Development of clinical prediction models for renal and cardiovascular outcomes and mortality in patients with type 2 diabetes and chronic kidney disease using time-varying predictors

Ye, Wen; Ding, Xuemei; Putnam, Nathaniel M.; Farej, Ryan; Singh, Rakesh; Wang, Di; Kuo, Shihchen; Kong, Sheldon X.; Elliott, Jay C.; Lott, Jason P.; Herman, William H.

doi:10.1016/j.jdiacomp.2022.108180

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…For example, risk prediction and health economic models are increasingly looking at disease combinations rather than single diseases. 21 22 …”

Section: Discussionmentioning

confidence: 99%

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

Pasea,

Dashtban,

Mizani

et al. 2023

Open Heart

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BackgroundHeart failure (HF), type 2 diabetes (T2D) and chronic kidney disease (CKD) commonly coexist. We studied characteristics, prognosis and healthcare utilisation of individuals with two of these conditions.MethodsWe performed a retrospective, population-based linked electronic health records study from 1998 to 2020 in England to identify individuals diagnosed with two of: HF, T2D or CKD. We described cohort characteristics at time of second diagnosis and estimated risk of developing the third condition and mortality using Kaplan-Meier and Cox regression models. We also estimated rates of healthcare utilisation in primary care and hospital settings in follow-up.FindingsWe identified cohorts of 64 226 with CKD and HF, 82 431 with CKD and T2D, and 13 872 with HF and T2D. Compared with CKD and T2D, those with CKD and HF and HF and T2D had more severe risk factor profile. At 5 years, incidence of the third condition and all-cause mortality occurred in 37% (95% CI: 35.9%, 38.1%%) and 31.3% (30.4%, 32.3%) in HF+T2D, 8.7% (8.4%, 9.0%) and 51.6% (51.1%, 52.1%) in HF+CKD, and 6.8% (6.6%, 7.0%) and 17.9% (17.6%, 18.2%) in CKD+T2D, respectively. In each of the three multimorbid groups, the order of the first two diagnoses was also associated with prognosis. In multivariable analyses, we identified risk factors for developing the third condition and mortality, such as age, sex, medical history and the order of disease diagnosis. Inpatient and outpatient healthcare utilisation rates were highest in CKD and HF, and lowest in CKD and T2D.InterpretationHF, CKD and T2D carry significant mortality and healthcare burden in combination. Compared with other disease pairs, individuals with CKD and HF had the most severe risk factor profile, prognosis and healthcare utilisation. Service planning, policy and prevention must take into account and monitor data across conditions.

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“…For example, risk prediction and health economic models are increasingly looking at disease combinations rather than single diseases. 21 22 …”

Section: Discussionmentioning

confidence: 99%

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

Pasea,

Dashtban,

Mizani

et al. 2023

Open Heart

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A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

Jiu,

Wang,

Javier Somolinos-Simón

et al. 2024

Diabetes Research and Clinical Practice

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Using Machine Learning to Support Prostate Cancer Detection Without a Dedicated Biomarker

Salem,

Othman,

Broucqsault

et al. 2024

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

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Early disease detection through the analysis of laboratory tests is critical for the successful treatment of certain diseases. However, current medical knowledge relies on the use of certain biomarkers in order to diagnose associated diseases. This means that some diseases cannot be detected unless the corresponding biomarker is actually measured.In this paper, we will explore the possibility of using machine learning to detect abnormal values in biomarkers, even when they are not directly measured. This stems from the fact that, as an ensemble of features, even a set of common biomarkers contains sufficient information to suspect anomalies in a patient's analyses.We present a complete pipeline for processing medical laboratory data, taking into account the many challenges offered by this use case, such as data cleanliness, the abundance of missing values, and strong class imbalance. Using this framework, we perform a case study on prostate cancer and its associated biomarker, Prostate-Specific Antigen (PSA). Our implementation, based on an imbalance-aware ensemble prediction model, is tested on a real laboratory dataset of more than 8 million records. We find that basing the ensemble on Random Forest models provides the best results, with a ROC-AUC of 75.86%, indicating that it is indeed possible to detect abnormal PSA values without measuring it directly. We also show that a simple baseline of 24 commonly-measured biomarkers provides nearequivalent results to a full set of features. Our work offers new perspectives for routine disease screening.

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Development of clinical prediction models for renal and cardiovascular outcomes and mortality in patients with type 2 diabetes and chronic kidney disease using time-varying predictors

Cited by 4 publications

References 33 publications

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

Using Machine Learning to Support Prostate Cancer Detection Without a Dedicated Biomarker

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