Evidence map›Paper›PMID 35931880›Full record

ArticleSurgery today2023

A preliminary prediction model using a deep learning software program for prolonged hospitalization after cardiovascular surgery.

Ryota Murase, Yasushige Shingu, Satoru Wakasa

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Surgery today, 2023. 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
–field-weighted citation impact, top 79% 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, 0 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.

Ryota MuraseDepartment of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan.
Yasushige ShinguDepartment of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan. shingu@huhp.hokudai.ac.jp.
Satoru WakasaDepartment of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan.
Hokkaido University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction model of a prolonged LOS using a deep learning software program (Prediction One; Sony Network Communications Inc., Tokyo, Japan) using preoperative data. Subjects were 157 patients (121 for training data, 36 for validation data). A prolonged LOS was defined as a more than 30-day postoperative stay due to physical inactivity. The area under the receiver operating characteristic curve and the accuracy of the model in the validation data were 0.806 and 67%, respectively. In conclusion, the preliminary model demonstrated acceptable performance for the prediction of a prolonged LOS after cardiovascular surgery.

Indexed as

Deep LearningAgingHumansLength of StayRisk FactorsSoftwareArtificial intelligenceCardiovascular surgeryDeep learningProlonged hospital stay

Identifiers

PMID35931880
OpenAlexW4295152282

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.