Evidence map›Paper›PMID 34847905›Full record

ArticleBMC surgery2021

A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting.

HuanRui Zhang, Wen Tian, YuJiao Sun

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 32% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

HuanRui ZhangDepartment of Geriatric Cardiology, The First Affiliated Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China.
Wen TianDepartment of Geriatric Cardiology, The First Affiliated Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China.
YuJiao SunDepartment of Geriatric Cardiology, The First Affiliated Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China. yjsun@cmu.edu.cn.
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe long-term outcomes for patients after coronary artery bypass grafting (CABG) have been received more and more concern. The existing prediction models are mostly focused on in-hospital operative mortality after CABG, but there is still little research on long-term mortality prediction model for patients after CABG.

objectiveTo develop and validate a novel nomogram for predicting 3-year mortality in critically ill patients after CABG.

methodsData for developing novel predictive model were extracted from Medical Information Mart for Intensive cart III (MIMIC-III), of which 2929 critically ill patients who underwent CABG at the first admission were enrolled.

resultsA novel prognostic nomogram for 3-year mortality was constructed with the seven independent prognostic factors, including age, congestive heart failure, white blood cell, creatinine, SpO

conclusionThe novel nomogram had well performance to predict 3-year mortality in critically ill patients after CABG. The prediction model provided valuable information for treatment strategy and postdischarge management, which may be helpful in improving the long-term prognosis in critically ill patients after CABG.

Indexed as

Critical IllnessNomogramsAftercareCoronary Artery BypassHumansPatient DischargeRetrospective StudiesCoronary artery bypass graftingCritically illMortalityNomogramPrediction model

Identifiers

PMID34847905
PMCPMC8638264
OpenAlexW3217099003

What OpenQuestion holds

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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.