Gianfranco Sanson1, Cecilia Marino2, Andrea Valenti3, Umberto Lucangelo4, Giorgio Berlot5. 1. Clinical Department of Medical, Surgical and Health Sciences, Trieste University, Strada di Fiume 447, 34100 Trieste, Italy. Electronic address: gsanson@units.it. 2. Department of Perioperative Medicine, Intensive Care and Emergency, University Hospital, Trieste, Italy. Electronic address: cecilia_marino@tiscali.it. 3. Department of Perioperative Medicine, Intensive Care and Emergency, University Hospital, Trieste, Italy. Electronic address: andrea.valenti@asuits.sanita.fvg.it. 4. Clinical Department of Medical, Surgical and Health Sciences, Trieste University, Strada di Fiume 447, 34100 Trieste, Italy; Department of Perioperative Medicine, Intensive Care and Emergency, University Hospital, Trieste, Italy. Electronic address: u.lucangelo@fmc.units.it. 5. Clinical Department of Medical, Surgical and Health Sciences, Trieste University, Strada di Fiume 447, 34100 Trieste, Italy; Department of Perioperative Medicine, Intensive Care and Emergency, University Hospital, Trieste, Italy. Electronic address: berlot@inwind.it.
Abstract
BACKGROUND: Patients discharged from intensive care units (ICUs) are at risk for adverse events (AEs). Establishing safe discharge criteria is challenging. No available criteria consider nursing complexity among risk factors. OBJECTIVES: To investigate whether nursing complexity upon ICU discharge is an independent predictor for AEs. METHODS: Prospective observational study. The Patient Acuity and Complexity Score (PACS) was developed to measure nursing complexity. Its predictive power for AEs was tested using multivariate regression analysis. RESULTS: The final regression model showed a very-good discrimination power (AUC 0.881; p<0.001) for identifying patients who experienced AEs. Age, ICU admission reason, PACS, cough strength, PaCO2, serum creatinine and sodium, and transfer to Internal Medicine showed to be predictive of AEs. Exceeding the identified PACS threshold increased by 3.3 times the AEs risk. CONCLUSIONS: The level of nursing complexity independently predicts AEs risk and should be considered in establishing patient's eligibility for a safe ICU discharge.
BACKGROUND:Patients discharged from intensive care units (ICUs) are at risk for adverse events (AEs). Establishing safe discharge criteria is challenging. No available criteria consider nursing complexity among risk factors. OBJECTIVES: To investigate whether nursing complexity upon ICU discharge is an independent predictor for AEs. METHODS: Prospective observational study. The Patient Acuity and Complexity Score (PACS) was developed to measure nursing complexity. Its predictive power for AEs was tested using multivariate regression analysis. RESULTS: The final regression model showed a very-good discrimination power (AUC 0.881; p<0.001) for identifying patients who experienced AEs. Age, ICU admission reason, PACS, cough strength, PaCO2, serum creatinine and sodium, and transfer to Internal Medicine showed to be predictive of AEs. Exceeding the identified PACS threshold increased by 3.3 times the AEs risk. CONCLUSIONS: The level of nursing complexity independently predicts AEs risk and should be considered in establishing patient's eligibility for a safe ICU discharge.
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342