Evidence map›Paper›PMID 33297955›Full record

Observational studyBMC cardiovascular disorders2020

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI.

Nan Gao, Xiaoyong Qi, Yi Dang, Yingxiao Li, Gang Wang, Xiao Liu, Ning Zhu, Jinguo Fu

Abstract readMulticenter StudyObservational StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC cardiovascular disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Observational
  20. Observational
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

8 authors.

Nan GaoDepartment of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China.
Xiaoyong QiDepartment of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China. qixiaoyong123@126.com.ORCID 0000-0002-6240-8947
Yi DangDepartment of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China.
Yingxiao LiDepartment of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China.
Gang WangDepartment of Cardiology, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Xiao LiuDepartment of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China.
Ning ZhuDepartment of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China.
Jinguo FuDepartment of Cardiology, Cangzhou Central Hospital, Cangzhou, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI).

methodsThis was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.

resultsTotally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981-0.994, P = 0.003) and 0.990 (95% CI: 0.987-0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit.

conclusionsA novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

Indexed as

Decision Support TechniquesHospital MortalityNomogramsAgedAged, 80 and overClinical Decision-MakingFemaleHumansMaleMiddle AgedPercutaneous Coronary InterventionPredictive Value of TestsProspective StudiesReproducibility of ResultsRetrospective StudiesRisk AssessmentHospital mortalityNomogramPercutaneous coronary interventionPredictive value of testsST-elevated myocardial infarction

Identifiers

PMID33297955
PMCPMC7727168

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.