In Silico Examination of Initiation of Long-Acting Insulin Analogs Toujeo Compared to Lantus Under 3 Dosing Titration Rules in Virtual Type 2 Diabetes Subjects.

BACKGROUND: Despite the benefits and clinical necessity of insulin treatment in type 2 diabetes (T2D), healthcare providers are reluctant to initiate insulin, and patients are reluctant to start it for several reasons, one of these being the complexity of insulin treatment. Patients and their healthcare providers can benefit from titration algorithms (TAs) or rules that assist with the initiation and titration of insulin, performing the calculations that are needed to safely initiate and conservatively adjust. METHODS: The primary objective for this in silico study was to examine the effectiveness of 3 dose TAs (1-3) for optimization of basal insulin glargine (Gla-100 and Gla-300). In the simulations, 100 virtual subjects with T2D were included (50% men, age 62 ± 3 years, HbA1c 8.1% ± 2.9%, body weight 94 ± 16 kg). Subjects were studied under each TA (TA1 and TA2 fasting blood glucose [FBG] targets 90-130 mg/dL, TA3 FBG target 110-150 mg/dL). Initial dose of both insulins was based on 0.2 U/kg body weight. During 3 months, subjects reported their FBG to the LTHome web-based dose guidance system with a rules engine to safely guide long-acting insulin titration and maintenance. Subjects followed dose recommendations to reach designated FBG target ranges. RESULTS: All subjects reached stable doses under all TAs with both Gla-100 and Gla-300 insulin, and 93 or more of the 100 subjects, depending on the assigned TA, achieved the target FBG range within the 3-month simulation for all TAs. Mean FBG was lowered (Gla-100: 155 ± 40 to 118 ± 11 mg/dL with TA1 and TA2 and 132 ± 12 mg/dL for TA3; Gla-300: 125 ± 14 with TA1 and TA2 and 134 ± 15 mg/dL with TA3). Calculated HbA1c improved from 8.1% ± 2.9% to 7.1% ± 2.5% for TA1 and TA2 and 7.5% ± 2.5% for TA3, a reduction of 0.9% and 0.6% over 3 months for both insulins. Three subjects on Gla-100 and one subject on Gla-300 experienced mild hypoglycemia. CONCLUSION: All TAs delivered safe dose recommendations with minimal hypoglycemia, leading to a stable glucose control in the majority of subjects.

RETROSPECTIVE STUDY OF GLYCEMIC CONTROL FOLLOWING TRANSITION FROM THE INTENSIVE CARE UNIT IN A NATIONAL SAMPLE OF U.S. HOSPITALS.

OBJECTIVE: Retrospective study to evaluate glycemic control outcomes after transition from the intensive care unit (ICU) to a non-ICU area in a national sample of U.S. hospitals. METHODS: Mean point-of-care blood glucose (POC-BG) data were assessed overall and at 24 hours before and up to 72 hours after the transition. Comparisons in glucose variability (standard deviation of POC-BG data) were assessed. Impact on glycemic control was evaluated after accounting for hospital characteristics through logistic regression analysis. RESULTS: POC-BG data were obtained from 576 hospitals. Overall mean (SD) POC-BG values in ICU versus non-ICU areas were 176 (24) versus 169 (21) mg/dL (P<.01). Mean (SD) of the ICU POC-BG data were 76 (16) versus 73 (16) mg/dL in the non-ICU data (P<.01). However, when comparing values of POC-BG in the last 24-hour ICU period with those from up to 72 hours posttransition, we found no differences, indicative of overall stable glycemic control and variability after transition. Any deterioration of glucose control following the transition was significantly associated with hospital size (P<.01): the smallest hospitals had the highest percentage of these cases. In addition, geographic region showed significant variability (P = .04), with hospitals in the Midwest and West having the highest proportion of cases in which glycemic control worsened following the transition. CONCLUSION: Glycemic control and variability did not change after transition from the ICU, but outcomes may depend on certain hospital characteristics. Inpatient glycemic control assessment should move beyond just cross-sectional studies and consider the impact of transitioning across inpatient areas. Other statistical approaches to studying this question should be evaluated.

Accuracy of a novel noninvasive transdermal continuous glucose monitor in critically ill patients.

Stress hyperglycemia and hypoglycemia are associated with increased morbidity and mortality in the critically ill. Intermittent, random blood glucose (BG) measurements can miss episodes of hyper- and hypoglycemia. The purpose of this study was to determine the accuracy of the Symphony® continuous glucose monitor (CGM) in critically ill cardiac surgery patients. Fifteen adult cardiac surgery patients were evaluated immediately postoperatively in the intensive care unit. Prelude® SkinPrep prepared the skin and a sensor was applied to 2 test sites on each subject to monitor interstitial fluid glucose. Reference BG was sampled at 30- to 60-minute intervals. The skin at the test sites was inspected for adverse effects. Accuracy of the retrospectively analyzed CGM data relative to reference BG values was determined using continuous glucose-error grid analysis (CG-EGA) and mean absolute relative difference (MARD). Using 570 Symphony CGM glucose readings paired with reference BG measurements, CG-EGA showed that 99.6% of the readings were within zones A and B. BG measurements ranged from 73 to 251 mg/dL. The MARD was 12.3%. No adverse device effects were reported. The Symphony CGM system is able to safely, continuously, and noninvasively monitor glucose in the transdermal interstitial fluid of cardiac surgery intensive care unit patients with accuracy similar to that reported with other CGM systems. Future versions of the system will need real-time data analysis, fast warm-up, and less frequent calibrations to be used in the clinical setting.

Benchmarking glycemic control in u.s. Hospitals.

OBJECTIVE: Report data on glucose control from 635 U.S. hospitals. METHODS: Point-of-care blood glucose (POC-BG) test data from January through December 2012 from 635 facilities were extracted. Glucose control was evaluated using patient-day-weighted mean POC-BG values. We calculated hypoglycemia and hyperglycemia rates, stratified by presence or absence of intensive care unit (ICU) admission, and we evaluated the relationship between glycemic control and hospital characteristics. RESULTS: In total, 51,375,764 POC-BG measurements (non-ICU, 39,197,762; ICU, 12,178,002) from 2,612,966 patients (non-ICU, 2,415,209; ICU, 575,084) were analyzed. The mean POC-BG was 167 mg/dL for non-ICU patients and 170 mg/dL for ICU patients. The prevalence of hyperglycemia (defined as glucose value >180 mg/dL) was 32.3 and 28.2% in non-ICU and ICU patients, respectively. The prevalence of hypoglycemia (defined as glucose value <70 mg/dL) was 6.1 and 5.6% in non-ICU and ICU patients, respectively. In non-ICU and ICU settings, the patient-day-weighted mean glucose was highest in the smallest hospitals, in rural hospitals, and in hospitals located in the Northeast (all P<.01). For non-ICU patients, we observed a significant difference in the percentage of patient days with hypoglycemia by geographic region only (P<.001). In ICU patients, the prevalence of hypoglycemia varied significantly by hospital type (P<.03) and geographic region (P<.01). CONCLUSION: In this largest POC-BG data set analysis conducted to date, glycemic control varied according to hospital characteristics. Our findings remain consistent with previous reports. Among other variables, national benchmarking of inpatient glucose data will need to consider differences in hospital characteristics.

Software-guided insulin dosing: tight glycemic control and decreased glycemic derangements in critically ill patients.

OBJECTIVE: To determine whether glycemic derangements are more effectively controlled using software-guided insulin dosing compared with paper-based protocols. PATIENTS AND METHODS: We prospectively evaluated consecutive critically ill patients treated in a tertiary hospital surgical intensive care unit (ICU) between January 1 and June 30, 2008, and between January 1 and September 30, 2009. Paper-based protocol insulin dosing was evaluated as a baseline during the first period, followed by software-guided insulin dosing in the second period. We compared glycemic metrics related to hyperglycemia, hypoglycemia, and glycemic variability during the 2 periods. RESULTS: We treated 110 patients by the paper-based protocol and 87 by the software-guided protocol during the before and after periods, respectively. The mean ICU admission blood glucose (BG) level was higher in patients receiving software-guided intensive insulin than for those receiving paper-based intensive insulin (181 vs 156 mg/dL; P=.003, mean of the per-patient mean). Patients treated with software-guided intensive insulin had lower mean BG levels (117 vs 135 mg/dL; P=.0008), sustained greater time in the desired BG target range (95-135 mg/dL; 68% vs 52%; P=.0001), had less frequent hypoglycemia (percentage of time BG level was <70 mg/dL: 0.51% vs 1.44%; P=.04), and showed decreased glycemic variability (BG level per-patient standard deviation from the mean: ±29 vs ±42 mg/dL; P=.01). CONCLUSION: Surgical ICU patients whose intensive insulin infusions were managed using the software-guided program achieved tighter glycemic control and fewer glycemic derangements than those managed with the paper-based insulin dosing regimen.

Trends in glycemic control over a 2-year period in 126 US hospitals.

BACKGROUND: Cross-sectional data on inpatient glucose control in a large sample of US hospitals are now available, but little is known about changes in glycemic control over time in these institutions. OBJECTIVE: To evaluate trends in glycemic control in US hospitals over 2 years. DESIGN: Retrospective analysis. METHODS: Point-of-care blood glucose (POC-BG) test results at 126 hospitals during January to December 2007 and January to December 2009 were extracted using the Remote Automated Laboratory System-Plus (Medical Automation Systems, Charlottesville, VA), and patient-day-weighted mean glucose levels were compared. SETTING/PATIENTS: Hospitalized patients. RESULTS: A total of 12,541,929 POC-BG measurements from 1,010,705 patients were analyzed for 2007, and 10,659,418 POC-BG measurements from 656,206 patients were analyzed for 2009. Patient-day-weighted mean POC-BG in 2009 decreased by 5 mg/dL in the non-intensive care unit (non-ICU) data compared with that in 2007 (154 mg/dL vs 159 mg/dL, respectively; P < 0.001). However, POC-BG values were clinically unchanged in intensive care unit (ICU) data from 2009 vs 2007 (167 mg/dL vs 166 mg/dL; P < 0.001). From 2007 to 2009, the proportion of patient-day-weighted mean POC-BGs that were >180 mg/dL declined from 28% to 25% in non-ICU patients (P < 0.001), but not in ICU. Decreases in patient-day-weighted mean POC-BG values in non-ICU patients were significant regardless of hospital size, type, and geographic region (all P < 0.001), but similar decreases were not found in ICU data. CONCLUSIONS: In this first analysis of glucose changes in US hospitals, improvements over 2 years occurred in non-ICU patients. Ongoing analysis will determine whether this trend continues.

Characterizing glucose changes antecedent to hypoglycemic events in the intensive care unit.

OBJECTIVE: To determine whether patterns of glucose changes before hypoglycemia vary according to the severity of the event. METHODS: In this retrospective analysis, point-of-care blood glucose (POC-BG) data were obtained from the intensive care units (ICUs) of a convenience sample of hospitals that responded to a survey on inpatient diabetes management quality improvement initiatives. To evaluate POC-BG levels before hypoglycemic events, data from patients who experienced hypoglycemia during their time in the ICU were examined, and their glucose changes were assessed against a comparison group of patients who achieved a glycemic range of 80 to 110 mg/dL without ever experiencing hypoglycemia. Absolute glucose decrease, glucose rate of change, and glucose variability before hypoglycemic events (<40, 40-49, 50-59, and 60-69 mg/dL) were calculated. RESULTS: A total of 128,419 POC-BG measurements from 2942 patients in 89 ICUs were analyzed. Patients who experienced the most severe hypoglycemic episodes had the largest absolute drop in their glucose levels before the event (P<.001). The glucose rate of change before a hypoglycemic event increased with worsening hypoglycemia: mean (±standard deviation) glucose rate of change was -1.69 (±2.98) mg/dL per min before an episode with glucose values less than 40 mg/dL, -0.56 (±2.65) mg/dL per min before an episode with glucose values 60 to 69 mg/dL, but only -0.39 (±0.70) for patients who attained a glucose range of 80 to 110 mg/dL without hypoglycemia (P<.001). Glucose variability before an event progressively increased with worsening biochemical hypoglycemia and was least among patients achieving glucose concentrations in the 80 to 110-mg/dL range without hypoglycemia (P<.001). CONCLUSIONS: Antecedent glucose change and variability were greater for patients who experienced hypoglycemia. If monitored, these patterns could potentially alert clinicians and help them take preventive measures. Further examination of how these parameters interact with other predisposing risk factors for hypoglycemia is warranted.

Assessing inpatient glycemic control: what are the next steps?

Despite the emergence of glucometrics (i.e., systematic analysis of data on blood glucose levels of inpatients) as a subject of high interest, there remains a lack of standardization on how glucose parameters are measured and reported. This dilemma must be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines and that can also be supported by quality care organizations. In this article, we review some of the questions that need to be resolved through consensus and review of the evidence, and discuss some of the limitations in analyzing and reporting inpatient glucose data that must be addressed (or at least accepted as limitations) before hospitals can commit resources to gathering, compiling, and presenting inpatient glucose statistics as a health care quality measure. Standards must include consensus on which measures to report, the unit of analysis, definitions of targets for hyperglycemia treatment, a definition of hypoglycemia, determination of how data should be gathered (from chart review or from laboratory information systems), and which type of sample (blood or point of care) should be used for analysis of glycemic control. Hospitals and/or their representatives should be included in the discussion. For inpatient glucose control to remain a focus of interest, further dialogue and consensus on the topic are needed.

Update on inpatient glycemic control in hospitals in the United States.

OBJECTIVE: To provide data on glucose control in hospitals in the United States, analyzing measurements from the largest number of facilities to date. METHODS: Point-of-care bedside glucose (POC-BG) test results were extracted from 575 hospitals from January 2009 to December 2009 by using a laboratory information management system. Glycemic control for patients in the intensive care unit (ICU) and non-ICU areas was assessed by calculating patient-day-weighted mean POC-BG values and rates of hypoglycemia and hyperglycemia. The relationship between POC-BG levels and hospital characteristics was determined. RESULTS: A total of 49,191,313 POC-BG measurements (12,176,299 ICU and 37,015,014 non-ICU values) were obtained from 3,484,795 inpatients (653,359 in the ICU and 2,831,436 in non-ICU areas). The mean POC-BG was 167 mg/dL for ICU patients and 166 mg/dL for non-ICU patients. The prevalence of hyperglycemia (>180 mg/dL) was 32.2% of patient-days for ICU patients and 32.0% of patient-days for non-ICU patients. The prevalence of hypoglycemia (<70 mg/dL) was 6.3% of patient-days for ICU patients and 5.7% of patient-days for non-ICU patients. Patient-day-weighted mean POC-BG levels varied on the basis of hospital size (P<.01), type (P<.01), and geographic location (P<.01) for ICU and non-ICU patients, with larger hospitals (≥400 beds), academic hospitals, and US hospitals in the West having the lowest mean POC-BG values. The percentage of patient-days in the ICU characterized by hypoglycemia was highest among larger and academic hospitals (P<.05) and least among hospitals in the Northeast (P<.001). CONCLUSION: Hyperglycemia is common in hospitals in the United States, and glycemic control may vary on the basis of hospital characteristics. Increased hospital participation in data collection may support a national benchmarking process for the development of optimal practices to manage inpatient hyperglycemia.

Case discussion in blood glucose variability.

Stress-induced hyperglycemia has been associated with poor outcomes and death in critically ill patients. Blood glucose (BG) variability, a component of stress-related hyperglycemia has recently been reported as a significant independent predictor of intensive care unit and hospital mortality. We sought to evaluate three cases in which intensive insulin therapy was administered using a standardized insulin dosing protocol to normalize the BG and reduce glycemic variability. Point-of-care BG values and other clinical measures were obtained from the medical record of three patients who received intensive insulin therapy. This was a convenience sample of three patients where the BG level had stabilized on a consistent intravenous insulin dose rate for up to 20 hours in a surgical trauma intensive care unit. Data were collected manually and electronically using the Remote Automated Laboratory System-Tight Glycemic Control Module (RALS-TGCM) BG management and monitoring system. Each case presentation describes a critically ill, nondiabetic patient, requiring continuous intravenous insulin therapy for hyperglycemia. In each instance, BG variability was present in a worsening patient condition after a period of normalization of hyperglycemia with intensive insulin therapy. Although decreasing BG variability is an important aspect of hyperglycemia management, new onset events of variability may be a sentinel warning or occur as a physiologic response to a worsening patient condition. If so, these events warrant rapid investigation and treatment of the underlying problem.

Benchmarking glucose results through automation: the 2009 Remote Automated Laboratory System Report.

BACKGROUND: Hyperglycemia in the adult inpatient population remains a topic of intense study in U.S. hospitals. Most hospitals have established glycemic control programs but are unable to determine their impact. The 2009 Remote Automated Laboratory System (RALS) Report provides trends in glycemic control over 4 years to 576 U.S. hospitals to support their effort to manage inpatient hyperglycemia. METHODS: A proprietary software application feeds de-identified patient point-of-care blood glucose (POC-BG) data from the Medical Automation Systems RALS-Plus data management system to a central server. Analyses include the number of tests and the mean and median BG results for intensive care unit (ICU), non-ICU, and each hospital compared to the aggregate of the other hospitals. RESULTS: More than 175 million BG results were extracted from 2006-2009; 25% were from the ICU. Mean range of BG results for all inpatients in 2006, 2007, 2008, and 2009 was 142.2-201.9, 145.6-201.2, 140.6-205.7, and 140.7-202.4 mg/dl, respectively. The range for ICU patients was 128-226.5, 119.5-219.8, 121.6-226.0, and 121.1-217 mg/dl, respectively. The range for non-ICU patients was 143.4-195.5, 148.6-199.8, 145.2-201.9, and 140.7-203.6 mg/dl, respectively. Hyperglycemia rates of >180 mg/dl in 2008 and 2009 were examined, and hypoglycemia rates of <40 mg/dl (severe) and <70 mg/dl (moderate) in both 2008 and 2009 were calculated. CONCLUSIONS: From these data, hospitals can determine the current state of glycemic control in their hospital and in comparison to other hospitals. For many, glycemic control has improved. Automated POC-BG data management software can assist in this effort.

Diabetes and hyperglycemia quality improvement efforts in hospitals in the United States: current status, practice variation, and barriers to implementation.

OBJECTIVE: To determine the status of diabetes and hyperglycemia quality improvement efforts in hospitals in the United States. METHODS: We designed and administered a survey to a convenience sample of hospitals, and the responses were analyzed statistically. RESULTS: We received 269 responses from 1,151 requested surveys. The sample was similar to hospitals in the United States on the basis of hospital type and geographic region (P = no significant difference) but not on the basis of number of beds (P<.001). Among responding hospitals, 39%, 21%, and 15% had fully implemented inpatient diabetes and hyperglycemia quality improvement programs for critically ill, non-critically ill, and perioperative patients, respectively. Moreover, 77%, 44%, and 49% had fully implemented protocols for hypoglycemia, hyperglycemic crises, and diabetic ketoacidosis, respectively. Variations in glucose target ranges were noted. The responding hospitals had no standard biochemical definition of hypoglycemia; 47% defined hypoglycemia as a glucose level

Inpatient glucose control: a glycemic survey of 126 U.S. hospitals.

BACKGROUND: Despite increased awareness of the value of treating inpatient hyperglycemia, little is known about glucose control in U.S. hospitals. METHODS: The Remote Automated Laboratory System-Plus (RALS-Plus Medical Automation Systems, Charlottesville, VA) was used to extract inpatient point-of-care bedside glucose (POC-BG) tests from 126 hospitals for the period January to December 2007. Patient-day-weighted mean POC-BG and hypoglycemia/hyperglycemia rates were calculated for intensive care unit (ICU) and non-ICU areas. The relationship of POC-BG levels with hospital characteristics was determined. RESULTS: A total of 12,559,305 POC-BG measurements were analyzed: 2,935,167 from the ICU and 9,624,138 from the non-ICU. Patient-day-weighted mean POC-BG was 165 mg/dL for ICU and 166 mg/dL for non-ICU. Hospital hyperglycemia (>180 mg/dL) prevalence was 46.0% for ICU and 31.7% for non-ICU. Hospital hypoglycemia (<70 mg/dL) prevalence was low at 10.1% for ICU and 3.5% for non-ICU. For ICU and non-ICU there was a significant relationship between number of beds and patient-day-weighted mean POC-BG levels, with larger hospitals (> or = 400 beds) having lower patient-day weighted mean POC-BG per patient day than smaller hospitals (<200 beds, P < 0.001). Rural hospitals had higher POC-BG levels compared to urban and academic hospitals (P < 0.05), and hospitals in the West had the lowest values. CONCLUSIONS: POC-BG data captured through automated data management software can support hospital efforts to monitor the status of inpatient glycemic control. From these data, hospital hyperglycemia is common, hypoglycemia prevalence is low, and POC-BG levels vary by hospital characteristics. Increased hospital participation in data collection and reporting may facilitate the creation of a national benchmarking process for the development of best practices and improved inpatient hyperglycemia management.

Inpatient point-of-care bedside glucose testing: preliminary data on use of connectivity informatics to measure hospital glycemic control.

BACKGROUND: Point-of-care (POC) bedside glucose (BG) testing and timely evaluation of its effectiveness are important components of hospital inpatient glycemic control programs. We describe a new technology to evaluate inpatient POC-BG testing and report preliminary results of inpatient glycemic control from 10 U.S. hospitals. METHODS: We used the Remote Automated Laboratory System RALS-Tight Glycemic Control Module (TGCM) (Medical Automation Systems, Charlottesville, VA) connected to the RALS-Plus to extract and analyze inpatient POC-BG tests from 10 U.S. hospitals for a 3-month period. POC-BG measurements were evaluated in aggregate from all 10 facilities for intensive care unit (ICU), non-ICU, and ICU + non-ICU combined. RESULTS: A total of 742,154 POC-BGs were analyzed. The combined (ICU + non-ICU) mean POC-BG was 159 mg/dL, compared with 146 mg/dL for the ICU and 164 mg/dL for non-ICU. The proportion of hypoglycemic values (<70 mg/dL) was low at 4%, but the percentage of measurements that would be considered hyperglycemic (>180 mg/dL) was high, with more than 30% of values in the non-ICU and 20% in the ICU being elevated. CONCLUSIONS: POC-BG data can be captured through automated data management software and can support hospital efforts to evaluate and monitor the status of inpatient glycemic control. These preliminary data suggest that there is a need to conduct broad-based efforts to improve inpatient glucose management. Increasing hospital participation in data collection has the potential to create a national benchmarking process for the development of best practices and improved inpatient hyperglycemia management.

Evaluation of the effect of endovascular options on infrarenal abdominal aortic aneurysm repair.

Endovascular devices designed to exclude flow to infrarenal abdominal aortic aneurysms (AAA) were approved by the Food and Drug Administration in the United States in 1999. This action allowed widespread use of this technology for AAA exclusion. The purpose of this report is to examine trends for use of these modalities, rates of rupture of AAA, and to compare results of open AAA repair with endovascular repair. Results were collected for all hospitals, except for Veterans Administration hospitals, by a state-wide repository. Data for the years 1996 through 1998 and 2001 through 2002 were evaluated, and data from 1999 through 2000 were excluded because no separate codes were available to distinguish between open and endovascular repair. The information gathered is based on the All Patient Refined Diagnostic Related Group (APRDRG; 3M, St. Paul, MN). An average of 718 open, elective AAA was performed between 1996 and 1998. This dropped to 503 open repairs from 2001 to 2002 (P < 0.005). During that same interval, 308 endovascular elective AAA repairs were performed, therefore the total rate of elective repair increased by 100. The average rate of ruptured AAA repairs from 1996 to 1998 was 121 per year, and this dropped to 89 from 2001 to 2002 (P < 0.005). The mortality of open AAA repair during the 1996 to 1998 and 2001 to 2002 intervals was unchanged (4.7%). Mortality from endovascular AAA repair between 2001 and 2002 was 1.9 per cent (P = 0.003). Major morbidity was 14.5 per cent for open, elective AAA repair and 6.3 per cent for endovascular elective repair from 2001 to 2002 (P < 0.001). These data suggest that the advent of endovascular AAA repair has contributed to a reduction in the rate of ruptured AAA repairs, an increase in total procedures performed, and a significant decrease in perioperative deaths and major complications when compared with open AAA repair.

Independent risk factors for anemia in cancer patients receiving chemotherapy: results from the European Cancer Anaemia Survey.

OBJECTIVES: To develop a hitherto unavailable risk factor model for accurately predicting anemia development in cancer patients before chemotherapy (CT) administration. METHODS: 2,070 nonanemic patients from the European Cancer Anaemia Survey (ECAS) with hemoglobin (Hb) > or =12 g/dl at enrollment who received their first CT during ECAS and underwent at least two CT cycles were divided randomly into split half (SH) 1 and SH2 (n = 1,035 each). The model was developed on SH1 using logistic regression to simultaneously evaluate predictive factors, and was validated using SH2 and an additional similar subpopulation of 5,901 ECAS patients. Anemia risk values were assigned to the predictive factors and the sum of the predictive factors gave the total anemia risk score; lower-, higher-, and highest-risk cutoff points of the total anemia risk score were determined. RESULTS: Variables ultimately identified as significant predictive factors for anemia were: lower initial Hb (< or =12.9 g/dl in females, and < or =13.4 g/dl in males); having lung or gynecologic cancer versus gastrointestinal (GI)/colorectal cancer; cancer at any other site versus GI/colorectal cancer; treatment with platinum CT, and female gender. CONCLUSION: Using this evidence-based risk model, nonanemic patients who are at the highest risk of developing anemia prior to receiving CT can be identified clinically, allowing appropriate anemia management to be planned.

Carotid endarterectomy: update on the gold standard treatment for carotid stenosis.

Many prospective, randomized clinical trials evaluating the safety and efficacy of carotid endarterectomy (CEA) versus medical management in the prevention of ischemic stroke were performed in the 1990s. Clinical trials are underway that will compare CEA outcomes to carotid stenting; however, relatively few studies have examined the outcomes of modern CEA. The purpose of this report is to examine current outcomes of CEA and evaluate hospital costs and length of stay. Statewide results were collected for all hospitals, except Veterans Administration hospitals, by Virginia Health Information (VHI). Data for the years 1997-2001 were evaluated, and data were based on the All Patient Refined Diagnostic Related Group (APR-DRG; 3M Company). A total of 14,095 CEAs were performed in a 5-year period. The mortality of patients undergoing CEA was 0.5 per cent. The stroke rate was 1 per cent overall and decreased each year of the study. Mean and median lengths of hospital stay were 3 and 2 days, respectively. Length of stay decreased over the course of this study. Mean and median hospital costs were 14,331 dollars and 11,268 dollars. Higher rates of mortality and stroke and higher costs were observed at low-volume hospitals. The need for CEA is substantial. CEA is safe and inexpensive. The data presented here demonstrates continued refinement in CEA, leading to a very low rate of perioperative adverse events, declining lengths of stay, and low hospital costs.

Examination of sex as an independent risk factor for adverse events after carotid endarterectomy.

BACKGROUND: The incidence of adverse events after carotid endarterectomy (CEA) for women compared with men is controversial. This report compares the incidence of perioperative stroke and death in men and women by examining the effect of comorbidities and hospital setting on CEA outcomes. METHODS: All CEAs performed in non-Federal acute-care Virginia hospitals between 1997 and 2001 were reviewed. Patient demographics, comorbidities, and hospital characteristics were compared for possible relationships to perioperative adverse events. RESULTS: A total of 14,095 CEAs were performed in 34 urban and 28 rural hospitals (9 high-volume and 53 low-volume hospitals); 42% were performed on women, and 58% were performed on men. Women experienced a significantly higher stroke rate (1.23%) than men (0.87%; P = .04) with bivariate analysis. However, logistic regression analysis of comorbidities and hospital settings demonstrated that sex was actually not independently related to adverse outcomes in CEA ( P = .08). Preoperative neurologic symptoms could not be evaluated as risk factors for adverse events. Acute coronary ischemia, history of arrhythmia, end-stage renal disease, nonwhite race, advanced age, and low hospital volume were all significantly related to mortality. History of arrhythmia was the only factor that was significantly related to the incidence of stroke. CONCLUSIONS: Logistic regression analysis of comorbidities and hospital setting indicated that female sex is not independently associated with higher mortality or a higher stroke rate during CEA. These data indicate that patients with carotid stenosis frequently have multiple medical problems that need to be carefully examined and controlled before any single patient or hospital factor is designated as significantly related to adverse outcomes.

The aspect ratio (dome/neck) of ruptured and unruptured aneurysms.

OBJECT: In this retrospective study the authors examined the aspect ratio (AR; the maximum dimension of the dome/width of the neck of an aneurysm) and compared the distribution of this ratio in a group of ruptured and unruptured aneurysms. A similar comparison was performed in relation to the maximum dimension of the aneurysm alone. The authors sought to evaluate the utility of these measures for differentiating ruptured and unruptured aneurysms. METHODS: Measurements were made of 774 aneurysms in 532 patients at three medical centers. One hundred twenty-seven patients harbored only unruptured lesions, 290 only ruptured lesions, and 115 both ruptured and unruptured lesions. Cases were included if angiograms were available for measurement and the status of the individual patient's aneurysm(s) was known. The odds of a lesion falling in the ruptured aneurysm group increased with both the lesion's maximum size and the AR. The odds ratio for rupture rose progressively only for the AR. The distribution curves showed that ruptured aneurysms were larger and had greater ARs. The mean size of unruptured aneurysms was 7 mm and that of ruptured ones was 8 mm; the corresponding mean ARs were 1.8 and 3.4, respectively. The odds of rupture were 20-fold greater when the AR was larger than 3.47 compared with an AR less than or equal to 1.38. Only 7% of ruptured aneurysms had an AR less than 1.38 compared with 45% of unruptured lesions. CONCLUSIONS: The AR is probably a useful index to calculate. A high AR might reasonably influence the decision to treat actively an unruptured aneurysm independent of its maximum size. Prospective studies are warranted.