untestable. Residual confounding may have occurred despite using multivariate statistical techniques to adjust for confounding covariates, as additional confounding factors may not have been collected. Likewise, errors may have occurred in the classification of patients with respect to confounding variables. However, these limitations were mitigated by the large sample size, long follow-up duration, and robust joint modeling approach, which can be replicated in future studies.Our results may not be generalizable to broader NDD-CKD populations in the UK or elsewhere. Approximately 18% of Salford center patients receiving dialysis are non-white , whereasOur results show that patients with NDD-CKD were at an increased risk of any CV event, ACM, and RRT. In addition to traditional CV risk factors , risk factors such as decreases in eGFR values and serum albumin and history of CVD, PVD, or cerebrovascular disease were associated with increased mortality and CV event risk in this population. Novel biomarkers, while not explicitly evaluated in this study, may be appropriate to incorporate into future models . Event rSupplementary file1 (DOCX 543 kb)Below is the link to the electronic supplementary material.


Global burden of hypertension among young people continues to increase. There have been many studies examining the effect of aerobic and muscle-strengthening physical activity on blood pressure, many of them didn't consider interdependence between them. Conflicting results of health-related fitness, particularly handgrip strength, as intermediate outcomes of muscle-strengthening physical activity on blood pressure also emerged. This research will carry out a mediation-moderation analysis to find out the relationship between muscle strengthening physical activity and blood pressure among young adults by considering health-related fitness and 24-hour movement behavior.A cross-sectional study among 221 Indonesian young adults attending a physical activity intervention collected participant's muscle-strengthening physical activity, and 24 h movement behavior, including aerobic physical activity, sedentary and sleep behavior, and mental well-being using validated questionnaires. Mediation and moderation analyses were conducted using Process Macro model 10 on SPSS 25 to investigate the association of muscle-strengthening physical activity on blood pressure, with gender and blood pressure as moderator, mediators consist of handgrip strength, muscle mass percentage and cardiorespiratory fitness. A subgroup analysis was conducted based on participant's cardiorespiratory fitness level.Volume of muscle-strengthening physical activities in a week have a direct association with systolic blood pressure among prehypertensive male with an effect of 0,00989359 . Considering its volume as mediator, the frequency of muscle-strengthening physical activity contributed to a significant direct effect on diastolic blood pressure in both genders, but the duration of MSPA has a significant direct effect on systolic blood pressure in male subjects. There is no component of physical fitness that provides a significant mediating effect. After a subgroup analysis, the relationship between MSPA Volume and blood pressure is not significant for individuals with a high level of cardiorespiratory fitness.This study shows that increased participation in muscle strengthening physical activity, especially in subject with low cardiorespiratory fitness, could increase blood pressure in prehypertensive young adult male population without mediation by physical fitness. Further research is needed to investigate other mechanisms that influence this relationship. From 1990 to 2019, the number of persons with hypertension more than doubled, from 600 million to more than 1 billion . That shThe high rate of hypertension at a young age is inseparable from the reluctance of young people to do physical activity , 5. VariAlthough there have been many studies looking for a relationship between MSPA and blood pressure, many of them didn't consider the interdependence between them. While health- related fitness components such as muscle strength can be intermediates between MSE and blood pressure, some studies show inconsistent results. MSPA is generally associated with a lower risk of high blood pressure, but several studies have shown that higher blood pressure is associated with higher grip strength through mechanisms that are unclear. Therefore, it is necessary to do an analysis to assess the relationship between various factors , 9, 10.In addition, various health-related factors that can affect blood pressure conditions, such as age, mental health, sleeping behavior, and sedentary behavior are also rarely considered. Age is globally known as one of the biggest risk factor in high blood pressure incidence, including in Indonesia . Mental Moreover, gender and abnormal blood pressure as potential moderators. There are several studies that show differences in response to physical exercise between men and women. Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) reduction with muscle strengthening exercises differs statistically between men and women . AnotherGiven that there is still a knowledge gap where there are not many mediation analysis studies that link MSPA and blood pressure with consideration of the interdependence connection between them, this research will carry out a mediation-moderation analysis to find out the relationship between muscle strengthening physical activity and blood pressure by considering gender and abnormal blood pressure as moderator, muscle strength, muscle mass percentage and cardiorespiratory fitness as mediator and moderate-vigorous physical activity, age, sleep behavior, sedentary behaviour, mental wellbeing and BMI as covariates.2.2.1.Effectiveness of a virtual challenge with a social component in promoting physical activity, hydration, and fruit consumption among university students.” This study protocol has been registered with the Thai Clinical Registry with the registry number TCTR20230106003. We reported our current study following the strengthening the reporting of observational studies in epidemiology (STROBE) statement and A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational . This study is a sub-study of a quasi-experimental study entitled “vational .2.2.All Gadjah Mada University students, including those pursuing vocational, undergraduate, master, and doctoral degrees, were qualified to take part in the study. Subject age was limited to the range of 18–25. The study excluded participants who had experienced a heart attack within the previous three months, were recovering from surgery, and had physical or mental impairments that precluded them from taking part in all tests. Convenience sampling technique was adopted in this investigation. G*Power analysis was used to calculate the sample size to examine the link between the volume of muscle-strengthening activities and blood pressure . Using a2.3.The primary aim of this study was examining the direct effect of volume of MSPA on blood pressure. In addition, the study also aimed to analyze the indirect effect of various physical fitness components as potential mediators in the relationship between them by considering the role of sex and abnormal blood pressure as moderators.This study not only wants to see the direct effect of volume of MSPA on blood pressure but also the indirect effect of various physical fitness components as potential mediators in the relationship between them. This study also wants to see the role of sex and abnormal blood pressure as moderators.2.4.We used a modeling framework to examine whether physical fitness mediates the relationship between MSPA volume and blood pressure . MSPA se2.5.2.5.1.After a minimum of five minutes of rest, SBP and DBP were assessed in a sitting position using a certified digital instrument  . We meas2.5.2.The volume of Muscle Strength Physical Activity (MSPA) for a week was measured by the Muscle Strengthening Exercise Questionnaire (MSEQ), which consists of questions about the duration, intensity, frequency, and domain of muscle strengthening exercises . MSPA is2.5.3.2.5.3.1.Muscle strength was assessed with a Camry handgrip dynamometer Model EH101). All subjects were asked to sit down while holding the dynamometer in the dominant hand with their arms at a 90degree angle with the body without squeezing the arms against the body. Participants are allowed to do one trial. As in the previous study 1. All su, three t2.5.3.2.Omron Karada Scan HBF 375 Body Fat Composition Monitor Used to measure body weight in kilograms (kg) and percentage of skeletal muscle mass.2.5.3.3.Cardiorespiratory fitness examined by the 6-Minute Walking Test (6MWT) method. The result of the 6MWT distance in meters is converted to an estimate of V˙O2max by the formula . V˙O2max2.5.4.Covariates consist of Moderate-Vigorous physical activity (MVPA), sedentary behavior, sleep behavior, mental wellbeing, and BMI. MVPA, sedentary behavior, and sleeping habits are measured from questions on a questionnaire that has been tested for reliability and validity in our unpublished study . MVPA is2.6.Data were displayed using descriptive statistics in the form of a mean and standard deviation. The distribution of categorical variables is presented using frequency and proportion. If data is normally distributed, then Pearson correlation was used to see the relationship between volume of muscle strengthening exercises, blood pressure, muscle strength, and percentage of muscle mass. If the data is not normally distributed, then an alternative is using Spearman's correlation. The percentile bootstrapping linear regression using Hayes model was used to assess the mediating effect of muscle strength and muscle mass percentage. We present the results of the mediation-moderation analysis with data that has been adjusted by the covariates and without adjustment. A sensitivity analysis was carried out by considering the level of cardiopulmonary fitness. All analyses were performed using the SPSS version 25 application with a significance level of 0.05.3.257 undergraduate students who were among the 412 participants in the virtual challenge decided to take part in the study and completed the questionnaire. Ninety percent of them (235) attended the health and fitness examination, but 4 of them had an absolute contraindication to fitness measurement, resulting in a total of 231 study subjects. After excluding 5 missing and 5 invalid data, a total of 221 subjects were finally included in the analysis. Based on According to the R2 of the unadjusted models , muscle-β = 0.00191, 95% CI 0.00046 to 0.00335, t = 2.60425, p = 0.00987), but not in women . Looking at these data, it can be estimated that every 625 min of 4 MET (moderate intensity) muscle-strengthening physical activity in 1 week is associated with an increase in systolic blood pressure of 5 mmHg in the male population that has high blood pressure. Neither muscle mass, muscle strength nor cardiorespiratory fitness has a mediating effect on the relationship between MSPA volume and blood pressure.Looking at the results of the adjusted model that included covariates –5, it isp = 0.02064, 95% CI −1.63213 to −0.13681; female p = 0.03221, 95% CI −2.59071 to −0.11597). Meanwhile, the duration of MSPA has a significant direct effect on systolic blood pressure in male subjects . After looking at the direct and indirect effects in the model above, we conducted a sensitivity analysis by comparing the data on the relationship between VMSPA and blood pressure by grouping subjects based on their level of cardiopulmonary fitness, shown in p: 0.1057, 95% CI −0.0004 to 0.0040), compared to the group of all fitness levels  and in the low fitness group , which is significant. Comparing R2 among the three groups, we can see the good fitness group explains the variation in systolic blood pressure readings in the population by 75.44%, compared with 69.16% in the all fitness group and 65.39% in the low fitness group. As a result, in people with a high level of cardiorespiratory fitness, the relationship between the volume of muscle-strengthening exercises and blood pressure is not significant.According to the 4.4.1.This study shows statistically that there is a significant positive relationship between MSPA and blood pressure, after entering gender and abnormal blood pressure as moderators. Different results appear when the two variables are not considered, where there is no significant direct effect between MSPA and BP. This shows that sex and abnormal pressure function as moderator variables in the relationship between MSPA and BP.In previous studies that investigated the relationship between muscle strength and BP, there was a positive relationship between MS and BP. However, in the present study, the relationship between MS and BP was not significant. This could be influenced by many things, seeing as the number of samples and the age range used in previous studies were different. However, MSPA has a direct effect on SBP. In the components and domains of MSPA, it turns out that free weight also has a direct effect on SBP in men.However, there was no indirect effect from the mediators studied. This shows that there are other mechanisms or factors other than physical fitness that can mediate the relationship between MSPA and blood pressure. We suspect that there are three things that can explain why there are differences in the effects of MSPA on BP in men and women.First, hormone and autonomic response differences between gender. In women, there is hormone secretion, such as estrogen, which has an effect on nitric oxide synthesis, and provides a relaxing effect on peripheral resistance of blood vessels . In addiIt is well known that blood pressure is the product of cardiac output and peripheral resistance. Cardiac output is the product of the heart rate and stroke volume, where the heart rate component is strongly influenced by the work of the sympathetic and parasympathetic nerves. While, in resting conditions, young women have lower muscle sympathetic activity to support blood pressure than men , 33. ThiWomen typically have a poorer capacity for arterial bed vasoconstriction than males do under specific circumstances following physical activity. Following exercise, women's vascular resistance is lower than men's. This could be the reason why women's blood pressure tends to drop more quickly and effectively after exercise than men's does .Second, differences in metabolism during conditions of abnormal blood pressure. Because of the cross-sectional design of this study, it is not possible to determine whether the association between blood pressure and the amount of muscle-strengthening exercise is causative or allows for a two-way relationship. The increase in blood pressure may be due to adaptation to muscle-building activities, or the individual may already have high blood pressure before beginning resistance training. Regarding the relationship between muscle strengthening exercises and blood pressure, there is some evidence indicating that there is a disturbance of skeletal muscle oxygenation during exercise in adult subjects with hypertension . When coIn addition, acute weight training will have an impact on the activation of muscle fibers due to alterations in skeletal muscle metabolism that occur in people with high blood pressure. In individuals with high blood pressure, there is a shift from oxidative metabolism to predominantly glycolytic metabolism under hypoxic conditions in skeletal muscle during weight training This increased use of energy from glycolysis will change the recruitment of type II muscle fibers to be dominant over type I muscle fibers , 38, 39.Third, resistance training reduces arterial compliance and exacerbates aortic wave reflection, especially in young subjects , 41. In Fourth, there are non-biological factors that can affect a person's health, including how a person's reasons for doing physical activity and how a person's body responds to physical activity and produces blood pressure output . One of In addition to these four mechanisms, this study found that cardiorespiratory fitness level played a role in the relationship between the volume of muscle-strengthening exercises and blood pressure. It is known that there is a significant positive relationship between the volume of muscle strengthening exercises and systolic blood pressure in male subjects with high blood pressure. The volume of muscle strengthening exercises and systolic blood pressure relationship was not significant in male subjects with good fitness . From these results, it can be concluded that a good level of cardiorespiratory fitness has a protective effect on blood pressure in the young adult population, especially in male subjects with high blood pressure. Therefore, someone at a young age who does muscle strengthening exercises is advised to maintain cardiorespiratory fitness by doing aerobics activities as a companion to muscle strengthening exercises.4.2.There are two limitations to this study. First, we know that a person's cardio-metabolic state is influenced by hereditary factors. Because this study focuses on functional outcomes, especially aspects of fitness, we do not carry out tests that lead to biomarkers or genetic characteristics. Second, because this research is not an interventional study, the conclusions in this study cannot prove a definite causal relationship between the independent variable and the outcome. For future research, lab-based RCTs can be conducted to learn more about the mechanisms, or research in the form of mediation analysis of behavioral-based cluster interventions can be conducted to see a more in-depth dose-response in the population.5.This study investigates the relationship between MSPA and blood pressure in a young adult population, taking into account mediators, moderators, and various covariate variables. The results show that MSPA has a significant positive direct effect on systolic blood pressure, especially in the male population with high blood pressure and low cardiorespiratory fitness. It can be estimated that every 625 min of 4 MET MSPA per week is associated with an increase in systolic blood pressure of 5 mmHg in the male population that has high blood pressure. Seeing this, when exercising, the community, especially fitness practitioners, must consider the dose of exercise that is in accordance with a person's premorbid conditions. Looking at the results of the sensitivity analysis, the possibility of a harmful effect of MSPA on blood pressure can be minimized by increasing cardiopulmonary fitness by also doing aerobic activity in addition to MSPA. In addition, functional parameters such as physical fitness do not have a mediating effect on the relationship between MSPA and blood pressure, so it is necessary to study the mechanism to explain other pathways that can explain the pathway of the relationship between MSPA volume and blood pressure.


The synergistic effects of RAAS inhibition and intensive blood pressure lowering in reducing proteinuria have not been well studied. We aimed to study this effect using data from the AASK Trial where our data suggest there is an additive and synergistic effect between RAAS inhibition and intensive blood pressure inhibition in reducing proteinuria. Previous studies have shown that renin-angiotensin-aldosterone system (RAAS) inhibitors and intensive blood pressure (BP) lowering are both effective in lowering proteinuria –4. HowevThe data in this study were derived from the AASK Trial which are publicly available in the NIDDK Central Repository. Briefly, the AASK Trial was a 2 × 3 factorial design trial which tested the effect of two BP targets  and three different BP agents , a calcium channel blocker (CCB) (amlodipine), and a beta-blocker (metoprolol)) on the progression of kidney disease. To achieve the assigned BP targets, AASK Trial permitted the use of other anti-hypertensive agents besides the randomized intervention . Our priOur primary exposures were modeled as a six-category variable that accounted for both the randomly assigned BP target and agent, treating those receiving ACE-I and usual BP control as the reference group. The primary outcome in our study was the ratio of proportional change in PCR between baseline and month 6 compared to the reference group. The rationale for examining change in proteinuria over a 6-month period was because the AASK Trial aimed to achieve the assigned BP target over this timeframe and re-ascertained proteinuria at month 6 of study .Our primary analysis was unadjusted given the preservation of randomization in the definition of our exposure. However, in secondary analysis, we also included a minimally adjusted model (Model 1) adjusting only for baseline proteinuria, and a fully adjusted model (Model 2) that was additionally adjusted for baseline age, sex, estimated glomerular filtration rate, and systolic (BP).2, mean systolic blood pressure (BP) was 154 mmHg, and mean PCR was 0.65 g/g. These baseline characteristics were similar across all six groups.Although 358 patients were eligible for inclusion, 109 patients did not have PCR measured at 6 months post-randomization and were excluded; 2 developed end stage kidney disease and 7 died in the interim. For the 240 participants included in our analysis, mean age was 51 years, 62.9% were men, mean estimated glomerular filtration rate was 29 ml/min/1.73 mThe proportional change in PCR between baseline and month 6 in each of the six groups is shown in Table Intensive BP lowering was overall more effective in lowering proteinuria compared to usual BP control, regardless of the BP agent Table . IntensiOverall, our results suggest that ACE-I and beta-blockers achieved similar degrees of suppression of proteinuria when patients were already receiving an intensive blood pressure lowering regimen. However, there was an additive and synergistic effect of adding intensive BP control to ACE-I or beta-blockers compared to usual BP control. Furthermore, use of a CCB as previously known in the literature, was associated with a greater increase in proteinuria.The strength of our study was our ability to quantify the additive effects of intensive BP control on ACE-I and other anti-hypertensive agents within a randomized setting. There are a few studies to our knowledge which have tested such BP interventions in a factorial design trial. There are a few limitations to our study. Our findings may not apply to patients in the general population who may differ from trial participants. Second, the AASK Trial only recruited Black participants, and findings may not apply to those of other racial or ethnic groups. Third, we do not have data on whether the participants had hyperaldosteronism and its role on treatment effects or suppression of proteinuria. Fourth, only a subset of patients enrolled in the AASK Trial had substantial proteinuria. However, studies have shown that even small degrees of change in proteinuria are associated with a higher risk of adverse outcomes . Our dat


The Pharmacy Quality Alliance’s definition of proportion of days covered (PDC) and medication possession ratio (MPR) have not been examined as potential quality measures in the kidney transplant recipient population.To (a) describe the frequency distribution of MPR and PDC using mycophenolic acid products in a real-world kidney transplant recipient population and (b) evaluate associations between MPR and PDC with late (> 90 days after transplantation) biopsy-proven acute rejection (BPAR).This was a retrospective cohort study combining data from the Wisconsin Allograft Recipient Database with University of Wisconsin (UW) Health Specialty Pharmacy prescription claims and dispensing data from March 10, 2006, to June 30, 2012. Patients who met criteria for persistence filling mycophenolic acid prescriptions at UW Health Specialty Pharmacy in the first year following discharge from kidney transplantation surgery hospitalization were included. Patients were excluded if they were enrolled in a clinical trial, if they had BPAR within 90 days of transplantation, or if they did not have panel reactive antibody data available. PDC and MPR were calculated over 360 days after discharge, and multivariable analyses were performed to determine if there were associations between PDC or MPR with late BPAR within 3 years.This study included 388 patients. The incidence of 3-year late BPAR was 5.1% (n = 20). Characteristics of patients who experienced late BPAR were largely consistent with those who did not experience late BPAR, with the exception of number of hospital readmissions, which was higher among patients who experienced late BPAR. The frequency distribution of PDC and MPR exhibited a skewed left distribution, with a median PDC of 0.972 and a median MPR of 1.000. Higher PDC was associated with lower odds of late BPAR  in multivariable analysis, as was a higher MPR .MPR and PDC may be calculated from data available to pharmacies and health plans, and each was associated with 3-year late BPAR among patients who did not experience early BPAR. However, the construct validity of these medication adherence measures requires further study. What is already known about this subjectLifelong adherence to immunosuppressive medications following kidney transplantation is vital to prevent rejection of the allograft.Pharmacy prescription claims data have been used for the purpose of comparing health plans by national quality organizations in several chronic disease states, although this strategy has not been examined in kidney transplant recipients.What this study addsThis study provides a description of medication possession ratio (MPR) and proportion of days covered (PDC), using mycophenolic acid products (mycophenolate mofetil or enteric-coated mycophenolate sodium) in a real-life kidney transplant recipient population.MPR and PDC in the first year following kidney transplantation are associated with late biopsy-proven acute rejection within 3 years following kidney transplantation, yet the construct validity of these medication adherence measures requires further study.2 Health system attributes may influence medication adherence after kidney transplantation, as evidenced by differential adherence rates observed in American and European transplant recipients.4 However, there is a lack of literature describing medication adherence quality measures, such as those endorsed by national quality organizations, for the purpose of comparing health plans and specialty pharmacies serving kidney transplant recipient populations.5Care following kidney transplantation requires lifelong adherence to immunosuppressant medications. Nonadherence to these medications after kidney transplantation is a common, dynamic, and modifiable risk factor for costly acute rejection episodes and allograft loss secondary to chronic, active antibody-mediated rejection.7 Proportion of days covered (PDC) and medication possession ratio (MPR) can be used to track medication adherence from one year to the next.8 PDC is preferred over MPR, since it is less likely to overestimate adherence because of prospective adjustment for overlapping days supplied.9 The Centers for Medicare & Medicaid Services used the PDC adherence measures endorsed by the National Quality Forum for statins, renin angiotensin system antagonists, and oral diabetes medications in their Part D 2016 star ratings, many of which correlate with Part C intermediate outcomes.10 A PDC threshold of ≥ 0.800 (≥ 80%) is typically selected to dichotomize medication adherence in disease states such as schizophrenia, diabetes, hypertension, congestive heart failure, and hyperlipidemia based on hospitalization predictive value.12 However, neither PDC nor MPR has been applied to characterize use of oral immunosuppressant medication in a real-world kidney transplant recipient population, so distributions of adherence rates using PDC and MPR in this distinct clinical arena are unknown.The Pharmacy Quality Alliance has developed, tested, and endorsed measures intended to estimate medication adherence in many disease states, using data available to prescription drug plans and closed pharmacy systems.14 The results of this study were interpreted in the context of the unique epidemiology of kidney transplant recipient rehospitalization and immunosuppression standards of care following kidney transplantation.The objectives of this study were to (a) describe the frequency distribution of MPR and the PDC using mycophenolic acid product-dispensing records in a real-world kidney transplant recipient population, and (b) determine whether there is an association between MPR or PDC in the first year following kidney transplantation and the 3-year incidence of late biopsy-proven acute rejection (BPAR), adjusting for a limited number of patient characteristics in multivariable analysis. Late BPAR was chosen as the primary outcome because it is commonly associated with nonadherence to immunosuppressant medications, responds poorly to treatment, and frequently leads to accelerated allograft loss.Data from the Wisconsin Allograft Recipient Database (WisARD) were combined with prescription claims data from the University of Wisconsin (UW) Health Specialty Pharmacy. WisARD contains prospectively validated data on demographics, transplant- and nontransplant-related outcomes, treatment, and laboratory values for all patients who had a solid organ transplantation procedure at the UW Hospital from 1980 to the present. Patients with a kidney transplantation surgery discharge between March 10, 2006, and June 20, 2012  were included based on availability of data.14 BPAR within 90 days would also have limited the MPR and PDC observation period. Patients were also excluded if they were enrolled in a clinical trial and if panel reactive antibody (PRA) data were not available at the time of transplantation.Patients were excluded if they experienced BPAR within 90 days of transplantation, since this study was neither designed nor powered to evaluate factors that would predispose a patient to acute cellular or antibody-mediated rejection within this early time frame after transplantation.Mycophenolic acid products (mycophenolate mofetil and enteric-coated mycophenolate sodium) were chosen for measurement, since they are expected to be subject to fewer dose adjustments than calcineurin inhibitors and corticosteroids in the first year following transplantation, and all patients were expected to remain on mycophenolic acid throughout the first postoperative year as standard of care at the study institution. According to the recommendations of the Pharmacy Quality Alliance, the MPR and PDC denominator should include the patients who were continuously enrolled in the pharmacy during the observation period. Therefore, persistence on mycophenolic acid was defined as at least 1 mycophenolic acid prescription within 15 days of discharge, 1 mycophenolic acid prescription within 100 days of day 360 (to account for 90-day supplies), and at least 3 prescriptions within the observation period.The primary outcome was 3-year late BPAR. All subjects were observed for a period of 3 years following kidney transplantation, regardless of their transplantation date. We selected covariates among available data a priori, by those most associated with the primary outcome. The UW Health Sciences Minimal Risk Institutional Review Board approved this study.Medication possession ratio (MPR) was defined as the sum of days supplied for each prescription dispensed between the date of discharge and 360 days following discharge, divided by 360 days. The days supplied for the last fill before the end of the observation period was truncated by the number of days between the fill and the last day of the study period. The maximum MPR was limited to 1.000 (100%).15 Briefly, an array was developed to account for each of 360 days following each patient’s date of discharge. The days supplied by each mycophenolic acid prescription fill prospectively populated each day in the array with a value of 1 to indicate that medication was available that day or 0 to indicate no availability. Overlapping days supplied were credited forward to the day with the next 0. As with MPR, the days supplied for the last prescription fill within the observation period was truncated, and the maximum PDC was limited to 1.000 (100%).Proportion of days covered (PDC) was defined using a micro-array approach using days supplied as described by Leslie for the 2007 SAS Global Forum.16 Logistic regression was used to conduct the multivariable analyses, using incidence of late BPAR  as the primary outcome. All statistical analyses were performed in Stata 14 .Descriptive statistics and a multivariable analysis were conducted using continuous data for MPR and PDC in accordance with the International Society for Pharmacoeconomics and Outcomes Research because of the lack of empiric evidence for a cutpoint or “threshold” in this population.A total of 388 patients were included in the analysis. Of the initial 1,640 patients, 238 were excluded for BPAR within the first 90 days after transplantation; 45 were then excluded for enrollment in a clinical trial; 251 of the remaining were excluded because they did not fill a mycophenolic acid product at a UW pharmacy within 15 days of discharge; and an additional 712 did not meet criteria for persistence filling at a UW pharmacy for the entire 360-day observation period. Finally, 6 patients were excluded for missing PRA data.Distributions of MPR and PDC exhibited a skewed left distribution, with a median PDC of 0.972 (interquartile range [IQR] = 0.128) and a median MPR of 1.000 (IQR = 0.108). The mean PDC was 0.907 (standard deviation [SD] = 0.146) and mean MPR was 0.920 (SD = 0.143). A total of 139 patients had PDC capped at 1.000, and 202 patients had MPR capped at 1.000. There were 34 cases per 100 patients per year with a PDC < 1.000 and 28 cases per 100 patients per year with an MPR < 1.000.P = 0.96), sex (P = 0.64), race (P = 0.50), diabetes as the cause of native kidney disease (P = 0.39), previous solid organ transplantation (P = 0.49), donor type (P = 0.77), and PRA  and PDC  remained similarly associated with late BPAR (P = 0.002) or PDC .The incidence of late (>90 days after transplantation) BPAR within 3 years after transplantation in this cohort was 5.1% (n = 20), with a mean onset of 266.8 days  after kidney transplantation. Characteristics of patients who experienced late BPAR were largely consistent with those who did not experience late BPAR on the basis of age  to 34 (PDC) cases of nonadherence per 100 patients per year, similar to the incidence described in a 2007 meta-analysis of nonadherence in kidney transplant recipients of 36 cases of nonadherence per 100 patients per year.38 The PDC threshold of < 0.800 (80%) commonly selected for its hospitalization predictive value in chronic disease states has not been validated among kidney transplant recipients and may not have much utility in this setting. Future studies are needed to determine whether a higher threshold would be useful for predicting outcomes meaningful to the kidney transplant recipient population, such as hospitalization predictive value, allograft loss, or survival.The frequency distributions of MPR and PDC observed in this cohort were skewed further left when compared with other chronic disease states in which the MPR and PDC denominators have been similarly defined.39 However, hospitalization after kidney transplantation is prevalent and highly associated with recipient age; in 1 single center study, 4.2%-8.6% of patients spent 120 or more excess days in the hospital in the first year after kidney transplantation.41 Moreover, 51% of patients with allograft rejection may require prolonged hospital stays.42 It is plausible that hospitalization may mediate the observed association between MPR and PDC with late BPAR in this analysis, even though the observation period for the primary outcome was 3 times as long as the observation period for the primary independent variables . Additional information about hospitalization may be available to health plans and integrated health care systems that would be interested in using either MPR or PDC to estimate outpatient immunosuppressant medication use. Future research should investigate the influence of inpatient hospitalization within the observation period and the prospective hospitalization predictive value of MPR and PDC to further develop their potential utility in quality assessment and performance improvement.This analysis was limited by the lack of data on days spent hospitalized and inpatient medication use, since these data were not available in WisARD. Patients in this analysis were therefore assumed to be users of outpatient medications each day during the observation period. The Centers for Medicare & Medicaid Services have performed adjustments of PDC for inpatient, hospice, and skilled nursing facility stays and found little effect on the PDC measurement.43 For example, Pinsky et al. (2009) reported a modified MPR of 0.896 during the first year after kidney transplantation using a multidrug approach.44While most kidney transplant recipients will be initiated on a mycophenolic acid product after transplantation, some will be converted to another antiproliferative medication such as azathioprine. Frequent dose changes may influence MPR and PDC in the early period after transplantation if using refill data for medications such as calcineurin inhibitors. On the flip side, monthly belatacept infusions would be relatively straightforward to monitor. Taking into account the variety of regimens that may be used in solid organ transplantation, a multiclass approach may need to be taken.Finally, the generalizability of these data may also be limited because patients who choose to continue to fill their mycophenolic acid prescriptions at a health system pharmacy may not be representative of the overall population transplanted at this single center or at any other transplant center. There was limited racial heterogeneity among participants and fewer patients with end-stage renal disease secondary to diabetes than in many populations . While dIn the first year following kidney transplantation discharge, MPR and PDC of mycophenolic acid product prescriptions were each associated with late BPAR within 3 years following kidney transplantation. However, their utility as medication use quality measures is questionable because of their limited distribution even before adjustment for hospitalization. Further research should consider the construct validity of these medication adherence measures in a kidney transplant recipient population, as well as investigating censoring and long-term temporal relationships between claims-based medication adherence measurements and hospitalizations, kidney allograft loss, and survival.