Length of Stay in the Neonatal ICU is Predictable using Heart Rate: An Opportunity for Optimizing Managed Care

Author(s):  
Xinyu Ivy Zhang ◽  
Prahlad G Menon
Author(s):  
David J Whellan ◽  
Xin Zhao ◽  
Adrian F Hernandez ◽  
Eric D Peterson ◽  
Deepak L Bhatt ◽  
...  

Background: Heart failure (HF) admissions are frequent and result in significant expenditures. Identifying predictors of increased length of stay (LOS), particularly above the median LOS, may help providers set expectations for patients and target resources effectively. Methods: We analyzed HF admissions (n= 70,094) from January 2005 through April 2007 from 246 hospitals in the AHA's Get With The Guidelines-HF program. In a subset with BNP (n=44,535), baseline characteristics, admission vital signs and selected labs (BNP, creatinine, BUN, hemoglobin, and sodium) were included in a multivariable regression analysis to determine factors associated with LOS ≥4 days. Results: Patients were median age of 72, 45% female, 53% had ischemic etiology, and median LVEF was 35%. Median LOS was 4 days (25 th ,75 th 2,6). The most significant predictors of LOS ≥ 4 days were a higher admission BUN, higher heart rate, and lower SBP (Table 1). Age, insurance, race, creatinine, and LVEF were not. Conclusion: Upon admission for HF, certain vital signs, comorbidites, and laboratory values are associated with an increased likelihood of a LOS ≥ 4 days. These observations may be of value in the implementation of interventions aimed at reducing LOS and improving quality of care in HF. Variables Associated With Hospital LOS >/= 4 Days Variable Chi-Square OR Lower (95% CI) Upper (95% CI) P-value Admissioun BUN (/1 unit increase) 221.8 1.01 1.01 1.01 <.001 Admission SBP (/ 10-unit increase) 129.6 0.96 0.95 0.96 <.001 Heart Rate (/ 10-unit increase) 122.4 1.07 1.06 1.09 <.001 History of COPD/Asthma 45.8 1.19 1.13 1.25 <.001 Admission BNP (per 100-unit increase) 37.6 1.01 1.00 1.01 <.001 Female vs. Male 29.7 1.12 1.08 1.17 <.001 History of renal insufficiency 27.4 1.17 1.10 1.24 <.001 History of heart failure 18.0 0.89 0.85 0.94 <.001 Region: (MW vs. NE)
 (S vs NE)
 (W vs. NE) 17.3 0.71
 0.91
 0.71 0.60
 .077
 0.56 0.85
 1.08
 0.88 <.001


2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e14-e15
Author(s):  
Daryl Cheng ◽  
Caitlyn Hui ◽  
Kate Langrish ◽  
Carolyn Beck

Abstract BACKGROUND Paediatric intermediate care units (IC) function to provide a higher level of inpatient paediatric care such as frequent monitoring or nursing intervention compared to routine inpatient general paediatric care. A small subset of these patients in IC deteriorate further and require transfer to the paediatric intensive care unit (PICU). By identifying patient characteristics at the time of admission that predict secondary transfer, specific monitoring, resource allocation and early intervention may be implemented in order to improve quality of care. Appropriate and timely patient flow and length of stay (LOS) can also be optimized. DESIGN/METHODS The IC at our tertiary care institution admits predominantly general paediatric patients. Its admission criteria have been designed with input from stakeholders, and comprise a range of physiologic and resource based measures. Data were collected on patients who were admitted to IC, including those subsequently transferred to PICU, between July 2016 - June 2017. Patients whose index IC admission was from the PICU were excluded. Data included demographic and physiologic characteristics (heart rate, respiratory rate, temperature, oxygen therapy) and the bedside paediatric early warning system (BPEWS) score, a validated score based on vital signs. Quantitative and qualitative data were analyzed using Fisher and Mann-Whitney tests respectively. RESULTS 210 patient visits occurred in this time period, with 44 (20.95%) transferred to PICU (Table 1). Transferred patients showed no significant difference in age or sex. However, they had significantly higher median BPEWS, heart rate, respiratory rate and mean body temperature compared to non-transferred patients, as well as a significantly higher rate of respiratory support and shorter LOS on IC. There was a non-significant trend toward admission directly from the Emergency Department (ED) in transferred patients. Admission criteria and main organ systems affected were similar amongst both groups, with a predominance of respiratory conditions. PICU transfer was predicted by most physiological characteristics, including BPEWS. This coupled with a significantly shorter length of stay is a likely reflection of higher disease acuity in this group of patients and higher risk of deterioration and subsequent transfer to PICU. CONCLUSION The need for close monitoring of physiologic parameters remains paramount in predicting the need for transfer from the IC to PICU.


Author(s):  
Gonzalo Solís-García ◽  
Elena Maderuelo-Rodríguez ◽  
Teresa Perez-Pérez ◽  
Laura Torres-Soblechero ◽  
Ana Gutiérrez-Vélez ◽  
...  

Objective Analysis of longitudinal data can provide neonatologists with tools that can help predict clinical deterioration and improve outcomes. The aim of this study is to analyze continuous monitoring data in newborns, using vital signs to develop predictive models for intensive care admission and time to discharge. Study Design We conducted a retrospective cohort study, including term and preterm newborns with respiratory distress patients admitted to the neonatal ward. Clinical and epidemiological data, as well as mean heart rate and saturation, at every minute for the first 12 hours of admission were collected. Multivariate mixed, survival and joint models were developed. Results A total of 56,377 heart rate and 56,412 oxygen saturation data were analyzed from 80 admitted patients. Of them, 73 were discharged home and 7 required transfer to the intensive care unit (ICU). Longitudinal evolution of heart rate (p < 0.01) and oxygen saturation (p = 0.01) were associated with time to discharge, as well as birth weight (p < 0.01) and type of delivery (p < 0.01). Longitudinal heart rate evolution (p < 0.01) and fraction of inspired oxygen at admission at the ward (p < 0.01) predicted neonatal ICU (NICU) admission. Conclusion Longitudinal evolution of heart rate can help predict time to transfer to intensive care, and both heart rate and oxygen saturation can help predict time to discharge. Analysis of continuous monitoring data in patients admitted to neonatal wards provides useful tools to stratify risks and helps in taking medical decisions. Key Points


2005 ◽  
Vol 46 (5) ◽  
pp. 431-439 ◽  
Author(s):  
James A. Bourgeois ◽  
William S. Kremen ◽  
Mark E. Servis ◽  
Jacob A. Wegelin ◽  
Robert E. Hales

2011 ◽  
Vol 70 ◽  
pp. 37-37
Author(s):  
J R Moorman ◽  
W A Carlo ◽  
J Kattwinkel ◽  
R L Schelonka ◽  
P J Porcelli ◽  
...  

1998 ◽  
Vol 1 (1) ◽  
pp. 83
Author(s):  
JA O'Brien ◽  
MJ Pietrusewicz ◽  
D Pierce ◽  
JJ Caro
Keyword(s):  

2017 ◽  
Vol 18 (9) ◽  
pp. 907-908
Author(s):  
Kate Madden ◽  
Jeffrey P. Burns
Keyword(s):  

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