Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.

Numerous predictive models for Parkinson’s disease (PD) incidence have been published recently. However, the model performance and methodological quality of those available models are yet needed to be summarized and assessed systematically. In this systematic review, we systematically reviewed the published predictive models for PD incidence and assessed their risk of bias and applicability. Three international databases were searched. Cohort or nested case-control studies that aimed to develop or validate a predictive model for PD incidence were considered eligible. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used for risk of bias and applicability assessment. Ten studies covering 10 predictive models were included. Among them, four studies focused on model development, covering eight models, while the remaining six studies focused on model external validation, covering two models. The discrimination of the eight new development models was generally poor, with only one model reported C index > 0.70. Four out of the six external validation studies showed excellent or outstanding discrimination. All included studies had high risk of bias. Three predictive models (the International Parkinson and Movement Disorder Society [MDS] prodromal PD criteria, the model developed by Karabayir et al. and models validated by Faust et al.) are recommended for clinical application by considering model performance and resource-demanding. In conclusion, the performance and methodological quality of most of the identified predictive models for PD incidence were unsatisfactory. The MDS prodromal PD criteria, model developed by Karabayir et al. and model validated by Faust et al. may be considered for clinical use.

Background To summarize the up-to-date empirical evidence on trial-level characteristics of randomized controlled trials associated with treatment effect estimates. Methods A systematic review searched three databases up to August 2020. Meta-epidemiological (ME) studies of randomized controlled trials on intervention effect were eligible. We assessed the methodological quality of ME studies using a self-developed criterion. Associations between treatment effect estimates and trial-level characteristics were presented using forest plots. Results Eighty ME studies were included, with 25/80 (31%) being published after 2015. Less than one-third ME studies critically appraised the included studies (26/80, 33%), published a protocol (23/80, 29%), and provided a list of excluded studies with justifications (12/80, 15%). Trials with high or unclear (versus low) risk of bias on sequence generation (3/14 for binary outcome and 1/6 for continuous outcome), allocation concealment (11/18 and 1/6), double blinding (5/15 and 2/4) and smaller sample size (4/5 and 2/2) significantly associated with larger treatment effect estimates. Associations between high or unclear risk of bias on allocation concealment (5/6 for binary outcome and 1/3 for continuous outcome), double blinding (4/5 and 1/3) and larger treatment effect estimates were more frequently observed for subjective outcomes. The associations between treatment effect estimates and non-blinding of outcome assessors were removed in trials using multiple observers to reach consensus for both binary and continuous outcomes. Some trial characteristics in the Cochrane risk-of-bias (RoB2) tool have not been covered by the included ME studies, including using validated method for outcome measures and selection of the reported results from multiple outcome measures or multiple analysis based on results (e.g., significance of the results). Conclusions Consistently significant associations between larger treatment effect estimates and high or unclear risk of bias on sequence generation, allocation concealment, double blinding and smaller sample size were found. The impact of allocation concealment and double blinding were more consistent for subjective outcomes. The methodological and reporting quality of included ME studies were dissatisfactory. Future ME studies should follow the corresponding reporting guideline. Specific guidelines for conducting and critically appraising ME studies are needed.

Aims To evaluate the associations of baseline and long-term trajectories of lifestyle with incident ischemic heart diseases (IHD). Methods 29,164 participants in the UK Biobank who had at least one follow-up assessment and were free of IHD at the last follow-up assessment were included. We constructed a weighted unhealthy lifestyle score though summing five lifestyle factors (smoking, physical activity, diet, BMI, and sleep duration). Lifestyle assessed at baseline (2006-2009), the first follow-up assessment (2012-2013) and the second follow-up assessment (since 2014) were used to derive the trajectories of each individual. The joint categories were created through cross-classifying three baseline lifestyle categories (ideal, intermediate and poor) by three lifestyle trajectory categories (improve, maintain and decline). Results During a median follow-up period of 4.2 years, 868 IHD events were recorded. The hazard ratio (HR) of incident IHD associated with per unit increase in unhealthy lifestyle trajectory was 1.08 (95% confidence interval (CI): 0.99-1.17). Subgroup analyses indicated such association was stronger among individuals with hypertension (HR: 1.13, 95%CI: 1.03-1.24), diabetes (HR: 1.23, 95%CI: 0.96-1.58) or hyperlipidemia (HR: 1.09, 95%CI: 0.97-1.22). Compared with participants consistently adhering to an ideal lifestyle (ideal-maintain), the HRs of incident IHD were: 1.30 (1.07-1.58) for intermediate-maintain, 1.52 (1.23-1.88) for poor-maintain, 1.25 (0.93-1.68) for intermedia-improve, 1.48 (1.17-1.88) for poor-improve, 1.46 (1.08-1.99) for intermedia-decline and 1.77 (1.21-2.59) for poor-decline. Conclusions A declined lifestyle trajectory increased the risk of incident IHD, irrespective of baseline lifestyle levels. Individuals with hypertension, diabetes or hyperlipidemia were more predisposed to the influence of lifestyle change.

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