The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes
| Diabetologia Clinical and Experimental Diabetes and Metabolism |
| © The Author(s) 2012 |
| 10.1007/s00125-011-2418-8 |
Article
The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes
B. M. Shields1, T. J. McDonald2, S. Ellard2, M. J. Campbell3, C. Hyde4 and A. T. Hattersley1 
| (1) | Peninsula NIHR Clinical Research Facility, Peninsula Medical School, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK |
| (2) | Royal Devon and Exeter NHS Foundation Trust, Exeter, UK |
| (3) | School of Health and Related Research, University of Sheffield, Sheffield, UK |
| (4) | Peninsula Technology Assessment Group, Peninsula Medical School, University of Exeter, Salmon Pool Lane, Exeter, UK |
| A. T. Hattersley Email: Andrew.Hattersley@pms.ac.uk |
Received: 16 August 2011 Accepted: 24 November 2011 Published online: 5 January 2012
Abstract
Aims/hypothesis
Diagnosing MODY is difficult. To date, selection for molecular genetic testing for MODY has used discrete cut-offs of limited clinical characteristics with varying sensitivity and specificity. We aimed to use multiple, weighted, clinical criteria to determine an individual’s probability of having MODY, as a crucial tool for rational genetic testing.
Methods
We developed prediction models using logistic regression on data from 1,191 patients with MODY (n = 594), type 1 diabetes (n = 278) and type 2 diabetes (n = 319). Model performance was assessed by receiver operating characteristic (ROC) curves, cross-validation and validation in a further 350 patients.
Results
The models defined an overall probability of MODY using a weighted combination of the most discriminative characteristics. For MODY, compared with type 1 diabetes, these were: lower HbA1c, parent with diabetes, female sex and older age at diagnosis. MODY was discriminated from type 2 diabetes by: lower BMI, younger age at diagnosis, female sex, lower HbA1c, parent with diabetes, and not being treated with oral hypoglycaemic agents or insulin. Both models showed excellent discrimination (c-statistic = 0.95 and 0.98, respectively), low rates of cross-validated misclassification (9.2% and 5.3%), and good performance on the external test dataset (c-statistic = 0.95 and 0.94). Using the optimal cut-offs, the probability models improved the sensitivity (91% vs 72%) and specificity (94% vs 91%) for identifying MODY compared with standard criteria of diagnosis <25 years and an affected parent. The models are now available online at www.diabetesgenes.org.
Electronic supplementary material The online version of this article (doi:10.1007/s00125-011-2418-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
Keywords Maturity onset diabetes of the young – MODY – Prediction model – Clinical characteristics
Abbreviations
GCK
Glucokinase
- HNF1A Hepatocyte nuclear factor 1-α
- HNF4A Hepatocyte nuclear factor 4-α
- OHA Oral hypoglycaemic agent
- ROC Receiver operating characteristic
Background
MODY is a rare monogenic form of diabetes [1, 2]. Mutations in at least seven genes have been identified to date as causing MODY, with HNF1A, HNF4A and GCK accounting for approximately 94% of cases [3]. A correct diagnosis of MODY has implications for patient treatment: hepatocyte nuclear factor 1-α (HNF1A) and hepatocyte nuclear factor 4-α (HNF4A) MODY are very sensitive to sulfonylurea tablets [4, 5] whereas patients with glucokinase (GCK) MODY rarely require pharmacological treatment [6, 7]. In addition, a diagnosis of MODY predicts disease prognosis and identifies risk of diabetes in family members. It is estimated that more than 80% of individuals with MODY in the UK are currently undiagnosed, or misdiagnosed with type 1 or young-onset type 2 diabetes [3] and, consequently, inappropriately treated, often with insulin.
Genetic testing is expensive, so careful consideration is required when determining which patients should be tested. In young-onset patients, type 1 diabetes is the most prevalent form, accounting for around 74% of cases in those diagnosed under 35 (E. Pearson and R. McAlpine, personal communication). Type 2 diabetes is less common in young patients, but its likelihood increases with age and obesity. MODY is rare, with estimates of its prevalence between 0.3% and 2.4% of diabetes cases [3, 8–11], although it is recognised that these figures represent underestimates.
MODY is characterised by young age at diagnosis, strong family history of diabetes (due to autosomal dominant inheritance), and no insulin dependence [1, 2, 12]. Most clinical criteria for defining MODY have used absolute cut-offs (e.g. non-obese, age at diagnosis <25 years), rather than determining a probability based on continuous quantitative traits such as BMI or age at diagnosis. Criteria based on absolute cut-offs have shown poor sensitivity, picking up approximately only half of patients with MODY [3, 13]. Current guidelines for genetic testing are based on expert knowledge obtained from clinical observation [14], but not all of the characteristics described are routinely collected, and the likelihood of a positive diagnosis based on these clinical features, either separately or in combination, has not been quantified. Identifying a way of combining simple clinical criteria in a weighted manner, to produce a probability of MODY, would be a valuable aid in determining whether to test an individual for MODY.
Medical diagnostic decision models provide a way of determining a person’s probability of having a particular condition based on their clinical characteristics. Widely used models such as the Amsterdam criteria and Bethesda guidelines for hereditary non-polyposis colorectal cancer [15] and the Well’s score for deep vein thrombosis [16] provide practical screening tools for selecting patients for diagnostic testing. A clinical prediction model that generates a probability of MODY would aid clinicians in identifying patients that are most likely to benefit from genetic testing.
We aimed to produce a clinical prediction model for patients with young-onset diabetes (diagnosed ≤35 years) that uses clinical criteria to determine a patient’s probability of having MODY compared with the more common type 1 and type 2 diabetes. This can then be turned into a simple web-based algorithm for use by clinicians, patients and the molecular genetics diagnostic laboratory.
Methods
Participants and data
All participants were diagnosed between the ages of 1 and 35. Type 1 diabetes was defined as occurring in patients treated with insulin within 6 months of diagnosis [17]; otherwise patients were defined as having type 2 diabetes. MODY patients were identified based on a confirmed genetic diagnosis of HNF1A, HNF4A or GCK MODY.
Initial dataset
We identified 278 patients with type 1 diabetes criteria and 319 patients with type 2 diabetes criteria from five research databases (see electronic supplementary materials (ESM) Methods 1). These were compared with 594 probands with a genetic diagnosis of MODY (243 GCK, 296 HNF1A and 55 HNF4A) obtained from referrals to the Molecular Genetics Laboratory at the Royal Devon and Exeter NHS Foundation Trust, UK. Of the MODY probands, 177 (30%) were treated with insulin within 6 months of diagnosis and 417 (70%) were not, indicating the proportions of patients who were likely to have been misdiagnosed as having type 1 or type 2 diabetes at referral.
A core dataset comprising key variables common to all databases was established including sex, age at diabetes diagnosis, age at recruitment/referral, BMI, initial and current treatment (diet, oral hypoglycaemic agents [OHAs] or insulin), time to insulin treatment, HbA1c and parent affected with diabetes. As well as including the original reported BMI, for initial exploratory analysis, BMI in children was converted to the equivalent value in adults using the Child Growth Foundation reference standards [18] to enable direct comparison between adults and children. All patients were of white European origin. In the MODY cases, data were taken from the referrals database, thus reflecting the patient characteristics at referral and not following a genetic diagnosis.
Statistical approaches used for the discrimination models
Type 1 and type 2 diabetes are mutually exclusive subtypes according to the definitions used. Therefore, only two-way comparisons were considered: type 1 diabetes vs MODY and type 2 diabetes vs MODY. Three statistical approaches were used to produce the models: logistic regression, discriminant analysis and classification trees (CART). Further details are provided in ESM Methods 2.
Discriminative ability of the models
From the logistic regression and linear discriminant analysis, fitted probabilities were obtained from the data. Receiver operating characteristic (ROC) curves were plotted to determine the best cut-offs in terms of sensitivity and specificity, and the AUC (c-statistic) was used as a measure of overall performance. For classification trees, model performance was assessed by misclassification rates.
Validation
Jack-knife cross-validation was used on all models as a measure of internal validation. Following completion of the clinical model using the initial dataset, a further 350 patients were identified as a result of ongoing research recruitment/referral: 219 with MODY, of which 83 were probands and 136 were unrelated family members of probands used in the original model, plus a further 90 patients with type 1 diabetes and 41 patients with type 2 diabetes taken from community-based research cohorts (see ESM Methods 1). This served as an external test dataset.
Post-test probabilities of MODY
The pre-test probabilities for MODY were estimated as 0.7% and 4.6% for the type 1 diabetes vs MODY and type 2 diabetes vs MODY models, respectively (see ESM Methods 3 for derivation). Likelihood ratios for MODY were calculated using the sensitivity and specificity obtained from various cut-offs in fitted probabilities in the models. Post-test odds for MODY were obtained by multiplying the pre-test odds by the likelihood ratio, which could then be converted to post-test probabilities (positive predictive values).
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