Evidence map›Paper›PMID 37780569›Full record

ArticleFrontiers in medicine2023

A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States.

Zhongjian Wang, Jian Huang, Yang Zhang, Xiaozhu Liu, Tingting Shu, Minjie Duan, Haolin Wang, Chengliang Yin, Junyi Cao

Abstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
  3. Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
  4. 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

9 authors.

Zhongjian Wang *Artificial Intelligence Laboratory, Pharnexcloud Digital Technology (Chengdu) Co. Ltd., Chengdu, China.
Jian Huang *Graduate School, Guangxi University of Chinese Medicine, Nanning, China.
Yang Zhang *College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Xiaozhu LiuCollege of Medical Informatics, Chongqing Medical University, Chongqing, China.
Tingting ShuDepartment of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China.
Minjie DuanCollege of Medical Informatics, Chongqing Medical University, Chongqing, China.
Haolin WangCollege of Medical Informatics, Chongqing Medical University, Chongqing, China.
Chengliang YinFaculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.
Junyi CaoDepartment of Medical Quality Control, The First People's Hospital of Zigong City, Zigong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Heart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death. Methods and results: Data ( Conclusion: Decreased temperature, decreased SpO

Indexed as

big datacalculatordeathelderlyheart failureweb-based

Identifiers

PMID37780569
PMCPMC10541310

What Socratic 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 Socratic graph.