Intra-hospital transfers to a higher level of care: contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS).

BACKGROUND Patients who experience intra-hospital transfers to a higher level of care (eg, ward to intensive care unit [ICU]) are known to have high mortality. However, these findings have been based on single-center studies or studies that employ ICU admissions as the denominator. OBJECTIVE To employ automated bed history data to examine outcomes of intra-hospital transfers using all hospital admissions as the denominator. DESIGN Retrospective cohort study. SETTING A total of 19 acute care hospitals. PATIENTS A total of 150,495 patients, who experienced 210,470 hospitalizations, admitted to these hospitals between November 1st, 2006 and January 31st, 2008. MEASUREMENTS Predictors were age, sex, admission type, admission diagnosis, physiologic derangement on admission, and pre-existing illness burden; outcomes were: 1) occurrence of intra-hospital transfer, 2) death following admission to the hospital, 3) death following transfer, and 4) total hospital length of stay (LOS). RESULTS A total of 7,868 hospitalizations that began with admission to either a general medical surgical ward or to a transitional care unit (TCU) had at least one transfer to a higher level of care. These hospitalizations constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers. CONCLUSIONS Patients transferred to higher level of care following admission to the hospital have excess mortality and LOS.

[1]  Paul R. Rosenbaum,et al.  Comparing the Contributions of Groups of Predictors: Which Outcomes Vary with Hospital Rather than Patient Characteristics? , 1995 .

[2]  C. Clec’h,et al.  Diagnostic and prognostic value of procalcitonin in patients with septic shock , 2004, Critical care medicine.

[3]  D. Harrison,et al.  Reproducibility of physiological track-and-trigger warning systems for identifying at-risk patients on the ward , 2007, Intensive Care Medicine.

[4]  R. Tennant,et al.  Using care bundles to reduce in-hospital mortality: quantitative survey , 2010, BMJ : British Medical Journal.

[5]  J. Vincent,et al.  Serial evaluation of the SOFA score to predict outcome in critically ill patients. , 2001, JAMA.

[6]  C. Franklin,et al.  Developing strategies to prevent inhospital cardiac arrest: Analyzing responses of physicians and nurses in the hours before the event , 1994, Critical care medicine.

[7]  Carl van Walraven,et al.  The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. , 2010, Journal of clinical epidemiology.

[8]  E. M. Breed,et al.  Richardson score predicts short-term adverse respiratory outcomes in newborns >/=34 weeks gestation. , 2004, The Journal of pediatrics.

[9]  R. Wenzel,et al.  Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. , 1998, Critical care medicine.

[10]  J. Zimmerman,et al.  Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients* , 2006, Critical care medicine.

[11]  Sankey V. Williams,et al.  Hospital and Patient Characteristics Associated With Death After Surgery: A Study of Adverse Occurrence and Failure to Rescue , 1992, Medical care.

[12]  N. Cook Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction , 2007, Circulation.

[13]  L. Forni,et al.  Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department. , 2005, British journal of anaesthesia.

[14]  Mitchell J Barnett,et al.  Day of the Week of Intensive Care Admission and Patient Outcomes: A Multisite Regional Evaluation , 2002, Medical care.

[15]  C. Sprung,et al.  Clinical antecedents to in-hospital cardiopulmonary arrest. , 1990, Chest.

[16]  James Deddens,et al.  Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure* , 2005, Critical care medicine.

[17]  K. Hillman,et al.  Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial , 2005, The Lancet.

[18]  G. Escobar,et al.  Incorporation of physiological trend and interaction effects in neonatal severity of illness scores: an experiment using a variant of the Richardson score , 2007, Intensive Care Medicine.

[19]  Bruce H Fireman,et al.  Risk adjusting community-acquired pneumonia hospital outcomes using automated databases. , 2008, The American journal of managed care.

[20]  Peter J Pronovost,et al.  Rapid response teams--walk, don't run. , 2006, JAMA.

[21]  R. Hayward,et al.  Identifying poor-quality hospitals. Can hospital mortality rates detect quality problems for medical diagnoses? , 1996, Medical care.

[22]  Yuchiao Chang,et al.  Anticoagulation therapy for stroke prevention in atrial fibrillation: how well do randomized trials translate into clinical practice? , 2003, JAMA.

[23]  Bekele Afessa,et al.  The hospital mortality of patients admitted to the ICU on weekends. , 2004, Chest.

[24]  D. Hoaglin,et al.  Enhancement of claims data to improve risk adjustment of hospital mortality. , 2007, JAMA.

[25]  J. Birkmeyer,et al.  Surgical mortality as an indicator of hospital quality: the problem with small sample size. , 2004, JAMA.

[26]  H. Montenegro,et al.  Beyond the intensive care unit: a review of interventions aimed at anticipating and preventing in-hospital cardiopulmonary arrest. , 2007, Resuscitation.

[27]  A. Kramer,et al.  Effect of a rapid response system for patients in shock on time to treatment and mortality during 5 years* , 2007, Critical care medicine.

[28]  J. Selby,et al.  Linking Automated Databases for Research in Managed Care Settings , 1997, Annals of Internal Medicine.

[29]  Donald A Redelmeier,et al.  Improving patient care. The cognitive psychology of missed diagnoses. , 2005, Annals of internal medicine.

[30]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[31]  Gabriel J. Escobar,et al.  Risk-Adjusting Hospital Inpatient Mortality Using Automated Inpatient, Outpatient, and Laboratory Databases , 2008, Medical care.

[32]  Bertrand Guidet,et al.  Mortality among patients admitted to intensive care units during weekday day shifts compared with “off” hours* , 2007, Critical care medicine.