scholarly journals Risk prediction models for acute kidney injury in adults: An overview of systematic reviews

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248899
Author(s):  
Paulien Van Acker ◽  
Wim Van Biesen ◽  
Evi V. Nagler ◽  
Muguet Koobasi ◽  
Nic Veys ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
High Risk ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Prediction Models ◽  
Kidney Injury ◽  
Risk Of Bias ◽  
Bias Assessment ◽  
Risk Prediction Models

Background The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI. Methods The protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs. Results Eight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report. Conclusion The methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient. Trial registration PROSPERO registration number is CRD 42020204236, available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=204236.

scholarly journals Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Aziz Sheikh ◽  
Ulugbek Nurmatov ◽  
Huda Amer Al-Katheeri ◽  
Rasmeh Ali Al Huneiti
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Ethnically Diverse ◽  
Risk Of Bias ◽  
Low Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Eligibility Criteria ◽  
Risk Prediction Models ◽  
Ascvd Risk

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population. Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population. Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models. Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains. Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

scholarly journals Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Maomao Cao ◽  
He Li ◽  
Dianqin Sun ◽  
Siyi He ◽  
Yadi Zheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Liver Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

scholarly journals Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systematic Review ◽  
Prediction Model ◽  
Acute Heart Failure ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Cochrane Library ◽  
Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

scholarly journals Rapid diagnostic test for antenatal syphilis screening in low-income and middle-income countries: a systematic review and meta-analysis

BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e018132 ◽  
Author(s):  
Carmen Phang Romero Casas ◽  
Marrissa Martyn-St James ◽  
Jean Hamilton ◽  
Daniel S Marinho ◽  
Rodolfo Castro ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Diagnostic Accuracy ◽  
Pregnant Women ◽  
Sensitivity And Specificity ◽  
Methodological Quality ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Antenatal Clinics

ObjectivesTo undertake a systematic review and meta-analysis to evaluate the test performance including sensitivity and specificity of rapid immunochromatographic syphilis (ICS) point-of-care (POC) tests at antenatal clinics compared with reference standard tests (non-treponemal (TP) and TP tests) for active syphilis in pregnant women.MethodsFive electronic databases were searched (PubMed, EMBASE, CRD, Cochrane Library and LILACS) to March 2016 for diagnostic accuracy studies of ICS test and standard reference tests for syphilis in pregnant women. Methodological quality was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). A bivariate meta-analysis was undertaken to generate pooled estimates of diagnostic parameters. Results were presented using a coupled forest plot of sensitivity and specificity and a scatter plot.ResultsThe methodological quality of the five included studies with regards to risk of bias and applicability concern judgements was either low or unclear. One study was judged as high risk of bias for patient selection due to exclusion of pregnant women with a previous history of syphilis, and one study was judged at high risk of bias for study flow and timing as not all patients were included in the analysis. Five studies contributed to the meta-analysis, providing a pooled sensitivity and specificity for ICS of 0.85 (95% CrI: 0.73 to 0.92) and 0.98 (95% CrI: 0.95 to 0.99), respectively.ConclusionsThis review and meta-analysis observed that rapid ICS POC tests have a high sensitivity and specificity when performed in pregnant women at antenatal clinics. However, the methodological quality of the existing evidence base should be taken into consideration when interpreting these results.PROSPERO registration numberCRD42016036335.

scholarly journals A prospective study of acute kidney injury in the intensive care unit: development and validation of a risk prediction model

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Qi Wang ◽  
Yi Tang ◽  
Jiaojiao Zhou ◽  
Wei Qin
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Risk Prediction Model ◽  
Risk Patients

Abstract Background Acute kidney injury (AKI) has high morbidity and mortality in intensive care units (ICU). It can also lead to chronic kidney disease (CKD), more costs and longer hospital stay. Early identification of AKI is important. Methods We conducted this monocenter prospective observational study at West China Hospital, Sichuan University, China. We recorded information of each patient in the ICU within 24 h after admission and updated every two days. Patients who reached the primary outcome were accepted into the AKI group. Of all patients, we randomly drew 70% as the development cohort and the remaining 30% as the validation cohort. Using binary logistic regression we got a risk prediction model of the development cohort. In the validation cohort, we validated its discrimination by the area under the receiver operator curve (AUROC) and calibration by a calibration curve. Results There were 656 patients in the development cohorts and 280 in the validation cohort. Independent predictors of AKI in the risk prediction model including hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male. In the validation cohort, the AUROC is 0.783 (95% CI 0.730–0.836) and the calibration curve shows good calibration of this prediction model. The optimal cut-off value to distinguish high-risk and low-risk patients is 4.5 points (sensitivity is 78.4%, specificity is 73.2% and Youden’s index is 0.516). Conclusions This risk prediction model can help to identify high-risk patients of AKI in ICU to prevent the development of AKI and treat it at the early stages. Trial registration TCTR, TCTR20170531001. Registered 30 May 2017, http://www.clinicaltrials.in.th/index.php?tp=regtrials&menu=trialsearch&smenu=fulltext&task=search&task2=view1&id=2573

scholarly journals Developing Risk Prediction Models for Kidney Injury and Assessing Incremental Value for Novel Biomarkers

2014 ◽  
Vol 9 (8) ◽  
pp. 1488-1496 ◽  
Author(s):  
Kathleen F. Kerr ◽  
Allison Meisner ◽  
Heather Thiessen-Philbrook ◽  
Steven G. Coca ◽  
Chirag R. Parikh
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Kidney Injury ◽  
Risk Prediction Models ◽  
Incremental Value ◽  
Novel Biomarkers
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