Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review
Objectives: This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness. Methods: As of November 1, 2023, Cochrane Library, PubMed, Embase, CINAHL, Web of Science, PsycInfo,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | International Journal of Nursing Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235201322400111X |
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| author | Pengfei Yang Fu Yang Qi Wang Fang Fang Qian Yu Rui Tai |
| author_facet | Pengfei Yang Fu Yang Qi Wang Fang Fang Qian Yu Rui Tai |
| author_sort | Pengfei Yang |
| collection | DOAJ |
| description | Objectives: This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness. Methods: As of November 1, 2023, Cochrane Library, PubMed, Embase, CINAHL, Web of Science, PsycInfo, China National Knowledge Infrastructure (CNKI), SinoMed, Wanfang database, and China Science and Technology Journal Database (VIP) were searched. Following the literature screening process, we extracted data encompassing participant sources, post-intensive care syndrome (PICS) outcomes, sample sizes, missing data, predictive factors, model development methodologies, and metrics for model performance and evaluation. We conducted a review and classification of the PICS domains and predictive factors identified in each study. The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies. Results: This systematic review included a total of 16 studies, comprising two cognitive impairment studies, four psychological impairment studies, eight physiological impairment studies, and two studies on all three domains. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68–0.90. The predictive performance of most models was excellent, but most models were biased and overfitted. All predictive factors tend to encompass age, pre-ICU functional impairment, in-ICU experiences, and early-onset new symptoms. Conclusions: This review identified 16 prediction models and the predictive factors for PICS. Nonetheless, due to the numerous methodological and reporting shortcomings identified in the studies under review, clinicians should exercise caution when interpreting the predictions made by these models. To avert the development of PICS, it is imperative for clinicians to closely monitor prognostic factors, including the in-ICU experience and early-onset new symptoms. |
| format | Article |
| id | doaj-art-e25dfe33c5de4245a327be019951256b |
| institution | Kabale University |
| issn | 2352-0132 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Nursing Sciences |
| spelling | doaj-art-e25dfe33c5de4245a327be019951256b2025-01-26T05:03:59ZengElsevierInternational Journal of Nursing Sciences2352-01322025-01-011218188Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic reviewPengfei Yang0Fu Yang1Qi Wang2Fang Fang3Qian Yu4Rui Tai5School of Nursing, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Nursing, Shanghai General Hospital, Shanghai, ChinaDepartment of Nursing, Shanghai General Hospital, Shanghai, ChinaSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Nursing, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Nursing, Shanghai General Hospital, Shanghai, China; Corresponding author. School of Nursing, Shanghai Jiao Tong University School of Medicine, Shanghai, China.Department of Nursing, Shanghai General Hospital, Shanghai, ChinaDepartment of Nursing, Shanghai General Hospital, Shanghai, ChinaObjectives: This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness. Methods: As of November 1, 2023, Cochrane Library, PubMed, Embase, CINAHL, Web of Science, PsycInfo, China National Knowledge Infrastructure (CNKI), SinoMed, Wanfang database, and China Science and Technology Journal Database (VIP) were searched. Following the literature screening process, we extracted data encompassing participant sources, post-intensive care syndrome (PICS) outcomes, sample sizes, missing data, predictive factors, model development methodologies, and metrics for model performance and evaluation. We conducted a review and classification of the PICS domains and predictive factors identified in each study. The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies. Results: This systematic review included a total of 16 studies, comprising two cognitive impairment studies, four psychological impairment studies, eight physiological impairment studies, and two studies on all three domains. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68–0.90. The predictive performance of most models was excellent, but most models were biased and overfitted. All predictive factors tend to encompass age, pre-ICU functional impairment, in-ICU experiences, and early-onset new symptoms. Conclusions: This review identified 16 prediction models and the predictive factors for PICS. Nonetheless, due to the numerous methodological and reporting shortcomings identified in the studies under review, clinicians should exercise caution when interpreting the predictions made by these models. To avert the development of PICS, it is imperative for clinicians to closely monitor prognostic factors, including the in-ICU experience and early-onset new symptoms.http://www.sciencedirect.com/science/article/pii/S235201322400111XCritical carePost-intensive care syndromePrediction modelSystematic review |
| spellingShingle | Pengfei Yang Fu Yang Qi Wang Fang Fang Qian Yu Rui Tai Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review International Journal of Nursing Sciences Critical care Post-intensive care syndrome Prediction model Systematic review |
| title | Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review |
| title_full | Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review |
| title_fullStr | Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review |
| title_full_unstemmed | Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review |
| title_short | Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review |
| title_sort | risk prediction models for post intensive care syndrome of icu discharged patients a systematic review |
| topic | Critical care Post-intensive care syndrome Prediction model Systematic review |
| url | http://www.sciencedirect.com/science/article/pii/S235201322400111X |
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