A multivariate logistic regression equation to screen for dysglycaemia: development and validation

Aims  To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes]. Methods  A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG) ≥ 6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG) ≥ 7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening. Results  The predictive equation was calculated with the following logistic regression parameters: P  = 1 + 1/(1 + e −X ) = where X = −8.3390 + 0.0214 (age in years) + 0.6764 (if female) + 0.0335 (BMI in kg/m 2 ) + 0.0934 (post-prandial time in hours) + 0.0141 (systolic blood pressure in mmHg) − 0.0110 (HDL in mmol/l) + 0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability ≥ 0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%. Conclusions  This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator. Diabet. Med. 22, 599–605 (2005)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75603/1/j.1464-5491.2005.01467.x.pd

Initial impact and cost of a nationwide population screening campaign for diabetes in Brazil: A follow up study

<p>Abstract</p> <p>Background</p> <p>In 2001 Brazilian citizens aged 40 or older were invited to participate in a nationwide population screening program for diabetes. Capillary glucose screening tests and procedures for diagnostic confirmation were offered through the national healthcare system, diagnostic priority being given according to the severity of screening results. The objective of this study is to evaluate the initial impact of the program.</p> <p>Methods</p> <p>Positive testing was defined by a fasting capillary glucose ≥ 100 mg/dL or casual glucose ≥ 140 mg/dL. All test results were tabulated locally and aggregate data by gender and clinical categories were sent to the Ministry of Health. To analyze individual characteristics of screening tests performed, a stratified random sample of 90,106 tests was drawn. To describe the actions taken for positive screenees, a random sub-sample of 4,906 positive screenees was actively followed up through home interviews.</p> <p>Main outcome measures considered were the number of diabetes cases diagnosed and cost per case detected and incorporated into healthcare.</p> <p>Results</p> <p>Of 22,069,905 screening tests performed, we estimate that 3,417,106 (95% CI 3.1 – 3.7 million) were positive and that 346,168 (290,454 – 401,852) new cases were diagnosed (10.1% of positives), 319,157 (92.2%) of these being incorporated into healthcare. The number of screening tests needed to detect one case of diabetes was 64. As many cases of untreated but previously known diabetes were also linked to healthcare providers during the Campaign, the estimated number needed screen to incorporate one case into the healthcare system was 58. Total screening and diagnostic costs were US$26.19 million, the cost per diabetes case diagnosed being US$ 76. Results were especially sensitive to proportion of individuals returning for diagnostic confirmation.</p> <p>Conclusion</p> <p>This nationwide population-based screening program, conducted through primary healthcare services, demonstrates the feasibility, within the context of an organized national healthcare system, of screening campaigns for chronic diseases. Although overall costs were significant, cost per new case diagnosed was lower than previously reported. However, cost-effectiveness analysis based on more clinically significant outcomes needs to be conducted before this screening approach can be recommended in other settings.</p

Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis

Background: Self-monitoring of blood pressure (BP) appears to reduce BP in hypertension but important questions remain regarding effective implementation and which groups may benefit most. This individual patient data (IPD) meta-analysis was performed to better understand the effectiveness of BP self-monitoring to lower BP and control hypertension.Methods and findings:Medline, Embase, and the Cochrane Library were searched for randomised trials comparing self-monitoring to no self-monitoring in hypertensive patients (9June 2016). Two reviewers independently assessed articles for eligibility and the authors of eligible trials were approached requesting IPD. Of 2,846 articles in the initial search, 36 were eligible. IPD were provided from 25 trials, including 1 unpublished study. Data for the primary outcomes-change in mean clinic or ambulatory BP and proportion controlled below target at 12 months-were available from 15/19 possible studies (97,138/8,292 [86%] of randomised participants). Overall, self-monitoring was associated with reduced clinic systolic blood pressure (9sBP) compared to usual care at 12 months (-3.2 mmHg, [95% CI -4.9, -1.6 mmHg]). However, this effect was strongly influenced by the intensity of co-intervention ranging from no effect with self-monitoring alone (-1.0 mmHg [-3.3, 1.2]), to a 6.1 mmHg (-9.0, -3.2) reduction when monitoring was combined with intensive support. Self-monitoring was most effective in those with fewer antihypertensive medications and higher baseline sBP up to 170 mmHg. No differences in efficacy were seen by sex or by most comorbidities. Ambulatory BP data at 12 months were available from 4 trials (91,478 patients), which assessed selfmonitoring with little or no co-intervention. There was no association between self-monitoring and either lower clinic or ambulatory sBP in this group (9clinic -0.2 mmHg [-2.2, 1.8]; ambulatory 1.1 mmHg [-0.3, 2.5]). Results for diastolic blood pressure (9dBP) were similar. The main limitation of this work was that significant heterogeneity remained. This was at least in part due to different inclusion criteria, self-monitoring regimes, and target BPs in included studies.Conclusions: Self-monitoring alone is not associated with lower BP or better control, but in conjunction with co-interventions (9including systematic medication titration by doctors, pharmacists, or patients; education; or lifestyle counselling) leads to clinically significant BP reduction which persists for at least 12 months. The implementation of self-monitoring in hypertension should be accompanied by such co-interventions.</p

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p

Privacy-preserving computations of predictive medical models with minimax approximation and non-adjacent form

© International Financial Cryptography Association 2017. In 2014, Bos et al. introduced a cloud service scenario to provide private predictive analyses on encrypted medical data, and gave a proof of concept implementation by utilizing homomorphic encryption (HE) scheme. In their implementation, they needed to approximate an analytic predictive model to a polynomial, using Taylor approximations. However, their approach could not reach a satisfactory compromise so that they just restricted the pool of data to guarantee suitable accuracy. In this paper, we suggest and implement a new efficient approach to provide the service using minimax approximation and Non-Adjacent Form (NAF) encoding. With our method, it is possible to remove the limitation of input range and reduce maximum errors, allowing faster analyses than the previous work. Moreover, we prove that the NAF encoding allows us to use more efficient parameters than the binary encoding used in the previous work or balaced base-B encoding. For comparison with the previous work, we present implementation results using HElib. Our implementation gives a prediction with 7-bit precision (of maximal error 0.0044) for having a heart attack, and makes the prediction in 0.5 s on a single laptop. We also implement the private healthcare service analyzing a Cox Proportional Hazard Model for the first time.N