Exploratory clinical predictors of prolonged hospital stay in patients with patent foramen ovale undergoing transthoracic echocardiography- or X-ray-guided closure
- 1Department of Cardiac Surgery, Gansu Provincial People’s Hospital, Lanzhou, Gansu Province, China.
Abstract
Patent foramen ovale (PFO) can be managed using different image-guided treatment modalities; however, the determinants of prolonged hospitalization remain insufficiently defined. This retrospective study aimed to identify clinical predictors associated with length of hospital stay and to develop a machine learning model for predicting prolonged hospitalization in patients with PFO. Clinical data from 216 patients with PFO treated at Gansu Provincial People’s Hospital were retrospectively analyzed. Among them, 106 patients underwent transthoracic echocardiography (TTE)-guided treatment and 110 underwent X-ray-guided treatment. According to the median length of stay, patients were divided into a short-stay group (≤7 days) and a prolonged-stay group (>7 days). Baseline variables were compared between groups. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Five machine learning algorithms were then developed and compared in the training cohort, followed by validation in an independent validation cohort. Thirteen baseline variables showed significant between-group differences. LASSO regression retained 10 candidate predictors for model construction. Among the evaluated models, the random forest model showed the best overall performance. In the validation cohort, the model achieved an accuracy of 0.692, a precision of 0.708, a recall of 0.850, an F1-score of 0.773, and an AUC of 0.711, indicating moderate discrimination. The random forest model demonstrated moderate predictive discrimination for prolonged hospital stay. Given the limited sample size and single-center retrospective design, the model should be considered exploratory and requires external validation in larger independent cohorts before clinical use.
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BibTeX
@article{zhang2026exploratory,
title = {{Exploratory clinical predictors of prolonged hospital stay in patients with patent foramen ovale undergoing transthoracic echocardiography- or X-ray-guided closure}},
author = {Peng Zhang and Meng Chen and Xinhuan Dang and Fangfei Guo and Xiaodong Hou and Guolei Zhou and Yanling Yang and Xuan Wei and Tao You},
journal = {Medicine},
year = {2026},
month = oct,
doi = {10.1097/md.0000000000051265},
url = {https://europepmc.org/article/PMC/PMC13727161},
note = {CC BY},
}
RIS
TY - JOUR
TI - Exploratory clinical predictors of prolonged hospital stay in patients with patent foramen ovale undergoing transthoracic echocardiography- or X-ray-guided closure
AU - Peng Zhang
AU - Meng Chen
AU - Xinhuan Dang
AU - Fangfei Guo
AU - Xiaodong Hou
AU - Guolei Zhou
AU - Yanling Yang
AU - Xuan Wei
AU - Tao You
PY - 2026
DA - 2026/10/30
JO - Medicine
DO - 10.1097/md.0000000000051265
UR - https://europepmc.org/article/PMC/PMC13727161
AB - Patent foramen ovale (PFO) can be managed using different image-guided treatment modalities; however, the determinants of prolonged hospitalization remain insufficiently defined. This retrospective study aimed to identify clinical predictors associated with length of hospital stay and to develop a machine learning model for predicting prolonged hospitalization in patients with PFO. Clinical data from 216 patients with PFO treated at Gansu Provincial People’s Hospital were retrospectively analyzed. Among them, 106 patients underwent transthoracic echocardiography (TTE)-guided treatment and 110 underwent X-ray-guided treatment. According to the median length of stay, patients were divided into a short-stay group (≤7 days) and a prolonged-stay group (>7 days). Baseline variables were compared between groups. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Five machine learning algorithms were then developed and compared in the training cohort, followed by validation in an independent validation cohort. Thirteen baseline variables showed significant between-group differences. LASSO regression retained 10 candidate predictors for model construction. Among the evaluated models, the random forest model showed the best overall performance. In the validation cohort, the model achieved an accuracy of 0.692, a precision of 0.708, a recall of 0.850, an F1-score of 0.773, and an AUC of 0.711, indicating moderate discrimination. The random forest model demonstrated moderate predictive discrimination for prolonged hospital stay. Given the limited sample size and single-center retrospective design, the model should be considered exploratory and requires external validation in larger independent cohorts before clinical use.
SN - 0304-5412
SN - 1536-5964
SN - 0025-7974
ER -