Research Topics
| Andrew A KramerSummaryAffiliation: Cerner Corporation Country: USA Publications
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Detail Information
Publications
Predictive mortality models are not like fine wineAndrew A Kramer
Cerner Corporation, 1953 Gallows Road, Suite 570, Vienna, VA 22182, USA
Crit Care 9:636-7. 2005..Thus, the updated SAPS II model may be interesting for historical purposes, but it is doubtful that it can be an accurate tool for benchmarking data from contemporary populations...
Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patientsJack E Zimmerman
George Washington University, Washington, DC, USA
Crit Care Med 34:1297-310. 2006..S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated...
The association between ICU readmission rate and patient outcomesAndrew A Kramer
Cerner Corporation, Vienna, VA, USA
Crit Care Med 41:24-33. 2013..To examine the association between ICU readmission rates and case-mix-adjusted outcomes...- Intensive care unit readmissions in U.S. hospitals: patient characteristics, risk factors, and outcomesAndrew A Kramer
Cerner Corporation, Vienna, VA, USA
Crit Care Med 40:3-10. 2012..To examine which patient characteristics increase the risk for intensive care unit readmission and assess the association of readmission with case-mix adjusted mortality and resource use... - The relationship between hospital and intensive care unit length of stayAndrew A Kramer
Cerner Corporation, Vienna, VA, USA
Crit Care Med 39:1015-22. 2011..To assess variations in case-mix-adjusted hospital and intensive care unit length of stay and to examine the relationship between intensive care unit and hospital stay... - Institutional variations in frequency of discharge of elderly intensive care survivors to postacute care facilitiesAndrew A Kramer
Cerner Corporation, Washington, DC, USA
Crit Care Med 38:2319-28. 2010.... - A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stayAndrew A Kramer
Cerner Corporation, Suite 500, Vienna, Virginia 22182, USA
BMC Med Inform Decis Mak 10:27. 2010..Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay... - Predicting outcomes for cardiac surgery patients after intensive care unit admissionAndrew A Kramer
Cerner Corporation, Vienna, Virginia 22182, USA
Semin Cardiothorac Vasc Anesth 12:175-83. 2008..Thus, the equations are not designed for predicting individual patients' outcome but have proven useful in performance comparisons and for quality improvement initiatives... - Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisitedAndrew A Kramer
Cerner Corporation, Vienna, VA, USA
Crit Care Med 35:2052-6. 2007..To examine the Hosmer-Lemeshow test's sensitivity in evaluating the calibration of models predicting hospital mortality in large critical care populations... - Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IVJack E Zimmerman
George Washington University, Washington, DC, USA
Crit Care Med 34:2517-29. 2006..APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay... - A multicenter prospective study of interobserver agreement using the Full Outline of Unresponsiveness score coma scale in the intensive care unitAndrew A Kramer
Cerner Corporation, Vienna, VA, USA
Crit Care Med 40:2671-6. 2012..This manuscript reports on a study that examined the inter-rater reliability of the Full Outline of Unresponsiveness score in five intensive care units... - Outcome prediction in critical care: the Acute Physiology and Chronic Health Evaluation modelsJack E Zimmerman
Anesthesia and Critical Care Medicine, The George Washington University, Washington, District of Columbia, USA
Curr Opin Crit Care 14:491-7. 2008..We also compare APACHE IV with other systems and address the issue of model complexity... - A model for identifying patients who may not need intensive care unit admissionJack E Zimmerman
The Department of Anesthesia and Critical Care Medicine, George Washington University, Washington, DC, USA
J Crit Care 25:205-13. 2010..This study presents a new model for identifying patients who might be too well to benefit from intensive care unit (ICU) care... - Transferring critically ill patients out of hospital improves the standardized mortality ratio: a simulation studyJeremy M Kahn
Division of Pulmonary and Critical Care, Harborview Medical Center, University of Washington, Seattle WA, USA
Chest 131:68-75. 2007..We sought to quantify the effect of out-of-hospital transfers on the standardized mortality ratio (SMR), an outcome-based measure of ICU performance... - Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III)Thomas L Higgins
Critical Care Division, Baystate Medical Center, Springfield, MA, USA
Crit Care Med 35:827-35. 2007..To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data... - Validating predictive models of mortality: more than meets the eyeAndrew A Kramer
Crit Care Med 36:1357-8. 2008 - Hospital volume and the outcomes of mechanical ventilationJeremy M Kahn
Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle 98104, USA
N Engl J Med 355:41-50. 2006..Further research is needed to determine the mechanism of the relationship between volume and outcome among patients with a critical illness. Copyright 2006 Massachusetts Medical Society...