How to measure temporal changes in care pathways for chronic diseases using health care registry data

Natural History of Nonmetastatic Prostate Cancer Managed With Watchful Waiting

Importance

It is uncertain to what extent watchful waiting (WW) in men with nonmetastatic prostate cancer (PCa) and a life expectancy of less than 10 years is associated with adverse consequences.

Objective

To report transitions to androgen deprivation therapy (ADT), castration-resistant prostate cancer (CRPC), death from PCa, or death from other causes in men treated with a WW strategy.

Design, Setting, and Participants

This nationwide, population-based cohort study included men with nonmetastatic PCa diagnosed since 2007 and registered in the National Prostate Cancer Register of Sweden with WW as the primary treatment strategy and with life expectancy less than 10 years. Life expectancy was calculated based on age, the Charlson Comorbidity Index (CCI), and a drug comorbidity index. Observed state transition models complemented observed data to extend follow-up to more than 20 years. Analyses were performed between 2022 and 2023.

Exposure

Nonmetastatic PCa.

Main Outcomes and Measures

Transitions to ADT, CRPC, death from PCa, and death from other causes were measured using state transition modeling.

Results

The sample included 5234 men (median [IQR] age at diagnosis, 81 [79-84] years). After 5 years, 954 men with low-risk PCa (66.2%) and 740 with high-risk PCa (36.1%) were still alive and not receiving ADT. At 10 years, the corresponding proportions were 25.5% (n = 367) and 10.4% (n = 213), respectively. After 10 years, 59 men with low-risk PCa (4.1%) and 221 with high-risk PCa (10.8%) had transitioned to CRPC. Ten years after diagnosis, 1330 deaths in the low-risk group (92.3%) and 1724 in the high-risk group (84.1%) were from causes other than PCa.

Conclusions and Relevance

These findings suggest that the WW management strategy is appropriate for minimizing adverse consequences of PCa in men with a baseline life expectancy of less than 10 years.

Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data

Background

Little is known about disease trajectories for men with castration-resistant prostate cancer (CRPC).

Objective

To create a state transition model that estimates time spent in the CRPC state and its outcomes.

Design, setting, and participants

The model was generated using population-based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state.

Outcome measurements and statistical analysis

We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category.

Results and limitations

Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data.

Conclusions

There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category.

Patient summary

It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.

Long-term Outcomes Among Men Undergoing Active Surveillance for Prostate Cancer in Sweden

IMPORTANCE

The long-term outcomes among men with prostate cancer (PC) whose disease is managed with active surveillance (AS) remains unknown.

OBJECTIVE

To develop a simulation model with a 30-year follow-up for men with PC managed with AS.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study, a state transition model was created using data from Prostate Cancer data Base Sweden (PCBaSe) on 23 655 men diagnosed with PC and managed with deferred treatment to estimate treatment trajectories. A simulation was performed with 100 000 men in each combination of age at diagnosis, Charlson Comorbidity Index, and PC risk with a follow-up of 30 years.

MAIN OUTCOMES AND MEASURES

Death from PC and death from other causes were estimated, and the proportion of time without active PC treatment was assessed until date of death or age 85 years.

RESULTS

This study included 23 655 men from PCBaSe with a median age at diagnosis of 69 years (IQR, 64-74 years). Of these, 16 177 men underwent active surveillance for PC and 7478 underwent watchful waiting. The proportion of men who were diagnosed at age 55 years and died of PC before age 85 years was 9% for very low-risk PC, 13% for low-risk PC, and 15% for intermediate-risk PC. Among men with a Charlson Comorbidity Index of 0 who were diagnosed at age 70 years, the corresponding percentages were 3%, 6%, and 7%, respectively. The mean proportion of remaining life-years without active PC treatment for men diagnosed at age 55 years was 12 of 25 years (48%) for very low-risk PC, 9 of 25 years (36%) for low-risk PC, and 7 of 25 (29%) for intermediate-risk PC. For men aged 70 years, the corresponding numbers were 10 of 13 years (77%), 9 of 13 years (66%), and 8 of 13 years (60%), respectively. Men with intermediate-risk PC who were younger than 60 years at diagnosis had a high risk of PC death (12%-15%) and fewer remaining life-years without active PC treatment (29%-33%). In contrast, men with low-risk PC who were older than 65 years at diagnosis had a lower risk of PC death (3%-5%) and more remaining life-years without active PC treatment (62%-77%).

CONCLUSIONS AND RELEVANCE

The findings of this Swedish cohort study suggest that active surveillance may be a safe strategy for disease management among men with PC who were older than 65 years at diagnosis.

Simulation model of disease incidence driven by diagnostic activity

It is imperative to understand the effects of early detection and treatment of chronic diseases, such as prostate cancer, regarding incidence, overtreatment and mortality. Previous simulation models have emulated clinical trials, and relied on extensive assumptions on the natural history of the disease. In addition, model parameters were typically calibrated to a variety of data sources. We propose a model designed to emulate real‐life scenarios of chronic disease using a proxy for the diagnostic activity without explicitly modeling the natural history of the disease and properties of clinical tests. Our model was applied to Swedish nation‐wide population‐based prostate cancer data, and demonstrated good performance in terms of reconstructing observed incidence and mortality. The model was used to predict the number of prostate cancer diagnoses with a high or limited diagnostic activity between 2017 and 2060. In the long term, high diagnostic activity resulted in a substantial increase in the number of men diagnosed with lower risk disease, fewer men with metastatic disease, and decreased prostate cancer mortality. The model can be used for prediction of outcome, to guide decision‐making, and to evaluate diagnostic activity in real‐life settings with respect to overdiagnosis and prostate cancer mortality.