5 results on '"Aaron B Waxman"'
Search Results
2. Improving decision making for massive transfusions in a resource poor setting: a preliminary study in Kenya.
- Author
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Elisabeth D Riviello, Stephen Letchford, Earl Francis Cook, Aaron B Waxman, and Thomas Gaziano
- Subjects
Medicine ,Science - Abstract
The reality of finite resources has a real-world impact on a patient's ability to receive life-saving care in resource-poor settings. Blood for transfusion is an example of a scarce resource. Very few studies have looked at predictors of survival in patients requiring massive transfusion. We used data from a rural hospital in Kenya to develop a prediction model of survival among patients receiving massive transfusion.Patients who received five or more units of whole blood within 48 hours between 2004 and 2010 were identified from a blood registry in a rural hospital in Kenya. Presenting characteristics and in-hospital survival were collected from charts. Using stepwise selection, a logistic model was developed to predict who would survive with massive transfusion versus those who would die despite transfusion. An ROC curve was created from this model to quantify its predictive power.Ninety-five patients with data available met inclusion criteria, and 74% survived to discharge. The number of units transfused was not a predictor of mortality, and no threshold for futility could be identified. Preliminary results suggest that initial blood pressure, lack of comorbidities, and indication for transfusion are the most important predictors of survival. The ROC curve derived from our model demonstrates an area under the curve (AUC) equal to 0.757, with optimism of 0.023 based on a bootstrap validation.This study provides a framework for making prioritization decisions for the use of whole blood in the setting of massive bleeding. Our analysis demonstrated an overall survival rate for patients receiving massive transfusion that was higher than clinical perception. Our analysis also produced a preliminary model to predict survival in patients with massive bleeding. Prediction analyses can contribute to more efficient prioritization decisions; these decisions must also include other considerations such as equity, acceptability, affordability and sustainability.
- Published
- 2015
- Full Text
- View/download PDF
3. Plasma gelsolin depletion and circulating actin in sepsis: a pilot study.
- Author
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Po-Shun Lee, Sanjay R Patel, David C Christiani, Ednan Bajwa, Thomas P Stossel, and Aaron B Waxman
- Subjects
Medicine ,Science - Abstract
Depletion of the circulating actin-binding protein, plasma gelsolin (pGSN) has been described in septic patients and animals. We hypothesized that the extent of pGSN reduction correlates with outcomes of septic patients and that circulating actin is a manifestation of sepsis.We assayed pGSN in plasma samples from non-surgical septic patients identified from a pre-existing database which prospectively enrolled patients admitted to adult intensive care units at an academic hospital. We identified 21 non-surgical septic patients for the study. Actinemia was detected in 17 of the 21 patients, suggesting actin released into circulation from injured tissues is a manifestation of sepsis. Furthermore, we documented the depletion of pGSN in human clinical sepsis, and that the survivors had significantly higher pGSN levels than the non-survivors (163+/-47 mg/L vs. 89+/-48 mg/L, p = 0.01). pGSN levels were more strongly predictive of 28-day mortality than APACHE III scores. For every quartile reduction in pGSN, the odds of death increased 3.4-fold.We conclude that circulating actin and pGSN deficiency are associated with early sepsis. The degree of pGSN deficiency correlates with sepsis mortality. Reversing pGSN deficiency may be an effective treatment for sepsis.
- Published
- 2008
- Full Text
- View/download PDF
4. Hemodynamic and metabolic characteristics associated with development of a right ventricular outflow tract pressure gradient during upright exercise
- Author
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Berto J. Bouma, David M. Systrom, Amil M. Shah, Barbara J.M. Mulder, Michael J. Landzberg, Bradley A. Maron, Alexander R. Opotowsky, Aaron B. Waxman, Annelieke C.M.J. van Riel, Rudolf K.F. Oliveira, Cardiology, Graduate School, APH - Personalized Medicine, APH - Aging & Later Life, Amsterdam Cardiovascular Sciences, ACS - Heart failure & arrhythmias, and ACS - Pulmonary hypertension & thrombosis
- Subjects
Male ,Cardiac Catheterization ,Supine position ,Cardiac index ,Hemodynamics ,lcsh:Medicine ,Blood Pressure ,030204 cardiovascular system & hematology ,Vascular Medicine ,0302 clinical medicine ,Heart Rate ,Medicine and Health Sciences ,Ventricular outflow tract ,Public and Occupational Health ,Pulmonary Arteries ,Cardiac Output ,lcsh:Science ,Multidisciplinary ,Central venous pressure ,Hematology ,Arteries ,Middle Aged ,Systolic Pressure ,Sports Science ,Echocardiography ,Cardiology ,Female ,Anatomy ,Research Article ,Adult ,medicine.medical_specialty ,Pulmonary Artery ,03 medical and health sciences ,Internal medicine ,Heart rate ,medicine ,Ventricular Pressure ,Humans ,Sports and Exercise Medicine ,Exercise ,Pressure gradient ,business.industry ,lcsh:R ,Biology and Life Sciences ,Physical Activity ,Blood pressure ,030228 respiratory system ,Physical Fitness ,Cardiovascular Anatomy ,Ventricular Function, Right ,Blood Vessels ,lcsh:Q ,business - Abstract
BACKGROUND We recently reported a novel observation that many patients with equal resting supine right ventricular(RV) and pulmonary artery(PA) systolic pressures develop an RV outflow tract(RVOT) pressure gradient during upright exercise. The current work details the characteristics of patients who develop such an RVOT gradient. METHODS We studied 294 patients (59.7±15.5 years-old, 49% male) referred for clinical invasive cardiopulmonary exercise testing, who did not have a resting RVOT pressure gradient defined by the simultaneously measured peak-to-peak difference between RV and PA systolic pressures. RESULTS The magnitude of RVOT gradient did not correspond to clinical or hemodynamic findings suggestive of right heart failure; rather, higher gradients were associated with favorable exercise findings. The presence of a high peak RVOT gradient (90th percentile, ≥33mmHg) was associated with male sex (70 vs. 46%, p = 0.01), younger age (43.6±17.7 vs. 61.8±13.9 years, p
- Published
- 2017
5. Improving Decision Making for Massive Transfusions in a Resource Poor Setting: A Preliminary Study in Kenya
- Author
-
Thomas A. Gaziano, Stephen Letchford, Elisabeth D. Riviello, Aaron B. Waxman, and Earl Francis Cook
- Subjects
Male ,medicine.medical_specialty ,Blood transfusion ,medicine.medical_treatment ,Decision Making ,lcsh:Medicine ,Logistic regression ,Models, Biological ,Disease-Free Survival ,medicine ,Humans ,Blood Transfusion ,Hospital Mortality ,Registries ,lcsh:Science ,Intensive care medicine ,Survival rate ,Whole blood ,Multidisciplinary ,business.industry ,Mortality rate ,lcsh:R ,Area under the curve ,Kenya ,Survival Rate ,Clinical trial ,Wounds and Injuries ,lcsh:Q ,Female ,business ,Trauma surgery ,Research Article - Abstract
Background The reality of finite resources has a real-world impact on a patient’s ability to receive life-saving care in resource-poor settings. Blood for transfusion is an example of a scarce resource. Very few studies have looked at predictors of survival in patients requiring massive transfusion. We used data from a rural hospital in Kenya to develop a prediction model of survival among patients receiving massive transfusion. Methods Patients who received five or more units of whole blood within 48 hours between 2004 and 2010 were identified from a blood registry in a rural hospital in Kenya. Presenting characteristics and in-hospital survival were collected from charts. Using stepwise selection, a logistic model was developed to predict who would survive with massive transfusion versus those who would die despite transfusion. An ROC curve was created from this model to quantify its predictive power. Results Ninety-five patients with data available met inclusion criteria, and 74% survived to discharge. The number of units transfused was not a predictor of mortality, and no threshold for futility could be identified. Preliminary results suggest that initial blood pressure, lack of comorbidities, and indication for transfusion are the most important predictors of survival. The ROC curve derived from our model demonstrates an area under the curve (AUC) equal to 0.757, with optimism of 0.023 based on a bootstrap validation. Conclusions This study provides a framework for making prioritization decisions for the use of whole blood in the setting of massive bleeding. Our analysis demonstrated an overall survival rate for patients receiving massive transfusion that was higher than clinical perception. Our analysis also produced a preliminary model to predict survival in patients with massive bleeding. Prediction analyses can contribute to more efficient prioritization decisions; these decisions must also include other considerations such as equity, acceptability, affordability and sustainability.
- Published
- 2015
- Full Text
- View/download PDF
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