Search

Background: UK guidelines recommend the QFracture tool to predict the risk of major osteoporotic fracture and hip fracture, but QFracture calibration is poor, partly because it does not account for competing mortality risk. The aim of this study was to derive and validate a competing risk model to predict major osteoporotic fracture and hip fracture (CFracture) and compare its performance with that of QFracture in UK primary care. Methods: We used UK linked primary care data from the Clinical Practice Research Datalink GOLD database to identify people aged 30–99 years, split into derivation and validation cohorts. In the derivation cohort, we derived models (CFracture) using the same covariates as QFracture with Fine-Gray competing risk modelling, and included the Charlson Comorbidity Index score as an additional predictor of non-fracture death. In a separate validation cohort, we examined discrimination (using Harrell's C-statistic) and calibration of CFracture compared with QFracture. Reclassification analysis examined differences in the characteristics of patients reclassified as higher risk by CFracture but not by QFracture. Findings: The derivation cohort included 1 831 606 women and 1 789 820 men, and the validation cohort included 915 803 women and 894 910 men. Overall discrimination of CFracture was excellent (C-statistic=0·813 [95% CI 0·810–0·816] for major osteoporotic fracture and 0·914 [0·908–0·919] for hip fracture in women; 0·734 [0·729–0·740] for major osteoporotic fracture and 0·886 [0·877–0·895] for hip fracture in men) and was similar to QFracture. CFracture calibration overall and in people younger than 75 years was generally excellent. CFracture overpredicted major osteoporotic fracture and hip fracture in older people and people with comorbidity, but was better calibrated than QFracture. Patients classified as high-risk by CFracture but not by QFracture had a higher prevalence of current smoking and previous fracture, but lower prevalence of dementia, cancer, cardiovascular disease, renal disease, and diabetes. Interpretation: CFracture has similar discrimination to QFracture but is better calibrated overall and in younger people. Both models performed poorly in adults aged 85 years and older. Competing risk models should be recommended for fracture risk prediction to guide treatment recommendations. Funding: National Institute for Health and Care Research, Wellcome Trust, Health Data Research UK.

Background: Acute kidney injury (AKI) is common and associated with adverse outcomes as well as important healthcare costs. However, evidence examining the epidemiology of acute kidney disease (AKD)—recently defined as AKI persisting between 7 and 90 days—remains limited. The aims of this study were to establish the rates of early AKI recovery, progression to AKD and non-recovery; examine risk factors associated with non-recovery and investigate the association between recovery timing and adverse outcomes, in a population-based cohort. Methods: All adult residents of Tayside & Fife, Scotland, UK, with at least one episode of community or hospital-managed AKI using KDIGO creatinine-based definition during the period 1 January 2010 to 31 December 2018 were identified. Logistic regression was used to examine factors associated with non-recovery, and Cox modelling was used to establish associations between AKI recovery timing and risks of mortality and development of de novo CKD. Results: Over 9 years, 56,906 patients with at least one AKI episode were identified with 18,773 (33%) of these progressing to AKD. Of those progressing to AKD, 5059 (27%) had still not recovered at day 90 post AKI diagnosis. Risk factors for AKD included: increasing AKI severity, pre-existing cancer or chronic heart failure and recent use of loop diuretics. Compared with early AKI recovery, progression to AKD was associated with increased hazard of 1-year mortality and de novo CKD (HR = 1.20, 95% CI 1.13 to 1.26 and HR = 2.21, 95% CI 1.91 to 2.57 respectively). Conclusions: These findings highlight the importance of early AKI recognition and management to avoid progression to AKD and long-term adverse outcomes.

BACKGROUND: This trial aimed to determine the efficacy of leucine and/or perindopril in improving physical function in older people with sarcopenia. METHODS: Placebo-controlled, parallel group, double-blind, randomized two-by-two factorial trial. We recruited adults aged?70years with sarcopenia, defined as low gait speed (

Additional file 1: Figure S1. Flow chart for cohort identification from CPRD GOLD. Table S1. Missing data handling for variables included in the model. Table S3. Incidence rates of cardiovascular disease per 1000 person years in derivation cohort Table S4. Adjusted subdistribution hazard ratios for CVD in women in the derivation cohort for CRISK-CCI. Table S5. Adjusted subdistribution hazard ratios for CVD in men in the derivation cohort for CRISK-CCI. Table S6. Characteristics of patients reclassified by CRISK-CCI in the validation cohort.

Additional file 1: Figure S1: Survival curve stratified by AKI category. Table S1: Recovery status at 7- and 90-days post-AKI diagnosis. Table S2: Characteristics of tested versus untested patients with CA-CM AKI. Table S3: 90-day status of patients untested within the first 7 days post AKI diagnosis. Figure S2: Association between individual predictors and progression to AKD (main analysis). Table S4 and Figure S3: Association between individual predictors and non-recovery at day 7 (sensitivity analysis). Figure S4: Association between individual predictors and non-recovery at day 90 in patients who entered the AKD phase (main analysis). Table S5 and Figure S5: Association between individual predictors and non-recovery at day 90 in patients who entered the AKD phase (sensitivity analysis). Figure S6: Correlation matrix for pairs of candidate risk factors. Table S6: Association between progression to AKD and subsequent risk of death or development of de novo CKD.

Additional file 1.

Additional file 2.

Additional file 2: Table S2. Follow-up status at 10 years by sex, age and co-morbidity in the derivation cohort.