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Background Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI). Methods One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model. Results A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI–age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability. Conclusions We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.

Background: Sodium-glucose co-transporter-2 inhibitors (SGLT2is) are widely used in clinical practice for their demonstrated cardiorenal benefits, but multiple adverse events (AEs) have been reported. We aimed to describe the distribution of SGLT2i-related AEs in different systems and identify important medical event (IME) signals for SGLT2i.Methods: Data from the first quarter (Q1) of 2013–2021 Q2 in FAERS were selected to conduct disproportionality analysis. The definition of AEs and IMEs relied on the system organ classes (SOCs) and preferred terms (PTs) by the Medical Dictionary for Regulatory Activities (MedDRA-version 24.0). Two signal indicators, the reported odds ratio (ROR) and information component (IC), were used to estimate the association between SGLT2is and IMEs.Results: A total of 57,818 records related to SGLT2i, with 22,537 SGLT2i-IME pairs. Most SGLT2i-related IMEs occurred in monotherapy (N = 21,408, 94.99%). Significant signals emerged at the following SOCs: “metabolism and nutrition disorders” (N = 9,103; IC025 = 4.26), “renal and urinary disorders” (3886; 1.20), “infections and infestations” (3457; 0.85). The common strong signals were observed in diabetic ketoacidosis, ketoacidosis, euglycaemic diabetic ketoacidosis and Fournier’s gangrene. Unexpected safety signals such as cellulitis, osteomyelitis, cerebral infarction and nephrolithiasis were detected.Conclusion: Our pharmacovigilance analysis showed that a high frequency was reported for IMEs triggered by SGLT2i monotherapy. Different SGLT2is caused different types and the association strengths of IMEs, while they also shared some specific PTs. Most of the results are generally consistent with previous studies, and more pharmacoepidemiological studies are needed to validate for unexpected AEs. Based on risk-benefit considerations, clinicians should be well informed about important medical events that may be aggravated by SGLT2is.

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Background/Aims: The relationship between carotid artery plaque burden, phenotype and serum cystatin C at normal and impaired renal function is still unclear. Methods: Demographic characteristics, carotid ultrasonography and other relevant information of 1,477 patients were collected. The association of carotid artery plaque burden, plaque phenotype with serum cystatin C was evaluated by strategy analysis based on renal function. Results: Serum cystatin C (OR=2.05, 95% CI: 1.83-2.29, P<.01) was a risk factor of stable plaque among patients with normal glomerular filtration rate. However, in the patients with mild impaired renal function, serum cystatin C was not only a risk factor for stable plaque (OR=1.60, 95%CI: 1.43-1.78, P<.001) but also was a risk factor for unstable plaque (OR=1.21, 95%CI: 1.10-1.32, P<.001). The smoothing function curve and a three-piecewise linear regression revealed that a nonlinear relationship was observed between serum cystatin C and plaque burden. When serum cystatin C was in the range of 0.75-1.30 (mg/L), the plaque burden tended to increase. Conclusion: In normal renal function, serum cystatin C may confer stability of plaques. In mildly impaired renal function, serum cystatin C is a risk predictor of plaques. In normal renal function circumstances, serum cystatin C may benefit to the stability of plaques. In mild impaired renal function circumstances, serum cystatin C are a risk predictors of plaques.