Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease

Importance: Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. Objective: To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts. Design, Setting, and Participants: This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023. Main Outcomes and Measures: Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c[mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated. Results: Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5. Conclusions and Relevance: In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.</p

CDSSs for CVD Risk Management: An Integrative Review

Cardiovascular disease (CVD) is a preventable disease affecting almost half of adults in the United States (U.S.) and can have significant negative outcomes such as stroke and myocardial infarction, which can be fatal. Utilizing clinical decision support systems (CDSSs) in the primary care and community health setting can improve primary prevention of CVD by supporting evidence-based decision making at the point of care. This integrative review synthesizes the most up-to-date literature on the use of clinical decision support (CDS) tools to support guideline-based management of CVD risk. Using Whittemore and Knafl’s framework for integrative reviews, a systematic search of CINAHL, Cochrane, and Medline and ancestry search yielded 492 results; 17 articles were included in the final review after applying inclusion and exclusion criteria. Evidence-based CDSSs for CVD prevention improved guideline-based initiation and intensification of pharmacological treatment, increased frequency and accuracy of CVD risk screening, and facilitated shared decision-making discussions with patients about CVD risk; however, they were not effective in promoting smoking cessation and only sometimes effective in improving blood pressure (BP) control. This integrative review supports future evidence-based practice projects implementing CDSSs designed to improve guideline-based primary prevention of CVD as an, albeit partial, solution to improving prevention of CVD in the U.S. and globally

A systematic review of diagnostic and prognostic models of chronic kidney disease in low- and middle- income countries

Objective: To summarize available chronic kidney disease (CKD) diagnostic and prognostic models in Low- and Middle-Income countries (LMIC) Method: Systematic review (PRISMA guidelines). We searched Medline, EMBASE, Global Health (these three through OVID), Scopus and Web of Science from inception to April 9th, 2021, April 17th, 2021 and April 18th, 2021, respectively . We first screened titles and abstracts, and then studied in detail the selected reports; both phases were conducted by two reviewers independently. We followed the CHARMS recommendations and used the PROBAST for risk of bias assessment. Results: The search retrieved 14,845 results, 11 reports were studied in detail and nine (n= 61,134) were included in the qualitative analysis. The proportion of women in the study population varied between 24.5%-76.6%, and the mean age ranged between 41.8-57.7 years. Prevalence of undiagnosed chronic kidney disease ranged between 1.1%-29.7%. Age, diabetes mellitus and sex were the most common predictors in the diagnostic and prognostic models. Outcome definition varied greatly, mostly consisting of urinary albumin-to-creatinine ratio and estimated glomerular filtration rate. The highest performance metric was the negative predictive value. All studies exhibited high risk of bias, and some had methodological limitations. Conclusion: There is no strong evidence to support the use of a CKD diagnostic or prognostic model throughout LMIC. The development, validation and implementation of risk scores must be a research and public health priority in LMIC to enhance CKD screening to improve timely diagnosis

Investigation of over-fitting and optimism in prognostic models

This work seeks to develop a high quality prognostic model for the CARE-HF data; see (Richardson et al. 2007). The CARE-HF trial was a major study into the effects of cardiac resynchronization. Cardiac resynchronization has been shown to reduce mortality in patients suffering heart failure due to electrical problems in the heart. The prognostic model presented in this work was motivated by the question as to which patient characteristics may modify the effect of cardiac resynchronization. This is a question of great importance to clinicians. Efforts are made to produce a high quality prognostic model in part through the application of methods to reduce the risk of over-fitting. One method discussed in this work is the strategy proposed by Frank Harrell Jr. The various aspects of Harrell’s approach are discussed. An attempt is made to extend Harrell’s strategy to frailty models. Key issues such as missing data and imputation, specification of the functional form of the model, and validation are examined in relation to the prognostic model for the CARE-HF data. Material is presented covering survival analysis, maximum likelihood methods, model selection criteria (AIC, BIC), specification of functional form (cubic splines and fractional polynomials) and validation methods (cross-validation, bootstrap methods). The concepts of over-fitting and optimism are examined. The author concludes that whilst Harrell’s strategy is valuable it is still quite possible to produce models that are over-fitted. MDL (Minimum Description Length) is suggested as potentially useful methods by which statistical models can be obtained that have an in built resistance to over-fitting. The author also recommends that concepts such as over-fitting, optimism and model validation are introduced earlier in more elementary courses on statistical modelling

Prescribing in paediatric kidney impairment

Kidney impairment is common in the paediatric population. This includes patients with an acute deterioration in kidney function during an episode of acute kidney injury (AKI), patients with chronic kidney disease (CKD) and patients undergoing renal replacement therapy (RRT) or with kidney transplants. Patients with kidney impairment are at increased risk of adverse events associated with errors in drug prescribing and administration.1 This article aims to highlight several key principles prescribers should be aware of when managing these patients, with a particular focus on management in secondary care

Description and pilot evaluation of the Metabolic Irregularities Narrowing down Device software: a case analysis of physician programming

Background: There is a gap between the abilities and the everyday applications of Computerized Decision Support Systems (CDSSs). This gap is further exacerbated by the different ‘worlds’ between the software designers and the clinician end-users. Software programmers often lack clinical experience whereas practicing physicians lack skills in design and engineering. Objective: Our primary objective was to evaluate the performance of Metabolic Irregularities Narrowing down Device (MIND) intelligent medical calculator and differential diagnosis software through end-user surveys and discuss the roles of CDSS in the inpatient setting. Setting: A tertiary care, teaching community hospital. Study participants: Thirty-one responders answered the survey. Responders consisted of medical students, 24%; attending physicians, 16%, and residents, 60%. Results: About 62.5% of the responders reported that MIND has the ability to potentially improve the quality of care, 20.8% were sure that MIND improves the quality of care, and only 4.2% of the responders felt that it does not improve the quality of care. Ninety-six percent of the responders felt that MIND definitely serves or has the potential to serve as a useful tool for medical students, and only 4% of the responders felt otherwise. Thirty-five percent of the responders rated the differential diagnosis list as excellent, 56% as good, 4% as fair, and 4% as poor. Discussion: MIND is a suggesting, interpreting, alerting, and diagnosing CDSS with good performance and end-user satisfaction. In the era of the electronic medical record, the ongoing development of efficient CDSS platforms should be carefully considered by practicing physicians and institutions

Developing a Recommendation-Based Application to Help Endocrinologists Treat Type II Diabetes Mellitus

Diabetes Mellitus type II is a disease characterized by abnormally high levels of glucose in the bloodstream (hyperglycemia) due to decreased insulin secretion, insulin resistance, or both. It affects approximately 425 million adults worldwide and is the 7th most common chronic condition according to the CDC (Figure 1).[1] Patients with this disease typically have increased urination, increased thirst, and fatigue and can even be vulnerable to many types of infections. Patients with type II diabetes see diabetes specialists and endocrinologists to effectively treat their disease. Currently, however, there is a massive shortage of endocrinologists in the United States due to a growing demand of chronic diseases such as diabetes and osteoporosis.[2] In one study, the majority of endocrinologists surveyed believed the process of treating diabetes is difficult for these four reasons: the shortage of physicians, constantly evolving diabetes research, rapidly changing medication guidelines, and the rate at which medications are being added to the market.[3] Another major problem in the diabetes community is the risk of potentially inappropriate medications (PIMs), which are defined as prescribing medications that have a greater risk of potentially severe adverse effects. 74% of elderly patients with type II diabetes are prescribed at least one PIM when hospitalized.[4] The studies conducted by Healy et al. and Sharma et al. reveal that the process of treating type II diabetes is difficult because of 3 main reasons: The shortage of endocrinologists, rapidly evolving medication recommendations by diabetes associations, and the health risk to elderly diabetic patients due to PIMs. There is a growing need for technology that assists endocrinologists in prescribing medication based on factors that adjust to the evolving recommendations by the American Diabetes Association and uses patient biomarkers along with other factors to recommend appropriate medications for patients.Undergraduat

X-linked hypophosphatemia caused by the prevailing North American PHEX variant c.*231A\u3eG; exon 13–15 duplication is often misdiagnosed as ankylosing spondylitis and manifests in both men and women

Inactivating mutations of the gene coding for phosphate-regulating endopeptidase homolog X-linked (PHEX) cause X-linked hypophosphatemia (XLH). A nove