ArticleEuropean journal of medical research2025
Characteristics of symptoms and establishment of a predictive model for PICS in mechanically ventilated patients with severe pneumonia: a retrospective study.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe 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.
methodsA 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.
resultsA 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.
conclusionThe 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.
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