Characteristics of symptoms and establishment of a predictive model for PICS in mechanically ventilated patients with severe pneumonia: a retrospective study - Juhong Ding; Dongli Wang; Xiaoling Zhou; Yumei Lu; Ke Ren; Yu Zhu; Yun Cao; Lei Ding (European Journal of Medical Research)
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.