Evidence map›Paper›PMID 39735250›Full record

ArticleFrontiers in pediatrics2024

Nomogram prediction model for length of hospital stay following laparoscopic appendectomy in pediatric patients: a retrospective study.

Ming Liu, Ping Yang, Yunpeng Gou, Qiang Chen, Dong Xu

Abstract read
In one paragraph

Article in Frontiers in pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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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.

Ming LiuDepartment of Pediatric Surgery, Suining Central Hospital, Suining, Sichuan Province, China.
Ping YangDepartment of Pediatric Surgery, Suining Central Hospital, Suining, Sichuan Province, China.
Yunpeng GouDepartment of Pediatric Surgery, Suining Central Hospital, Suining, Sichuan Province, China.
Qiang ChenDepartment of Pediatric Surgery, Suining Central Hospital, Suining, Sichuan Province, China.
Dong XuDepartment of Pediatric Surgery, Suining Central Hospital, Suining, Sichuan Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this research was to develop and internally validate a nomogram for forecasting the length of hospital stay following laparoscopic appendectomy in pediatric patients diagnosed with appendicitis. Methods: We developed a prediction model based on a training dataset of 415 pediatric patients with appendicitis, and hospitalization data were collected retrospectively from January 2021 and December 2022. The primary outcome measure in this study was hospital length of stay (LOS), with prolonged LOS defined as admission for a duration equal to or exceeding the 75th percentile of LOS, including the discharge day. Risk factor analysis was conducted through univariate and multivariate logistic regression analyses. Based on the regression coefficients, a nomogram prediction model was developed. The discriminative performance of the predicting model was evaluated using the C-index, and an adjusted C-index was computed through bootstrapping validation. Calibration curves were generated to assess the accuracy of the nomogram. Decision curve analysis was conducted to determine the clinical utility of the predicting model. Results: Predictors contained in the prediction nomogram included Age, neutrophil-to-lymphocyte ratio, C-reactive protein level, operative time, appendiceal fecalith, and drainage tube. The C-index of the prediction nomogram was determined to be 0.873 (95% CI: 0.838-0.908), with a corrected C-index of 0.8625 obtained through bootstrapping validation (1,000 resamples), indicating the model's favorable discrimination. Calibration curves illustrated a strong agreement between predicted and observed outcomes. According to the decision curve analysis of the nomogram, the predictive model demonstrates a net benefit at threshold probabilities exceeding 2%. Conclusion: This nomogram, incorporating variables such as Age, neutrophil-to-lymphocyte ratio, C-reactive protein level, operative time, appendiceal fecalith, and drainage tube, offers a convenient method for assessing the duration of hospitalization in pediatric patients with appendicitis.

Indexed as

appendicitischildrenlength of staynomogrampredictors

Identifiers

PMID39735250
PMCPMC11671485

What Socratic 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 Socratic graph.