Cindy L. Christiansen

Preoperative Renal Risk Stratification

BACKGROUND After cardiac surgery, acute renal failure (ARF) requiring dialysis develops in 1% to 5% of patients and is strongly associated with perioperative morbidity and mortality. Prior studies have attempted to identify predictors of ARF but have had insufficient power to perform multivariable analyses or to develop risk stratification algorithms. METHODS AND RESULTS We conducted a prospective cohort study of 43 642 patients who underwent coronary artery bypass or valvular heart surgery in 43 Department of Veterans Affairs medical centers between April 1987 and March 1994. Logistic regression analysis was used to identify independent predictors of ARF requiring dialysis. A risk stratification algorithm derived from recursive partitioning was constructed and was validated on an independent sample of 3795 patients operated on between April and December 1994. The overall risk of ARF requiring dialysis was 1.1%. Thirty-day mortality in patients with ARF was 63.7%, compared with 4.3% in patients without ARF. Ten clinical variables related to baseline cardiovascular disease and renal function were independently associated with the risk of ARF. A risk stratification algorithm partitioned patients into low-risk (0.4%), medium-risk (0.9% to 2.8%), and high-risk (> or = 5.0%) groups on the basis of several of these factors and their interactions. CONCLUSIONS The risk of ARF after cardiac surgery can be accurately quantified on the basis of readily available preoperative data. These findings may be used by physicians and surgeons to provide patients with improved risk estimates and to target high-risk subgroups for interventions aimed at reducing the risk and ameliorating the consequences of this serious complication.

Improving the Statistical Approach to Health Care Provider Profiling

For reports on the performance of health care providers to be effective, profiling must be done using the best statistical methods. Commonly used profiling methods often contain some of the following deficiencies. They ignore important relevant information. They use statistical standards where medical standards would serve better. They use the probability that the observed outcome is extreme, assuming that a hospitals true performance is acceptable to identify providers with extreme rates; this is not the probability that the medical units true mortality rate exceeds a given standard. (The true mortality rate is the rate that would have occurred if the hospital had served a very large number of patients.) The 1993 report on coronary artery bypass graft surgery from the New York State Department of Health [1] listed mortality data (deaths before leaving the hospital) and profile statistics for 31 hospitals. These profiles aimed to identify hospitals that had excessively high or low mortality rates associated with coronary artery bypass graft surgery. In this article, we review 3 of the 31 hospitals to see how profile results improve with the use of more information. Two of the hospitals were chosen for their high observed mortality rates (1 had a mortality rate that was substantially higher than the 1992 statewide rate of 2.78%), and 1 was chosen for its low mortality rate. (It is important to keep in mind that the performance of the hospitals may have changed since 1992.) Hierarchical models use information from the available data obtained from all health care providers being examined. These models are so named because they apply to situations with two or more levels of random variation. In the mortality rate example to follow, the first level of the hierarchy specifies a distribution for the random number of observed deaths at a given hospital and the second level specifies possible distributions for the true mortality rates in several hospitals. To use terminology from analysis of variance, level 1 in the hierarchical model concerns the variation of rates within providers and level 2 concerns the variation between true rates of the hospitals. We urge the use of medical standards that specify the largest or smallest medically acceptable true mortality rate in the setting being profiled. How standards are set depends on their purpose; standards meant to encourage quality improvement, for example, may differ from standards meant to distribute pay incentives. Major improvements to profiles will result from the use of medically appropriate performance standards. Case-mix adjustments are made in almost all profile analyses to account for the differences in provider performances attributable solely to differences in the populations served. Hierarchical models accommodate these crucial adjustments. In addition, standard profiling procedures typically ignore units with small caseloads, such as those with fewer than 50 patients. This practice gives little information on the performance of low-volume providers and can provide unfair gaming opportunities (for example, a hospital with a very high mortality rate for 49 patients might refuse to admit further patients in the given category). Hierarchical models require no minimum sample size for a particular health care provider, provided that the ensemble of all providers analyzed has adequate data. Ensemble data are used to correct for regression-to-the-mean bias. In this paper, we review standard statistical profiling methods, showing how successively better results are obtained as more information is included. We recommend the use of hierarchical models to extract ensemble information and advocate the use of more directly relevant standards. The advantages of a hierarchical approach are as follows: 1) The probabilities of performance standards are calculated, 2) comparisons of units are based on medically relevant standards, 3) regression-to-the-mean bias is removed, and 4) providers with small sample sizes remain in the analysis. The benefits of hierarchical modeling apply not only to the mortality data considered here but also to profiles of patient satisfaction, referral rates, and other outcomes. Profiling Data We estimated the true mortality rates associated with coronary artery bypass graft surgery for 31 hospitals in New York State [1] by using data on the number of patients, the number of deaths, and the case-mix difficulty of the patients treated at each hospital. For this analysis, we focused on data for two hospitals (H1 and H2) that had high observed mortality rates. The simplest profile analysis would compare the number of deaths in each hospital. Because 22 deaths occurred at H2, it seems to be a much worse hospital than H1, at which 3 deaths occurred. However, the number of patients served should also be considered: that is, mortality rates, not raw counts, should be analyzed. The disparity in the number of deaths can be completely explained by the caseload of 484 coronary artery bypass graft procedures at H2 compared with 67 at H1. The mortality rates of 4.48% (3 of 67 patients) at H1 and 4.55% (22 of 484 patients) at H2 are almost indistinguishable. This improvement makes a fairer comparison by adjusting for caseload while retaining the basic concept of comparing the number of deaths. The improvements stem from obtaining and using appropriate additional information. An even better approach accounts for case-mix differences. Data from the New York state study [1] show that the patients who had coronary artery bypass graft surgery at H2 were less healthy than those at H1. On average, patients at H1 had 51.1% of the risk for death of all patients who had coronary artery bypass graft surgery (expected mortality rate, 1.42% for the case mix of patients at H1 compared with 2.78% statewide; 1.42 2.78 = 0.511). When we adjusted for this, the 3 deaths at H1 equivalently resulted from 34.2 (0.511 67) procedures done on patients with an average case mix. The risk-adjusted mortality rate is therefore 8.77% (3 34.2) at H1. The risk-adjusted mortality rate at H2 was 5.77%. (With many deaths and an exceptionally healthy patient case mix, it is possible for the relative risk case-mix adjustment method to produce adjusted rates exceeding 1.00.) Risk adjustments contribute vitally to reducing unfair profile evaluations. The need for risk adjustment has led to vigorous research on ways to account for case-mix differences [2-8]. For our purposes, we used the case-mix data and the risk adjustment methods as they were used in the report from New York State [1]. Models and Tests of Statistical Significance Developing good profiling procedures requires specifying probability distributions for the observed outcomes and choosing the standards of acceptable care that define the hypotheses to be tested. Standards that are based on input from medical professionals and from users of the profiles will be the most useful and the most meaningful. When statistical convention alone determines these choices, they are likely to produce inaccurate conclusions and lead to poor decisions. Probability Distributions Once the hospital rates have been adjusted for case mix, a probability distribution is needed to perform a statistical test. The observed count is governed by the hospitals true mortality rate; here, it is the rate that could be observed only if the hospital had treated a very large number of patients with the average case mix. The true mortality rates are unknown and must be estimated from the data. The Poisson distribution is appropriate for these data because the probability of death after coronary artery bypass graft surgery was small (2.78% state-wide). An alternative, the binomial distribution, is well approximated by the Poisson distribution in this case. If the expected number of deaths is very small or if some individual probabilities are large, then normal, Poisson, and binomial assumptions may not be valid and an alternate calculation may be necessary [9]. Commonly Used Decision Criteria Profile analyses often require tests of the null hypothesis that a providers true mortality rate equals the average rate for all providers. The hypothesis is tested at a specified significance level. Following this convention, the New York State report [1] used the statewide mortality rate of 2.78% as the standard and set the significance level at 0.025. This hypothesis is not very useful: Taken literally, it means that if the true hospital mortality rates differ even by tiny amounts (which one would expect), many of the hospitals would have true rates that exceed the population mean. P Values When a distribution and a standard are specified, the P value can be calculated [10, 11]. A normal approximation to the Poisson distribution will work poorly for the profiles of New York State hospitals in which coronary artery bypass graft surgery was performed, because fewer than 10 deaths were expected at many of the hospitals. The P value for H1 is the probability of observing 3 or more deaths (because 3 deaths occurred at H1), assuming that H1 performed with a true mortality rate of 2.78% for patients with an average case mix. Had H1 performed at this average rate, 0.95 deaths would have been expected. A normal approximation produces a P value of 0.018. Small P values, such as this, identify high true mortality rates; H1 would therefore be identified by this approximate calculation. However, the exact P value based on the Poisson distribution is 0.072 and is too large to identify H1 as a poor performer. Profile procedures that use normal approximations result in incorrect profile estimates if the approximation is inaccurate. Errors such as this are unnecessary; many statistical computer packages make it easy to calculate exact P values for the Poisson and other common distributions. Hierarchical Bayesian Models for Profile Analyses A P value computed to test the hypothesis that a hospital

Hierarchical Poisson Regression Modeling

Abstract The Poisson model and analyses here feature nonexchangeable gamma distributions (although exchangeable following a scale transformation) for individual parameters, with standard deviations proportional to means. A relatively uninformative prior distribution for the shrinkage values eliminates the ill behavior of maximum likelihood estimators of the variance components. When tested in simulation studies, the resulting procedure provides better coverage probabilities and smaller risk than several other published rules, and thus works well from Bayesian and frequentist perspectives alike. The computations provide fast, accurate density approximations to individual parameters and to structural regression coefficients. The computer program is publicly available through Statlib.

Self-Reported vs Administrative Race/Ethnicity Data and Study Results

Concerns about administrative data on race/ethnicity have led some researchers to consider self-reported race/ethnicity as superior.1–5 However, few studies have examined the differential impact of the source of race/ethnicity data, that is, observed or selfreported, on study outcomes. We investigated whether differences in reporting of race/ethnicity led to different results with regard to the use of one therapeutic dental procedure, root canal therapy.

Hospital-Based Palliative Care Consultation: Effects on Hospital Cost

CONTEXT Palliative care consultation teams in hospitals are becoming increasingly more common. Palliative care improves the quality of hospital care for patients with advanced disease. Less is known about its effects on hospital costs. OBJECTIVE To evaluate the relationship between palliative care consultation and hospital costs in patients with advanced disease. DESIGN, SETTING, AND PATIENTS An observational study of 3321 veterans hospitalized with advanced disease between October 1, 2004 and September 30, 2006. The sample includes 606 (18%) veterans who received palliative care and 2715 (82%) who received usual hospital care. October 1, 2004 and September 30, 2006. MAIN OUTCOME MEASURES We studied the costs and intensive care unit (ICU) use of palliative versus usual care for patients in five Veterans Affairs hospitals over a 2-year period. We used an instrumental variable approach to control for unmeasured characteristics that affect both treatment and outcome. RESULTS The average daily total direct hospital costs were

Assessment of optimal size and composition of the U.S. National Registry of hematopoietic stem cell donors.

464 a day lower for the 606 patients receiving palliative compared to the 2715 receiving usual care (p < 0.001). Palliative care patients were 43.7 percentage points less likely to be admitted to ICU during the hospitalization than usual care patients (p < 0.001). COMMENTS Palliative care for patients hospitalized with advanced disease results in lower costs of care and less utilization of intensive care compared to similar patients receiving usual care. Selection on unobserved characteristics plays an important role in the determination of costs of care.

Health status among 28,000 women veterans. The VA Women's Health Program Evaluation Project.

Background. The National Marrow Donor Program (NMDP) receives federal funding to operate a registry of over 4 million volunteer donors for patients in need of a hematopoietic stem cell transplant. Because minority patients are less likely to find a suitably matched donor than whites, special efforts have been aimed toward recruitment of minorities. Significant financial resources are required to recruit and tissue type additional volunteer donors. Methods. Population genetics models have been constructed to project likelihoods of finding a human leukocyte antigen (HLA)-matched donor for patients of various racial/ethnic groups. These projections have been made under a variety of strategies for expansion of the NMDP Registry. Cost-effectiveness calculations incorporated donor unavailability and other barriers to transplantation. Results. At current recruitment rates, the probability of an available HLA-A,B,DRB1 matched donor is projected to increase from 27% to 34%; 45% to 54%; 75% to 79%; and 48% to 55%, for blacks, Asians/Pacific Islanders, whites and Hispanics, respectively, by the year 2007. Substantial increases in minority recruitment would have only modest impacts on these projections. These projections are heavily affected by donor availability rates, which are less than 50% for minority volunteers. Conclusions. Continued recruitment of additional volunteers can improve the likelihood of finding an HLA-matched donor, but will still leave significant numbers of patients of all racial/ethnic groups without a match. Efforts to improve donor availability (especially among minorities) and to increase the number of patients with access to the NMDP Registry may prove to be more cost-effective means of increasing transplants.

Screening for colorectal cancer with flexible sigmoidoscopy by nonphysician endoscopists.

AbstractBACKGROUND: Male veterans receiving Veterans Health Administration (VA) care have worse health than men in the general population. Less is known about health status in women veteran VA patients, a rapidly growing population. OBJECTIVE: To characterize health status of women (vs men) veteran VA patients across age cohorts, and assess gender differences in the effect of social support upon health status. DESIGN AND PATIENTS: Data came from the national 1999 Large Health Survey of Veteran Enrollees (response rate 63%) and included 28,048 women and 651,811 men who used VA in the prior 3 years. MEASUREMENTS: Dimensions of health status from validated Veterans Short Form-36 instrument; social support (married, living arrangement, have someone to take patient to the doctor). RESULTS: In each age stratum (18 to 44, 45 to 64, and ≥65 years), Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were clinically comparable by gender, except that for those aged≥65, mean MCS was better for women than men (49.3 vs 45.9, P<.001). Patient gender had a clinically insignificant effect upon PCS and MCS after adjusting for age, race/ethnicity, and education. Women had lower levels of social support than men; in patients aged <65, being married or living with someone benefited MCS more in men than in women. CONCLUSIONS: Women veteran VA patients have as heavy a burden of physical and mental illness as do men in VA, and are expected to require comparable intensity of health care services. Their ill health occurs in the context of poor social support, and varies by age.

Using patient safety indicators to estimate the impact of potential adverse events on outcomes.

PURPOSE Screening with sigmoidoscopy reduces the risk of death from colorectal cancer. Only 30% of eligible patients have undergone sigmoidoscopy, in part because of a limited supply of endoscopists. We evaluated the performance and safety of screening sigmoidoscopic examinations by trained nonphysician endoscopists in comparison with board-certified gastroenterologists. SUBJECTS AND METHODS Asymptomatic patients 50 years or older without evidence of fecal occult blood and no personal history or family history of a first-degree relative with colorectal cancer under age 55 years were offered sigmoidoscopy. All examinations were performed either by a gastroenterologist or a trained nonphysician endoscopist at a staff model health maintenance organization. Outcomes included the depth of examination, number and histology of polyps, and complications. RESULTS Nonphysicians performed 2,323 sigmoidoscopic examinations, and physicians performed 1,378 examinations. The mean (+/-SD) depth of sigmoidoscopy examinations performed by nonphysicians was 52 +/- 10 cm compared with 55 +/- 9 cm (P <0.001) in physicians. Nonphysicians detected neoplastic polyps in a greater proportion of patients (7.8%) than physicians (5.8%), but this difference was not significant after adjusting for differences in the age, sex, and family history of the patients (P = 0.35). No major complications occurred. The cost per examination, including the nonphysician training cost, was lower for nonphysicians (

Efficient Identification of Postdischarge Surgical Site Infections: Use of Automated Pharmacy Dispensing Information, Administrative Data, and Medical Record Information

186 per examination) than for physicians (