Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.
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SUBMITTER: Burlacu A
PROVIDER: S-EPMC8227302 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundChronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.Methods and findingsWe systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.ConclusionsThe development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored.
| S-EPMC3502517 | biostudies-literature
Project description:Cardiovascular disease (CVD) mortality is a leading cause of death in adult chronic kidney disease (CKD), with exceptionally high rates in young adults, according to the Task Force on Cardiovascular Disease. Recent data indicate that cardiovascular complications are already present in children with CKD. This review summarizes the current literature on cardiac risk factors, mortality and morbidity in children with CKD.
| S-EPMC2100430 | biostudies-other
Project description:ObjectiveTo provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).DesignScoping review.Data sourcesMedline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.Study selectionAll English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.Data extractionEligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.ResultsFrom 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.ConclusionsResearchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.
| S-EPMC9321365 | biostudies-literature
Project description:BackgroundMany publications report the prevalence of chronic kidney disease (CKD) in the general population. Comparisons across studies are hampered as CKD prevalence estimations are influenced by study population characteristics and laboratory methods.MethodsFor this systematic review, two researchers independently searched PubMed, MEDLINE and EMBASE to identify all original research articles that were published between 1 January 2003 and 1 November 2014 reporting the prevalence of CKD in the European adult general population. Data on study methodology and reporting of CKD prevalence results were independently extracted by two researchers.ResultsWe identified 82 eligible publications and included 48 publications of individual studies for the data extraction. There was considerable variation in population sample selection. The majority of studies did not report the sampling frame used, and the response ranged from 10 to 87%. With regard to the assessment of kidney function, 67% used a Jaffe assay, whereas 13% used the enzymatic assay for creatinine determination. Isotope dilution mass spectrometry calibration was used in 29%. The CKD-EPI (52%) and MDRD (75%) equations were most often used to estimate glomerular filtration rate (GFR). CKD was defined as estimated GFR (eGFR) <60 mL/min/1.73 m(2) in 92% of studies. Urinary markers of CKD were assessed in 60% of the studies. CKD prevalence was reported by sex and age strata in 54 and 50% of the studies, respectively. In publications with a primary objective of reporting CKD prevalence, 39% reported a 95% confidence interval.ConclusionsThe findings from this systematic review showed considerable variation in methods for sampling the general population and assessment of kidney function across studies reporting CKD prevalence. These results are utilized to provide recommendations to help optimize both the design and the reporting of future CKD prevalence studies, which will enhance comparability of study results.
| S-EPMC4514069 | biostudies-literature
Project description:BackgroundThe prevalence of chronic kidney disease (CKD) is high in general populations around the world. Targeted testing and screening for CKD are often conducted to help identify individuals that may benefit from treatment to ameliorate or prevent their disease progression.AimsThis systematic review examines the methods used in economic evaluations of testing and screening in CKD, with a particular focus on whether test accuracy has been considered, and how analysis has incorporated issues that may be important to the patient, such as the impact of testing on quality of life and the costs they incur.MethodsArticles that described model-based economic evaluations of patient testing interventions focused on CKD were identified through the searching of electronic databases and the hand searching of the bibliographies of the included studies.ResultsThe initial electronic searches identified 2,671 papers of which 21 were included in the final review. Eighteen studies focused on proteinuria, three evaluated glomerular filtration rate testing and one included both tests. The full impact of inaccurate test results was frequently not considered in economic evaluations in this setting as a societal perspective was rarely adopted. The impact of false positive tests on patients in terms of the costs incurred in re-attending for repeat testing, and the anxiety associated with a positive test was almost always overlooked. In one study where the impact of a false positive test on patient quality of life was examined in sensitivity analysis, it had a significant impact on the conclusions drawn from the model.ConclusionFuture economic evaluations of kidney function testing should examine testing and monitoring pathways from the perspective of patients, to ensure that issues that are important to patients, such as the possibility of inaccurate test results, are properly considered in the analysis.
| S-EPMC4605841 | biostudies-literature
Project description:ObjectivesIn this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.MethodsWe included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate.ResultsFrom a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.ConclusionsMachine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
| S-EPMC10227138 | biostudies-literature
Project description:Chronic kidney disease (CKD) is a global health burden and is independently associated with increased cardiovascular disease risk. Assessment of cardiovascular risk in the general population using prognostic models based on routinely collected risk factors is embedded in clinical practice. In CKD, prognostic models may misrepresent risk due to the interplay of traditional atherosclerotic and non-traditional risk factors. This systematic review's aim was to identify routinely collected risk factors for inclusion in a CKD-specific cardiovascular prognostic model.Systematic review and meta-analysis of observational cohort studies and randomized controlled trials. Studies identified from MEDLINE and Embase searches using a pre-defined and registered protocol (PROSPERO ID-2016:CRD42016036187). The main inclusion criteria were individuals ?18 years of age with non-endstage CKD. Routinely collected risk factors where multi-variable adjustment for established cardiovascular risk factors had occurred were extracted. The primary outcome was fatal and non-fatal cardiovascular events.The review of 3,232, abstracts identified 29 routinely collected risk factors of which 20 were presented in more than 1 cohort. 21 cohorts were identified in relation to 27,465 individuals and 100,838 person-years. In addition to established traditional general population cardiovascular risk factors, left ventricular hypertrophy, serum albumin, phosphate, urate and hemoglobin were all found to be statistically significant in their association with future cardiovascular events.These non-traditional risk factors should be assessed in the development of future cardiovascular prognostic models for use in individuals with CKD.
| S-EPMC5862400 | biostudies-literature
Project description:BackgroundChronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds.ObjectiveThe aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview.MethodsA systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models.ResultsThe final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015.ConclusionsThe clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.
| S-EPMC6238865 | biostudies-literature
Project description:Background and objectivesNative kidney biopsies are commonly performed in the diagnosis of acute kidney diseases and CKD. Because of the invasive nature of the procedure, bleeding-related complications are not uncommon. The National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases-sponsored Kidney Precision Medicine Project requires that all participants undergo a kidney biopsy; therefore, the objective of this analysis was to study complication rates of native kidney biopsies performed using automated devices under kidney imaging.Design, setting, participants, & measurementsThis is a systematic review and meta-analysis of the literature published from January 1983 to March 2018. The initial PubMed search yielded 1139 manuscripts. Using predetermined selection criteria, 87 manuscripts were included in the final analysis. A random effects meta-analysis for proportions was used to obtain combined estimates of complication rates. Freeman-Tukey double-arcsine transformations were used to stabilize variance as complications were rare.ResultsA total of 118,064 biopsies were included in this study. Patient age ranged from 30 to 79 years, and 45% of patients were women. On the basis of our meta-analysis, pain at the site of biopsy is estimated to occur in 4.3% of biopsied patients, hematomas are estimated to occur in 11%, macroscopic hematuria is estimated to occur in 3.5%, bleeding requiring blood transfusions is estimated to occur in 1.6%, and interventions to stop bleeding are estimated to occur in only 0.3%. Death attributed to native kidney biopsy was a rare event, occurring only in an estimated 0.06% of all biopsies but only 0.03% of outpatient biopsies. Complication rates were higher in hospitalized patients and in those with acute kidney disease. The reported complications varied on the basis of study type and geographic location.ConclusionsAlthough the native kidney biopsy is an invasive diagnostic procedure, the rates of bleeding complications are low. Albeit rare, death can occur postbiopsy. Complications are more frequently seen after kidney biopsies of hospitalized patients with AKI.
| S-EPMC7646247 | biostudies-literature
Project description:BackgroundAs well as being a risk factor for cardiovascular disease, hypertension is also a health condition in its own right. Risk prediction models may be of value in identifying those individuals at risk of developing hypertension who are likely to benefit most from interventions.Methods and findingsTo synthesize existing evidence on the performance of these models, we searched MEDLINE and EMBASE; examined bibliographies of retrieved articles; contacted experts in the field; and searched our own files. Dual review of identified studies was conducted. Included studies had to report on the development, validation, or impact analysis of a hypertension risk prediction model. For each publication, information was extracted on study design and characteristics, predictors, model discrimination, calibration and reclassification ability, validation and impact analysis. Eleven studies reporting on 15 different hypertension prediction risk models were identified. Age, sex, body mass index, diabetes status, and blood pressure variables were the most common predictor variables included in models. Most risk models had acceptable-to-good discriminatory ability (C-statistic>0.70) in the derivation sample. Calibration was less commonly assessed, but overall acceptable. Two hypertension risk models, the Framingham and Hopkins, have been externally validated, displaying acceptable-to-good discrimination, and C-statistic ranging from 0.71 to 0.81. Lack of individual-level data precluded analyses of the risk models in subgroups.ConclusionsThe discrimination ability of existing hypertension risk prediction tools is acceptable, but the impact of using these tools on prescriptions and outcomes of hypertension prevention is unclear.
| S-EPMC3702558 | biostudies-literature