The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice

Incidence Rate of Coronary Atherosclerosis among Cancer Types during Survivorship: A Systematic Review and Meta-Analysis

BACKGROUND

Although coronary atherosclerosis is an important contributor to cardiovascular burden in adult cancer survivors, it remains unclear as to who is at risk. This systematic review sought to identify the incidence rate of coronary atherosclerosis in different cancer types and risk factors that contributed to the risk difference.

METHODS

A search of papers on the prevalence of coronary atherosclerosis among cancer survivors was conducted on 14th October 2024. The outcome was to compare the risk of coronary atherosclerosis between survivors from specific cancer types and the general population. Final output was reported as risk difference (RD).

RESULTS

In 18 studies (8,099,752 individuals with cancer history), coronary atherosclerosis was assessed through medical records or confirmation on imaging. Compared to the non-cancer population, survivors of hematological, renal and testicular cancer were more likely to be diagnosed with coronary atherosclerosis. In addition, survivors had a significant increase in coronary atherosclerosis risk (absolute risk for survivors vs controls: 17% vs 8%; RD: 0.12 [95% CI: 0.03-0.22], p<0.001) over a longer follow-up period (>10 years). Among cardiovascular risk factors, hypercholesterolemia (β=0.012, p<0.001), hypertension (β=0.008, p=0.017) and obesity (β=0.008, p=0.021) were significant contributors to the risk difference in coronary atherosclerosis development. A lack of treatment data limits the potential to elucidate the impact of therapy on coronary atherosclerosis development among cancer survivors. Overall, the risks of bias of the included studies were low, and no evidence of publication bias was found.

CONCLUSIONS

Cancer survivors are at an increased risk of developing coronary atherosclerosis compared to the non-cancer population, especially over a longer follow-up period. Future studies should investigate contributors to coronary atherosclerosis, particularly cancer treatments such as chest radiotherapy and immune checkpoint inhibitors, among cancer survivors. This information could guide screening strategies and allow early initiation of treatment to prevent coronary atherosclerosis progression.

Cardiac Troponins and Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

BACKGROUND

The extent to which high-sensitivity cardiac troponin can predict cardiovascular disease (CVD) is uncertain.

OBJECTIVES

We aimed to quantify the potential advantage of adding information on cardiac troponins to conventional risk factors in the prevention of CVD.

METHODS

We meta-analyzed individual-participant data from 15 cohorts, comprising 62,150 participants without prior CVD. We calculated HRs, measures of risk discrimination, and reclassification after adding cardiac troponin T (cTnT) or I (cTnI) to conventional risk factors. The primary outcome was first-onset CVD (ie, coronary heart disease or stroke). We then modeled the implications of initiating statin therapy using incidence rates from 2.1 million individuals from the United Kingdom.

RESULTS

Among participants with cTnT or cTnI measurements, 8,133 and 3,749 incident CVD events occurred during a median follow-up of 11.8 and 9.8 years, respectively. HRs for CVD per 1-SD higher concentration were 1.31 (95% CI: 1.25-1.37) for cTnT and 1.26 (95% CI: 1.19-1.33) for cTnI. Addition of cTnT or cTnI to conventional risk factors was associated with C-index increases of 0.015 (95% CI: 0.012-0.018) and 0.012 (95% CI: 0.009-0.015) and continuous net reclassification improvements of 6% and 5% in cases and 22% and 17% in noncases. One additional CVD event would be prevented for every 408 and 473 individuals screened based on statin therapy in those whose CVD risk is reclassified from intermediate to high risk after cTnT or cTnI measurement, respectively.

CONCLUSIONS

Measurement of cardiac troponin results in a modest improvement in the prediction of first-onset CVD that may translate into population health benefits if used at scale.

Early Prediction of Radiation Pneumonitis in Patients With Lung Cancer Treated With Immunotherapy Through Monitoring of Plasma Chemokines

PURPOSE

This study is aimed to identify biomarkers for symptomatic radiation pneumonitis (RP) in patients with lung cancer treated with immune checkpoint inhibitors (ICIs).

METHODS AND MATERIALS

This multicenter, prospective study enrolled patients with lung cancer receiving thoracic radiation therapy (RT) between 2021 and 2023. Plasma cytokines were measured using Luminex assays. Cox proportional hazards model was used to identify risk factors and biomarkers for RP. Sensitivity analysis was conducted using Fine-Gray competing risk analyses. Receiver operating characteristic curves were used to assess the predictive value of the cytokines.

RESULTS

A total of 214 patients receiving thoracic RT were included in this study, with 75 (35.05%) patients experiencing symptomatic RP. Among the 71 patients with prior ICI treatment, 32 (45.07%) developed symptomatic RP. Patients with prior ICI treatment had higher incidence of symptomatic RP and plasma chemokines than those without prior ICI treatment. For patients with prior ICI treatment, plasma CXCL10 before RT (hazard ratio [HR], 1.29; 95% CI, 1.03-1.61) and at 2 weeks (HR, 1.28; 95% CI, 1.03-1.59) and 4 weeks during RT (HR, 1.65; 95% CI, 1.19-2.28) were significantly associated with RP. The area under the curves (AUC) of plasma CXCL10 at baseline, 2 weeks and 4 weeks during RT were 0.625, 0.680, and 0.679, respectively. Plasma CXCL14 before RT and CXCL2 during RT were also predictors of RP. A risk score integrating CXCL10, CXCL14, CXCL2, and mean lung dose showed better predictive performance than individual factors (AUC = 0.757).

CONCLUSIONS

In this prospective study, plasma chemokines predict future risk of symptomatic RP in patients with lung cancer who have received prior immunotherapy. Despite with moderate AUC, the scoring system based on plasma chemokines and mean lung dose is a feasible tool for predicting symptomatic RP, aiding in tailoring personalized and optimal treatment for patients.

Risk prediction of cardiovascular disease in the Asia-Pacific region: the SCORE2 Asia-Pacific model

BACKGROUND AND AIMS

To improve upon the estimation of 10-year cardiovascular disease (CVD) event risk for individuals without prior CVD or diabetes mellitus in the Asia-Pacific region by systematic recalibration of the SCORE2 risk algorithm.

METHODS

The sex-specific and competing risk-adjusted SCORE2 algorithms were systematically recalibrated to reflect CVD incidence observed in four Asia-Pacific risk regions, defined according to country-level World Health Organization age- and sex-standardized CVD mortality rates. Using the same approach as applied for the original SCORE2 models, recalibration to each risk region was completed using expected CVD incidence and risk factor distributions from each region.

RESULTS

Risk region-specific CVD incidence was estimated using CVD mortality and incidence data on 8 405 574 individuals (556 421 CVD events). For external validation, data from 9 560 266 individuals without previous CVD or diabetes were analysed in 13 prospective studies from 12 countries (350 550 incident CVD events). The pooled C-index of the SCORE2 Asia-Pacific algorithms in the external validation datasets was .710 [95% confidence interval (CI) .677-.744]. Cohort-specific C-indices ranged from .605 (95% CI .597-.613) to .840 (95% CI .771-.909). Estimated CVD risk varied several-fold across Asia-Pacific risk regions. For example, the estimated 10-year CVD risk for a 50-year-old non-smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and high-density lipoprotein cholesterol of 1.3 mmol/L, ranged from 7% for men in low-risk countries to 14% for men in very-high-risk countries, and from 3% for women in low-risk countries to 13% for women in very-high-risk countries.

CONCLUSIONS

The SCORE2 Asia-Pacific algorithms have been calibrated to estimate 10-year risk of CVD for apparently healthy people in Asia and Oceania, thereby enhancing the identification of individuals at higher risk of developing CVD across the Asia-Pacific region.

Revealing the limitations of 10-year MACE observations: 20-year observed total cardiovascular burden in the EPIC-Norfolk study

BACKGROUND

Primary prevention strategies for cardiovascular disease (CVD) conventionally rely on 10-year risk estimates of major adverse cardiovascular events (MACE). However, communicating longer-term total CVD risk may better facilitate informed preventive decisions. Therefore, we aimed to quantify how well 10-year observed incidence reflects 20-year observed incidence and how MACE reflects total CVD events across demographic groups, using observations in long-term prospective data.

METHODS

In individuals aged 40-79 without CVD or diabetes from the population-based EPIC-Norfolk cohort, we compared the first occurrence of 10 and 20 years (1) 3-point MACE events (non-fatal myocardial infarction+non-fatal stroke+fatal CVD) and (2) total CVD events (all non-fatal and fatal CVD events leading to hospitalisation), stratified by sex and age.

RESULTS

Among 22 569 participants (57% women), incident 10-year and 20-year 3-point MACE was 5.3% and 15.5%, respectively, yielding 20/10 year ratios from 2.2 (in older men) to 4.5 (in younger women). Total CVD increased from 10.5% at 10 years to 26.9% at 20 years, with ratios ranging from 1.9 (older men) to 3.9 (younger women). Ratios between 10-year MACE and 20-year total CVD varied substantially, ranging from 3-fold in (older men) to 10-fold (younger women).

CONCLUSIONS

The observed incidence of CVD roughly triples from 10 to 20 years of follow-up, with 10-year MACE observations underestimating 20-year total CVD burden by a factor ranging from 3 (older men) to 10 (younger women). These findings highlight the limitations of communicating 10-year MACE risk assessments to facilitate informed decisions in longer-term CVD prevention-particularly in younger women.

The long-term effects of cardiometabolic risk factors on mortality and life expectancy: evidence from a health check-up cohort study

OBJECTIVE

Cardiometabolic risk factors significantly contribute to disease burden. This study explored the effects of hypertension (HTN), diabetes mellitus (DM), and hyperlipidemia (HLP) on mortality. It stratified findings by age group and comorbidity severity using the Charlson Comorbidity Index (CCI) score. Additionally, it assessed the compounded effects of comorbid conditions to estimate life expectancy (LE) and years of life lost (YLL) in individuals with various cardiometabolic risk factor combinations.

METHODS

Using data from the MJ Health Check-up Database (2002-2017), linked with the National Health Insurance Research Database (2000-2017) and the Death Registry (2002-2019), this study employed Cox proportional hazards models to determine mortality risk associated with various cardiometabolic risk factors. Adjusted Kaplan-Meier curves were constructed to evaluate survival rates across different risk factors and CCI scores. Survival rates were extrapolated to estimate confounder-adjusted LE and YLL for age-comorbidity combinations.

RESULTS

Among the three age groups (20-39, 40-59, 60-79), HLP was the most common single risk factor, followed by HTN. In participants with dual risk factors, HTN and HLP were the most frequent pair, with diabetes and HLP second. An increased number of cardiometabolic risk factors elevated mortality risk, particularly in the 20-39 age group. LE, adjusted for confounders, declined with age, higher CCI scores, and more risk factors. YLL decreased with age but increased with higher CCI scores and more risk factors.

CONCLUSIONS

Promoting health awareness, early disease detection, and timely medical access can reduce cardiometabolic risk factors and associated comorbidities, thereby alleviating disease burden.

Adding ethnicity to cardiovascular risk prediction: External validation and model updating of SCORE2 using data from the HELIUS population cohort

BACKGROUND

Current prediction models for mainland Europe do not include ethnicity, despite ethnic disparities in cardiovascular disease (CVD) risk. SCORE2 performance was evaluated across the largest ethnic groups in the Netherlands and ethnic backgrounds were added to the model.

METHODS

11,614 participants, aged between 40 and 70 years without CVD, from the population-based multi-ethnic HELIUS study were included. Fine and Gray models were used to calculate sub-distribution hazard ratios (SHR) for South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin groups, representing their CVD risk relative to the Dutch group, on top of individual SCORE2 risk predictions. Model performance was evaluated by discrimination, calibration and net reclassification index (NRI).

RESULTS

Overall, 274 fatal and non-fatal CVD events, and 146 non-cardiovascular deaths were observed during a median of 7.8 years follow-up (IQR 6.8-8.8). SHRs for CVD events were 1.86 (95 % CI 1.31-2.65) for the South-Asian Surinamese, 1.09 (95 % CI 0.76-1.56) for the African-Surinamese, 1.48 (95 % CI 0.94-2.31) for the Ghanaian, 1.63 (95 % CI 1.09-2.44) for the Turkish, and 0.67 (95 % CI 0.39-1.18) for the Moroccan origin groups. Adding ethnicity to SCORE2 yielded comparable calibration and discrimination [0.764 (95 % CI 0.735-0.792) vs. 0.769 (95 % CI 0.740-0.797)]. The NRI for adding ethnicity to SCORE2 was 0.24 (95 % CI 0.18-0.31) for events and - 0.12 (95 % CI -0.13-0.12) for non-events.

CONCLUSIONS

Adding ethnicity to the SCORE2 risk prediction model in a middle-aged, multi-ethnic Dutch population did not improve overall discrimination but improved risk classification, potentially helping to address CVD disparities through timely treatment.

Comparison of seven models for the progression patterns of multiple chronic conditions in longitudinal studies

Introduction

Studies investigating the relationship between patterns of multimorbidity and risk of a new condition have typically defined the patterns at a baseline time and used Kaplan-Meier (KM) or Cox proportional hazards regression. These methods do not consider the competing risk of death or the changes in the patterns of conditions over time. This study illustrates how these methodological limitations can be overcome in the setting of progression from cardiometabolic conditions to dementia.

Methods

Data from 11 930 women who participated in the Australian Longitudinal Study on Women's Health were used to define patterns of diabetes, heart disease and stroke and estimate the cumulative incidence or HRs of subsequent dementia. Seven methods were compared. For cumulative incidence these were KM method, cumulative incidence function (CIF) (to account for the competing risk of death) and multistate model with Aalen-Johansen estimates (to account also for the progression of conditions over time). For HRs, the corresponding methods were Cox model and Fine and Gray model (for sub-HRs) with the cardiometabolic patterns treated as time-invariant (from baseline) or as time-varying predictors.

Results

The estimated cumulative incidence of dementia using the KM method declined when the competing risk of death was considered. For example, for women with no cardiometabolic condition at baseline, the KM and CIF estimates were 35.7% (95% CI 34.6%, 36.8%) and 27.3% (26.4%, 28.2%) but these women may have developed cardiometabolic conditions during the study which would increase their risk. The Aalen-Johansen multistate estimate for women with no cardiometabolic condition over the whole study period was 11.0% (10.4%, 11.7%). Comparing models to estimate HRs, the estimates in the Fine and Gray models were lower than those in the Cox models.

Conclusions

Multistate and time-varying survival analysis models should be used to study the natural development of multimorbidity.

Development and validation of a lifetime prediction model for incident type 2 diabetes in patients with established cardiovascular disease: the CVD2DM model

AIMS

Identifying patients with established cardiovascular disease (CVD) who are at high risk of type 2 diabetes (T2D) may allow for early interventions, reducing the development of T2D and associated morbidity. The aim of this study was to develop and externally validate the CVD2DM model to estimate the 10-year and lifetime risks of T2D in patients with established CVD.

METHODS AND RESULTS

Sex-specific, competing risk-adjusted Cox proportional hazard models were derived in 19 281 participants with established CVD and without diabetes at baseline from the UK Biobank. The core model's pre-specified predictors were age, current smoking, family history of diabetes mellitus, body mass index, systolic blood pressure, fasting plasma glucose, and HDL cholesterol. The extended model also included HbA1c. The model was externally validated in 3481 patients from the UCC-SMART study. During a median follow-up of 12.2 years (interquartile interval 11.3-13.1), 1628 participants with established CVD were diagnosed with T2D in the UK Biobank. External validation c-statistics were 0.79 [95% confidence interval (CI) 0.76-0.82] for the core model and 0.81 (95% CI 0.78-0.84) for the extended model. Calibration plots showed agreement between predicted and observed 10-year risk of T2D.

CONCLUSION

The 10-year and lifetime risks of T2D can be estimated with the CVD2DM model in patients with established CVD, using readily available clinical predictors. The model would benefit from further validation across diverse ethnic groups to enhance its applicability. Informing patients about their T2D risk could motivate them further to adhere to a healthy lifestyle.

Competing risk analysis to estimate amputation incidence and risk in lower-extremity peripheral artery disease

Background: Patients with peripheral artery disease face high amputation and mortality risk. When assessing vascular outcomes, consideration of mortality as a competing risk is not routine. We hypothesize standard time-to-event methods will overestimate major amputation risk in chronic limb-threatening ischemia (CLTI) and non-CLTI. Methods: Patients undergoing peripheral vascular intervention from 2017 to 2018 were abstracted from the Vascular Quality Initiative registry and stratified by mean age (⩾ 75 vs < 75 years). Mortality and amputation data were obtained from Medicare claims. The 2-year cumulative incidence function (CIF) and risk of major amputation from standard time-to-event analysis (1 - Kaplan-Meier and Cox regression) were compared with competing risk analysis (Aalen-Johansen and Fine-Gray model) in CLTI and non-CLTI. Results: A total of 7273 patients with CLTI and 5095 with non-CLTI were included. At 2-year follow up, 13.1% of patients underwent major amputation and 33.4% died without major amputation in the CLTI cohort; 1.3% and 10.7%, respectively, in the non-CLTI cohort. In CLTI, standard time-to-event analysis overestimated the 2-year CIF of major amputation by 20.5% and 13.7%, respectively, in patients ⩾ 75 and < 75 years old compared with competing risk analysis. The standard Cox regression overestimated adjusted 2-year major amputation risk in patients ⩾ 75 versus < 75 years old by 7.0%. In non-CLTI, the CIF was overestimated by 7.1% in patients ⩾ 75 years, and the adjusted risk was overestimated by 5.1% compared with competing risk analysis. Conclusions: Standard time-to-event analysis overestimates the incidence and risk of major amputation, especially in CLTI. Competing risk analyses are alternative approaches to estimate accurately amputation risk in vascular outcomes research.

Lifetime and 10-year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE-T1D model

AIMS

To develop and externally validate the LIFE-T1D model for the estimation of lifetime and 10-year risk of cardiovascular disease (CVD) in individuals with type 1 diabetes.

MATERIALS AND METHODS

A sex-specific competing risk-adjusted Cox proportional hazards model was derived in individuals with type 1 diabetes without prior CVD from the Swedish National Diabetes Register (NDR), using age as the time axis. Predictors included age at diabetes onset, smoking status, body mass index, systolic blood pressure, glycated haemoglobin level, estimated glomerular filtration rate, non-high-density lipoprotein cholesterol, albuminuria and retinopathy. The model was externally validated in the Danish Funen Diabetes Database (FDDB) and the UK Biobank.

RESULTS

During a median follow-up of 11.8 years (interquartile interval 6.1-17.1 years), 4608 CVD events and 1316 non-CVD deaths were observed in the NDR (n = 39 756). The internal validation c-statistic was 0.85 (95% confidence interval [CI] 0.84-0.85) and the external validation c-statistics were 0.77 (95% CI 0.74-0.81) for the FDDB (n = 2709) and 0.73 (95% CI 0.70-0.77) for the UK Biobank (n = 1022). Predicted risks were consistent with the observed incidence in the derivation and both validation cohorts.

CONCLUSIONS

The LIFE-T1D model can estimate lifetime risk of CVD and CVD-free life expectancy in individuals with type 1 diabetes without previous CVD. This model can facilitate individualized CVD prevention among individuals with type 1 diabetes. Validation in additional cohorts will improve future clinical implementation.

Heart Failure-Related Death in Subjects With Atrial Fibrillation in the United States, 1999 to 2020

BACKGROUND

Population-based data on heart failure (HF)-related death in patients with atrial fibrillation (AF) are lacking. We assessed HF-related death in people with AF in the United States over the past 21 years and examined differences by age, sex, race, ethnicity, urbanization, and census region.

METHODS AND RESULTS

Data were extracted from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research to determine trends in age-adjusted mortality rates per 100 000 people, due to HF-related death among subjects with AF aged ≥15 years. To calculate nationwide annual trends, we assessed the average annual percent change (AAPC) and annual percent change with relative 95% CIs using joinpoint regression. Between 1999 and 2020, 916 685 HF-related deaths (396 205 men and 520 480 women) occurred among US adults having a concomitant AF. The overall age-adjusted mortality rates increased (AAPC: +4.1% [95% CI, 3.8-4.4]; P<0.001), especially after 2011 (annual percent change, +6.8% [95% CI, 6.2-7.4]; P<0.001) in men (AAPC, +4.8% [95% CI, 4.4-5.1]; P<0.001), in White subjects (AAPC: +4.2% [95% CI, 3.9 to 4.6]; P<0.001) and in subjects aged <65 years (AAPC: +7.5% [95% CI, 6.7-8.4]; P<0.001). The higher percentage of deaths were registered in the South (32.8%). During the first year of the COVID-19 pandemic, a significant excess in HF-related deaths among patients with AF aged >65 years was observed.

CONCLUSIONS

A worrying increase in the HF-related mortality rate among patients with AF has been observed in the United States over the past 2 decades.

Validation of Systematic Coronary Risk Evaluation 2 (SCORE2) and SCORE2-Older Persons in the EPIC-Norfolk prospective population cohort

AIMS

The European Systematic Coronary Risk Evaluation 2 (SCORE2) and SCORE2-Older Persons (OP) models are recommended to identify individuals at high 10-year risk for cardiovascular disease (CVD). Independent validation and assessment of clinical utility is needed. This study aims to assess discrimination, calibration, and clinical utility of low-risk SCORE2 and SCORE2-OP.

METHODS AND RESULTS

Validation in individuals aged 40-69 years (SCORE2) and 70-79 years (SCORE2-OP) without baseline CVD or diabetes from the European Prospective Investigation of Cancer (EPIC) Norfolk prospective population study. We compared 10-year CVD risk estimates with observed outcomes (cardiovascular mortality, non-fatal myocardial infarction, and stroke). For SCORE2, 19 560 individuals (57% women) had 10-year CVD risk estimates of 3.7% [95% confidence interval (CI) 3.6-3.7] vs. observed 3.8% (95% CI 3.6-4.1) [observed (O)/expected (E) ratio 1.0 (95% CI 1.0-1.1)]. The area under the curve (AUC) was 0.75 (95% CI 0.74-0.77), with underestimation of risk in men [O/E 1.4 (95% CI 1.3-1.6)] and overestimation in women [O/E 0.7 (95% CI 0.6-0.8)]. Decision curve analysis (DCA) showed clinical benefit. Systematic Coronary Risk Evaluation 2-Older Persons in 3113 individuals (58% women) predicted 10-year CVD events in 10.2% (95% CI 10.1-10.3) vs. observed 15.3% (95% CI 14.0-16.5) [O/E ratio 1.6 (95% CI 1.5-1.7)]. The AUC was 0.63 (95% CI 0.60-0.65) with underestimation of risk across sex and risk ranges. Decision curve analysis showed limited clinical benefit.

CONCLUSION

In a UK population cohort, the SCORE2 low-risk model showed fair discrimination and calibration, with clinical benefit for preventive treatment initiation decisions. In contrast, in individuals aged 70-79 years, SCORE2-OP demonstrated poor discrimination, underestimated risk in both sexes, and limited clinical utility.

Periodontal disease and subsequent risk of cardiovascular outcome and all-cause mortality: A meta-analysis of prospective studies

Studies reported periodontal disease (PD) periodontal disease is associated with many systemic diseases, including cardiovascular outcomes and all-cause mortality. However, the precise mechanistic link for these relationship remained unclear. We therefore performed a meta-analysis of cohort studies to investigate the association of PD with the risk of cardiovascular outcomes and all-cause mortality. We systematically searched the databases of PubMed, EmBase, and the Cochrane library to identify eligible studies until April 2023. The investigated outcomes included major adverse cardiovascular events (MACEs), coronary heart disease (CHD), myocardial infarction (MI), stroke, cardiac death, and all-cause mortality. The summary relative risk (RR) with 95% confidence interval (CI) were calculated using the random-effects model. Thirty-nine cohort studies with 4,389,263 individuals were selected for final meta-analysis. We noted PD were associated with elevated risk of MACEs (RR: 1.24; 95%CI: 1.15-1.34; P<0.001), CHD (RR: 1.20; 95%CI: 1.12-1.29; P<0.001), MI (RR: 1.14; 95%CI: 1.06-1.22; P = 0.001), stroke (RR: 1.26; 95%CI: 1.15-1.37; P<0.001), cardiac death (RR: 1.42; 95%CI: 1.10-1.84; P = 0.007), and all-cause mortality (RR: 1.31; 95%CI: 1.07-1.61; P = 0.010). Sensitivity analyses indicated the pooled conclusions for cardiovascular outcomes and all-cause mortality are robustness. The associations of PD with the risk of ardiovascular outcomes and all-cause mortality could affected by region, study design, PD definition, follow-up duration, and study quality. This study found the risk of cardiovascular outcomes and all-cause mortality were elevated in PD patients, and the intervention for PD should be applied to prevent the risk of cardiovascular outcomes.