Developing and validating clinical prediction models in hepatology - An overview for clinicians.

Prediction models are everywhere in clinical medicine. We use them to assign a diagnosis or a prognosis, and there have been continuous efforts to develop better prediction models. It is important to understand the fundamentals of prediction modelling, thus, we herein describe nine steps to develop and validate a clinical prediction model with the intention of implementing it in clinical practice: Determine if there is a need for a new prediction model; define the purpose and intended use of the model; assess the quality and quantity of the data you wish to develop the model on; develop the model using sound statistical methods; generate risk predictions on the probability scale (0-100%); evaluate the performance of the model in terms of discrimination, calibration, and clinical utility; validate the model using bootstrapping to correct for the apparent optimism in performance; validate the model on external datasets to assess the generalisability and transportability of the model; and finally publish the model so that it can be implemented or validated by others.

Misclassified Alcohol-related Liver Disease is Common in Presumed Metabolic Dysfunction-associated Steatotic Liver Disease and Highly Increases Risk for Future Cirrhosis.

BACKGROUND & AIMS: Alcohol overconsumption is a risk factor for disease progression in patients with presumed metabolic dysfunction-associated steatotic liver disease (MASLD). How commonly this occurs and how it affects progression to major adverse liver outcomes (MALOs) is not well known. METHODS: We did a register-based cohort study, including all patients with a diagnosis of MASLD in Sweden between 1987 and 2020. Patients were stratified on co-occurrence of diagnoses of alcohol-related liver disease (ALD) or alcohol use disorder (AUD) prior to MASLD diagnosis. Incident MALOs were derived from national registers. Cox regression was used to calculate hazard ratios (HRs) for incident MALO. RESULTS: A total of 15,107 patients with MASLD were identified. The median age was 55 years, and 52% were female. Of the patients, 1843 (12%) had a prior diagnosis of ALD or AUD. During follow-up, a further 787 patients (5.2%) received a diagnosis of ALD or AUD. Patients with previous ALD or AUD diagnoses at or before baseline had considerably higher rates of MALOs compared with patients without (19.5% vs 7.8%; adjusted HR, 3.12; 95% confidence interval, 2.74-3.55). Acquiring an ALD or AUD diagnosis after MASLD diagnosis was associated with higher rates of MALOs (adjusted HR, 5.81; 95% confidence interval, 4.90-6.88). CONCLUSIONS: ALD or AUD is commonly diagnosed prior to or after MASLD diagnosis. Such patients have considerably higher rates of progression to MALOs. Correctly separating between MASLD and ALD is vital to assess prognosis.

Four-fold increased mortality rate in patients with Wilson's disease: A population-based cohort study of 151 patients.

BACKGROUND AND AIMS: Few studies have investigated mortality rates in patients with Wilson's disease and compared these to the general population. Here, we examined several clinical outcomes (including cardiovascular, psychiatric, neurologic conditions) in a population-based study of patients with Wilson's disease. METHOD: We used nationwide registers to identify all patients with a first diagnosis of Wilson's disease between 2002 and 2020 in Sweden. Each patient was matched by age, sex, and municipality with up to 10 reference individuals from the general population. Validated registers were used to investigate outcomes up to 19 years after baseline in patients and reference individuals. RESULTS: We identified 151 patients with Wilson's disease matched with 1441 reference individuals. Median age at baseline was 26 years (IQR 17-42) and 50% were males. During a median follow-up of 6.6 years (IQR 2.9-12.9), 10 (6.6%) patients with Wilson's disease died compared with 31 (2.2%) reference individuals. This translated to a hazard ratio (HR) of 3.8 (95%CI = 1.8-8.1). Mortality was higher among Wilson's disease patients with baseline neuropsychiatric diagnoses (HR 7.9, 95%CI = 2.9-21.8). Cumulative mortality over 10 years was 9.3% (95%CI = 5.0-16.8) in Wilson's disease, compared to 2.4% (95%CI = 1.6-3.6) in reference individuals. We observed significantly elevated risks in the Wilson's disease group for incident cardiovascular disease, and incident psychiatric and neurological conditions when considering liver transplantation or death from other causes as competing events. CONCLUSION: In this large population-based cohort study, patients with Wilson's disease had an almost four-fold increased mortality rate compared with matched individuals from the general population.

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study.

Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48-71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21-57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness-in addition to breast density-should be considered when assigning complementary screening modalities and personalized screening intervals.

Novel predictions of invasive breast cancer risk in mammography screening cohorts.

Mammography screening programs are aimed at reducing mortality due to breast cancer by detecting tumors at an early stage. There is currently interest in moving away from the age-based screening programs, and toward personalized screening based on individual risk factors. To accomplish this, risk prediction models for breast cancer are needed to determine who should be screened, and when. We develop a novel approach using a (random effects) continuous growth model, which we apply to a large population-based, Swedish screening cohort. Unlike existing breast cancer prediction models, this approach explicitly incorporates each woman's individual screening visits in the prediction. It jointly models invasive breast cancer tumor onset, tumor growth rate, symptomatic detection rate, and screening sensitivity. In addition to predicting the overall risk of invasive breast cancer, this model can make separate predictions regarding specific tumor sizes, and the mode of detection (eg, detected at screening, or through symptoms between screenings). It can also predict how these risks change depending on whether or not a woman will attend her next screening. In our study, we predict, given a future diagnosis, that the probability of having a tumor less than (as opposed to greater than) 10-mm diameter, at detection, will be, on average, 2.6 times higher if a woman in the cohort attends their next screening. This indicates that the model can be used to evaluate the short-term benefit of screening attendance, at an individual level.

Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study.

With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.

Estimating Distributions of Breast Cancer Onset and Growth in a Swedish Mammography Screening Cohort.

BACKGROUND: In recent years, biologically motivated continuous tumor growth models have been introduced for breast cancer screening data. These provide a novel framework from which mammography screening effectiveness can be studied. METHODS: We use a newly developed natural history model, which is unique in that it includes a carcinogenesis model for tumor onset, to analyze data from a large Swedish mammography cohort consisting of 65,536 participants, followed for periods of up to 6.5 years. Using patient data on age at diagnosis, tumor size, and mode of detection, as well as screening histories, we estimate distributions of patient's age at onset, (inverse) tumor growth rates, symptomatic detection rates, and screening sensitivities. We also allow the growth rate distribution to depend on the age at onset. RESULTS: We estimate that by the age of 75, 13.4% of women have experienced onset. On the basis of a model that accounts for the role of mammographic density in screening sensitivity, we estimated median tumor doubling times of 167 days for tumors with onset occurring at age 40, and 207 days for tumors with onset occurring at age 60. CONCLUSIONS: With breast cancer natural history models and population screening data, we can estimate latent processes of tumor onset, tumor growth, and mammography screening sensitivity. We can also study the relationship between the age at onset and tumor growth rates. IMPACT: Quantifying the underlying processes of breast cancer progression is important in the era of individualized screening.