Dataset Information

Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review.

ABSTRACT:

Introduction

Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI.

Methods and analysis

Relevant literature from a comprehensive search across six databases will be imported into Covidence. Abstract screening and full-text review will be conducted independently by two team members, and any conflicts will be resolved by a third member. Studies to be included are cohort studies and randomised controlled trials with at least 100 subjects, adult hospitalised patients, with AKI. Only those studies evaluating multivariable predictive models reporting a statistical measure of accuracy (area under the receiver operating curve or C-statistic) and predicting resolution of AKI, progression of AKI, subsequent dialysis and mortality will be included. Data extraction will be performed independently by two team members, with a third reviewer available to resolve conflicts. Results will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias will be assessed using Prediction model Risk Of Bias ASsessment Tool.

Ethics and dissemination

We are committed to open dissemination of our results through the registration of our systematic review on PROSPERO and future publication. We hope that our review provides a platform for future work in realm of using artificial intelligence to predict outcomes of common diseases.

Prospero registration number

CRD42019137274.

SUBMITTER: Arora T

PROVIDER: S-EPMC7757434 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review.

Arora Tanima T Martin Melissa M Grimshaw Alyssa A Mansour Sherry S Wilson Francis P FP

BMJ open 20201222 12

<h4>Introduction</h4>Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI.<h4>Methods and analysis</h4>Relevant literature from a comprehensive sea ...[more]

PMID: 33371041

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Project description:IntroductionAcute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review.Methods and analysisA systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool.Ethics and disseminationEthical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences.Osf registration number10.17605/OSF.IO/X25AT.

Project description:BackgroundAcute kidney injury (AKI) is a common complication of hospitalization with high morbidity and mortality for which no effective treatments exist and for which current diagnostic tools have limitations for earlier identification. MicroRNAs (miRNAs) are small non-coding RNAs that have been implicated in the pathogenesis of AKI, and some miRNAs have shown promise as therapeutic tools in animal models of AKI. However, less is known about the role of miRNAs in human AKI.ObjectiveTo evaluate the role of miRNAs in human subjects with AKI.DesignSystematic review and meta-analysis.MeasurementsQuantification of miRNA levels from human blood, urine, or kidney biopsy samples, and measures of renal function as defined in the study protocol.MethodsA comprehensive search strategy for Ovid MEDLINE All, Embase, Web of Science, and CENTRAL will be developed to identify investigational studies that evaluated the relationship between miRNA levels and human AKI. Primary outcomes will include measurements of kidney function and miRNA levels. Study screening, review and data extraction will be performed independently by 2 reviewers. Study quality and certainty of evidence will be assessed with validated tools. A narrative synthesis will be included and the possibility for meta-analysis will be assessed according to characteristics of clinical and statistical heterogeneity between studies.LimitationsThese include (1) lack of randomized trials of miRNAs for the prevention or treatment of human AKI, (2) quality of included studies, and (3) sources of clinical and statistical heterogeneity that may affect strength and reproducibility of results.ConclusionPrevious studies of miRNAs in different animal models of AKI have generated strong interest on their use for the prevention and treatment of human AKI. This systematic review will characterize the most promising miRNAs for human research and will identify methodological constraints from miRNA research in human AKI to help inform the design of future studies.Systematic review registrationPROSPERO CRD42020201253.

Project description:BACKGROUND:Acute kidney injury (AKI) causes significant morbidity and mortality in humans, and there are currently no effective treatments to enhance renal recovery. MicroRNAs (miRNAs) are short chain nucleotides that regulate protein expression and have been implicated in the pathogenesis of AKI. Recently, preclinical studies in vivo have uncovered a therapeutic role for administration of specific miRNAs in AKI. However, the overall benefits of this strategy in preclinical studies have not been systematically reviewed, and the potential for translation to human studies is unclear. AIM:The primary aim is to conduct a systematic review of the therapeutic properties of miRNAs in preclinical studies of AKI. The secondary aim is to determine potential adverse effects of miRNA administration in these studies. METHODS:A comprehensive search strategy will identify relevant studies in AKI in vivo models, using the MEDLINE, EMBASE, OVID, PUBMED, and Web of Science databases. The search strategy will include terms for mammalian (non-human) AKI models, including injury related to ischemia/reperfusion, nephrotoxicity, sepsis, contrast agents, cardio-pulmonary bypass, and hemorrhagic shock. Interventions will be defined as direct administration of exogenous miRNAs or antagonists of miRNAs, as well as maneuvers that alter expression of miRNAs that are mechanistically linked to AKI outcomes. The primary outcomes will be indices of kidney function and structure, and there will be no restriction on comparator interventions. Two independent investigators will initially screen abstracts, and selected articles that meet eligibility criteria will be reviewed for data abstraction and analysis. The SYRCLE RoB tool for animal studies will determine risk of bias, and meta-analysis will be performed as appropriate. The GRADE methodology will assess the quality of evidence. DISCUSSION:The administration of selective miRNA mimics or antagonists exerts beneficial effects in mammalian models of AKI, although multiple obstacles must be addressed prior to translation to human clinical trials. The proposed systematic review will document key miRNA candidates, and determine effect size estimates and sources of outcome bias. The review will also identify gaps in knowledge and guide future directions in AKI research. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42019128854.

Project description:BACKGROUND:Contrast-induced acute kidney injury (CI-AKI) is defined as worsening of renal function after the administration of iodinated contrast material. In patients with cardiovascular disease, kidney disease, and/or diabetes, renin-angiotensin system blockers, non-steroidal anti-inflammatory drugs, diuretics, and metformin can increase the risk of CI-AKI when undergoing contrast imaging. Despite CI-AKI being the leading iatrogenic cause of acute kidney injury, there is a lack of sufficient scientific evidence supporting which drugs should be stopped, when they should be stopped, and when they should be resumed. The purpose of this systematic review is to assess (1) the effect of withholding medication before contrast procedures on the risk of CI-AKI and other clinical outcomes and (2) the incidence of adverse events occurring after withholding these drugs prior to contrast procedures. This protocol has been registered with PROSPERO, https://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016033178 . METHODS:An information specialist will assist in searching MEDLINE, Embase, and the Cochrane Library databases to identify randomized controlled trials, observational studies, case reports, and case series. Relevant abstracts from professional society meetings and web-based registries of clinical trials will also be included. Studies included will compare patients aged ≥ 18 years instructed to continue taking the drugs of interest and those advised to stop taking them before undergoing contrast procedures. If these drugs are not withheld prior to contrast procedures, the studies must compare patients who are administered these drugs and those who are not before undergoing contrast procedures. Two reviewers will independently screen the titles and abstracts of the studies obtained from the search using pre-defined inclusion criteria and will then extract data from the full texts of selected studies. The quality of the studies will be assessed by two independent reviewers using the Cochrane Risk of Bias 2.0 tool for randomized trials and the Newcastle-Ottawa Scale for observational studies. DISCUSSION:This systematic review will provide a synthesis of current evidence on the discontinuation of drugs prior to contrast procedures and its effect on CI-AKI and other clinical outcomes. These findings will provide clinicians with guidelines and serve as a strong research base for future studies in this field. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42016033178.

Project description:ObjectiveCritically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations.DesignSystematic review.Data sourcesMedline, Embase and Web of Science until November 2016.EligibilityStudies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal.Results14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found.ConclusionsAKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.

Dataset Information

Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review.

Introduction

Methods and analysis

Ethics and dissemination

Prospero registration number

Publications

Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review.

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