{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:17:46Z","timestamp":1787015866783,"version":"build-2736575974"},"reference-count":42,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nursing in Critical Care"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post\u2010traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post\u2010ICU patients is an understudied problem.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer\u2013Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The risk factors of mental disorders were Sepsis\u2010related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.249, the area under the ROC curve\u2009=\u20090.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post\u2010ICU patients was developed based on the regression model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The prediction model provides a reference for clinically screening patients at high risk of developing post\u2010ICU mental disorders, to enable the implementation of timely preventive management measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance to Clinical Practice<\/jats:title>\n                    <jats:p>The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nicc.12978","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T22:42:28Z","timestamp":1694558548000},"page":"646-660","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Construction of a risk prediction model for detecting postintensive care syndrome\u2014mental disorders"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-016X","authenticated-orcid":false,"given":"Faying","family":"Wang","sequence":"first","affiliation":[{"name":"Clinical Nursing Teaching Department Second Affiliated Hospital of Harbin Medical University  Harbin China"},{"name":"Office of General Affairs, School of Nursing Harbin Medical University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingshu","family":"Li","sequence":"additional","affiliation":[{"name":"Clinical Nursing Teaching Department Second Affiliated Hospital of Harbin Medical University  Harbin China"},{"name":"Office of General Affairs, School of Nursing Harbin Medical University  Harbin China"},{"name":"Hemodialysis Center The Second Affiliated Hospital of Harbin Medical University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuying","family":"Fan","sequence":"additional","affiliation":[{"name":"Clinical Nursing Teaching Department Second Affiliated Hospital of Harbin Medical University  Harbin China"},{"name":"Office of General Affairs, School of Nursing Harbin Medical University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaona","family":"Qi","sequence":"additional","affiliation":[{"name":"Clinical Nursing Teaching Department Second Affiliated Hospital of Harbin 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