Jianfang Liu1, Elaine Larson, Amanda Hessels, Bevin Cohen, Philip Zachariah, David Caplan, Jingjing Shang. 1. Jianfang Liu, PhD, is Assistant Professor, School of Nursing, Columbia University, New York, New York. Elaine Larson, RN, PhD, FAAN, CIC, is Associate Dean for Research and Anna C. Maxwell Professor of Nursing Research, School of Nursing, and Professor of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York. Amanda Hessels, PhD, MPH, RN, CIC, CPHQ, FAPIC, is Assistant Professor, School of Nursing, Columbia University, New York, New York, and Nurse Scientist, Hackensack Meridian Health, Neptune, New Jersey. Bevin Cohen, PhD, MPH, RN, is Associate Research Scientist, School of Nursing, Columbia University, New York, New York. Philip Zachariah, MD, MS, is Assistant Professor, Columbia University Medical Center & New York-Presbyterian Morgan Stanley Children's Hospital. David Caplan, BS, is Senior Technical Specialist, Division of Quality Analytics, New York-Presbyterian Hospital. Jingjing Shang, PhD, RN, is Associate Professor, School of Nursing, Columbia University, New York, New York.
Abstract
BACKGROUND: Patient risk adjustment is critical for hospital benchmarking and allocation of healthcare resources. However, considerable heterogeneity exists among measures. OBJECTIVES: The performance of five measures was compared to predict mortality and length of stay (LOS) in hospitalized adults using claims data; these include three comorbidity composite scores (Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, and V W Elixhauser age-comorbidity score), 3 M risk of mortality (3 M ROM), and 3 M severity of illness (3 M SOI) subclasses. METHODS: Binary logistic and zero-truncated negative binomial regression models were applied to a 2-year retrospective dataset (2013-2014) with 123,641 adult inpatient admissions from a large hospital system in New York City. RESULTS: All five measures demonstrated good to strong model fit for predicting in-hospital mortality, with C-statistics of 0.74 (95% confidence interval [CI] [0.74, 0.75]), 0.80 (95% CI [0.80, 0.81]), 0.81(95% CI [0.81, 0.82]), 0.94 (95% CI [0.93, 0.94]), and 0.90 (95% CI [0.90, 0.91]) for Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, V W Elixhauser age-comorbidity score, 3 M ROM, and 3 M SOI, respectively. The model fit statistics to predict hospital LOS measured by the likelihood ratio index were 0.3%, 1.2%, 1.1%, 6.2%, and 4.3%, respectively. DISCUSSION: The measures tested in this study can guide nurse managers in the assignment of nursing care and coordination of needed patient services and administrators to effectively and efficiently support optimal nursing care.
BACKGROUND:Patient risk adjustment is critical for hospital benchmarking and allocation of healthcare resources. However, considerable heterogeneity exists among measures. OBJECTIVES: The performance of five measures was compared to predict mortality and length of stay (LOS) in hospitalized adults using claims data; these include three comorbidity composite scores (Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, and V W Elixhauser age-comorbidity score), 3 M risk of mortality (3 M ROM), and 3 M severity of illness (3 M SOI) subclasses. METHODS: Binary logistic and zero-truncated negative binomial regression models were applied to a 2-year retrospective dataset (2013-2014) with 123,641 adult inpatient admissions from a large hospital system in New York City. RESULTS: All five measures demonstrated good to strong model fit for predicting in-hospital mortality, with C-statistics of 0.74 (95% confidence interval [CI] [0.74, 0.75]), 0.80 (95% CI [0.80, 0.81]), 0.81(95% CI [0.81, 0.82]), 0.94 (95% CI [0.93, 0.94]), and 0.90 (95% CI [0.90, 0.91]) for Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, V W Elixhauser age-comorbidity score, 3 M ROM, and 3 M SOI, respectively. The model fit statistics to predict hospital LOS measured by the likelihood ratio index were 0.3%, 1.2%, 1.1%, 6.2%, and 4.3%, respectively. DISCUSSION: The measures tested in this study can guide nurse managers in the assignment of nursing care and coordination of needed patient services and administrators to effectively and efficiently support optimal nursing care.
Authors: Dan Olson; Nicole L Davis; Robert Milazi; Norman Lufesi; William C Miller; Geoffrey A Preidis; Mina C Hosseinipour; Eric D McCollum Journal: Trop Med Int Health Date: 2013-07 Impact factor: 2.622
Authors: Karim S Ladha; Kevin Zhao; Sadeq A Quraishi; Tobias Kurth; Matthias Eikermann; Haytham M A Kaafarani; Eric N Klein; Raghu Seethala; Jarone Lee Journal: BMJ Open Date: 2015-09-08 Impact factor: 2.692