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. 2025 Apr 10;30(1):264.
doi: 10.1186/s40001-025-02547-x.

Characteristics of symptoms and establishment of a predictive model for PICS in mechanically ventilated patients with severe pneumonia: a retrospective study

Juhong Ding  1 , Dongli Wang  1 , Xiaoling Zhou  1 , Yumei Lu  1 , Ke Ren  1 , Yu Zhu  1 , Yun Cao  1 , Lei Ding  2
Affiliations

Characteristics of symptoms and establishment of a predictive model for PICS in mechanically ventilated patients with severe pneumonia: a retrospective study

Juhong Ding et al. Eur J Med Res. .

Abstract

Purpose: The study aimed to characterize the symptoms of post-intensive care unit (ICU) syndrome in mechanically ventilated patients with severe pneumonia and establish a predictive model for this syndrome.

Methods: A retrospective study was conducted on critically ill pneumonia patients requiring mechanical ventilation. Patients were categorized into non-ICU-acquired complication and post-intensive care syndrome (PICS) groups based on the development of ICU-acquired complications. Various demographic, clinical, laboratory, imaging, and symptom-related parameters were collected and analyzed.

Results: A total of 133 patients including 62 patients with non-ICU-Acquired Complications Group and 71 patients with PICS Group were included. Significant differences between the non-ICU-acquired complication and PICS groups were observed in demographic characteristics, such as age, body mass index (BMI), and Acute Physiology and Chronic Health Evaluation (APACHE) II score (p < 0.05). Clinical parameters, including PaO2/FiO2 (P/F) ratio, white blood cell (WBC) count, serum creatinine, and procalcitonin levels, showed statistical significance (p < 0.05). Ventilation and ICU stay characteristics, laboratory parameters at 72 h, imaging findings, and symptom characteristics also displayed significant differences between the groups (p < 0.05). The study's joint model exhibited an area under the curve (AUC) value of 0.786 (95% CI 0.746-0.833), indicating a moderate-to-good predictive value for PICS.

Conclusion: The study's findings highlight the potential utility of a multi-faceted predictive model integrating demographic, clinical, laboratory, imaging, and symptom-related parameters for identifying patients at risk for PICS.

Keywords: Mechanically ventilated; PICS; Predictive model; Severe pneumonia; Symptoms.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study received approval from the Institutional Review Board and Ethics Committee of the Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University. Based on the guidelines set forth by our institution's Institutional Review Board and Ethics Committee, informed consent was waived for this retrospective study as it solely involved de-identified patient data, thus presenting no risk or impact on patient care. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of patient selection process
Fig. 2
Fig. 2
ROC curve analysis for predictive indicators of pics in mechanically ventilated patients with severe pneumonia. AUC = 0.786. ROC Receiver-operating characteristic, PICS post-intensive care syndrome, AUC area under the curve
See this image and copyright information in PMC

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