Quintile Of Hospitals Research Articles

Variation and Impact of Multiple Complications on Failure to Rescue After Inpatient Surgery.

To examine the extent to which multiple, sequential complications impacts variation in institutional postoperative mortality rates. Failure to rescue (FTR) has been proposed as an underlying factor in hospital variation in surgical mortality. However, little is currently known about hospital variation in FTR after multiple complications or the contribution of sequential complications to variation. Retrospective cohort study of 266,101 patients within the Veterans Affairs Surgical Quality Improvement Program (2000-2014) who underwent a subset of high-mortality inpatient general, vascular, or thoracic procedures. The association between number of postoperative complications (0, 1, 2, or ≥3) and 30-day mortality across quintiles of hospital risk-adjusted mortality was evaluated with multivariable, multilevel mixed-effects models. Among patients who had a complication, over half (60.9%) had 1, but those with more than 1 accounted for the majority of the deaths (63.1%). Across hospital quintiles, there were no differences in complications (23.5% very low mortality vs 23.6% very high mortality; trend test P = 0.15). FTR increased significantly (12.0% vs 18.1%; trend test P < 0.001) with an incremental impact as complications accrued (6.7% 1 complication vs 26.1% ≥3, lowest quintile; 11.7% 1 complication vs 33.0% ≥3, highest quintile). However, the risk of FTR associated with increasing complications remained relatively constant across hospital quintiles and was not explained by differences in patients presenting with multiple complications on the index complicated day. FTR occurs predominantly among patients who have more than 1 complication with a dose-response relationship as complications accrue. As this dose-response relationship is observed across hospitals, surgical quality improvement efforts may benefit by shifting focus to broader interventions designed to prevent subsequent complications at all hospitals.

Complications and Failure to Rescue After Inpatient Noncardiac Surgery in the Veterans Affairs Health System

ImportanceThe quality of surgical care in the Veterans Health Administration improved markedly in the 1990s after implementation of the Veterans Affairs (VA) National Surgical Quality Improvement Program (now called the VA Surgical Quality Improvement Program). Although there have been many recent evaluations of surgical care in the private sector, to date, a contemporary global evaluation has not been performed within the VA health system.ObjectiveTo provide a contemporaneous report of noncardiac postoperative outcomes in the VA health system during the past 15 years.Design, Setting, and ParticipantsA retrospective cohort study was conducted using data from the VA Surgical Quality Improvement Program among veterans who underwent inpatient general, vascular, thoracic, genitourinary, neurosurgical, orthopedic, or spine surgery from October 1, 1999, through September 30, 2014.Main Outcomes and MeasuresRates of 30-day morbidity, mortality, and failure to rescue (FTR) over time.ResultsAmong 704 901 patients (mean [SD] age, 63.7 [11.8] years; 676 750 [96%] male) undergoing noncardiac surgical procedures at 143 hospitals, complications occurred in 97 836 patients (13.9%), major complications occurred in 66 816 (9.5%), FTR occurred in 12 648 of the 97 836 patients with complications (12.9%), FTR after major complications occurred in 12 223 of the 66 816 patients with major complications (18.3%), and 18 924 patients (2.7%) died within 30 days of surgery. There were significant decreases from 2000 to 2014 in morbidity (8202 of 59 421 [13.8%] vs 3368 of 32 785 [10.3%]), major complications (5832 of 59 421 [9.8%] vs 2284 of 32 785 [7%]), FTR (1445 of 8202 [17.6%] vs 351 of 3368 [10.4%]), and FTR after major complications (1388 of 5832 [23.8%] vs 343 of 2284 [15%]) (trend test, P < .001 for all). Although there were no clinically meaningful differences in rates of complications and major complications across hospital risk-adjusted mortality quintiles (any complications: lowest quintile, 20 945 of 147 721 [14.2%] vs highest quintile, 18 938 of 135 557 [14%]; major complications: lowest quintile, 14 044 of 147 721 [9.5%] vs highest quintile, 12 881 of 135 557 [9.5%]), FTR rates (any complications: lowest quintile, 2249 of 20 945 [10.7%] vs highest quintile, 2769 of 18 938 [14.6%]; major complications: lowest quintile, 2161 of 14 044 [15.4%] vs highest quintile, 2663 of 12 881 [20.7%]) were significantly higher with increasing quintile (P < .001). However, across hospital quintiles, there were significant decreases in morbidity (20.6%-29.9% decrease; trend test, P < .001 for all) and FTR (29.2%-50.6% decrease; trend test, P < .001 for all) during the study period. After hierarchical modeling, the odds of postoperative mortality, FTR, and FTR after a major complication were approximately 40% to 50% lower in the most recent study year compared with 15 years ago (P < .001 for all).Conclusions and RelevanceFor the past 15 years, morbidity, mortality, and FTR have improved within the VA health system. Other integrated health systems providing a high volume of surgical care for their enrollees may benefit by critically evaluating the system-level approaches of the VA health system to surgical quality improvement.

Hospitals with the highest intensive care utilization provide lower quality pneumonia care to the elderly.

Quality of care for patients admitted with pneumonia varies across hospitals, but causes of this variation are poorly understood. Whether hospitals with high ICU utilization for patients with pneumonia provide better quality care is unknown. We sought to investigate the relationship between a hospital's ICU admission rate for elderly patients with pneumonia and the quality of care it provided to patients with pneumonia. Retrospective cohort study. Two thousand eight hundred twelve U.S. hospitals. Elderly (age≥65 years) fee-for-service Medicare beneficiaries with either a (1) principal diagnosis of pneumonia or (2) principal diagnosis of sepsis or respiratory failure and secondary diagnosis of pneumonia in 2008. None. We grouped hospitals into quintiles based on ICU admission rates for pneumonia. We compared rates of failure to deliver pneumonia processes of care (calculated as 100-adherence rate), 30-day mortality, hospital readmissions, and Medicare spending across hospital quintile. After controlling for other hospital characteristics, hospitals in the highest quintile more often failed to deliver pneumonia process measures, including appropriate initial antibiotics (13.0% vs 10.7%; p<0.001), and pneumococcal vaccination (15.0% vs 13.3%; p=0.03) compared with hospitals in quintiles 1-4. Hospitals in the highest quintile of ICU admission rate for pneumonia also had higher 30-day mortality, 30-day hospital readmission rates, and hospital spending per patient than other hospitals. Quality of care was lower among hospitals with the highest rates of ICU admission for elderly patients with pneumonia; such hospitals were less likely to deliver pneumonia processes of care and had worse outcomes for patients with pneumonia. High pneumonia-specific ICU admission rates for elderly patients identify a group of hospitals that may deliver inefficient and poor-quality pneumonia care and may benefit from interventions to improve care delivery.

Variation in Inpatient Consultation Among Older Adults in the United States.

Differences among hospitals in the use of inpatient consultation may contribute to variation in outcomes and costs for hospitalized patients, but basic epidemiologic data on consultations nationally are lacking. The purpose of the study was to identify physician, hospital, and geographic factors that explain variation in rates of inpatient consultation. This was a retrospective observational study. This work included 3,118,080 admissions of Medicare patients to 4,501 U.S. hospitals in 2009 and 2010. The primary outcome measured was number of consultations conducted during the hospitalization, summarized at the hospital level as the number of consultations per 1,000 Medicare admissions, or "consultation density." Consultations occurred 2.6 times per admission on average. Among non-critical access hospitals, use of consultation varied 3.6-fold across quintiles of hospitals (933 versus 3,390 consultations per 1,000 admissions, lowest versus highest quintiles, p < 0.001). Sicker patients received greater intensity of consultation (rate ratio [RR] 1.18, 95% CI 1.17-1.18 for patients admitted to ICU; and RR 1.19, 95% CI 1.18-1.20 for patients who died). However, even after controlling for patient-level factors, hospital characteristics also predicted differences in rates of consultation. For example, hospital size (large versus small, RR 1.31, 95% CI 1.25-1.37), rural location (rural versus urban, RR 0.78, CI 95% 0.76-0.80), ownership status (public versus not-for-profit, RR 0.94, 95% CI 0.91-0.97), and geographic quadrant (Northeast versus West, RR 1.17, 95% CI 1.12-1.21) all influenced the intensity of consultation use. Hospitals exhibit marked variation in the number of consultations per admission in ways not fully explained by patient characteristics. Hospital "consultation density" may constitute an important focus for monitoring resource use for hospitals or health systems.

Patients' Perspectives of Care and Surgical Outcomes in Michigan

To determine the relationship between postoperative morbidity and mortality and patients' perspectives of care. Priorities in health care quality research are shifting to place greater emphasis on patient-centered outcomes. Whether patients' perspectives of care correlate with surgical outcomes remains unclear. Retrospective cohort study. Using data from the Michigan Surgical Quality Collaborative clinical registry (2008-2012), we identified 41,833 patients undergoing major elective general or vascular surgery. Our exposure variables were the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Total and Base Scores derived from the Hospital Value-Based Purchasing Patient Experience of Care Domain. Using multilevel, mixed-effects logistic regression models, we adjusted hospitals' rates of morbidity and mortality for patient comorbidities and case mix. We stratified reporting of outcomes by quintiles of hospitals' Total and Base Scores. Risk-adjusted morbidity (13.6%-28.6%) varied widely across hospitals. There were no significant differences in risk-adjusted morbidity rates between hospitals with the lowest and highest HCAHPS Total Scores (24.5% vs 20.2%, P = 0.312). The HCAHPS Base Score, which quantifies sustained achievement or improvement in patients' perspectives of care, was not associated with a reduction in postoperative morbidity over the study period despite an overall decrease of 2.5% for all centers. We observed a similar relationship between HCAHPS Total and Base Scores and postoperative mortality. Patients' perspectives of care do not correlate with the incidence of morbidity and mortality after major surgery. Improving patients' perspectives and objective outcomes may require separate initiatives for surgeons in Michigan.

Reliability of Surgical Outcomes for Predicting Future Hospital Performance

Because of small sample sizes and low event rates, risk-adjusted surgical outcomes often do not meet reliability benchmarks for distinguishing hospital performance. Nonetheless, it is unclear whether these measures may still be useful for predicting future hospital surgical performance. We used national Medicare data to analyze patients undergoing colectomy from 2007 to 2010 (n=462,959 patients). We first quantified 2007-2008 outcome reliability (ability to differentiate quality differences) and ranked hospitals based on their 2007-2008 risk-adjusted outcome rates. To assess the ability of adjusted outcomes to predict true performance, we evaluated future (2009-2010) outcomes across quintiles of past performance. We then systematically sampled 2007-2008 cases to evaluate performance prediction when hospitals' past performance was measured with progressively lower reliability levels. Outcomes in 2007-2008 were good predictors of outcomes in the next 2 years (2009-2010), but predictive strength depended upon reliability. With progressive sampling of 2007-2008 caseloads, outcome reliability and predictive strength decreased. With 100% sampling of 2007-2008 caseloads, the worst versus best hospital quintile based on past performance had 1.52 [95% confidence interval (CI), 1.44-1.60] times the odds of mortality and 1.50 (95% CI, 1.44-1.56) times the odds of complications in 2009-2010. With 10% sampling, outcome reliability was well below commonly accepted benchmarks, but the worst quintile of hospitals in 2007-2008 still had 1.12 (95% CI, 1.06-1.19) times the odds of mortality and 1.16 (95% CI, 1.11-1.21) times the odds of complications in 2009-2010 compared with the best quintile of hospitals. Even at very low reliability levels, risk-adjusted outcome measures may distinguish best and worst hospitals' surgical performance. This study suggests that commonly accepted reliability thresholds may be too high, especially in the context of selective referral.

Ranking Trauma Center Quality

To explore whether trauma center quality metrics based on historical data can reliably predict future trauma center performance. The goal of the American College of Surgeons Trauma Quality Improvement Program is to create a new paradigm in which high-quality trauma centers can serve as learning laboratories to identify best practices. This approach assumes that trauma quality reporting can reliably identify high-quality centers using historical data. We performed a retrospective observational study on 122,408 patients in 22 level I and level II trauma centers in Pennsylvania. We tested the ability of the Trauma Mortality Prediction Model to predict future hospital performance based on historical data. Patients admitted to the lowest performance hospital quintile had a 2-fold higher odds of mortality than patients admitted to the best performance hospital quintile using either 2-year-old data [adjusted odds ratio (AOR): 2.11; 95% confidence interval (CI): 1.36-3.27; P < 0.001] or 3-year-old data (AOR: 2.12; 95% CI: 1.34-3.21; P < 0.001). There was a trend toward increased mortality using 5-year-old data (AOR: 1.70; 95% CI: 0.98-2.95; P = 0.059). The correlation between hospital observed-to-expected mortality ratios in 2009 and 2007 demonstrated moderate agreement (intraclass correlation coefficient = 0.56; 95% CI: 0.22-0.77). The intraclass correlation coefficients for observed-to-expected mortality ratios obtained using 2009 data and 3-, 4-, or 5-year-old data were not significantly different from zero. Trauma center quality based on historical data is associated with subsequent patient outcomes. Patients currently admitted to trauma centers that are classified as low-quality centers using 2- to 5-year-old data are more likely to die than patients admitted to high-quality centers. However, although the future performance of individual trauma centers can be predicted using performance metrics based on 2-year-old data, the performance of individual centers cannot be predicted using data that are 3 years or older.

Composite Quality Measures for Common Inpatient Medical Conditions

Public reporting on quality aims to help patients select better hospitals. However, individual quality measures are suboptimal in identifying superior and inferior hospitals based on outcome performance. To combine structure, process, and outcome measures into an empirically derived composite quality measure for heart failure (HF), acute myocardial infarction (AMI), and pneumonia (PNA). To assess how well the composite measure predicts future high and low performers, and explains variance in future hospital mortality. Using national Medicare data, we created a cohort of older patients treated at an acute care hospital for HF (n=1,203,595), AMI (n=625,595), or PNA (n=1,234,299). We ranked hospitals on the basis of their July 2005 to June 2008 performance on the composite. We then estimated the odds of future (July to December 2009) 30-day, risk-adjusted mortality at the worst versus best quintile of hospitals. We repeated this analysis using 2005-2008 performance on existing quality indicators, including mortality. The composite (vs. Hospital Compare) explained 68% (vs. 39%) of variation in future AMI mortality rates. In 2009, if an AMI patient had chosen a hospital in the worst versus best quintile of performance using 2005-2008 composite (vs. Hospital Compare) rankings, he or she would have had 1.61 (vs. 1.39) times the odds of dying in 30 days (P-value for difference <0.001). Results were similar for HF and PNA. Composite measures of quality for HF, AMI, and PNA performed better than existing measures at explaining variation in future mortality and predicting future high and low performers.

Trends in the Hospitalization of Ischemic Stroke in the United States, 1998–2007

The late 1990s/early 2000s was a time of change in both the prevention and acute care of ischemic stroke, with primary prevention driven by increased utilization of antihypertensive, antiplatelet, anticoagulation, and lipid-lowering agents. To examine whether ischemic stroke hospitalization rates and outcomes in the United States have changed. We retrospectively identified 894 169 hospitalizations with a primary diagnosis of ischemic stroke from 1 January 1998 through to 31 December 2007 in the Nationwide Inpatient Sample, the largest all-payer healthcare database in the United States. Annual, national case estimates were combined with US Census data to derive age-adjusted and age-specific population hospitalization rates. Temporal trends were tested using linear regression. From 1998 through 2007, there were an estimated 4 382 336 ischemic stroke hospitalizations in the United States. Overall, the age-adjusted rate of ischemic stroke hospitalization decreased from 184 to 128 per 100 000 (P < 0·0001). Age-specific rates decreased among those 55+ years old (P < 0·0001), but increased among those 25-34 and 35-44 years old (P < 0·001 and P < 0·0001, respectively). Rates among those <25 and 45-54 years old were unchanged. In-hospital mortality decreased from 7·0% (standard error 0·1) to 5·4% (standard error 0·1) (P < 0·0001). Case proportion at the highest quintile of hospitals by annual caseload increased from 54·0% (standard error 2·1) to 61·8% (standard error 2·0) (P < 0·0001). Mean adjusted hospitalization costs increased from $9273 (standard deviation 199) to $10 524 (standard deviation 77) (P < 0·0001). In 1998 through to 2007, the overall rate of ischemic stroke hospitalization in the United States decreased. However, rates among young adults increased. In-hospital mortality rates decreased over the study period.

Hospital Volume and Failure to Rescue With High-risk Surgery

Although the relationship between surgical volume and mortality is well established, the mechanisms underlying these associations remain uncertain. We sought to determine whether increased mortality at low-volume centers was due to higher complication rates or less success in rescuing patients from complications. Using 2005 to 2007 Medicare data, we identified patients undergoing 3 high-risk cancer operations: gastrectomy, pancreatectomy, and esophagectomy. We first ranked hospitals according to their procedural volume for these operations and divided them into 5 equal groups (quintiles) based on procedure volume cutoffs that most closely resulted in an equal distribution of patients through the quintiles. We then compared the incidence of major complications and "failure to rescue" (ie, case fatality among patients with complications) across hospital quintiles. We performed this analysis for all operations combined and for each operation individually. With all 3 operations combined, failure to rescue had a much stronger relationship to hospital volume than postoperative complications. Very low-volume (lowest quintile) hospitals had only slightly higher complications rates (42.7% vs. 38.9%; odds ratio 1.17, 95% confidence interval, 1.02-1.33), but markedly higher failure-to-rescue rates (30.3% vs. 13.1%; odds ratio 2.89, 95% confidence interval, 2.40-3.48) compared with very high-volume hospitals (highest quintile). These relationships also held true for individual operations. For example, patients undergoing pancreatectomy at very low-volume hospitals were 1.7 times more likely to have a major complication than those at very high-volume hospitals (38.3% vs. 27.7%, P<0.05), but 3.2 times more likely to die once those complications had occurred (26.0% vs. 9.9%, P<0.05). Differences in mortality between high and low-volume hospitals are not associated with large differences in complication rates. Instead, these differences seem to be associated with the ability of a hospital to effectively rescue patients from complications. Strategies focusing on the timely recognition and management of complications once they occur may be essential to improving outcomes at low-volume hospitals.

Comparison of Composite Measure Methodologies for Rewarding Quality of Care

Composite indices of health care performance are an aggregation of underlying individual performance measures and are increasingly being used to rank hospitals. We sought to conduct an observational analysis to determine the influence of the opportunity-based and all-or-none composite performance measures on hospital rankings. We examined 194 245 patients hospitalized with acute myocardial infarction between July 2006 and June 2009 from 334 hospitals participating in the Get With The Guidelines--Coronary Artery Disease (GWTG-CAD) quality improvement program. We analyzed hospital opportunity-based and all-or-none composite scores and 30-day risk-standardized all-cause mortality and readmission rates. We found that the median calculated opportunity-based score for these hospitals was 95.5 (interquartile range, 90.4, 98.0). The median all-or-none score was 88.9 (interquartile range, 79.7, 94.4). The 2 scoring methods were significantly correlated with one another (r=0.98, P<0.001). Rankings generated by the two methods were significantly correlated (r=0.93, P<0.001). The two methods had a modest correlation with the 30-day risk-standardized mortality rate (opportunity-based score: r=-0.25, P<0.001; all-or-none score: r=-0.24, P<0.001). Neither composite measure correlated with the 30-day risk-standardized readmission rate. Over time, the number of hospitals new to the top and bottom quintiles of hospital rankings diminished similarly for both composite measures. When including additional performance measures into the composite score, the two methods produced similar changes in hospital rankings. The opportunity-based and all-or-none coronary artery disease composite indices are highly correlated and yield similar ranking of the top and bottom quintiles of hospitals. The two methods provide similarly modest correlations with 30-day mortality, but not readmission.

Ranking hospitals on surgical mortality: the importance of reliability adjustment.

We examined the implications of reliability adjustment on hospital mortality with surgery. We used national Medicare data (2003-2006) for three surgical procedures: coronary artery bypass grafting (CABG), abdominal aortic aneurysm (AAA) repair, and pancreatic resection. We conducted an observational study to evaluate the impact of reliability adjustment on hospital mortality rankings. Using hierarchical modeling, we adjusted hospital mortality for reliability using empirical Bayes techniques. We assessed the implication of this adjustment on the apparent variation across hospitals and the ability of historical hospital mortality rates (2003-2004) to forecast future mortality (2005-2006). The net effect of reliability adjustment was to greatly diminish apparent variation for all three operations. Reliability adjustment was also particularly important for identifying hospitals with the lowest future mortality. Without reliability adjustment, hospitals in the "best" quintile (2003-2004) with pancreatic resection had a mortality of 7.6 percent in 2005-2006; with reliability adjustment, the "best" hospital quintile had a mortality of 2.7 percent in 2005-2006. For AAA repair, reliability adjustment also improved the ability to identify hospitals with lower future mortality. For CABG, the benefits of reliability adjustment were limited to the lowest volume hospitals. Reliability adjustment results in more stable estimates of mortality that better forecast future performance. This statistical technique is crucial for helping patients select the best hospitals for specific procedures, particularly uncommon ones, and should be used for public reporting of hospital mortality.

Variation in Hospital Mortality Associated with Inpatient Surgery

Hospital mortality that is associated with inpatient surgery varies widely. Reducing rates of postoperative complications, the current focus of payers and regulators, may be one approach to reducing mortality. However, effective management of complications once they have occurred may be equally important. We studied 84,730 patients who had undergone inpatient general and vascular surgery from 2005 through 2007, using data from the American College of Surgeons National Surgical Quality Improvement Program. We first ranked hospitals according to their risk-adjusted overall rate of death and divided them into five groups. For hospitals in each overall mortality quintile, we then assessed the incidence of overall and major complications and the rate of death among patients with major complications. Rates of death varied widely across hospital quintiles, from 3.5% in very-low-mortality hospitals to 6.9% in very-high-mortality hospitals. Hospitals with either very high mortality or very low mortality had similar rates of overall complications (24.6% and 26.9%, respectively) and of major complications (18.2% and 16.2%, respectively). Rates of individual complications did not vary significantly across hospital mortality quintiles. In contrast, mortality in patients with major complications was almost twice as high in hospitals with very high overall mortality as in those with very low overall mortality (21.4% vs. 12.5%, P<0.001). Differences in rates of death among patients with major complications were also the primary determinant of variation in overall mortality with individual operations. In addition to efforts aimed at avoiding complications in the first place, reducing mortality associated with inpatient surgery will require greater attention to the timely recognition and management of complications once they occur.

Effects of Hospital Volume of Primary Percutaneous Coronary Interventions on Angiographic Results and In-Hospital Outcomes for Acute Myocardial Infarction

Several clinical studies have demonstrated an inverse relationship between hospital volume of primary percutaneous coronary interventions (PCI) and in-hospital mortality. However, the relationships among hospital primary PCI volume, angiographic results, and in-hospital prognosis in patients with acute myocardial infarction (AMI) have not been fully investigated in Japan. Using the AMI-Kyoto Multi-Center Risk Study database between January 2000 and December 2005, hospitals were classified into quintiles based on their annual volume of primary PCI. The fifth quintile of hospitals was labeled as high-volume, and the other quintiles were combined and defined as low-volume. Although patients undergoing primary PCI in high-volume hospitals (high-volume group, n=764) had a larger number of diseased vessels at initial coronary angiography and lower Thrombolysis In Myocardial Infarction (TIMI) flow grade in the infarct-related artery before PCI, compared with those in low-volume hospitals (low-volume group, n=1,021), the rates of achieving TIMI flow grade 3 just after PCI in the high-volume group was significantly higher than that in the low-volume group. The overall in-hospital mortality did not differ between the 2 groups. On multivariate analysis, in AMI patients undergoing primary PCI, Killip class >or=3 at admission, multivessel disease or left main trunk (LMT) as culprit lesion, number of diseased vessels >or=2 or diseased LMT, and age were the independent positive predictors of in-hospital mortality, whereas the TIMI flow grade 3 after primary PCI and elapsed time <24 h were the negative ones, but not low-volume hospital. Angiographic results of primary PCI in high-volume hospitals were superior to those in low-volume hospitals, but there was no significant difference in the in-hospital mortality between AMI patients in high-volume hospitals and those in low-volume hospitals.