Cost‐effectiveness of a risk‐based secondary screening programme of type 2 diabetes

Nagy, Balázs; Zsólyom, A.; Nagyjánosi, László; Merész, Gergő; Steiner, Tamás; Papp, E.; Dessewffy, Z.; Jermendy, György; Winkler, Gábor; Kaló, Zoltán; Vokó, Zoltán

doi:10.1002/dmrr.2791

Cited by 10 publications

(11 citation statements)

References 75 publications

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“…Eastman[ 33 , 87 ], Caro[ 76 , 77 ], IQVIA-CORE[ 93 ], Michigan model[ 97 ], Tilden[ 104 ], ODEM[ 98 ], Sheffield[ 101 ], Syreon[ 103 ], PROSIT[ 99 ], and ECHO[ 88 ], and PREDICT[ 15 ]…”

Section: Resultsmentioning

confidence: 99%

“…Older HE models estimated their own prediction models (e.g., Eastman, Archimedes, UKPDS and EAGLE), and newer HE models used or re-estimated existing prediction models, with a few exceptions (e.g., BRAVO and JJCEM). The UKPDS risk engine (n = 20, 59%) is the most frequently used set of prediction models (Table 2), followed by the Eastman [33,87], Caro [76,77], IQVIA-CORE [93], Michigan model [97], Tilden [104], ODEM [98], Sheffield [101], Syreon [103], PROSIT [99], and ECHO [88], and PREDICT [ GDM [16], UKPDS-OM1 [25], EAGLE [86], Cardiff [75], JADE [5], DMM [85], UKPDS-OM2 [11], TTM [105], SPHR [102], BRAVO [74], COMT [82], RAMP-DM [100], Cornerstone [83], CHIME [81], and PRIME [53] Individual trajectories were simulated as a sequence of events by repeatedly applying prediction models, using a discrete time cycle, usually a fixed annual cycle Risks for events were based on existing or self-developed prediction models, reflecting the risk to develop a certain complication ADVANCE and QRisk2) were only applicable for macrovascular disease. In many models, microvascular disease was estimated using diabetes duration stratified constant hazard ratios, assuming implicit exponential survival models.…”

Section: Application Of Prediction Modelsmentioning

confidence: 99%

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Prediction of complications in health economic models of type 2 diabetes: a review of methods used

Wang

et al. 2023

Acta Diabetol

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Aim Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. Methods PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. Results The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the ‘sunflower method’ (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. Conclusions The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

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“…Eastman[ 33 , 87 ], Caro[ 76 , 77 ], IQVIA-CORE[ 93 ], Michigan model[ 97 ], Tilden[ 104 ], ODEM[ 98 ], Sheffield[ 101 ], Syreon[ 103 ], PROSIT[ 99 ], and ECHO[ 88 ], and PREDICT[ 15 ]…”

Section: Resultsmentioning

confidence: 99%

Section: Application Of Prediction Modelsmentioning

confidence: 99%

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

Wang

et al. 2023

Acta Diabetol

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“…While a five-state model structure was most common, models with as few as three statesmodeling pre-ESKD and ESKD states in addition to death [82, 83]-and as many as eight states were identified. Models with more states either modeled CKD in greater detail, e.g., by distinguishing between early and advanced overt nephropathy and different types of RRT [95] or between detected and undetected kidney disease states [102], or by modeling individual CKD stages [12,78], and/or modeled CKD sequelae such as CVD [65,100].…”

Section: State-transition Modelsmentioning

confidence: 99%

“…There was also a broad trend towards larger sample sizes in more recent primary data sources. The largest sample sizes were from analyses of registry data, including more than 1.1 million adults from the Kaiser Permanente Renal Registry [126], used in the ECHO-T2DM model, and 228,552 adults from the USRDS [127], used in the Syreon Diabetes Control Model [102] and the CORE Diabetes Model [37], as well as the 57,594 patients from the Taiwanese Chronic Fig. 2 Model-source co-occurrence network for clinical effects.…”

Section: Network Of Models and Primary Sourcesmentioning

confidence: 99%

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

Pöhlmann¹,

Bergenheim

Sánchez

et al. 2022

Diabetes Ther

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Introduction: As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.Methods: This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources. Results: The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were

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“…Recent surveys indicate an increased prevalence of self-reporting of DM 29 and the cost-effectiveness of the screening program has been well established. 30 With early diagnosis of prediabetes, lifestyle and diet changes can be made that prevent the development of DM in most patients. Early treatment of both prediabetes and DM can save not only lives, but also a very large amount of money to the health care system.…”

Section: Point Of Care Hba 1c Testingmentioning

confidence: 99%

Point of care testing in the diagnosis and monitoring of diabetes mellitus

Ábel¹

2017

Diabetologia Hungarica

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In the past half-century, routine central laboratory testing has become increasingly automated and efficient. The majority of clinical chemistry, immunochemistry and hematology testing is performed using high throughput instrumentation, with sophisticated automation. Point of care testing offers considerable advantages over central laboratory testing such as fast and simple specimen handling, and simpler sample requirement (no additives and mostly blood from finger stick, or urine). The advantage of short turnaround time is particularly important in the inpatient setting for blood glucose monitoring to achieve tight glycaemic control. HbA 1c screening of inpatients, either with a central laboratory method or by means of point of care testing identifies patients previously not diagnosed with diabetes and enables enrolling of these patients in a diabetes treatment program. In the outpatient setting, point of care glucose and HbA 1c monitoring allow adjustment of therapy without the need of a second appointment in the doctor's office. In recent years, technology enabled patients to use point of care devices at home to assure tighter glucose control. However, point of care testing also has a downside: testing is more expensive than central laboratory testing, the accuracy and reproducibility of many point of care systems are no match to the analytical performance of the central laboratory tests, and performing the assays at home, by the patient's bedside, or in the office requires some skill. In addition, the accuracy of point of care testing in critically ill patients has been questioned recently, and the analytical performance of several commercially available point of care testing systems for HbA 1c has been found unacceptable. This article succinctly reviews the clinical utility of point of care blood glucose and HbA 1c testing in the diagnosis and monitoring of patients with type 2 diabetes mellitus, and discusses the caveats and limitations of point of care glucose and HbA 1c testing.

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Cost‐effectiveness of a risk‐based secondary screening programme of type 2 diabetes

Abstract: From the economic perspective introduction of the 3-yearly screening programme is justifiable and it provides a good value for money. Copyright © 2016 John Wiley & Sons, Ltd.

Cited by 10 publications

References 75 publications

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

Point of care testing in the diagnosis and monitoring of diabetes mellitus

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