Evidence map›Paper›PMID 28159819›Full record

Trial reportJournal of the American Heart Association2017

Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

Wen-Jun Yin, Yi-Hu Yi, Xiao-Feng Guan, Ling-Yun Zhou, Jiang-Lin Wang, Dai-Yang Li, Xiao-Cong Zuo

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of the American Heart Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  20. Prediction Models for AKI in ICU: A Comparative Study.International journal of general medicine · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Wen-Jun YinClinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Yi-Hu YiXiangya School of Medical Science of Central South University, Changsha, Hunan, China.
Xiao-Feng GuanClinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Ling-Yun ZhouClinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jiang-Lin WangClinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Dai-Yang LiClinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Xiao-Cong ZuoClinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China zuoxc08@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. METHODS AND

resultsA total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level.

conclusionsThe newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.

Indexed as

ForecastingAcute Kidney InjuryChinaContrast MediaCoronary AngiographyCoronary Artery DiseaseFemaleGlomerular Filtration RateHumansIncidenceInjections, IntravenousMaleMiddle AgedMyocardial RevascularizationPreoperative PeriodProspective StudiesContrast Mediacontrast‐induced nephropathycontrast mediapercutaneous coronary interventionrisk factorrisk prediction

Identifiers

PMID28159819
PMCPMC5523753

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.