Evidence map›Paper›PMID 40291398›Full record

ArticleInternational journal of general medicine2025

A Nomogram for Predicting the Risk of Death in Patients with Prolonged Hospital Stays in Internal Medicine Wards: A Retrospective Study.

Huiqing Pan, Xinran Liu, Bing Wang, Hua Hang, Sheng Ye

Abstract read
In one paragraph

Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

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

5 authors.

Huiqing Pan *Emergency Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, People's Republic of China.
Xinran Liu *Graduate School, Wannan Medical College, Wuhu, Anhui, People's Republic of China.
Bing WangEmergency Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, People's Republic of China.
Hua HangMedical Records Management Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, People's Republic of China.
Sheng YeEmergency Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Prolonged hospital length of stay (PLOS) is associated with adverse outcomes, including increased healthcare costs, higher risk of complications, and increased mortality. This study aimed to investigate the relationship between PLOS and mortality among patients hospitalized in internal medicine wards and to develop a nomogram to predict the risk of death in this patient population. Methods: This retrospective study included patients hospitalized for more than 30 days in internal medicine wards between January 1, 2022, and December 31, 2022. Multivariate logistic regression analysis was used to identify independent risk factors for in-hospital mortality. The nomogram was constructed based on the independent factors. Calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of the nomogram, and decision curve analysis (DCA) was conducted to assess its clinical utility. Results: A total of 1042 patients were included in this study, resulting in a mortality rate of 10.17%. Multivariate logistic regression analysis showed that age ( Conclusion: This nomogram effectively evaluates the risk of death for prolonged hospitalization of patients in internal medicine wards and holds significant potential for promotion in clinical practice.

Indexed as

ADL scorein-hospital mortalityinternal medicine wardsMNA-SF scoreprolonged hospital length of stay

Identifiers

PMID40291398
PMCPMC12034287

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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.