Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study.
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ABSTRACT:
SUBMITTER: Moon S
PROVIDER: S-EPMC8333254 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionWe present a new diabetes risk score developed and validated in a multi-ethnic population in Guyana, South America. Measurement of in-country diabetes prevalence is a vital epidemiologic tool to combat the pandemic. It is believed that for every person diagnosed with type 2 diabetes there is another undiagnosed. The International Diabetes Federation (IDF) recommends a two-step detection programme using a risk score questionnaire to identify high-risk individuals followed by glycaemic measure.MethodsData on 798 persons from the 2016 STEPwise Approach to Chronic Disease Risk Factor Surveillance (STEPS) were used to correlate responses to 36 questions with glycated haemoglobin (HbA1C) and fasting plasma glucose (FPG) results. Bootstrapping was used to internally validate the derived seven-variable model. This model with the addition of family history questions was tested in a convenience sample of 659 Guyanese adults and externally validated in a cohort of another 528.ResultsAn 8-item Guyana Diabetes Risk Score (GDRS) was derived. The final model performed with an area under the curve (AUC) of 0.812 CONCLUSIONS: The validated eight-item Guyana Diabetes Risk Score will be extremely useful in identifying individuals at high risk of having diabetes in Caribbean, Black or East Indian populations.
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Project description:BACKGROUND:Researchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed. METHODS:We performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (?1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ?2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI. RESULTS:The study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI. CONCLUSIONS:Clinicians may consider implementing the SPARK index and classifications to stratify patients' PO-AKI risks before performing noncardiac surgery.
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Project description:ContextMultimorbidity is highly prevalent among older adults and associated with a high mortality. Prediction of mortality in multimorbid people would be clinically useful but there is no mortality risk index designed for this population. Our objective was therefore to develop and internally validate a 1-year mortality prognostic index for older multimorbid adults.MethodsWe analysed data of the OPERAM cohort study in Bern, Switzerland, including 822 adults aged 70 years or more with multimorbidity (3 or more chronic medical conditions) and polypharmacy (use of 5 drugs or more for >30 days). Time to all-cause mortality was assessed up to 1 year of follow-up. We performed a parametric Weibull regression model with backward stepwise selection to identify mortality risk predictors. The model was internally validated and optimism corrected using bootstrapping techniques. We derived a point-based risk score from the regression coefficients. Calibration and discrimination were assessed by the calibration slope and C statistic.Results805 participants were included in the analysis. During 1-year of follow-up, 158 participants (20%) had died. Age, Charlson-Comorbidity-Index, number of drugs, body mass index, number of hospitalizations, Barthel-Index (functional impairment), and nursing home residency were predictors of 1-year mortality in a multivariable model. Using these variables, the 1-year probability of dying could be predicted with an optimism-corrected C statistic of 0.70. The optimism-corrected calibration slope was 0.93. Based on the derived point-based risk score to predict mortality risk, 7% of the patients classified at low-risk of mortality, 19% at moderate-risk, and 37% at high-risk died after one year of follow-up. A simpler mortality score, without the Charlson-Comorbidity-Index and Barthel-Index, showed reduced discriminative power (optimism-corrected C statistic: 0.59) compared to the full score.ConclusionWe developed and internally validated a mortality risk index including for the first-time specific predictors for multimorbid adults. This new 1-year mortality prediction point-based score allowed to classify multimorbid older patients into three categories of increasing risk of mortality. Further validation of the score among various populations of multimorbid patients is needed before its implementation into practice.
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Project description:AimsCardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model.Methods and resultsWe developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02-0.11] in the derivation cohort and 0.14 (95% CI 0.04-0.25) in the validation cohort.ConclusionOur recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.
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Project description:To develop a simple systemic lupus erythematosus (SLE) severity index that requires knowledge of only American College of Rheumatology (ACR) criteria and subcriteria.This study used demographic, mortality and medical records data of 1915 patients with lupus from the Lupus Family Registry and Repository. The data were randomly split (2:1 ratio) into independent training and validation sets. A logistic regression with ridge penalty was used to model the probability of being prescribed major immunosuppressive drugs-a surrogate indicator of lupus severity. ACR criteria and subcriteria were used as predictor variables in this model, and the resulting regression coefficient estimates obtained from the training data were used as item weightings to construct the severity index.The resulting index was tested on the independent validation dataset and was found to have high predictive accuracy for immunosuppressive use and early mortality. The index was also found to be strongly correlated with a previously existing severity score for lupus. In addition, demographic factors known to influence lupus severity (eg, age of onset, gender and ethnicity) all showed robust associations with our severity index that were consistent with observed clinical trends.This new index can be easily computed using ACR criteria, which may be among the most readily available data elements from patient medical records. This tool may be useful in lupus research, especially large dataset analyses to stratify patients by disease severity, an important prognostic indicator in SLE.
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Project description:BackgroundAnemia is a common complication of chronic kidney disease (CKD), but its incidence in nephrology settings is poorly investigated. Similarly, the risks of adverse outcomes associated with new-onset anemia are not known.MethodsWe performed a pooled analysis of three observational cohort studies including 1031 non-anemic CKD patients with eGFR <60 mL/min/1.73 m2 regularly followed in renal clinics. We estimated the incidence of mild anemia (hemoglobin 11-12 g/dL in women and 11-13 g/dL in men) and severe anemia (hemoglobin <11 g/dL or use of erythropoiesis-stimulating agents) during a 3-year follow-up period. Thereafter we estimated the risk of end-stage kidney disease (ESKD) and all-cause death associated with new-onset mild and severe anemia.ResultsThe mean age was 63 ± 14 years, 60% were men and 20% had diabetes. The mean estimated glomerular filtration rate (eGFR) was 37 ± 13 mL/min/1.73 m2 and the median proteinuria was 0.4 g/day [interquartile range (IQR) 0.1-1.1]. The incidence of mild and severe anemia was 13.7/100 patients-year and 6.2/100 patients-year, respectively. Basal predictors of either mild or severe anemia were diabetes, lower hemoglobin, higher serum phosphate, eGFR <30 mL/min/1.73 m2 and proteinuria >0.50 g/day. Male sex, moderate CKD (eGFR 30-44 mL/min/1.73 m2) and moderate proteinuria (0.15-0.50 g/day) predicted only mild anemia. The incidence of anemia increased progressively with CKD stages (from 8.77 to 76.59/100 patients-year) and the proteinuria category (from 13.99 to 25.02/100 patients-year). During a median follow-up of 3.1 years, 232 patients reached ESKD and 135 died. Compared with non-anemic patients, mild anemia was associated with a higher adjusted risk of ESKD {hazard ratio [HR] 1.42 [95% confidence interval (CI) 1.02-1.98]} and all-cause death [HR 1.55 (95% CI 1.04-2.32)]. Severe anemia was associated with an even higher risk of ESKD [HR 1.73 (95% CI 1.20-2.51)] and death [HR 1.83 (95% CI 1.05-3.19)].ConclusionsNew-onset anemia is frequent, particularly in patients with more severe renal damage and in those with diabetes mellitus. The occurrence of anemia, even of a mild degree, is associated with mortality risk and faster progression towards ESKD.
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Project description:BackgroundKidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia.MethodsData included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model.ResultsTwo models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration).ConclusionThis index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.
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Project description:ObjectiveTo develop, validate and apply a generic clinical severity index applicable to all adult patients with inflammatory bowel disease (IBD).DesignA review of the literature and an expert focus group consultation were carried out in order to draw out relevant items from existing literature. The new index was called the IBD Index (IBDEX). Standard psychometric analysis was carried out. The construct validity was assessed against biochemical markers, clinical and endoscopic indices. The new index was completed again within 6 weeks to check responsiveness and reproducibility.ResultsIBDEX was used to assess 255 adult patients with IBD (125 with Crohn's disease and 130 with ulcerative colitis), and 64 patients were re-evaluated within 6 weeks. It had good internal consistency (Cronbach's α=0.79) and correlated very well with the Harvey Bradshaw Index (r=0.94), the Simple Clinical Colitis Activity Index (r=0.92), the Mayo Clinic Index (r=0.87) and the Simple Endoscopic Score (r=0.76), all with p values <0.05. IBDEX had a moderate but positive correlation with C reactive protein (r=0.51) and erythrocyte sedimentation rate (r=0.36) p values both <0.05. The test-retest reliability was good (intraclass correlation coefficient 0.97) and responsiveness ratio was 2.27.ConclusionsIBDEX is the first properly validated Clinical Disease Severity Index in IBD. Our results showed that it is valid, reliable and reproducible and has the potential to be used in clinical practice.
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Project description:INTRODUCTION:Several previous studies have reported the incidence of new-onset diabetes mellitus (NODM) after pancreatectomy. Nevertheless, the results were inconsistent. The true rate of NODM after distal pancreatectomy (DP) is still unknown. RESEARCH DESIGN AND METHODS:The aim of this study was to investigate the incidence of and the risk factors for NODM after DP. This study enrolled patients who underwent DP between January 2004 and February 2016 at Peking Union Medical College Hospital. Patients with preoperative diabetes mellitus or diagnosed with pancreatic cancer were excluded. The primary outcome was NODM. RESULTS:A total of 485 patients were enrolled. The median (IQR) of follow-up duration was 30.95 (9.26-180.30) months. The accumulative incidence of NODM was 8.9% at postoperative 6 months, 14.0% at postoperative year one, 22.3% at year three, 27.1% at year five, and 35.5% at year ten. Multivariate analysis showed that the risk of postoperative NODM was positively correlated with age (HR 1.029 (1.013-1.045), p<0.001), preoperative body mass index (BMI) (HR 1.042 (1.003-1.083), p=0.001), operative blood loss (HR 1.0003 (1.0002-1.0010), p<0.001), and length of resected pancreas (HR 1.079 (1.013-1.148), p=0.017). Moreover, concomitant splenectomy (HR 2.001 (1.202-3.331), p=0.008) was associated with significantly higher risk of postoperative NODM. CONCLUSION:NODM incidence increased with postoperative time progression. Age, BMI, surgical blood loss, length of resected pancreas and splenectomy were independent risk factors for NODM after DP. TRIAL REGISTRATION NUMBER:NCT03030209.
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