Ilona W M Verburg1, Rebecca Holman2, Dave Dongelmans3, Evert de Jonge4, Nicolette F de Keizer5. 1. Academic Medical Center, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. Electronic address: i.w.verburg@amc.uva.nl. 2. Clinical Research Unit, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. 3. Department of Intensive Care, Leiden University Medical Center, Leiden, The Netherlands. 4. Department of Intensive Care, Academic Medical Center, University of Amsterdam, The Netherlands. 5. Academic Medical Center, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Abstract
PURPOSE: We described the association between Intensive care units (ICU) characteristics and ICU Length of stay (LoS), after correcting for patient characteristics. We also compared the predictive performances of models including either patient and ICU characteristics or only patient characteristics. MATERIALS AND METHODS: We included all admissions of 38 ICUs participating in the Dutch National Intensive Care Evaluation registry (NICE) between 2014 and 2016. We performed mixed effect regression including, one ICU characteristic in each model and a random intercept per ICU. Furthermore, we developed a prediction model containing multiple ICU characteristics and patients characteristics. RESULTS: We found negative associations for the number of hospital beds; number of ICU beds; availability of fellows in training for intensivist; full-time equivalent ICU nurses; and discharged in a shift with 100% bed occupancy. Furthermore, we found a U-shaped association with the nurses to patient ratio as spline function. The performance based on R2 was between 0.30 and 0.32 for both the model containing only patient characteristics and the model also containing ICU characteristics. CONCLUSION: After correcting for patient characteristics, we found statistically significant associations between ICU LoS and six ICU characteristics, mainly describing staff availability. Furthermore, we conclude that including ICU characteristics did not significantly improve ICU LoS prediction.
PURPOSE: We described the association between Intensive care units (ICU) characteristics and ICU Length of stay (LoS), after correcting for patient characteristics. We also compared the predictive performances of models including either patient and ICU characteristics or only patient characteristics. MATERIALS AND METHODS: We included all admissions of 38 ICUs participating in the Dutch National Intensive Care Evaluation registry (NICE) between 2014 and 2016. We performed mixed effect regression including, one ICU characteristic in each model and a random intercept per ICU. Furthermore, we developed a prediction model containing multiple ICU characteristics and patients characteristics. RESULTS: We found negative associations for the number of hospital beds; number of ICU beds; availability of fellows in training for intensivist; full-time equivalent ICU nurses; and discharged in a shift with 100% bed occupancy. Furthermore, we found a U-shaped association with the nurses to patient ratio as spline function. The performance based on R2 was between 0.30 and 0.32 for both the model containing only patient characteristics and the model also containing ICU characteristics. CONCLUSION: After correcting for patient characteristics, we found statistically significant associations between ICU LoS and six ICU characteristics, mainly describing staff availability. Furthermore, we conclude that including ICU characteristics did not significantly improve ICU LoS prediction.