Development and clinical empirical validation of the chronic critical illness prognosis prediction model

Article type: Research Article

Authors: Zhao, Li^{a; b} | Xu, Wen-Kui^a | Wang, Ying^a | Lu, Wei-Yan^c | Wu, Yong^{d; *} | Hu, Rong^{a; *}

Affiliations: [a] School of Nursing, Fujian Medical University, Fuzhou, Fujian, China | [b] Intensive Care Unit, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China | [c] Department of Orthopaedic Trauma, Foot and Ankle Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China | [d] Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China

Correspondence: [*] Corresponding author: Rong Hu, School of Nursing, Fujian Medical University, 1 Xueyuan Road, Shangjie Town, Minhou County, Fuzhou, Fujian 350000, China. E-mail: [email protected]; Yong Wu, Department of Hematology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, Fujian 350000, China. E-mail: [email protected].

Abstract: BACKGROUND: The evolution of critical care medicine and nursing has aided and enabled the rescue of a large number of patients from numerous life-threatening diseases. However, in many cases, patient health may not be quickly restored, and the long-term prognosis may not be optimistic. OBJECTIVES: In this study, we aimed to develop and validate a prediction model for accurate, precise, and objective identification of the severity of chronic critical illness (CCI) in patients. METHODS: We used a retrospective case-control and prospective cohort study with no interventions. Patients diagnosed with CCI admitted to the ICU of a large metropolitan public hospital were selected. In the case-control study, 344 patients (case: 172; control:172) were enrolled to develop the prognosis prediction model of chronic critical illness (PPCCI Model); 88 patients (case:44; control: 44) in a prospective cohort study, served as the validation cohort. The discrimination of the model was measured using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). RESULTS: Age, prolonged mechanical ventilation (PMV), sepsis or other severe infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding were the nine predictors included in the model. In both cohorts, the PPCCI model outperformed the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA) in identifying deceased patients with CCI (development cohort: AUC, 0.934; 95%CI, 0.908–0.960; validation cohort: AUC, 0.965; 95% CI, 0.931–0.999). CONCLUSION: The PPCCI model can provide ICU medical staff with a standardized measurement tool for assessing the condition of patients with CCI, enabling them to allocate ward monitoring resources rationally and communicate with family members.

Keywords: Chronic critical illness, critical care, model, prediction, prognosis, score

DOI: 10.3233/THC-230359

Journal: Technology and Health Care, vol. 32, no. 2, pp. 977-987, 2024