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Background Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. Methods We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. Results Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P Conclusions Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

Cardiac scintigraphy has emerged as a key diagnostic test for transthyretin cardiac amyloidosis (ATTR-CA). However, there are potential limitations and pitfalls in the interpretation of cardiac scintigraphy for ATTR-CA that are worth noting. We present here a series of three cases which illustrate some of these important principles.

Shared decision making (SDM) improves the likelihood that patients will receive care in a manner consistent with their priorities. To facilitate SDM, decision aids (DA) are commonly used, both to prepare a patient before their clinician visit, as well as to facilitate discussion during the visit. However, the relative efficacy of patient-focused or encounter-based DAs on SDM and patient outcomes remains largely unknown. We aim to directly estimate the comparative effectiveness of two DA's on SDM observed in encounters to discuss stroke prevention strategies in patients with atrial fibrillation (AF).The study aims to recruit 1200 adult patients with non-valvular AF who qualify for anticoagulation therapy, and their clinicians who manage stroke prevention strategies, in a 2x2 cluster randomized multi-center trial at six sites. Two DA's were developed as interactive, online, non-linear tools: a patient decision aid (PDA) to be used by patients before the encounter, and an encounter decision aid (EDA) to be used by clinicians with their patients during the encounter. Patients will be randomized to PDA or usual care; clinicians will be randomized to EDA or usual care.Primary outcomes are quality of SDM, patient decision making, and patient knowledge. Secondary outcomes include anticoagulation choice, adherence, and clinical events.This trial is the first randomized, head-to-head comparison of the effects of an EDA versus a PDA on SDM. Our results will help to inform future SDM interventions to improve patients' AF outcomes and experiences with stroke prevention strategies.

Cardiac scintigraphy has emerged as a key diagnostic test for transthyretin cardiac amyloidosis (ATTR-CA). However, there are potential limitations and pitfalls in the interpretation of cardiac scintigraphy for ATTR-CA that are worth noting. We present here a series of three cases which illustrate some of these important principles.

Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning.Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation.Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation.Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

This study uses data from a large academic medical center to estimate the accuracy of the criteria used by Medicare to represent the burden of hospitalizations for heart failure.

Background: Co-management of hospitalized heart failure (HHF) patients by hospitalists and cardiologists may enhance care quality. Aided by an enterprise data warehouse (EDW) screen for HHF patients, we implemented a house surveillance model of heart failure consultation, whereby cardiology consultation and multidisciplinary heart failure intervention was offered for patients meeting criteria. Objective: To analyze the impact of house surveillance for HHF on cardiology consultation rates for patients coding into heart failure diagnosis-related groups (DRGs) on medicine units. Methods: An EDW screen for HHF was deployed, and services with HHF patients were offered cardiology consultation. The intervention was deployed 7/2015; chart review for patients 6 months pre- and post-intervention was conducted to ascertain consultation rate. Results: There were 386 patient discharges from a non-cardiac service with a heart failure DRG. In the six months prior to intervention, 40% of patients had cardiology consultation. This figure rose to 69% post-intervention, a highly statistically significant result. Conclusions: EDW-enabled active surveillance for HHF increases cardiology consultation rate, allowing for multidisciplinary intervention, co-management, and potentially improved outcomes for HHF patients.

Background— Aortic 3-dimensional blood flow was analyzed to investigate altered ascending aorta (AAo) hemodynamics in bicuspid aortic valve (BAV) patients and its association with differences in cusp fusion patterns (right-left, RL versus right-noncoronary, RN) and expression of aortopathy. Methods and Results— Four-dimensional flow MRI measured in vivo 3-dimensional blood flow in the aorta of 75 subjects: BAV patients with aortic dilatation stratified by leaflet fusion pattern (n=15 RL-BAV, mid AAo diameter=39.9±4.4 mm; n=15 RN-BAV, 39.6±7.2 mm); aorta size controls with tricuspid aortic valves (n=30, 41.0±4.4 mm); healthy volunteers (n=15, 24.9±3.0 mm). Aortopathy type (0–3), systolic flow angle, flow displacement, and regional wall shear stress were determined for all subjects. Eccentric outflow jet patterns in BAV patients resulted in elevated regional wall shear stress ( P Conclusions— The presence and type of BAV fusion was associated with changes in regional wall shear stress distribution, systolic flow eccentricity, and expression of BAV aortopathy. Hemodynamic markers suggest a physiological mechanism by which the valve morphology phenotype can influence phenotypes of BAV aortopathy.

Limited data have suggested that an "obesity paradox" exists for mortality and cardiovascular outcomes in patients undergoing coronary artery bypass grafting. Much less is known about the role of the preoperative body mass index (BMI) in patients undergoing valve surgery. We evaluated 2,640 consecutive patients who underwent valve surgery between April 2004 and March 2011. The patients were classified by the World Health Organization standards as "underweight" (BMI 11.5 to 18.4 kg/m(2), n = 61), "normal weight" (BMI 18.5 to 24.9 kg/m(2), n = 865), "overweight" (BMI 25 to 29.9 kg/m(2), n = 1,020), and "obese" (BMI 30 to 60.5 kg/m(2), n = 694). Mortality was ascertained using the Social Security Death Index. Hazard ratios (HRs), adjusted for known preoperative risk factors, were obtained using Cox regression models. The mean follow-up was 31.9 ± 20.5 months. The long-term mortality rate was 1.21, 0.52, 0.32, and 0.44 per 10 years of person-time for underweight, normal, overweight, and obese patients, respectively. Compared to the normal BMI category, overweight patients (adjusted HR 0.60, 95% confidence interval 0.46 to 0.79, p

Background: Multidisciplinary, collaborative care improves outcomes and reduces costs but may unintentionally increase the operational overhead to manage the complexity of increasingly sophisticated teamwork. Objective: To quantify complexity of a standardized discharge planning process by closely examining team continuity, composition, and experience for both explicitly defined, deliberately organized teams and implicitly defined, organically assembled teams. Methods: We examined discharge planning team membership for inpatient cardiology encounters with a length of stay (LOS) greater than 48 hours for a three-year period from 2012-2014 in a large academic medical center. By constructing a co-affiliation graph from transactional EHR data, we determined the frequency of team occurrence and size. We then calculated the shared experience of teammates using a pairwise similarity metric and identified modularity, groups of healthcare personnel with dense connections, within the graph by applying a community detection algorithm. Results: We identified 52,254 transactions for 3,213 encounters with an average LOS of 8.7 days for the time period. The standard discharge planning team accounted for 41,101 (79%) transactions, consisted of 7 team member types, and comprised 709 individual providers performing 36 activities. We identified 569 additional discharge planning team members, consisting of 22 additional provider types. When constrained to only explicitly defined teams, 404 unique teams with an average size of 5.9 members occurred with a mean of 8 (min=1, max=118) shared encounters. When unconstrained, organically assembled teams with no explicit definition occurred 3,209 times (mean=1; min=1, max=3) with an average size of 7.3 members. Single-encounters accounted for 21,107 (62%) of all provider pairs. However, over 50% of all discharges had at least 1 pair sharing over 99 encounters (median=4 pairs). Community detection found 9 modules (range: 12-275 members) among pairs with more than 5 shared encounters. Conclusions: We have shown by deconstructing digital interactions via the EHR, a core group of providers, defined by role and activity, anchor most discharge planning teams. However, the EHR can also identify identify the 20% of teams with constantly recombining membership due to situational care; this can impose overhead when targeting team-wide process improvements, communication strategies, or educational initiatives.

Background: The vulnerable period for hospital readmission begins upon hospital discharge, a complex, interdisciplinary process that necessitates close teamwork for accurate execution of the discharge plan. Thus, better understanding of the quality of teamwork throughout the discharge process may inform strategies to reduce hospital readmissions rates. Novel methods using a network analytics approach to quantify teamwork may better characterize this critical clinical process, facilitate quality improvement (QI), and become an important tool in learning healthcare systems. Methods: We extracted three years of patient, provider, and activity data related to discharge planning for an inpatient cardiology unit from Northwestern Medicine’s Enterprise Data Warehouse. We then created a provider-patient network to identify providers who shared patients and calculated readmissions rates for provider pairs. Using these data, we calculated a novel parameter, the Shared Positive Outcome Ratio (SPOR), an objective composite measure that quantifies the concentration of positive outcomes over a set of shared patients. To identify significant low-readmission and high-readmission collaborative relationships, we compared the observed network to 1000 sample networks containing randomized readmission values. Results: We identified 133,927 actions distributed among 38 discharge activity types performed during 13,720 patient encounters. The collaborative network was composed of 1,542 providers including 503 nurses, 432 residents, 207 physicians, 111 physical and occupational therapists, 59 medical students, 32 dieticians, and other medical and administrative staff. The average encounter involved 4 providers performing 10 discharge-related actions. After pruning the network to include only provider pairs with 6 or more shared patients, we found that 6% of collaborative interactions had a significantly low SPOR, indicating lower than expected readmission rates. Conversely, 12% of collaborative interactions had a significantly high SPOR, indicating higher than expected readmission rates. We identified 21 providers who had a low SPOR for a significant percentage of their collaborations, indicating potential top performers in the teamwork domain. Conclusions: Readmission rates appear to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform various QI strategies, such as optimizing team staffing, identifying high-performing teams who can share their best practices, and redesigning discharge care processes. Ongoing work on this model is focused on accurately risk-adjusting outcomes, which will increase the robustness of this method.

Background: Multidisciplinary transitional care teams represent a model for reducing heart failure readmissions. Within this context, early identification of patients hospitalized with acute decompensated heart failure (ADHF) permits meaningful transitional care plan development. Improving the efficiency of early identification of the higher risk ADHF patient represents an area not well studied in hospitalized heart failure (HF). Objective: To validate the sensitivity and specificity of an enterprise data warehouse (EDW)-based strategy for early identification of patients with ADHF. Methods: An EDW query was constructed to identify patients with ADHF based on clinical and diagnosis-related parameters, including BNP level and administration of intravenous diuretics. The EDW query was run daily; expert clinicians verified the diagnosis of ADHF based on comprehensive chart review. This classification was used to determine specificity of the query for ADHF. We computed the sensitivity of the EDW-based approach by matching query results to heart failure diagnosis related group (DRG) data and primary discharge diagnosis data from separate hospital systems. Results: During the study period of 70 days, a total of 2354 charts were screened (33.6 charts per day). A total of 410 patients were identified by chart review as having heart failure requiring active management, for a specificity of 17.4%. Sensitivity was computed using both heart failure DRG data and primary discharge diagnosis data. Of the 114 patients discharged with a heart failure DRG (291, 292, or 293), all 114 were detected a priori by the admission EDW screen, for a sensitivity of 100%. A similar analysis conducted using HF principal diagnoses, which includes cardiac surgery-related admissions, yielded a sensitivity of 97.2%. Conclusions: EDW-based screening of patients based on simple clinical parameters early in the hospitalization is highly sensitive for detection of ADHF hospitalizations, but specificity is low. Brief chart review by expert clinicians is rapid, and identifies a specific cohort of patients that can be targeted for multidisciplinary HF transitional care. A better delineation of risk has broad outpatient workflow implications. Ongoing process improvements will demonstrate if early identification of at-risk patients yields significant reduction in HF readmissions.

Objectives: Failure Mode and Effects Analysis (FMEA) is a frequently-used approach for prospective risk assessment and quality improvement in healthcare, particularly for high-risk care processes such as hospital discharge planning. Our goal was to evaluate whether secondary use of metadata collected by the electronic health record (EHR) during daily practice can inform assembly of a comprehensive FMEA team by showing: 1) discrepancies between expected and observed process activities and individuals involved, and 2) the presence of individuals who may be appropriate to include in an FMEA based on their variable familiarity with a process. Methods: We extracted discharge planning data for an inpatient cardiology unit from the Enterprise Data Warehouse (EDW) and compared it to a hand-drawn map (HDM) indicating clinicians’ understanding of discharge activities and providers expected to complete each activity. We assessed the presence of providers highly experienced in the process, the diversity of involved disciplines, and the accuracy of the HDM compared to observation from EDW data. Findings: Over 500 providers completed nearly 35,000 discharge-related activities across 18 activity types over 2,000 encounters. Experience was skewed such that 90% (510 of 569) of providers completed between 0 and 99 activities while the remaining 10% (59 of 569) performed up to 1,200 activities. Frequent performers completed similar activities to their peers, but did so as many as 12 times more frequently than average for their discipline. Expectation of who performed an activity closely matched observation for 11 discipline-specific activities, such as case management assessment. However, providers from up to 10 different disciplines performed the remaining 7 activities, such as scheduling a follow-up visit or ordering a therapy consult. Overall, 35% (12,183 of 34,939) of activities were performed by an unexpected provider. Conclusions: Analyzing metadata from EHRs is a novel method to inform FMEA of high-risk processes. This study provides a framework for assessing process activities and the providers involved. In the discharge planning process, there appears to be significant discrepancy between clinicians’ understanding and the actual discharge process and team, which suggests the presence of providers who could be overlooked during typical FMEA team construction. This methodology can empirically enrich the FMEA team and highlight quality improvement target areas.

Background: Heart failure (HF) readmissions remain a major driver of cost and health care utilization. Timely follow-up of patients post-discharge represents an evidence-based intervention proven to reduce readmission rates. A previously unexplored characteristic of hospital discharges is variability in discharge caseload. This variability thwarts the timeliness of follow-up, negates the benefit of transition care planning and may lead to a higher risk of HF readmissions. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to determine if caseload can be determined more precisely in a manner that sufficiently accommodates HF discharge variability. Objective: To analyze the impact of hospital discharge rate variability on outpatient clinic capacity needs using HF hospitalization discharge data and operations management approaches. Methods: Higher risk hospitalizations requiring active transitional care heart failure management were detected using an enterprise data warehouse-supported process over the study period. Queuing theory approaches were used to model the impact of HF discharge clinic capacity on wait time to an appointment. Discharge clinic was modeled as a single 7-day follow-up appointment, with an acceptable scheduling window of 5 to 9 days post-discharge. Results: During the study period of 100 days, 566 HF discharges were made, for a median of 5.66 discharges daily, or 39.6 discharges weekly. The distribution of daily discharges was skewed rightward (mode = 3, range = 0 to 18, standard deviation = 3.3, coefficient of variation = 0.58). Current clinic design: Providing one discharge slot for every hospital discharge (100% utilization) leads to an average wait of 18.3 days prior to an appointment, with only 31.9% of appointments scheduled within 7 days, and 38.9% of appointments scheduled within 9 days. Clinic re-design (queuing theory): Providing five extra discharge appointment slots per week (88% utilization or 13.6% excess capacity) reduces the expected waiting period to 1.1 days, with 99.8% of patients seen within 7 days, and virtually all patients seen within 9 days of discharge. Conclusions: Deployment of queuing theory allows for a more precise quantification of needed clinical capacity to accomplish appropriate HF follow-up with a reasonable degree of certainty. Our simplified model demonstrates that variability in hospital discharge rates leads to excessive clinic wait times in the absence of a modest capacity buffer and consequently exposes patients to a higher risk of HF readmission. We show using single center HF discharge data that a 10-15% increase in capacity is needed to ensure an adequate follow-up service level. Ongoing process of care work will demonstrate if optimization of clinic load yields a significant reduction in HF readmissions.

ObjectiveEarly readmission in patients hospitalized for medical congestive heart failure is common, expensive, and associated with a worse late survival. Our objective was to compare late survival in patients’ readmission for congestive heart failure with readmission for other causes in patients undergoing cardiac surgery.MethodsOf 3654 consecutive patients undergoing cardiac surgery at a single institution between April 2004 and June 2010, 3492 (96%) were discharged from the hospital before 30 days and analyzed. Survival curves by readmission reason were compared using the log-rank test. Multivariable analyses adjusted for patient demographics, known preoperative cardiac risk factors, and surgical characteristics.ResultsThe readmission rate at 30 days was 13% (465/3492): 23% for arrhythmias/heart block, 12% for congestive heart failure, 40% for surgery related causes, 14% for infection, and 11% for noncardiac causes. Independent risk factors for readmission include age, gender, congestive heart failure, and cardiopulmonary bypass time. Eight percent (268/3492) of discharged patients died within the 6-year study: 14% in the readmission group versus 7% in the nonreadmission group (P

Background Recent data have suggested that statins are associated with reduced early mortality and cardiovascular events after valvular heart surgery. The midterm effects of preoperative statin therapy in the setting of valvular heart surgery are presently unclear. Methods All patients (n = 2,120) who underwent a valvular procedure between April 2004 and April 2010 were identified. Patients undergoing concomitant coronary artery bypass graft surgery were excluded. Two patient groups were studied: those who received preoperative statin therapy (n = 663; 31.3%) and those who did not (n = 1,457; 68.7%). Propensity score matching resulted in 381 matched pairs, thus addressing baseline risk imbalances. Thirty-day mortality, readmission rates, postoperative complications, and length of stay were analyzed. Late survival was ascertained by the Social Security Death Index. Results In the matched group, 30-day mortality was 1.3% (5 of 381) for statin-treated patients versus 4.2% (16 of 381) for statin-untreated patients ( p = 0.03). After a mean follow-up of 33 ± 23 months, statin therapy was associated with significantly reduced mortality (hazard ratio 0.63, 95% confidence interval: 0.43 to 0.93, p = 0.019), independent of known cardiac risk factors. Weighted log rank tests revealed that the mortality difference between the two cohorts occurred early after surgery ( p = 0.015). Statin users were less likely to be readmitted to the intensive care unit (3.4% versus 8.1%, p = 0.01). There were no other significant differences between the two groups in terms of postoperative complications and length of stay. Conclusions Preoperative statin administration is associated with early reductions in mortality among patients undergoing isolated valvular heart surgery, leading to improved late survival. Future prospective analyses are warranted to optimize statin therapy in this patient population.

Methods Right-left coronary leaflet fusion BAV patients on b-blockers (BB+) (n = 30, M:F = 23:7, age: 46 ± 14 years) and not on b-blockers (BB-) (n=30, M:F = 23:7, age: 46 ± 13 years) and healthy controls (n=15, age:43±11 years) underwent time-resolved, 3D phase contrast (4D flow) MRI. Patient groups were matched by systolic blood pressure (SBP), degree of aortic stenosis (AS), and AAo diameter (3.9 ± 0.7 vs. 3.9 ± 0.6 cm, p = 0.70). A 3D segmentation of the thoracic aorta was performed (MIMICS, Materlise, Belgium). Systolic 3D WSS was calculated in the thoracic aorta from 4D flow velocity acquisition and a sagittal maximum intensity projection (MIP) of WSS was generated.

We thank Hope and colleagues for their interesting and pertinent letter on our recent study in patients with bicuspid aortic valve (BAV), in which the relationship between bicuspid aortic valve morphology, the resulting flow patterns, and the aortopathy phenotype was examined.1 To confirm their initial comment, indeed, the ascending aortic flow displacement parameter was not normalized to the individual vessel diameter. We agree with Hope et al that flow displacement should ideally be normalized by the lumen diameter to account for interindividual differences in aortic geometry. Nonetheless, aortic diameters in the aorta size control group and the BAV patient groups in our study cohort were similar (4.1 versus 4.0 cm, respectively). Thus, the process of normalization did not affect the …

Background Beta-blockers are the recommended medical treatment for slowing ascending aorta (AAo) dilation in patients with bicuspid aortic valve (BAV). Wall shear stress (WSS) has been shown to promote endothelial cell dysfunction [1] and AAo WSS may play a role in aortic aneurysm growth. Time-resolved 3D phase contrast (4D flow) MRI allows for the quantification of 3D WSS regionally in the thoracic aorta. The aim of this study was to assess changes in thoracic aorta WSS associated with b-blocker therapy in BAV patients.

We present pre- and postoperative imaging findings in a 29-year-old man with a bicuspid aortic valve (BAV) with fusion of the right and left coronary leaflets and a 6.1 cm × 6.3 cm ascending aorta aneurysm (Figure 1). The patient underwent a valve-sparing aortic root replacement with a 34-mm Dacron graft and BAV repair with resection of the raphe of the fused leaflet and leaflet shortening of the noncoronary cusp. Figure 1. Sagital ( A ) and axial ( B ) preoperative MRI demonstrating a 6.1×6.3 cm ascending aorta (AAo) aneurysm. To better understand the preoperative flow characteristics in the large aneurysm and the impact of surgical repair and reconstruction on thoracic aorta hemodynamics, time-resolved 3-dimensional phase contrast (4D flow) MRI was performed before and after surgery. The MRI examinations (pre: 3.0 T Skyra, post: 1.5 T Avanto, Siemens, Germany) included both 4D flow MRI (velocity sensitivity pre:150 cm/s, post: 250 cm/s, spatial/temporal resolution pre: 3.14×2.13×3.2 mm3/39 ms, post: 2.88×2.13×2.15 mm3/38ms) and dynamic 2-D cine imaging of the heart (steady-state free precession, spatial/temporal resolution pre: 1.25×1.25×6 mm3/29 ms post:1.8×1.8×6 mm3/39 ms). The study was approved by the local institutional review board, and informed consent was obtained from the patient. Blood flow patterns within the thoracic aorta were visualized using time-resolved 3D pathlines to illustrate complex blood flow dynamics over the entire cardiac cycle (EnSight, CEI). In addition, 3D velocity streamlines drawn tangent to the time-resolved velocity field were used to illustrate instantaneous flow dynamics in the thoracic aorta. Forward and …

Ejection fraction (EF) and end-systolic volume index (ESVI) are established predictors of outcomes following ST-segment elevation myocardial infarction (STEMI). We sought to assess the relative impact of infarct size, EF and ESVI on clinical outcomes and left ventricular (LV) remodelling.Prospective cohort study.Academic hospital in Chicago, USA.122 patients with STEMI following acute percutaneous reperfusion.Death, recurrent myocardial infarction (MI) and heart failure.Cardiac magnetic resonance imaging was obtained within 1 week following STEMI in 122 subjects. ESVI, EF and infarct size were tested for the association with outcomes over 2 years in 113 subjects, and a repeat study was obtained 4 months later to assess LV remodelling in 91 subjects.Acute infarct size correlated linearly with the initial ESVI (r = 0.69, p0.001), end-diastolic volume index (EDVI) (r = 0.42, p0.001) and EF (r = -0.75, p0.001). All were independently associated with outcomes (one death, one recurrent MI and 16 heart failure admissions). However, infarct size was the only significant predictor of adverse outcomes (p0.05) by multivariate analysis. The smallest infarct size tertile had an increased EF (49% (SD 8%) to 53% (6%); p = 0.002) and unchanged EDVI (p = 0.7). In contrast, subjects with the largest infarct tertile also had improved EF (32% (9%) to 36% (11%); p = 0.002) at the expense of a dramatic increase in EDVI (86 (19) to 95 (21) ml/m(2); p = 0.005).Infarct size, EF and ESVI can predict the development of future cardiac events. Acute infarct size, which is independent of LV stunning and loading, directly relates to LV remodelling and is a stronger predictor of future events than measures of LV systolic performance.

Previous studies have suggested that ST-segment depression with adenosine myocardial perfusion imaging (MPI) may be a marker of significant coronary artery disease (CAD). It is unclear if the significance of ST depression differs between men and women. We investigated the diagnostic accuracy of ST-segment depression with adenosine radionuclide MPI as a marker of significant CAD in men and women. Consecutive patients who had angina or suspected CAD and underwent an adenosine stress test and subsequent angiography were retrospectively analyzed. The inclusion criteria were met by 959 patients. Mean age was 64 +/- 11 years, and 43% were women. ST depression occurred in 7.6% of the cohort and more often in women (64% women vs 36% men, p0.001). Among men and women, patients with ST-segment depression had a significantly higher peak rate-pressure product, more chest pain, and a higher ejection fraction in response to the adenosine infusion compared with those without ST-segment depression. ST-segment depression occurred more often in the presence of stenotic lesions (/=50% and/=70%), and left main or 3-vessel disease, regardless of gender. Transient ischemic dilation occurred more often in men with ST-segment depression. The logistic regression analysis demonstrated that the only significant predictors of left main or 3-vessel CAD were gender, an abnormal result on MPI, transient ischemic dilation, and ST-segment depression. In conclusion, ST-segment depression during adenosine MPI is an important marker of angiographically significant CAD in men and women. The presence of ST-segment depression is associated with left main disease and 3-vessel CAD.

We have recently shown that glucose and glucosamine regulate the transcription of transforming growth factor-alpha (TGF alpha) in rat aortic smooth muscle (RASM) cells. Based on the increased potency of glucosamine compared to glucose, we hypothesized that stimulation of TGF alpha transcription by glucose is mediated through the hexosamine biosynthesis pathway. The yeast cDNA for the rate-limiting enzyme of this pathway, glutamine:fructose-6-phosphate amidotransferase (GFA), was therefore expressed in RASM cells. GFA-transfected cells showed an increase in GFA activity, exhibiting a 2.2-fold increase in the synthesis of glucosamine-6-phosphate, the first product of the hexosamine biosynthetic pathway. To test the effect of GFA overexpression on TGF alpha transcriptional activity, cells were transiently cotransfected with GFA along with a reporter plasmid containing the firefly luciferase gene under control of the TGF alpha promoter. GFA-transfected cells exhibited a glucose-dependent 2-fold increase in TGF alpha activity compared to control cells. Maximal stimulation of TGF alpha-luciferase activity by glucosamine, however, was equivalent in GFA-and control-transfected cells, confirming that the stimulation observed by both agents operated through the same pathway. This increase in TGF alpha activity was inhibited (85% at 0.5 mM glucose and 69% at 30 mM glucose) by the glutamine analog and inhibitor of GFA, 6-diazo-5-oxonorleucine (10 microM). Control studies confirmed that the increased TGF alpha-luciferase activity in the GFA-expressing cells was not an artifact of altered growth, survival, or transfection efficiency.(ABSTRACT TRUNCATED AT 250 WORDS)