Abstract
Increased length of stay (LOS) in intensive care units is directly associated with the financial burden, anxiety, and increased mortality risks. In the current study, we have incorporated the association of day-to-day nutrition and medication data of the patient during its stay in hospital with its predicted LOS. To demonstrate the same, we developed a model to predict the LOS using risk factors (a) perinatal and antenatal details, (b) deviation of nutrition and medication dosage from guidelines, and (c) clinical diagnoses encountered during NICU stay. Data of 836 patient records (12 months) from two NICU sites were used and validated on 211 patient records (4 months). A bedside user interface integrated with EMR has been designed to display the model performance results on the validation dataset. The study shows that each gestation age group of patients has unique and independent risk factors associated with the LOS. The gestation is a significant risk factor for neonates < 34 weeks, nutrition deviation for < 32 weeks, and clinical diagnosis (sepsis) for ≥ 32 weeks. Patients on medications had considerable extra LOS for ≥ 32 weeks’ gestation. The presented LOS model is tailored for each patient, and deviations from the recommended nutrition and medication guidelines were significantly associated with the predicted LOS.
Highlights
Increased length of stay (LOS) in intensive care units is directly associated with the financial burden, anxiety, and increased mortality risks
Clinical factorbased prediction studies have highlighted the relationship of LOS with different clinical diagnoses and have used different severity scores, including the Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), and Mortality Probability Model (MPM)[6]
Our study presents the retrospective analysis of 16 months of data collected from two neonatal intensive care units (NICUs) study sites
Summary
We present a LOS model that incorporates independent variables, referred to as risk factors, based on gestational ages in neonatal population. These risk factors represent patient-specific aspects of the CCU clinical course, including (a) antenatal and perinatal factors, (b) nutritional orders, (c) medication orders, and (d) clinical diagnosis data. These categories were (a) Antenatal and Perinatal factors, (b) Nutrition orders and Medication orders, and (c) Clinical diagnosis These data were utilized to predict LOS and associated weights of risk factors displayed on the bedside tablet interface. The predicted LOS is generated as a linear model based on averages of dependent variables (such as antenatal, nutrition and medication deviations, and co-morbidities) over a reference grid. The interface was designed and implemented to show validation stage results, but it was not used at bedside for daily rounds
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