Evidence map›Paper›PMID 36321466›Full record

ArticleDiabetes, obesity & metabolism2023

Nomogram model and risk score to predict 5-year risk of progression from prediabetes to diabetes in Chinese adults: Development and validation of a novel model.

Yong Han, Haofei Hu, Yufei Liu, Zhibin Wang, Dehong Liu

Open access · hybridAbstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.7field-weighted citation impact, top 14% of its field
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

16 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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

5 authors at 1 institution in 1 country.

Yong HanDepartment of Emergency, Shenzhen Second People's Hospital, Shenzhen, China.
Haofei HuDepartment of Nephrology, Shenzhen Second People's Hospital, Shenzhen, China.ORCID 0000-0001-6061-6796
Yufei LiuDepartment of Neurosurgery, Shenzhen Second People's Hospital, Shenzhen, China.
Zhibin WangDepartment of Emergency, Shenzhen Second People's Hospital, Shenzhen, China.
Dehong LiuDepartment of Emergency, Shenzhen Second People's Hospital, Shenzhen, China.ORCID 0000-0001-9615-5119
Shenzhen Second People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes.

methodsThere were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance.

resultsWe developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively.

conclusionsWe developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Indexed as

Diabetes MellitusPrediabetic StateAdultEast Asian PeopleHumansNomogramsProspective StudiesRetrospective StudiesRisk Factorsincident diabetesnomogramprediabetesprediction performancerisk score

Identifiers

PMID36321466
PMCPMC10107751
OpenAlexW4307972799

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.