Abstract
Background A vast number of patients with chronic critical illness (CCI) have died of delayed organ failure in the intensive care unit (ICU). The weak organ function of patients needed appropriate tool to evaluate, which could provide reference for clinical decisions and communication with family members. The objective of this study was to develope and validate a prediction model for accurate, timely, simple, and objective identification of the critical degree of the patients' condition. Methods This study used a retrospective case–control and a prospective cohort study, with no interventions. Patients identified as CCI from a comprehensive ICU of a large metropolitan public hospital were selected. A total of 344 (case 172; control 172) patients were included to develop the Prognosis Prediction Model of Chronic Critical Illness (PPCCI Model) in this case-control study; 88 (case, 44; control 44) patients were included for the validation cohort in a prospective cohort study. The discrimination of the model was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). Results The model comprised 9 predictors: age, prolonged mechanical ventilation (PMV), sepsis/other serious infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding.In both cohorts, the PPCCI Model could better identify the dead CCI patients (development cohort: AUC, 0.934; 95% CI, 0.908–0.960; validation cohort: AUC, 0.965; 95% CI, 0.931–0.999), and showed better discrimination than the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA). Conclusions The PPCCI Model can provide a standardized measurement tool for ICU medical staff to evaluate the condition of CCI patients, to facilitate rational allocation of ward-monitoring resources or communicate with family members.
Development and clinical empirical validation of the Prognosis Prediction Model of Chronic Critical Illness
