Frequency and risk factors of post-intensive care syndrome components in a multicenter randomized controlled trial of German sepsis survivors

Kosilek, Robert P; Schmidt, Konrad; Baumeister, Sebastian E.; Gensichen, Jochen

doi:10.1016/j.jcrc.2021.07.006

Cited by 31 publications

(26 citation statements)

References 21 publications

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“…Yuan et al , in a concept analysis, proposed PICS as a co-occurrence of these physical, psychological, cognitive and social impairments 15. Around 50%–80% of admissions survive an ICU stay8 16 and the prevalence of PICS can reach 80% of survivors 8 9 17–19. Not only do individuals experience a deteriorated quality of life,20 but health systems confront enormous challenges related to treatment, care and support for those with PICS 3 21.…”

Section: Introductionmentioning

confidence: 99%

Instruments to measure postintensive care syndrome: a scoping review protocol

et al. 2022

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IntroductionThere is an increasing need for evaluating postintensive care syndrome in adults concerning their long-term physical, psychological, cognitive and/or social outcomes, yet there is no consensus regarding the choice of instruments to measure these. This scoping review aims to identify and examine instruments used to measure postintensive care syndrome in adults.Methods and analysisThis scoping review will be conducted following the Arksey and O’Malley and its extended framework, and the Joanna Briggs Institute guideline. It will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Review checklists. Medline via EBSCO, CINAHL complete, EMBASE, Web of Science, AME and APA PsycINFO databases and grey literature will be searched from 2010 to the present. Reference lists of included studies will be manually checked to identify additional sources. The quality of included studies will be appraised using the Crowe Critical Appraisal Tool. All review steps will involve at least two reviewers. Data charting will be performed narratively, comprising study characteristics and findings, and instrument properties. This review will also aim to identify research gaps.Ethics and disseminationThere is no ethics disclosure for this review protocol. This scoping review will identify instruments used to measure postintensive care syndrome in adults. The findings will be disseminated through professional bodies, conferences and research papers.

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Section: Introductionmentioning

confidence: 99%

Instruments to measure postintensive care syndrome: a scoping review protocol

et al. 2022

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“…Studies have shown that cognitive dysfunction is mainly manifested as slow thinking, delirium, memory loss, etc. ; psychological dysfunction is anxiety, depression and post-traumatic stress disorder (PTSD); physical dysfunction is mainly fatigue, sleep disturbance, bathing, shopping, decline in daily living skills such as going up and down stairs [5] . Only 50% of patients discharged from the ICU return to school or work after 1 year, and the social integration and quality of life are low.…”

Section: Introductionmentioning

confidence: 99%

Reliability and validity of ICU nurses' knowledge-attitude-practice scale for post-ICU syndrome in critically ill patients

cheng

zhang

xia

et al. 2022

Preprint

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Background: Post-ICU syndrome has not been paid enough attention in Chinese medical institutions. Due to the large population base in my country, the number of people with post-ICU syndrome is also large, which has a great impact on the quality of life of patients in the later period.Objective To develop and verify ICU nurses' knowledge, attitude and practice questionnaire on post-ICU syndrome in critically ill patients, to provide a basis for the investigation of post-ICU syndrome status.Methods Using the principle of Knowledge, Attitude and Practice (KAP), a questionnaire item database was created through literature review and semi-structured interviews. The questionnaire was modified by Delphi expert correspondence method and pre-investigation. 451 ICU nurses were selected for formal investigation, and the reliability of the questionnaire was evaluated degree test.Results A questionnaire of knowledge, attitude, and practice of ICU nurses on post-ICU syndrome in critically ill patients was formed, in which the number of items in the three dimensions of knowledge, attitude, and practice were 11, 7, and 13, respectively. The Pearson correlation coefficient between dimensions and the total score of the questionnaire ranged from 0.526 to 0.860, and the correlation coefficients between items and dimensions ranged from 0.517 to 0.921 (P＜0.001). Item content validity (I-CVI) ranged from 0.867 to 1, and questionnaire content validity (S-CVI) =0.989. Four common factors were extracted by exploratory factor analysis, and the cumulative variance explanation rate was 76.214%. Confirmatory factor analysis: Chi-square ratio of degrees of freedom (χ2/df) =2.789, RMSEA=0.077, RMR=0.048, GFI=0.947, CFI=0.922, NFI=0.943, IFI=0.922, TLI=0.915. The Cronbach's alpha coefficients of the questionnaire and dimension were 0.948, 0.976, 0.966, 0.910, split-half reliability = 0.951, and test-retest reliability = 0.927.Conclusion The reliability and validity of the questionnaire meets the requirements of questionnaire preparation, and can reflect the knowledge, belief, and behavior of ICU nurses in post-ICU syndrome.

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“…Longer duration of delirium showed an independent association with poor cognitive function [8] , and repeated delirium exposure was associated with a greater risk of developing cognitive de cit compared to single delirium episode [10] . Older age has not yet been conclusively determined as an independent risk factor, as no agedependent increase in the prevalence of cognitive de cit was observed after ICU admission [8] ; however, other studies support the hypothesis of a higher risk at older ages [11][12][13] . Some ICU-related parameters such as longer ICU length of stay and mechanical ventilation showed a signi cant positive association with the occurrence of cognitive impairment [13] .…”

Section: Introductionmentioning

confidence: 99%

“…Older age has not yet been conclusively determined as an independent risk factor, as no agedependent increase in the prevalence of cognitive de cit was observed after ICU admission [8] ; however, other studies support the hypothesis of a higher risk at older ages [11][12][13] . Some ICU-related parameters such as longer ICU length of stay and mechanical ventilation showed a signi cant positive association with the occurrence of cognitive impairment [13] . However, a meta-analysis con rmed that delirium is the sole risk factor for cognitive impairment [14] .…”

Section: Introductionmentioning

confidence: 99%

Logistic regression technique is comparable to machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome

Wang

Wei

et al. 2022

Preprint

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Objective: The aim of this study was to explore whether machine learning (ML) algorithms are more accurate than traditional statistical models in predicting cognitive impairment related to post intensive care syndrome (PICS-CI). Research Methodology: We conducted a prospective observational study of ICU patients at two tertiary hospitals. A cohort of 2079 patients was screened, and finally 481 patients were included. Six different ML models were considered, decision tree (DT), random forest (RF), XGBoost, neural network (NN), naïve Bayes (NB), and support vector machine (SVM), and compared with logistic regression (LR). Discriminative ability was evaluated by area under the receiver operating characteristic curve (AUC), calibration belt plots, and Hosmer-Lemeshow test was used to assess calibration. Decision curve analysis was performed to quantify clinical utility. Results: All ML models showed good performance (AUC range: 0.822–0.906). NN model had the highest AUC (0.906 [95%CI: 0.857–0.955]), which was slightly higher than, but not significantly different from that of LR (0.898 [95%CI: 0.847–0.949]) (P>0.05, Delong test). Except for DT, XBGoost, and NB models, the other ML models demonstrated good agreement between the predicted and observed probability of PICS-CI (Hosmer and Lemeshow Test, P>0.05). Decision curve analysis showed higher net benefit of most of the ML models. Given the overfitting and complexity of some ML models, the LR model was then used to develop a web-based risk calculator to aid decision-making (https://model871010.shinyapps.io/dynnomapp/). Conclusion: In a low dimensional data, logistic regression may yield as good performance as ML models to predict cognitive impairment after ICU hospitalization.

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Frequency and risk factors of post-intensive care syndrome components in a multicenter randomized controlled trial of German sepsis survivors

Cited by 31 publications

References 21 publications

Instruments to measure postintensive care syndrome: a scoping review protocol

Instruments to measure postintensive care syndrome: a scoping review protocol

Reliability and validity of ICU nurses' knowledge-attitude-practice scale for post-ICU syndrome in critically ill patients

Logistic regression technique is comparable to machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome

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