Tailoring surveillance and treatment strategies for stage II colon cancer (CC) after curative surgery remains challenging, and personalized approaches are lacking. We aimed to identify a gene methylation panel capable of stratifying high-risk stage II CC patients for recurrence beyond traditional clinical variables.

Genome-wide tumor tissue DNA methylation data were analyzed from 562 stage II CC patients who underwent surgery in Germany (DACHS study). The cohort was divided into a training set (N = 395) and an internal validation set (N = 131), with external validation performed on 97 stage II CC patients from Spain. DNA methylation markers were primarily selected using the Elastic Net Cox model. The resulting prognostic index (PI), a combination of clinical factors and selected methylation markers, was compared to baseline models using clinical variables or microsatellite instability (MSI), with discrimination and prediction accuracy assessed through time-dependent receiver operating characteristic curves (AUC) and Brier scores.

The final PI incorporated age, sex, tumor stage, location, and 27 DNA methylation markers. The PI consistently outperformed the baseline model including age, sex, and tumor stage in time-dependent AUC across validation cohorts (e.g., 1-year AUC and 95 % confidence interval: internal validation set, PI: 0.66, baseline model: 0.52; external validation set, PI: 0.72, baseline model: 0.64). In internal validation, the PI also showed a consistently improved time-dependent AUC compared with a combination of MSI and tumor stage only. Nevertheless, the PI did not improve the prediction accuracy of CC recurrence compared to the baseline model.

This study identified 27 tumor tissue DNA methylation biomarkers that improved the discriminative power in classifying recurrence risk among stage II colon cancer patients. While this methylation panel alone lacks sufficient prediction accuracy for clinical application, its discriminative improvement suggests potential value as part of a multimodal risk-stratification tool.

Residential green space has the potential to benefit cardiometabolic health. However, little is known about its impacts on cardiometabolic multimorbidity (CMM). Furthermore, the capacity of access to green space to reduce health inequalities during the development of CMM is unclear, and longitudinal evidence is urgently needed. Hence, a large prospective study based on the UK Biobank was conducted. CMM was defined as the coexistence of at least two conditions including type 2 diabetes, ischemic heart disease, and stroke. Multi-state models were used to assess the impacts of green space on the transitions of CMM, from free of cardiometabolic disease to first cardiometabolic disease (FCMD), subsequently to CMM, and finally death. This study included 328,260 participants with a median follow-up of 12.5 years. The benefits of access to green space were observed for transitions from baseline to FCMD, from baseline to death, and from FCMD to death, with HRs of 0.975 (95 % CI: 0.959, 0.991), 0.956 (95 % CI: 0.931, 0.982) and 0.943 (95 % CI: 0.897, 0.990) per IQR increase in the percentage of green space, respectively. Although access to green space benefited early transitions related to FCMD, its protective role was not found in the progression from FCMD to CMM and finally death. Furthermore, access to green space was associated with a reduction in health inequalities influenced by sex and socioeconomic status in the earliest transition from healthy to FCMD. Future policies should prioritize green space investments to sustainably enhance cardiometabolic health and improve health inequalities.

In cardiovascular (CV) trials, analyzing the total number of events, rather than just time-to-first event, enhances understanding of participants' health. Adapting Cox models to account for between-subject heterogeneity in multiple events and understanding its impact plays crucial roles in total event analysis.

This study compares effect sizes from first event and total event analyses in three cardiovascular trials: ORIGIN (N = 12,537, median follow-up of 6.2 years), COMPASS (N = 18,278, median follow-up of 1.8 years), TRANSCEND (N = 5,926, median follow-up of 1.1 years). It also examines the impact of heterogeneity, measured by the negative binomial overdispersion parameter. Treatment effects were assessed using the Cox model for first events and the negative binomial (NB), Andersen-Gill (AG), Prentice-Williams-Peterson (PWP), Wei-Lin-Weissfeld (WLW), and Lin-Wei-Yang-Ying (LWYY) models for total events. Hazard ratios (HRs) or risk ratios (RRs), 95% confidence intervals (CIs), and CI widths were reported. The risk ratio applies to negative binomial. The first composite was consisted of myocardial infarction (MI), stroke, cardiovascular death. Simulations assessed Type I error, power, and mean squared error across the different approaches.

In ORIGIN, the incidence per 100 years increased from 2.9 to 3.8 for the first composite with a heterogeneity of 2.4. The HR or RR for the first composite was 1.03 (95% CI, 0.94-1.12, CI width = 0.18) using Cox, 1.01 (95% CI, 0.92-1.11, CI width = 0.19) for NB, 1.01 (95% CI, 0.94-1.09, CI width = 0.15) for AG, 1.02 (95% CI, 0.94-1.10, CI width = 0.16) for PWP total, 1.01 (95% CI, 0.94-1.09, CI width = 0.15) for PWP gap, 1.03 (95% CI, 0.94-1.12, CI width = 0.18) for WLW and 1.01 (95% CI, 0.92-1.11, CI width = 0.19) for LWYY. Similar trends were observed in other studies. Our simulation results showed that total event approaches had approximately 5% higher power than the Cox model, though power declined exponentially across all methods with increasing heterogeneity. Among the total event methods, AG, PWP gap, and LWYY demonstrated better power, with AG and LWYY also achieving the smallest mean squared error (MSE).

High heterogeneity arises when a small number of patients experience a disproportionately large number of events. This effect is more pronounced when the overall event incidence is low and few patients experience any events. The effect size and CI width stayed consistent with low heterogeneity across different approaches. Power decreased with high heterogeneity. The AG and LWYY approaches slightly outperformed the other approaches.

ORIGIN (NCT00069784), COMPASS (NCT01776424), TRANSCEND (NCT00153101).

The role of biological age (BA) acceleration in longitudinal disease progression from health to cardiometabolic disease (CMD), then to post-CMD dementia (including vascular dementia (VaD) and Alzheimer's disease (AD)), and finally to death remains unclear.

Using data from 284,723 UK Biobank participants, two established BA measures (Klemera-Doubal Method Biological Age [KDM-BA] and PhenoAge) were generated on the basis of baseline clinical biomarkers. Post-CMD dementia was defined as dementia that occurred after the first occurrence of CMD. Multistate analysis was constructed to examine the association between BA accelerations and longitudinal progression of post-CMD dementia. We further explored the role of two BA accelerations in CMD-specific transitions and dementia-specific transitions, respectively.

Over a median follow-up of 13.7 years, 47,150 participants developed CMD, and 999 developed post-CMD dementia. Biologically older participants demonstrated robustly higher risks from healthy to CMD, then to post-CMD dementia, and finally to death. For the transition from baseline to CMD, adjusted HRs (95% CI) were 1.34 (1.32, 1.35) for each SD increase in KDM-BA acceleration and 1.19 (1.18, 1.20) for PhenoAge acceleration. For the transition from CMD to post-CMD dementia, HRs were 1.12 (1.04, 1.20) for KDM-BA acceleration and 1.10 (1.04, 1.17) for PhenoAge acceleration. Both BA accelerations were more strongly associated with the transition from CMD to post-CMD VaD than with the transition to post-CMD AD.

BA accelerations hold promise for identifying the disease progression of post-CMD dementia in routine clinical practice and slowing down disease progression through the interventions that slow down biological aging.

This study aimed to implement a multi-state risk prediction model to predict the progression of COVID-19 cases among hospitalized patients in Kurdistan province by analyzing hospital care data.

This retrospective analysis consisted of data from 17,286 patients admitted to hospitals with COVID-19 from March 23, 2019, to December 19, 2021, in various areas in the Kurdistan province. A multi-state prediction model was used to show that each transition is predicted by a different set of variables. These variables include underlying diseases (like diabetes, hypertension, etc.) and sociodemographic information (like sex and age). Model aims to predict the likelihood of recovery, the need for critical care intervention (e.g., transfer to isolation units or the ICU), or exits from the hospitalization course. We performed the statistical analysis using R software and the mstate package.

Of the hospitalized patients studied, 5.6% died of the disease, 6.6% were admitted to ICUs, and 38.72% were treated in isolation units. Mortality rates in general wards, isolation units, and the ICU were 3.48%, 4.56%, and 26.6%, respectively. Significant predictors for ICU admission include age over 60 years (HR: 1.46, 95% CI 1.37-1.55), kidney-related conditions (HR: 2.19, 95% CI 1.65-2.91), cardiovascular diseases (HR: 1.68, 95% CI 1.46-1.94), lung disease (HR: 1.89,‏95% CI 1.43-2.05), and cancer (HR: 2.46,‏95% CI 1.77-3.41). The likelihood of in-hospital death is significantly increased by age over 60 years (HR: 2.40, 95% CI 2.09-2.76), diabetes (HR: 1.97, 95% CI 1.45-2.68), high blood pressure (HR: 2.30, 95% CI 1.78-2.97), and history of heart disease (HR: 3.01, 95% CI 2.29-3.95).

The model helps the provider and policymakers to make an informed decision depending on patient management and resource allocation within the health care systems.

Two Cox-based multistate modeling approaches are compared for modeling a complex multicohort event history process. The first approach incorporates cohort information as a fixed covariate, thereby providing a direct estimation of the cohort-specific effects. The second approach includes the cohort as a stratum variable, which offers an extra flexibility in estimating the transition probabilities. Additionally, both approaches may include possible interaction terms between the cohort and a given prognostic predictor. Furthermore, the Markov property conditional on observed prognostic covariates is assessed using a global score test. Whenever departures from the Markovian assumption are revealed for a given transition, the time of entry into the current state is incorporated as a fixed covariate, yielding a semi-Markov process. The two proposed methods are applied to a three-wave dataset of COVID-19-hospitalized adults in the southern Barcelona metropolitan area (Spain), and the corresponding performance is discussed. While both semi-Markovian approaches are shown to be useful, the preferred one will depend on the focus of the inference. To summarize, the cohort-covariate approach enables an insightful discussion on the behavior of the cohort effects, whereas the stratum-cohort approach provides flexibility to estimate transition-specific underlying risks according to the different cohorts.

Cohort studies with comprehensive follow-up periods that track patients with chronic kidney disease (CKD) and gather extensive health data are important for understanding the diverse progression patterns of CKD. This review explores the potential of emerging analytical techniques that can be applied in addition to conventional analysis approaches to enhance CKD research by offering more detailed insights into disease progression. To maximize the insights available from CKD cohort data with extended follow-up, we examined two advanced approaches: analysis of disease trajectories and analysis of recurrent events. The analysis of trajectories examines the timing and relationships between events, uncovering progression patterns and identifying key events that could signal future outcomes. In contrast, the application of recurrent event analysis facilitates the examination of repeated occurrences of significant events, thereby providing a more nuanced understanding of the evolution of risk over time. Using data from the German Chronic Kidney Disease study, this review illustrates how these approaches can enhance conventional analyses. The application of these supplementary methodologies to CKD research has the potential to facilitate a transition towards a more personalized approach to patient care. The insights gained may inform the development of tailored treatment strategies and contribute to enhanced overall patient outcomes.

Frailty is an underappreciated but modifiable clinical syndrome, but little about how air quality improvements could influence frailty progression is known. Here, we utilized two Chinese cohorts with repeated follow-up visits to address this knowledge gap and explored the underlying DNA methylation mechanisms. We first conducted a multistate modeling analysis in the Chinese Longitudinal Healthy Longevity Study (CLHLS), a nationwide cohort with 21,654 older adults who had participated in at least two survey waves. An interquartile range reduction in PM2.5 exposure increased the likelihood of improvement for frail/prefrail individuals by more than 50% while lowering their risks of worsening frailty or mortality. A quasi-experimental study within a CLHLS subcohort of 1816 adults, leveraging the implementation of China's Clean Air Act, further validated these findings. Additionally, in the Guangxi Eco-Environmental Health and Aging Study─a regional prospective cohort based in Guilin, China─we included 235 older adults with follow-up data and identified three frailty-related CpG sites that were associated with PM2.5 exposure. The CpG site cg25453797 mapped to the PRKCE gene was robustly associated with the change in frailty. These findings demonstrate that air quality improvement benefits older adults by alleviating the frailty burden. DNA methylation may serve as a potential biomarker to capture the health benefits of environmental policy interventions.

The aim of this study was to evaluate the long-term results of seizure recurrence after antiseizure medication (ASM) withdrawal vs continuation in patients with diffuse glioma, grades 2 and 3.

A prospective multicenter observational study was conducted, and patients were recruited from January 2014 until May 2016 from 3 neuro-oncology outpatient clinics in the Netherlands. The main inclusion criteria were as follows: history of ≥1 seizure, for which ASM was started; clinically and radiologically stable disease for ≥12 months; and seizure freedom for ≥12 months from the date of last antitumor treatment or seizure freedom for ≥24 months from the last seizure if seizures occurred after the last antitumor treatment. The primary outcome was time to recurrent seizure. A competing risk model was used to estimate cumulative incidences of recurrent seizure for ASM groups (i.e., ASM withdrawal vs ASM continuation) with death as the competing event. The proportional hazard assumption was violated for the ASM group; therefore, 2 Cox models were constructed for different time intervals (<48 months and ≥48 months since study inclusion).

A total of 71 patients were included (39 men [55%] and 58 older than 40 years [82%]); 46 patients with glioma (65%) were in the ASM withdrawal group and 25 (35%) in the ASM continuation group. The cumulative incidence of a recurrent seizure at 48 and 96 months was 48% (95% CI 33%-61%) and 66% (95% CI 48%-78%) for the ASM withdrawal group vs 28% (95% CI 12%-46%) and 52% (95% CI 31%-70%) for the ASM continuation group. The risk of a recurrent seizure differed in the 2 time intervals between the ASM continuation group (reference) and the ASM withdrawal group (cause-specific adjusted hazard ratio [aHR] 2.32 [95% CI 0.93-5.81], p = 0.071, during <48 months, and cause-specific aHR 0.73 [95% CI 0.21-2.49], p = 0.611, during ≥48 months since study inclusion).

Risk of recurrent seizure when withdrawing ASM was not statistically significantly higher in patients continuing ASM. However, a clinically relevant higher percentage of patients had a recurrent seizure in the ASM withdrawal group compared with the ASM continuation group. The lack of a statistical difference may be explained by the small sample size. Larger studies are needed to confirm these findings. Our results suggest that ASM withdrawal should be initiated cautiously and only when necessary.

This study provides Class III evidence that withdrawal of ASM does not significantly increase the risk of recurrent seizures in patients with glioma with stable disease and no seizures for >1 year. Confidence intervals do not exclude a clinically important increased risk of seizures.

Clonal hematopoiesis of indeterminate potential (CHIP) is defined as the aging-related clonal expansion of preleukemic mutations in hematopoietic stem cells. While CHIP has been studied in cardiometabolic diseases (CMDs), its role in the long-term progression from the absence of CMD to the development of a single CMD, cardiometabolic multimorbidity (CMM), and eventual mortality remains uncertain. This study aimed to investigate the association between CHIP and gene-specific CHIP subtypes with the progression of CMD transitions.

We included UK Biobank participants without CMD at baseline. The primary outcomes were the first CMD, CMM, and death. We evaluated associations between any CHIP (variant allele fraction [VAF] ≥ 2%), large CHIP (VAF ≥ 10%), and gene-specific CHIP subtypes (DNMT3 A, TET2, ASXL1, JAK2, PPM1D/TP53 [DNA damage genes], and SF3B1/SRSF2/U2 AF1 [spliceosome genes]) with CMD transitions via multistate model analyses. We estimated multivariable-adjusted hazard ratios (HRs) and 95% CIs with age as the time scale, and adjusted for sex, race, Townsend Deprivation Index, body mass index (BMI), smoking, alcohol, physical activity, sleep duration, and hypertension.

The study included 371,544 participants, with a mean age of 56.60 (± 8.03) years, and 44.2% of whom were male (CHIP: n = 11,570 [3.1%]; large CHIP: n = 7156 [1.9%]). During a median follow-up period of 14.49 years, 54,805 individuals developed at least one CMD, 8090 experienced CMM, and 26,218 died. In the fully adjusted multistate models, CHIP and large CHIP were associated with adjusted hazard ratios (HR) of 1.11 (95% CI 1.07-1.16) and 1.14 (95% CI 1.08-1.20), respectively, for transitioning from a CMD-free condition to a single CMD. The mortality risk associations were strongest, with adjusted HR of 1.45 (95% CI 1.36-1.55) and 1.64 (95% CI 1.52-1.77) for those without CMD, 1.39 (95% CI 1.26-1.54) and 1.59 (95% CI 1.41-1.79) for individuals with single CMD, and 1.58 (95% CI 1.31-1.91) and 1.61 (95% CI 1.29-2.02) for those with CMM. No significant association was observed with CMM development. Gene-specific analyses identified DNMT3 A, TET2, DNA damage genes, and spliceosome genes as the primary contributors to increased CMD risk. While CHIP showed no association with CMM progression, spliceosome genes were linked to a 1.72-fold higher risk (adjusted HR 1.72, 95% CI 1.14-2.59) of recurrent CMD events. All CHIP subtypes were strongly related to a heightened risk of mortality, with JAK2 presenting the highest adjusted odds ratio at 6.79 (95% CI 4.12-11.2).

CHIP serves as an independent risk factor for transitioning to the first CMD incidence and for mortality but is not associated with CMM development. CHIP-targeted management may represent a promising strategy for the primary prevention of CMD and for reducing mortality risk.

Cardiovascular death (CVD) represents a significant determinant affecting the long-term survival outcomes of cancer patients, independent of primary tumor effects. Consequently, this study aims to identify prognostic factors in patients with primary bone diffuse large B-cell lymphoma (PB-DLBCL) using CVD as a competing risk and to develop a competing risk nomogram.

Data for patients diagnosed with PB-DLBCL from 2000 to 2015 were sourced from the Surveillance Epidemiology, and End Results (SEER) database and a total of 1,224 PB-DLBCL patients were eventually included in this study. The approach of multiple imputation is utilized to address the issue of missing data. Univariate Cox regression analysis and the best subset selection method are utilized for variable screening, from which overlapping independent risk factors are identified for subsequent multivariate Cox analysis and multivariate competing risk analysis. The Fine-Gray test was applied for univariate competing risk analysis. Significant variables from the multivariate competing risk analysis were selected as independent prognostic factors to construct a competing risk nomogram for predicting 1-, 5-, and 10-year cancer-specific survival (CSS). The model's performance was evaluated by Harrell concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and calibration curves.

Compared with the competing risk model, the conventional Cox regression model overestimates the impact of variables on the incidence of cancer-specific death (CSD). Age, income, B symptoms, Ann Arbor stage, primary site, laterality, chemotherapy, and systemic therapy were identified as independent risk factors for CSD. A competing risk nomogram was developed incorporating these variables to predict CSS. In the training set, the areas under the curve (AUC) for 1-, 5-, and 10-year CSS were 0.879, 0.848, and 0.839, respectively, while in the testing set, the AUC values were 0.794, 0.781, and 0.790, respectively. The C-index of the model was 0.853, 0.823, and 0.819 for 1-, 5-, and 10-year survival in the training set, and 0.777, 0.757, and 0.754 in the testing set. The calibration curve indicated favorable consistency for the competing risk nomogram.

The competing risk nomogram was effectively utilized to predict CSS in patients with PB-DLBCL It exhibited robust predictive performance and holds potential for enhancing treatment decision-making in clinical practice.

The prevalence of cardiometabolic multimorbidity (CMM) has increased substantially in recent years. Previous studies have established the associations between vitamin D, vitamin D receptor (VDR) polymorphisms, and the risk of individual cardiometabolic disease (CMD). However, the role of these factors in the progression of CMD to CMM or mortality remains unclear. This study aimed to investigate the associations between vitamin D, VDR polymorphisms, and the dynamic progression of CMM, as well as to explore the potential modification effect of VDR polymorphisms.

Data for this cohort study were extracted from the UK Biobank. CMM was defined as the coexistence of at least two CMDs, including type 2 diabetes (T2D), coronary heart disease (CHD), and stroke. A multi-state model was used to analyze associations between serum 25(OH)D, VDR polymorphisms and the dynamic progression of CMM.

The sample included 396,192 participants. Over a median follow-up of 13.8 years, 55,772 individuals experienced at least one CMD and 28,624 died. Compared to participants with 25(OH)D < 25 nmol/L, those with 25(OH)D ≥ 75 nmol/L had HRs of 0.70 (95% CI, 0.67, 0.72) for baseline to first CMD (FCMD), 0.74 (95% CI, 0.67, 0.82) for FCMD to CMM, 0.66 (95% CI, 0.62, 0.70) for baseline to death, 0.84 (95% CI, 0.77, 0.92) for FCMD to death, and 0.85 (95% CI, 0.70, 1.03) for CMM to death. L-shaped relationships of these associations were noted, with a threshold around 45 nmol/L. The rs1544410 (BsmI) T alleles may have a detrimental effect, while the rs11568820 (Cdx2) T alleles may exert a protective effect in the early stages of CMM progression. Additionally, VDR polymorphisms significantly modified the association between serum 25(OH)D and certain stages of CMM progression.

Maintaining adequate vitamin D levels, as a readily implementable intervention strategy, not only reduces the risk of initial CMD but also delays the progression to CMM or death. Risk stratification based on VDR polymorphisms provides further insights for developing personalized prevention strategies.

Vulnerable subgroups of the population, such as care home residents, often face elevated mortality risks during crises like pandemics or wars. To correctly model and interpret the excess mortality of vulnerable groups during crises, a distinction must be made between the pre-existing heightened mortality of the vulnerable group, the general population's excess mortality during the crisis, and the crisis-specific excess mortality unique to the vulnerable group.

We introduce the concept of "excess excess" mortality, which captures the extra excess mortality experienced by vulnerable groups during crises, beyond what can be explained by their excess mortality due to being vulnerable and general population excess mortality. Using individual-level data from Statistics Netherlands, we model the excess excess mortality of Dutch care home residents aged 70 and older during the Covid-19 pandemic. We extend standard relative survival methods by incorporating multiple excess mortality components and use an additive hazards model to accommodate periods of negative excess hazard.

The findings confirm the severe impact of the Covid-19 pandemic on care home residents. In general, men and older age groups experienced higher excess excess mortality, both in absolute and relative terms.

Our approach offers a new perspective on how to model and interpret excess mortality in vulnerable groups during a crisis and provides a methodological foundation for investigating excess excess mortality in other contexts.

Insulin resistance and central obesity are major risk factors for cardiometabolic diseases. The triglyceride-glucose index (TyG) and lipid accumulation product (LAP) are markers that independently predict cardiometabolic risk. However, their combined long-term trajectories and impact on cardiometabolic multimorbidity (CMM) development remain unclear.

This cohort study utilized data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, which tracked 3467 participants at baseline. Dual-trajectory of TyG and LAP were identified using a group-based dual-trajectory model. Cox proportional hazards models were employed to assess the relationships between dual-trajectory groups and primary cardiometabolic outcomes, including first cardiometabolic disease (FCMD), CMM (two or more conditions such as type 2 diabetes, coronary heart disease, or stroke), and all-cause mortality. Multi-state models were performed to assess the associations of dual-trajectory with CMM development.

The study included 3467 participants with a mean age of 25.08 years (SD = 3.59). Of these, 43.4% (n = 1505) were male, and 53.2% (n = 1561) were White. Three distinct dual-trajectory groups were identified: low-increasing (61.5%), high-amplitude fluctuation (7.6%), and high-increasing (30.9%). After multivariate adjustment, compared with the low-increasing group, the high-amplitude fluctuation group exhibited significantly higher risks for FCMD (hazard ratio [HR] 1.38, 95% confidence interval [CI]: 1.08-1.77), CMM (HR 2.63, 95% CI 1.21-5.71), and all-cause mortality (HR 2.16, 95% CI 1.30-3.56), as well as elevated risks for transitions from baseline to FCMD (HR 1.41, 95% CI 1.17-1.63), FCMD to CMM (HR 2.07, 95% CI 1.53-3.96), CMM to death (HR 2.87, 95% CI 1.19-7.62). The high-increasing group showed similar results.

Elevated and fluctuating trajectories of TyG and LAP from early adulthood are associated with increased risks of CMM development in midlife.

Background/Objectives: Patients with chronic myeloid leukemia (CML) have a long life expectancy due to modern treatment. However, treatment may have adverse effects that hamper work ability. Methods: Patients aged 25-60 years diagnosed in 2002-2020 were included in this nationwide matched cohort study examining work ability from diagnosis (index date), including the need for permanent disability compensation (flexible job or disability pension). Each patient was matched 1:5 on sex, birth year, and level of comorbidity with citizens from the general Danish population without CML. The risks of requiring flexible job and disability pension were calculated by cause-specific hazard ratios (HRs) using Cox proportional hazards regression, and the Aalen-Johansen estimator was used to determine cumulative risks. Results: A total of 489 patients with CML and 2445 matched comparators were included. The median age was 46 years, and males comprised 59.5% of the cohort. Matched comparators were more likely to work at index date and 1, 3, 5, and 10 years after the index date (p < 0.001). The HRs of requiring both flexible job (HR 8.7 (95% confidence interval (CI): 6.1;12.2, p < 0.001)) and disability pension (HR 3.7 (95% CI: 2.8;4.9, p < 0.001)) were higher among patients diagnosed with CML compared to matched comparators. The cumulative risk of requiring flexible job and disability pension also increased in patients with CML compared to matched comparators (p < 0.001). Conclusions: Patients with CML have a reduced work ability compared to the general population. More research is needed to determine the cause of their loss of ability to work.

Social disconnection, including loneliness and social isolation, is associated with increased morbidity and death. However, its impact on the incidence and prognosis of atrial fibrillation (AF) remains inconclusive.

The present prospective cohort study enrolled 418 656 participants without AF and cardiovascular disease from the UK Biobank. A loneliness scale was constructed with 2 domains (loneliness feeling, inability to confide) and social isolation scale was constructed with 3 domains (living alone, lack of social support, and lack of social activity). We used a multistate model to analyze the impacts of the 2 scales on the progression from baseline to incident AF and subsequent major adverse cardiovascular events and further to death. Over a median follow-up of 14.7 years, 25 539 participants developed incident AF, among whom 7283 developed incident major adverse cardiovascular events, and 5165 died. Social isolation and loneliness scales were associated with both a higher incidence and worse prognosis of AF, with hazard ratios per 1-point increase of 1.06 (95% CI, 1.04-1.09) for the loneliness scale and 1.03 (95% CI, 1.02-1.05) for the social isolation scale for incident AF, and 1.12 to 1.14 for the loneliness scale (all P<0.001) and 1.12 to 1.27 for the social isolation scale (all P<0.001) after AF development. Loneliness feeling and living alone may be important contributors.

Loneliness and social isolation were both associated with a higher incidence and a worse prognosis of AF but to different extents. These observations highlight the importance of integrating social connection into the prevention and management of AF.

First cardiometabolic disorders (FCMDs) and dementia pose a significant burden on aging populations. Investigating the association between atmospheric pollutants and the progression of FCMDs and dementia is crucial.

A prospective analysis was conducted on 413 149 participants from the UK Biobank. Atmospheric pollutant concentrations were calculated using land-use regression models. We used a multistate model and calculated the polygenic risk score for dementia to analyze the associations of pollutants on the relevant trajectories from baseline to FCMD, subsequent dementia, and death.

Over 13 years, 65 336 patients with FCMD were identified, with 2584 developing dementia and 10 664 dying. Each interquartile range increase in pollutant concentrations showed the hazard ratios (HRs) of particulate matter with aerodynamic diameter ≤2.5 μm (HR, 1.03 [95% CI, 1.02-1.05]); nitrogen dioxide (HR, 1.04 [95% CI, 1.03-1.05]), and nitrogen oxides (HR, 1.03 [95% CI, 1.02-1.04]) for transitioning from baseline to FCMD; particulate matter with aerodynamic diameter ≤2.5 μm (HR, 1.11 [95% CI, 1.05-1.16]; nitrogen dioxide: HR, 1.12 [95% CI, 1.06-1.18]; and nitrogen oxides (HR, 1.07 [95% CI, 1.03-1.12]) for transitioning from FCMD to dementia, and the association between pollutants and this trajectory remained consistent after adjusting for genetic susceptibility to dementia (particulate matter with aerodynamic diameter ≤2.5 μm: HR, 1.06 [95% CI, 1.04-1.08]; nitrogen dioxide: HR, 1.05 ([95% CI, 1.03-1.07); and nitrogen oxides: HR, 1.05 [95% CI, 1.03-1.06]) for transitioning from baseline to death; and particulate matter with aerodynamic diameter ≤2.5 μm: HR, 1.03 ([95% CI, 1.00-1.05); nitrogen dioxide: HR, 1.05 ([95% CI, 1.02-1.08); nitrogen oxides: HR, 1.04 ([95% CI, 1.01-1.06]) for transitioning from subsequent FCMD to death. Particulate matter with aerodynamic diameter ≤10 μm was only significantly associated with the transition from baseline to FCMD.

Our study provides the first evidence that atmospheric pollutants are associated with the progression of FCMD and subsequent dementia.

Residential greenness may influence COPD mortality, but the causal links, risk trajectories, and mediation pathways between them remain poorly understood.

We aim to comprehensively identify the potential causal links, characterize the dynamic progression of hospitalization or posthospital risk, and quantify mediation effects between greenness and COPD.

This study was conducted using a community-based cohort enrolling individuals aged ≥ 18 years in southern China from January 1, 2009 to December 31, 2015. Greenness was characterized by normalized difference vegetation index (NDVI) around participants' residential addresses. We applied doubly robust Cox proportional hazards model, multi-state model, and multiple mediation method, to investigate the potential causal links, risk trajectories among baseline, COPD hospitalization, first readmission due to COPD or COPD-related complications, and all-cause death, as well as the multiple mediation pathways (particulate matter [PM], temperature, body mass index [BMI] and physical activity) connecting greenness exposure to COPD mortality.

Our final analysis included 581,785 participants (52.52% female; average age: 48.36 [Standard Deviation (SD): 17.56]). Each interquartile range (IQR: 0.06) increase in NDVI was associated with a reduced COPD mortality risk, yielding a hazard ratio (HR) of 0.88 (95 % CI: 0.81, 0.96). Furthermore, we observed per IQR (0.04) increase in NDVI was inversely associated with the risk of multiple transitions (baseline - COPD hospitalization, baseline - death, and readmission - death risks), especially a declined risk of all-cause death after readmission (HR = 0.66 [95 %CI: 0.44, 0.99]). Within the observed association between greenness and COPD mortality, three mediators were identified, namely PM, temperature, and BMI (HR for the total indirect effect: 0.773 [95 % CI: 0.703, 0.851]), with PM showing the highest mediating effect.

Our findings revealed greenness may be a beneficial factor for COPD morbidity, prognosis, and mortality. This protective effect is primarily attributed to the reduction in PM concentration.

Routinely collected data (RCD) from healthcare claims and encounters are increasingly used for outcomes in randomized trials; however, methods for estimating the validity and relative precision of RCD-derived outcomes compared to those from conventional outcome ascertainment are limited. We developed an approach to measuring validity and relative precision of RCD and quantifying uncertainty.

We reanalyzed data from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) cluster-randomized, controlled trial. Eighty-six primary care practices in 10 US healthcare systems were randomized to either a multifactorial intervention delivered by nurse falls care managers, or enhanced usual care, with 5451 persons age ≥ 70 at increased fall injury risk enrolled in the study. We estimated the hazard ratio (HR) and confidence interval (CI) for STRIDE's primary outcome (time to first serious fall injury) using original study data and RCD. The ratio of the RCD HR to original HR ("ratio of HRs") measured validity. The confidence limit ratio (CLR; upper divided by lower confidence limits of CI) measured precision, with the ratio of the CLR with RCD to the CLR from the original study data ("ratio of CLRs"), measuring relative precision. We estimated uncertainty around the ratio of HRs and ratio of CLRs using bootstrapped 95% CIs and performed sensitivity analyses to assess the effects of adaptations needed to use RCD.

Among the original sample of 5451 study participants, 5036 (92%) were linked to RCD. The intervention to control HR was 0.91 (95% CI: 0.78-1.07) in RCD, compared to 0.92 (95% CI: 0.80-1.06) in the original data. Using all RCD through STRIDE's administrative end date, the ratio of HRs was 1.00 (95% CI: 0.89-1.11) and ratio of CLRs was 1.03 (95% CI: 0.96-1.06). The CI around ratio of HRs was about three-fold wider for RCD than for the original STRIDE data in individuals who linked to RCD. Relative precision of RCD improved with increased length of follow-up.

Relying solely on RCD to ascertain the primary outcome in STRIDE would have resulted in similar point estimates and confidence limits for the treatment effect as in the original data. However, there was meaningful uncertainty around the estimate of validity. Efforts to validate RCD-derived outcomes for use as clinical trial endpoints should include measurement of uncertainty around validity estimates.

The current TNM staging system does not fully address the variations in glottic squamous cell carcinoma (SCC) extension and subsites involvement, especially if treated with transoral laser microsurgery (TOLMS). This study aims to evaluate the oncologic outcomes after TOLMS in intermediate-advanced glottic SCC, stratified by prognostic subcategories based on anatomical tumor extension.

This retrospective multicentric study analyzed 637 previously untreated patients with pT2-T3 glottic SCC treated by TOLMS following the same policies at four tertiary European centers. Patients were stratified into 5 subcategories (III, IV, Va, Vb, VI) based on three-dimensional local tumor extension, a refined definition of the previous classification proposed by Piazza et al. RESULTS: Out of 637 patients, 453 (71 %) were pT2, and 184 (29 %) pT3. The 5-year disease-specific survival for the entire cohort was 91 %, LCL 81 %, and LP 87 %. Subcategories Va (anterior paraglottic space [PGS] involvement) and Vb (posterior PGS involvement) showed significantly poorer disease-specific survival (Va: 91 %, Vb: 80 %) and local control with laser alone (Va: 76 %, Vb: 68 %) compared to subcategories III (tumors extending superficially to the supra- and/or subglottis) and IV (tumors infiltrating the vocal muscle). Tumors with posterior PGS involvement demonstrated the highest risk of local recurrence and total laryngectomy (HR: 3.70).

TOLMS is a viable treatment option for T2-T3 glottic SCC, offering high rates of laryngeal preservation and favorable oncologic outcomes in well-selected patients. Stratification based on tumor subsites involvement provides critical prognostic insights, with posterior PGS invasion serving as a key risk factor for poorer outcomes.

Evidence suggests that ozone is associated with an increased risk of hypertension, diabetes, or chronic kidney disease (CKD). However, the associations of ozone exposure with the dynamic progression of these diseases among Asian population remain unknown. This study included 9,256,945 participants from Taiwan's National Health Insurance Research Database between 2006 and 2021. Multimorbidity was defined as the coexistence of CKD and either hypertension or diabetes. The ordinary kriging method was used to estimate daily concentrations of ozone, sulfur dioxide, carbon monoxide, nitrogen dioxide, suspended fine particles, and suspended particles. Then, five-year average concentrations of pollutants were calculated. We performed multi-state survival models to analyze the association between ozone and dynamic progression of these diseases. During follow-up, 3,555,498 participants experienced hypertension, diabetes, or CKD; 656,515 experienced multimorbidity; and 792,555 died. Ozone exposure was significantly associated with incidence of the results in all transitions. The hazard ratios of each IQR (3.57 ppb) increment in ozone for the transition to incident disease were 1.016 [95 % confidence interval (CI): 1.014, 1.017], for the transition to death were 1.04 [95 % CI: 1.036, 1.043], for the transition to multimorbidity were 1.015 [95 % CI: 1.012, 1.017]. Furthermore, with each IQR increase of ozone, the hazard ratios for transition from the disease incidence to death and from multimorbidity to death were 1.03 [95 % CI: 1.026, 1.033] and 1.007 [95 % CI: 1.002, 1.013], respectively. Our results suggest long-term exposure to ozone might be an important determinant for the incidence and dynamic progression of hypertension, diabetes, and CKD in Taiwan.

Implant-based breast reconstruction is the most common technique after mastectomy. Breast implants are categorized by surface type as smooth, textured, or polyurethane-covered, each with specific attributed advantages and complication profiles. High-quality comparative studies are, however, limited. This study compared revision incidence and indications for revision among these implant types.

A prospective, nationwide cohort from the Dutch Breast Implant Registry was analysed. Permanent implants used between 2017 and 2022 for direct-to-implant or two-stage reconstruction were included. Surface-related revision was the primary outcome. Cumulative incidences were estimated using a competing risk model. Cause-specific hazard ratios (HRcs) were calculated using univariable and multivariable models, accounting for implant clustering and confounders. Subgroup analyses examined revisions for specific complications.

Of 3996 implants, 76.9% were textured, 12.4% smooth, and 10.8% polyurethane-covered. At 4 years, the cumulative incidence of revision surgeries did not differ between textured (11.1%; 95% c.i. = 9.9 to 12.5), smooth (13.0%; 95% c.i. = 8.5 to 18.4), and polyurethane-covered (16.1%; 95% c.i. = 12.4 to 20.2) implants. Multivariable analysis found no association between surface type and surface-related revision. Subgroup analysis however revealed that polyurethane-covered implants had increased hazards of revision for capsular contracture (HRcs = 2.49; 95% c.i. = 1.24 to 5.01) and asymmetry (HRcs = 2.31; 95% c.i. = 1.33 to 4.02).

After adjusting for confounders and clustering, surface-related revision in a reconstructive setting did not significantly different among breast implant surface types overall. Polyurethane-covered implants may, however, require more revisions due to capsular contracture and asymmetry.

Post hoc analyses of clinical trials suggest that sodium-glucose cotransporter-2 inhibitors (SGLT-2i) lower the risk of hyperkalemia and facilitate the use of renin-angiotensin system inhibitors (RASi) in people with type 2 diabetes. Whether this is also observed in routine care is unclear. We investigated whether SGLT-2i lowered the risk of hyperkalemia and RASi discontinuation as compared to dipeptidyl peptidase 4 inhibitors (DPP-4i).

Using the target trial emulation framework, we studied adults with type 2 diabetes (T2D) who started SGLT-2i or DPP-4i in Stockholm, Sweden (2014-2021). The outcomes were incident hyperkalemia (potassium >5.0 mmol/l), mild hyperkalemia (potassium >5-≤5.5 mmol/l), and moderate to severe hyperkalemia (potassium >5.5 mmol/l). Among RASi users, we studied time to RASi discontinuation through evaluation of pharmacy fills. Cox regression with inverse probability of treatment weighting was used to estimate per-protocol hazard ratios (HRs).

In total, 29 849 individuals (15 326 SGLT-2i and 14 523 DPP-4i initiators) were included (mean age 66 years, 37% women). About one-third of participants in each arm discontinued treatment within 1 year. Compared with DPP-4i, SGLT-2i use was associated with a lower rate of hyperkalemia (HR 0.77; 95% CI: 0.64-0.93), including both mild (0.76; 0.62-0.93) and moderate/severe (0.53; 0.40-0.69) hyperkalemia events. Of 19 116 participants who used RASi at baseline, 7% discontinued therapy. Initiation of SGLT-2i vs. DPP-4i was not associated with the rate of RASi discontinuation (0.97; 0.83-1.14). Results were consistent in intention-to-treat analysis and across strata of sex, cardiovascular disease, use of MRA, and use of RASi.

In patients with diabetes managed in routine clinical care, the use of SGLT-2i was associated with lower rates of hyperkalemia compared with DPP-4i. Possibly because of a relatively high rate of treatment discontinuations, this was not accompanied by higher persistence on RASi therapy.

To investigate the real-world effectiveness of COVID-19 vaccines in a large cohort of patients with diabetes mellitus (DM), we analyzed all >18-year-old patients with COVID-19 registered in a Brazilian nationwide surveillance database between February 2020 and February 2023. The primary outcome of interest was vaccine effectiveness against death, evaluated using multivariate logistic regression models. Among the 2,131,089 patients registered in the SIVEP-Gripe, 482,677 (22.6%) had DM. After adjusting for covariates, patients with DM had a higher risk of death than those without comorbidities (adjusted odds ratio [aOR] = 1.43, 95% CI, 1.39-1.47). For patients without comorbidities (72.7%, 95% CI, 70.5-74.7) and those with DM (73.4%, 95% CI, 68.2-76.7), vaccine effectiveness was similar after the booster dose. However, it was reduced in patients with DM associated with other comorbidities (60.5%; 95% CI, 57.5-63.2). The strongest factor associated with booster failure was the omicron variant (aOR = 27.8, 95% CI, 19.9-40.1). Our study revealed that COVID-19 vaccines provided robust protection against death in individuals with DM. However, our findings underscore the need to update vaccines and develop tailored strategies for individuals with diabetes, especially those with additional underlying conditions.

Nomograms are increasingly recognized as indispensable clinical prediction tools, offering individualized risk estimates through interpretable and visually intuitive formats. Their integration into urologic oncology has significantly advanced precision medicine by enabling refined risk stratification for patients with urological malignancies. This review provides a concise yet comprehensive overview of the development, clinical application, and future prospects of nomograms in urologic oncology. We first outline the essential methodological framework for constructing valid prediction models, including outcome definition, predictor selection, model building, and statistical evaluation using discrimination and calibration metrics. Internal validation techniques such as cross-validation and bootstrapping are highlighted as safeguards against overfitting, while external validation is emphasized to ensure generalizability across diverse clinical contexts. Twelve representative nomograms are examined, classified by display type and implementation format, to illustrate their clinical relevance and limitations. While regression-based models remain widely used, emerging approaches incorporating artificial intelligence and machine learning offer enhanced predictive accuracy but pose challenges in interpretability and integration into electronic health records. Interactive decision-support tools are also gaining prominence, promoting real-time, patient-centered care. Despite existing limitations, such as static outputs, dependence on retrospective data, and inconsistent methodological standards, nomograms continue to facilitate precise and evidence-based decision-making. Looking ahead, future models must prioritize transparency, dynamic updating, multimodal data integration, and adherence to established reporting guidelines. Through rigorous development and thoughtful implementation, nomograms will remain pivotal instruments in delivering personalized, high-quality care in urologic oncology.

Competing risk and immortal time bias can significantly affect the interpretation of analyses in infection disease research where patients may experience multiple outcomes. These biases can distort study results, leading to misestimation of event rates or therapy effects, and ultimately misinform clinical decisions.

This article aims to explain the concepts of competing risk and immortal time bias while providing a clear understanding of how these biases arise and offering practical methods for addressing them in clinical studies. A study of patients in the intensive care unit (ICU) is used as an illustrative example.

References were compiled through searches of MEDLINE/PubMed and Google Scholar up to December 2024.

Competing risk bias occurs when a patient is at risk for more than one mutually exclusive event, such as death from different causes, which can skew survival analyses if not properly accounted for. Immortal time bias arises when periods during which the outcome cannot occur for patients with an intermediate exposure are improperly included in a survival analysis. This leads to altered effects and duration of stay estimation. In the ICU data example, ignoring competing risks results in impossible probability estimates and relative overestimation of incidence by as much as 52%. Ignoring time dependencies leads to the flawed conclusion that bloodstream infection is protective against patient death in the ICU, whereas a valid approach results in a significant harmful effect. Solutions such as multi-state models or extended Cox regression models are presented to mitigate these biases. Examples from published literature with proper handling of these biases are provided.

Properly accounting for competing risk and immortal time biases ensures more reliable and valid results, ultimately guiding better clinical decision-making and improving patient outcomes in the management of infectious diseases.

Women with recent parity are at increased short-term breast cancer (BC) risk and face a worse prognosis. The effect of parity on response to neoadjuvant chemotherapy (NAC) is unstudied, and the influence of inherited susceptibility on parity-related short-term risk remains unclear.

A retrospective case-cohort study analyzed women age 50 years or younger with non-metastatic BC diagnosed between 2010 and 2020 who underwent genetic testing and were treated at Northwestern Medicine. Associations between NAC response and recency of parity were evaluated using multivariate logistic regression, stratified by tumor biologic subtypes. Relationships between germline mutations, recency of parity, and BC were explored via multi-state modeling and linear regression.

Among 1080 eligible women, 231 received NAC. Treatment response was poorer in parous women with triple-negative tumors than in nullipara women regardless of the recency of parity (P < 0.03). Among 122 women (11.3%) with detectable pathogenic mutations, adjusted analyses with both modeling approaches showed no indications that BRCA1/2 carriers had a greater hazard of a BC diagnosis in the decade after recent parity than nulliparous mutation carriers. For BRCA2 and PALB2 carriers, BC diagnosis occurred less frequently in the postpartum intervals.

This study showed a poor response to NAC in parous triple-negative BC (TNBC) patients than in nullipara patients. The effects of immunotherapy-based regimens deserve evaluation in the context of parity. Postpartum BC occurrence is not increased in BRCA1/2 carriers. The effects of rarer susceptibility genes may differ. These important effects of parity on BC in young women and those at genetic risk warrant larger prospective studies.

Although sarcopenia has been linked to a range of cardiometabolic diseases (CMDs, including coronary heart disease [CHD], stroke, and diabetes here), its role in the temporal progression from healthy to single CMD, subsequently to cardiometabolic multimorbidity (CMM, coexistence of ≥ 2 CMDs in an individual), and further to death remains unclear. In this study, we aimed to examine the associations of sarcopenia with the risk of CMDs, CMM, and mortality along the CMD progression trajectory.

We used data from UK Biobank of 413,326 participants free of CMDs at baseline. Multi-state models were used to analyze the transition-specific associations of sarcopenia status measured by handgrip strength, muscle mass, and gait speed (according to the 2019 European Working Group of Sarcopenia in Older People 2) with the progression from no CMD to single CMD, CMM, and ultimately to death. The role of specific sarcopenia components was also assessed.

During a median follow-up of 13.1 years, 51,705 participants experienced ≥ 1 CMD, 6,003 had CMM, and 24,495 died. Compared with people free of sarcopenia, participants with confirmed/severe sarcopenia had higher risk experiencing transitions from no CMD to single CMD or death (hazard ratio [HR] 1.42 and 2.08) and also higher risk from single CMD to CMM progression or death (HR 1.69 and 2.05). Significant associations were observed for participants with probable sarcopenia with smaller effect sizes. All three sarcopenia components increased the risk of most transitions, and stronger associations were observed for low gait speed. In stratified analyses, the associations between sarcopenia and mortality-related transitions were modified by specific lifestyles.

Sarcopenia is an independent risk factor of CMD, CMM progression, and all-cause mortality among middle-aged and older people.

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate end points and for patient management as clinical decision support tools. However, the effect of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes.

We included 11,046 kidney transplant recipients enrolled in ten countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine–Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modeling and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance.

Among 11,046 recipients in the derivation and validation cohorts, 1497 (14%) lost their graft and 1003 (9%) died with a functioning graft after a median follow-up postrisk evaluation of 4.7 years (interquartile range, 2.7–7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan–Meier and Aalen–Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138, and 0.0135 for Cox, Fine–Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors older than 65 years), the findings suggest a trend toward moderately improved calibration when using a competing risk approach.

Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

There is no consensus on de-escalation of monitoring during active surveillance (AS) for prostate cancer (PCa). Our objective was to determine clinical criteria that can be used in decisions to reduce the intensity of AS monitoring.

The global prospective AS cohort from the Global Action Plan prostate cancer AS consortium was retrospectively analyzed. The 24656 patients with complete outcome data were considered. The primary goal was to develop a model identifying a subgroup with a high ratio of other-cause mortality (OCM) to PCa-specific mortality (PCSM). Nonparametric competing-risks models were used to estimate cause-specific mortality. We hypothesized that the subgroup with the highest OCM/PCSM ratio would be good candidates for de-escalation of AS monitoring.

Cumulative mortality at 15 yr, accounting for censoring, was 1.3% for PCSM, 11.5% for OCM, and 18.7% for death from unknown causes. We identified body mass index (BMI) >25 kg/m2 and <11% positive cores at initial biopsy as an optimal set of criteria for discriminating OCM from PCSM. The 15-yr OCM/PCSM ratio was 34.2 times higher for patients meeting these criteria than for those not meeting the criteria. According to these criteria, 37% of the cohort would be eligible for de-escalation of monitoring. Limitations include the retrospective nature of the study and the lack of external validation.

Our study identified BMI >25 kg/m2 and <11% positive cores at initial biopsy as clinical criteria for de-escalation of AS monitoring in PCa.

We investigated factors that could help in deciding on when to reduce the intensity of monitoring for patients on active surveillance for prostate cancer. We found that patients with higher BMI (body mass index) and lower prostate cancer volume may be good candidates for less intensive monitoring. This model could help doctors and patients in making decisions on active surveillance for prostate cancer.