A comparison of statistical methods to predict the residual lifetime risk

Lifetime risk of lower extremity peripheral arterial disease: a Danish nationwide longitudinal study

BACKGROUND AND AIMS

Lower extremity peripheral arterial disease (PAD) presents a substantial disease burden, yet lifetime estimates remain scant. This nationwide study quantified the lifetime risk of PAD and its clinical outcomes in Denmark.

METHODS

This cohort study included 4 275 631 individuals in Denmark aged 40-99 years between 1998 and 2018. We estimated the lifetime risk using a modified survival analysis method, considering death as a competing risk event.

RESULTS

Over a median 15.5-year follow-up, 151 846 individuals were diagnosed with PAD (median age at diagnosis 71.5 years, interquartile range 63.1-79.2). The overall lifetime risk of PAD from age 40 was 11.6% (95% confidence interval 11.6%-11.7%), decreasing from 12.9% in 1998-2002 to 10.7% in 2013-18. Males had a higher lifetime risk than females (12.8% vs. 10.5%). Socioeconomic disparities were evident, with higher risks for those with lower educational levels (risk difference 3.4%, 95% confidence interval 3.2%-3.6%) and lower income (risk difference 0.4%, 95% confidence interval 0.2%-0.5%). One year after PAD diagnosis, 21.4% had undergone lower limb revascularization, 8.0% had experienced a major amputation, and 16.2% had died. At 5 years, the corresponding proportions were 26.4%, 10.8%, and 40.8%, respectively. The risk of lower limb revascularization showed little variation by sex and socioeconomic status, whereas there was a strong socioeconomic gradient for major amputation and all-cause death.

CONCLUSIONS

More than one in 10 Danish individuals are diagnosed with symptomatic PAD during their lifetime. Peripheral arterial disease diagnosis is associated with high morbidity and mortality at 1 and 5 years.

Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course

BACKGROUND

Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.

METHODS

A longitudinal study was performed using data from 2 cohort studies: the FOS (Framingham Offspring Study) with 3588 participants aged 19 to 57 years and the UKB (UK Biobank) with 327 837 participants aged 40 years to 70 years. A total of 134 765 and 3 831 734 person-time years were observed in FOS and UKB, respectively. Hazard ratios for CAD were calculated for polygenic risk score (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and pooled cohort equations in predicting CAD events was also evaluated by age groups.

RESULTS

The importance of CAD PRS diminished over the life course, with a hazard ratio of 3.58 (95% CI, 1.39-9.19) at the age of 19 years in FOS and a hazard ratio of 1.51 (95% CI, 1.48-1.54) by the age of 70 years in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed pooled cohort equations in identifying subsequent CAD events in the 40- to 45-year age group, with 3.2-fold more appropriately identified events. Overall, adding PRS improved the area under the receiving operating curve of the pooled cohort equations by an average of +5.1% (95% CI, 4.9%-5.2%) across all age groups; among individuals <55 years, PRS augmented the area under the receiver operater curve (ROC) of the pooled cohort equations by 6.5% (95% CI, 5.5%-7.5%; P<0.001).

CONCLUSIONS

Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies. All results are available at https://surbut.github.io/dynamicHRpaper/index.html.

MSGene: a multistate model using genetic risk and the electronic health record applied to lifetime risk of coronary artery disease

Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model's potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.

Development and validation of nomograms to predict survival of neuroendocrine carcinoma in genitourinary system: A population-based retrospective study

Neuroendocrine carcinoma (NEC) is a rare yet potentially perilous neoplasm. The objective of this study was to develop prognostic models for the survival of NEC patients in the genitourinary system and subsequently validate these models. A total of 7125 neuroendocrine neoplasm (NEN) patients were extracted. Comparison of survival in patients with different types of NEN before and after propensity score-matching (PSM). A total of 3057 patients with NEC, whose information was complete, were extracted. The NEC influencing factors were chosen through the utilization of the least absolute shrinkage and selection operator regression model (LASSO) and the Fine & Gary model (FGM). Furthermore, nomograms were built. To validate the accuracy of the prediction, the efficiency was verified using bootstrap self-sampling techniques and receiver operating characteristic curves. LASSO and FGM were utilized to construct three models. Confirmation of validation was achieved by conducting analyses of the area under the curve and decision curve. Moreover, the FGS (DSS analysis using FGM) model produced higher net benefits. To maximize the advantages for patients, the FGS model disregarded the influence of additional occurrences. Patients are expected to experience advantages in terms of treatment options and survival assessment through the utilization of these models.

Temporal trends in lifetime risks of atrial fibrillation and its complications between 2000 and 2022: Danish, nationwide, population based cohort study

OBJECTIVES

To examine how the lifetime risks of atrial fibrillation and of complications after atrial fibrillation changed over time.

DESIGN

Danish, nationwide, population based cohort study.

SETTING

Population of Denmark from 1 January 2000 to 31 December 2022.

PARTICIPANTS

3.5 million individuals (51.7% women and 48.3% men) who did not have atrial fibrillation at 45 years of age or older were followed up until incident atrial fibrillation, migration, death, or end of follow-up, whichever came first. All 362 721 individuals with incident atrial fibrillation (46.4% women and 53.6% men), but with no prevalent complication, were further followed up until incident heart failure, stroke, or myocardial infarction.

MAIN OUTCOME MEASURES

Lifetime risk of atrial fibrillation and lifetime risks of complications after atrial fibrillation over two prespecified periods (2000-10 v 2011-22).

RESULTS

The lifetime risk of atrial fibrillation increased from 24.2% in 2000-10 to 30.9% in 2011-22 (difference 6.7% (95% confidence interval 6.5% to 6.8%)). After atrial fibrillation, the most frequent complication was heart failure with a lifetime risk of 42.9% in 2000-10 and 42.1% in 2011-22 (-0.8% (-3.8% to 2.2%)). Individuals with atrial fibrillation lost 14.4 years with no heart failure. The lifetime risks of stroke and of myocardial infarction after atrial fibrillation decreased slightly between the two periods, from 22.4% to 19.9% for stroke (-2.5% (-4.2% to -0.7%)) and from 13.7% to 9.8% for myocardial infarction (-3.9% (-5.3% to -2.4%). No evidence was reported of a differential decrease between men and women.

CONCLUSION

Lifetime risk of atrial fibrillation increased over two decades of follow-up. In individuals with atrial fibrillation, about two in five developed heart failure and one in five had a stroke over their remaining lifetime after atrial fibrillation diagnosis, with no or only small improvement over time. Stroke risks and heart failure prevention strategies are needed for people with atrial fibrillation.

Assessment of Lifetime Risk for Cardiovascular Disease: Time to Move Forward

Over the past decades, there has been a notable increase in the risk of Cardiovascular Disease (CVD), even among younger individuals. Policymakers and the health community have revised CVD prevention programs to include younger people in order to take these new circumstances into account. A variety of CVD risk assessment tools have been developed in the past years with the aim of identifying potential CVD candidates at the population level; however, they can hardly discriminate against younger individuals at high risk of CVD.Therefore, in addition to the traditional 10-year CVD risk assessment, lifetime CVD risk assessment has recently been recommended by the American Heart Association/American College of Cardiology and the European Society of Cardiology prevention guidelines, particularly for young individuals. Methodologically, the benefits of these lifetime prediction models are the incorporation of left truncation observed in survival curves and the risk of competing events which are not considered equivalent in the common survival analysis. Thus, lifetime risk data are easily understandable and can be utilized as a risk communication tool for Public Health surveillance. However, given the peculiarities behind these estimates, structural harmonization should be conducted in order to create a sex-, race-specific tool that is sensitive to accurately identifying individuals who are at high risk of CVD. In this review manuscript, we present the most commonly used lifetime CVD risk tools, elucidate several methodological and critical points, their limitations, and the rationale behind their integration into everyday clinical practice.

MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record

Currently, coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. We designed a novel and general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. MSGene supports decision making about CAD prevention related to any of these states. We analyzed longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improved discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), with external validation. We also used MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore the potential public health value of our novel multistate model for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics.

Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course

Importance

Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. Understanding the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.

Objective

To assess the time-varying significance of genomic and clinical risk factors in CAD risk estimation across various age groups.

Design Setting and Participants

A longitudinal study was performed using data from two cohort studies: the Framingham Offspring Study (FOS) with 3,588 participants aged 19-57 years and the UK Biobank (UKB) with 327,837 participants aged 40-70 years. A total of 134,765 and 3,831,734 person-time years were observed in FOS and UKB, respectively.

Main Outcomes and Measures

Hazard ratios (HR) for CAD were calculated for polygenic risk scores (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and Pooled Cohort Equations (PCE) in predicting CAD events was also evaluated by age groups.

Results

The importance of CAD PRS diminished over the life course, with an HR of 3.58 (95% CI 1.39-9.19) at age 19 in FOS and an HR of 1.51 (95% CI 1.48-1.54) by age 70 in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed PCE in identifying subsequent CAD events in the 40-45-year age group, with 3.2-fold more appropriately identified events. The mean age of CAD events occurred 1.8 years earlier for those at high genomic risk but 9.6 years later for those at high clinical risk (p<0.001). Overall, adding PRS improved the area under the receiving operating curve of the PCE by an average of +5.1% (95% CI 4.9-5.2%) across all age groups; among individuals <55 years, PRS augmented the AUC-ROC of the PCE by 6.5% (95% CI 5.5-7.5%, p<0.001).

Conclusions and Relevance

Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies.

Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries

BACKGROUND

Information on the frequency and timing of mental disorder onsets across the lifespan is of fundamental importance for public health planning. Broad, cross-national estimates of this information from coordinated general population surveys were last updated in 2007. We aimed to provide updated and improved estimates of age-of-onset distributions, lifetime prevalence, and morbid risk.

METHODS

In this cross-national analysis, we analysed data from respondents aged 18 years or older to the World Mental Health surveys, a coordinated series of cross-sectional, face-to-face community epidemiological surveys administered between 2001 and 2022. In the surveys, the WHO Composite International Diagnostic Interview, a fully structured psychiatric diagnostic interview, was used to assess age of onset, lifetime prevalence, and morbid risk of 13 DSM-IV mental disorders until age 75 years across surveys by sex. We did not assess ethnicity. The surveys were geographically clustered and weighted to adjust for selection probability, and standard errors of incidence rates and cumulative incidence curves were calculated using the jackknife repeated replications simulation method, taking weighting and geographical clustering of data into account.

FINDINGS

We included 156 331 respondents from 32 surveys in 29 countries, including 12 low-income and middle-income countries and 17 high-income countries, and including 85 308 (54·5%) female respondents and 71 023 (45·4%) male respondents. The lifetime prevalence of any mental disorder was 28·6% (95% CI 27·9-29·2) for male respondents and 29·8% (29·2-30·3) for female respondents. Morbid risk of any mental disorder by age 75 years was 46·4% (44·9-47·8) for male respondents and 53·1% (51·9-54·3) for female respondents. Conditional probabilities of first onset peaked at approximately age 15 years, with a median age of onset of 19 years (IQR 14-32) for male respondents and 20 years (12-36) for female respondents. The two most prevalent disorders were alcohol use disorder and major depressive disorder for male respondents and major depressive disorder and specific phobia for female respondents.

INTERPRETATION

By age 75 years, approximately half the population can expect to develop one or more of the 13 mental disorders considered in this Article. These disorders typically first emerge in childhood, adolescence, or young adulthood. Services should have the capacity to detect and treat common mental disorders promptly and to optimise care that suits people at these crucial parts of the life course.

FUNDING

None.