Journal of Shanghai Jiao Tong University (Medical Science) >

2024 , Vol. 44 >Issue 6: 733 - 740

DOI: https://doi.org/10.3969/j.issn.1674-8115.2024.06.008

Clinical research

Early symptom network analysis of patients after transfer from intensive care unit

  • Ran DONG ,
  • Qian YU ,
  • Rui TAI ,
  • Fu YANG ,
  • Fang FANG
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  • 1.School of Nursing, Shanghai Jiao Tong University, Shanghai 200025, China
    2.Department of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
FANG Fang, E-mail: fang_fang0604@163.com.

Received date: 2023-12-15

  Accepted date: 2024-03-01

  Online published: 2024-06-18

Supported by

Nursing Development Program of Shanghai Jiao Tong University School of Medicine(SJTUHLXK2021);Standardized Technology Management and Promotion Project of Shanghai Hospital Development Center(SHDC22022219)

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Abstract

Objective ·To establish the early symptom network of adult intensive care unit (ICU) patients after transfer (post-ICU patients), identify the core symptoms and bridge symptoms, compare the symptom networks of two subgroups, i.e. mixed ICU and coronary care unit (CCU), and analyze the occurrence of symptoms. Methods ·From December 2022 to August 2023, a total of 328 adult patients transferred to wards from mixed ICU and CCU of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine were selected by convenience sampling. The general situation and clinical data questionnaire, and symptom questionnaires (including Hospital Anxiety and Depression Scale, Fatigue Severity Scale, Richards-Campbell Sleep Questionnaire, and Pain Numeric Rating Scale) were used. Based on Spearman correlation analysis and GLASSO algorithm, contemporaneous symptom network was built, and centrality indices and differences between subgroup symptom networks were computed. The edge accuracy and the stability of centrality indices of the network were tested. Results ·A total of 302 valid questionnaires were collected, and the effective rate was 92.1%. The results of the centrality indices computations showed that in the early symptom network of post-ICU patients, the highest strength was “feel cheerful” (rS=1.145), the highest closeness was “enjoy something” (rC=1.851×10-3), and the highest expected influence was “（fatigue） interferes with physical function” (rE=1.143). The top three highest bridge strengths of symptoms were “worrying thoughts” (rb=10.392), “enjoy something” (rb=10.359), and pain (rb=10.221). There were no significant differences in network structure (M=0.289) and overall connection strength (GSmixed ICU=13.876, GSCCU=13.838; S=0.039) of the early symptom networks between mixed ICU and CCU patients after being transferred to wards. When comparing the centrality indices, apart from the strength and expected influence of five symptoms showing statistically significant differences (all P<0.05), other indices were not significantly different. The edge accuracy and the stability of centrality indices in the early symptom network of post-ICU patients were fine. Conclusion ·Anxiety and depression are the core symptoms of adult post-ICU patients, and pain is one of the bridge symptoms. There is no significant difference in the incidence of early symptoms between mixed ICU and CCU patients after being transferred out. Medical care personnel should pay attention to the discomfort symptoms of post-ICU patients, and carry out targeted interventions to improve patients' comfort and promote the rehabilitation process.

Key words： critical illness; symptom management; network analysis; intensive care unit (ICU); transitional care

Cite this article

Ran DONG , Qian YU , Rui TAI , Fu YANG , Fang FANG . Early symptom network analysis of patients after transfer from intensive care unit[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024 , 44(6) : 733 -740 . DOI: 10.3969/j.issn.1674-8115.2024.06.008

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