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Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data
Epidemiological studies have shown that almost all physical illnesses coexist with psychiatric disorders or psychological problems, and the severity of mental illness is positively correlated with the duration and severity of physical illness. Liaison consultations are valuable in identifying and tr...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368869/ https://www.ncbi.nlm.nih.gov/pubmed/37502812 http://dx.doi.org/10.3389/fpsyt.2023.1171741 |
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author | Yanwen, Liu Mei, Li Wenwen, Zhang Huihui, Jing Hongbin, Lu Ying, Wang Ning, Liu Le, Han Xueyang, Han Xue, Zou |
author_facet | Yanwen, Liu Mei, Li Wenwen, Zhang Huihui, Jing Hongbin, Lu Ying, Wang Ning, Liu Le, Han Xueyang, Han Xue, Zou |
author_sort | Yanwen, Liu |
collection | PubMed |
description | Epidemiological studies have shown that almost all physical illnesses coexist with psychiatric disorders or psychological problems, and the severity of mental illness is positively correlated with the duration and severity of physical illness. Liaison consultations are valuable in identifying and treating psychiatric disorders, but the rate of psychiatric follow-up after consultation is low in outpatients. This study aimed to investigate the factors influencing post-discharge psychosomatic follow-up visits in patients undergoing psychiatric liaison consultation in general hospitals and construct a Nomogram prediction model for patients’ post-discharge psychosomatic follow-up visits. Medical record data of inpatients who received psychiatric liaison consultations at Xi’an International Medical Center Hospital in China from September 2019 to September 2020 were analyzed. Lasso regression and multivariate logistic regression analyses were conducted to screen independent influences on the occurrence of post-discharge psychosomatic follow-ups in patients undergoing psychiatric liaison consultations. Risk prediction column line graphs were constructed using R software, and the models were evaluated. Of the 494 inpatients who received psychiatric liaison consultations, 115 patients (23.279%) (mean age = 54.8 years) went for post-discharge psychosomatic follow-up, while 379 patients (mean age = 59.3 years) had no record of psychosomatic follow-up. Furthermore, occupation, interval.time, diagnosis, out.antipsychotics, and recommendations.followup were independent factors influencing post-discharge psychosomatic follow-up. The model accurately predicted post-discharge psychosomatic follow-up behavior of inpatients who received psychiatric liaison consultations. Lastly, the clinical decision curve analysis showed that the model had good validity for clinical application. Patients who received a psychiatric liaison consultation with a ≤ 10-day interval between admission to the hospital and application for consultation, were discharged with prescribed medication, and had a clear written medical order for a follow-up consultation had an increased probability of psychosomatic follow-up after discharge. |
format | Online Article Text |
id | pubmed-10368869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103688692023-07-27 Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data Yanwen, Liu Mei, Li Wenwen, Zhang Huihui, Jing Hongbin, Lu Ying, Wang Ning, Liu Le, Han Xueyang, Han Xue, Zou Front Psychiatry Psychiatry Epidemiological studies have shown that almost all physical illnesses coexist with psychiatric disorders or psychological problems, and the severity of mental illness is positively correlated with the duration and severity of physical illness. Liaison consultations are valuable in identifying and treating psychiatric disorders, but the rate of psychiatric follow-up after consultation is low in outpatients. This study aimed to investigate the factors influencing post-discharge psychosomatic follow-up visits in patients undergoing psychiatric liaison consultation in general hospitals and construct a Nomogram prediction model for patients’ post-discharge psychosomatic follow-up visits. Medical record data of inpatients who received psychiatric liaison consultations at Xi’an International Medical Center Hospital in China from September 2019 to September 2020 were analyzed. Lasso regression and multivariate logistic regression analyses were conducted to screen independent influences on the occurrence of post-discharge psychosomatic follow-ups in patients undergoing psychiatric liaison consultations. Risk prediction column line graphs were constructed using R software, and the models were evaluated. Of the 494 inpatients who received psychiatric liaison consultations, 115 patients (23.279%) (mean age = 54.8 years) went for post-discharge psychosomatic follow-up, while 379 patients (mean age = 59.3 years) had no record of psychosomatic follow-up. Furthermore, occupation, interval.time, diagnosis, out.antipsychotics, and recommendations.followup were independent factors influencing post-discharge psychosomatic follow-up. The model accurately predicted post-discharge psychosomatic follow-up behavior of inpatients who received psychiatric liaison consultations. Lastly, the clinical decision curve analysis showed that the model had good validity for clinical application. Patients who received a psychiatric liaison consultation with a ≤ 10-day interval between admission to the hospital and application for consultation, were discharged with prescribed medication, and had a clear written medical order for a follow-up consultation had an increased probability of psychosomatic follow-up after discharge. Frontiers Media S.A. 2023-07-12 /pmc/articles/PMC10368869/ /pubmed/37502812 http://dx.doi.org/10.3389/fpsyt.2023.1171741 Text en Copyright © 2023 Yanwen, Mei, Wenwen, Huihui, Hongbin, Ying, Ning, Le, Xueyang and Xue. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Yanwen, Liu Mei, Li Wenwen, Zhang Huihui, Jing Hongbin, Lu Ying, Wang Ning, Liu Le, Han Xueyang, Han Xue, Zou Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
title | Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
title_full | Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
title_fullStr | Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
title_full_unstemmed | Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
title_short | Construction of a Nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
title_sort | construction of a nomogram predictive model for post-discharge psychosomatic review of psychiatric liaison consultation patients based on medical record data |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368869/ https://www.ncbi.nlm.nih.gov/pubmed/37502812 http://dx.doi.org/10.3389/fpsyt.2023.1171741 |
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