Evidence map›Paper›PMID 27553666›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2017

Modeling preoperative risk factors for potentially lethal morbidities using a nationwide Japanese web-based database of patients undergoing distal gastrectomy for gastric cancer.

Chikara Kunisaki, Hiroaki Miyata, Hiroyuki Konno, Zenichiro Saze, Norimichi Hirahara, Hirotoshi Kikuchi, Go Wakabayashi, Mitsukazu Gotoh, Masaki Mori

Abstract read
PubMed Publisher
In one paragraph

Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Article
  7. Time-sequential prediction of postoperative complications after gastric cancer surgery using machine learning: a multicenter cohort study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

Chikara KunisakiThe Japanese Society of Gastroenterological Surgery, Working Group Database Committee, Tokyo, Japan. s0714@med.yokohama-cu.ac.jp.
Hiroaki MiyataThe Japanese Society of Gastroenterological Surgery, Database Committee, Tokyo, Japan.
Hiroyuki KonnoThe Japanese Society of Gastroenterological Surgery, Working Group Database Committee, Tokyo, Japan.
Zenichiro SazeThe Japanese Society of Gastroenterological Surgery, Working Group Database Committee, Tokyo, Japan.
Norimichi HiraharaThe Japanese Society of Gastroenterological Surgery, Database Committee, Tokyo, Japan.
Hirotoshi KikuchiThe Japanese Society of Gastroenterological Surgery, Working Group Database Committee, Tokyo, Japan.
Go WakabayashiThe Japanese Society of Gastroenterological Surgery, Working Group Database Committee, Tokyo, Japan.
Mitsukazu GotohThe Japanese Society of Gastroenterological Surgery, Working Group Database Committee, Tokyo, Japan.
Masaki MoriThe Japanese Society of Gastroenterological Surgery, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost risk models for mortality and morbidity after distal gastrectomy have been created based on relatively small retrospective studies, and a model originating from nationwide database has been lacking. This study aimed to identify preoperative risk factors that predict the postoperative morbidities closely associated with mortality in gastric cancer patients undergoing distal gastrectomy, using data from the National Clinical Database (NCD), a nationwide web-based database in Japan.

methodsWe analyzed records from the NCD for 65,906 patients who underwent distal gastrectomy in 1,986 hospitals during 2011 and 2012. Using 80 % of these patients, we identified independent preoperative predictors for morbidities closely related with mortality. These risk factors were then validated using the remaining 20 % of patients from 2011 to 2012 and the further 35,575 records from 2013.

resultsOperative mortality was 1.07 % and overall morbidity was 14.2 % for the development data set. We selected eight morbidities that were closely associated with postoperative mortality, and then identified between 13 and 25 independent preoperative risk factors for each of the eight morbidities. Of these, old age, female gender, and poor ADL were the factors most frequently associated with the morbidities. The C-indices for each morbidity from the 2011 to 2012 validation data set were favorable as follows: unplanned intubation (0.797), pneumonia (0.784), systemic sepsis (0.748), renal failure (0.832), cardiac events (0.728), large blood transfusions (0.700), central nervous system (CNS) events (0.779), and anastomotic leakage (0.658). Similar C-indices were obtained for each mortality using the 2013 validation data set.

conclusionsIn this study, we successfully constructed an acceptable risk model using preoperative risk factors to predict eight postoperative morbidities highly associated with mortality in gastric cancer patients. This risk model could help to tailor perioperative management and improve clinical outcomes for patients who undergo distal gastrectomy.

Indexed as

AgedComorbidityDatabases, FactualFemaleGastrectomyHumansJapanLogistic ModelsMaleModels, TheoreticalMorbidityPostoperative ComplicationsRisk AssessmentRisk FactorsStomach NeoplasmsDistal gastrectomyGastric cancerJapanese National Clinical DatabaseMorbidityMortality

Identifiers

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.