Evidence mapPaperPMID 39351189Full record

ReviewWorld journal of nephrology2024

Challenges in predictive modelling of chronic kidney disease: A narrative review.

Sukhanshi Khandpur, Prabhaker Mishra, Shambhavi Mishra, Swasti Tiwari

Abstract readReview
In one paragraph

Review in World journal of nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  5. Microbiota-gut-kidney axis in health and renal disease.International journal of biological sciences · 2026
    Review
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  7. Article
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  14. Anthraquinones fromDrug design, development and therapy · 2025
    Review
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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

4 authors.

Sukhanshi KhandpurDepartment of Molecular Medicine & Biotechnology, Sanjay Gandhi Post Graduate Institute of Medical Science, Lucknow 226014, Uttar Pradesh, India.
Prabhaker MishraDepartment of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India.
Shambhavi MishraDepartment of Statistics, University of Lucknow, Lucknow 226007, Uttar Pradesh, India.
Swasti TiwariDepartment of Molecular Medicine & Biotechnology, Sanjay Gandhi Post Graduate Institute of Medical Science, Lucknow 226014, Uttar Pradesh, India. tiwaris@sgpgi.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exponential rise in the burden of chronic kidney disease (CKD) worldwide has put enormous pressure on the economy. Predictive modeling of CKD can ease this burden by predicting the future disease occurrence ahead of its onset. There are various regression methods for predictive modeling based on the distribution of the outcome variable. However, the accuracy of the predictive model depends on how well the model is developed by taking into account the goodness of fit, choice of covariates, handling of covariates measured on a continuous scale, handling of categorical covariates, and number of outcome events per predictor parameter or sample size. Optimal performance of a predictive model on an independent cohort is desired. However, there are several challenges in the predictive modeling of CKD. Disease-specific methodological challenges hinder the development of a predictive model that is cost-effective and universally applicable to predict CKD onset. In this review, we discuss the advantages and challenges of various regression models available for predictive modeling and highlight those best for future CKD prediction.

Indexed as

Chronic kidney diseaseMethodologyPredictive modellingRegressionStatistical modelling

Identifiers

PMID39351189
PMCPMC11439095

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.