Time Under: Hospital and Patient Characteristics Affecting Anesthesia Duration

One aspect of the quality of surgical care is the length of the time patients spend in the operating room and under anesthesia. It is generally believed that the longer a surgical procedure, the greater the chance of a complication. But because obtaining procedure and anesthesia times usually involves a direct review of medical records, few large-scale studies are able to examine procedure times. This Issue Brief summarizes new work that validates the use of Medicare billing data as a proxy for anesthesia times, and illustrates how these data can shed light on hospital and patient characteristics that affect procedure duration and surgical quality

Hospital Nurse Staffing, Education, and Patient Mortality

A serious shortage of hospital nurses in the U.S., evident in the past decade, is expected to continue and worsen in the next 15 years. Increasingly, the public and the health professions are acknowledging that nurse understaffing represents a serious threat to patient safety in U.S. hospitals. Although anecdotal evidence has linked patient deaths to inadequate nurse staffing, the numbers and kinds of nurses needed for patient safety is unknown. This Issue Brief highlights two studies that clarify the impact of nurse staffing levels on surgical patient outcomes, and examine the effect of nurses’ experience and educational level on patient mortality in the 30 days after a surgical admission

Contrasting Evidence Within and Between Institutions that Provide Treatment in an Observational Study of Alternate Forms of Anesthesia

In a randomized trial, subjects are assigned to treatment or control by the flip of a fair coin. In many nonrandomized or observational studies, subjects find their way to treatment or control in two steps, either or both of which may lead to biased comparisons. By a vague process, perhaps affected by proximity or sociodemographic issues, subjects find their way to institutions that provide treatment. Once at such an institution, a second process, perhaps thoughtful and deliberate, assigns individuals to treatment or control. In the current article, the institutions are hospitals, and the treatment under study is the use of general anesthesia alone versus some use of regional anesthesia during surgery. For a specific operation, the use of regional anesthesia may be typical in one hospital and atypical in another. A new matched design is proposed for studies of this sort, one that creates two types of nonoverlapping matched pairs. Using a new extension of optimal matching with fine balance, pairs of the first type exactly balance treatment assignment across institutions, so each institution appears in the treated group with the same frequency that it appears in the control group; hence, differences between institutions that affect everyone in the same way cannot bias this comparison. Pairs of the second type compare institutions that assign most subjects to treatment and other institutions that assign most subjects to control, so each institution is represented in the treated group if it typically assigns subjects to treatment or, alternatively, in the control group if it typically assigns subjects to control, and no institution appears in both groups. By and large, in the second type of matched pair, subjects became treated subjects or controls by choosing an institution, not by a thoughtful and deliberate process of selecting subjects for treatment within institutions. The design provides two evidence factors, that is, two tests of the null hypothesis of no treatment effect that are independent when the null hypothesis is true, where each factor is largely unaffected by certain unmeasured biases that could readily invalidate the other factor. The two factors permit separate and combined sensitivity analyses, where the magnitude of bias affecting the two factors may differ. The case of knee surgery in the study of regional versus general anesthesia is considered in detail

Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes

In multivariate matching, fine balance constrains the marginal distributions of a nominal variable in treated and matched control groups to be identical without constraining who is matched to whom. In this way, a fine balance constraint can balance a nominal variable with many levels while focusing efforts on other more important variables when pairing individuals to minimize the total covariate distance within pairs. Fine balance is not always possible; that is, it is a constraint on an optimization problem, but the constraint is not always feasible. We propose a new algorithm that returns a minimum distance finely balanced match when one is feasible, and otherwise minimizes the total distance among all matched samples that minimize the deviation from fine balance. Perhaps we can come very close to fine balance when fine balance is not attainable; moreover, in any event, because our algorithm is guaranteed to come as close as possible to fine balance, the investigator may perform one match, and on that basis judge whether the best attainable balance is adequate or not. We also show how to incorporate an additional constraint. The algorithm is implemented in two similar ways, first as an optimal assignment problem with an augmented distance matrix, second as a minimum cost flow problem in a network. The case of knee surgery in the Obesity and Surgical Outcomes Study motivated the development of this algorithm and is used as an illustration. In that example, 2 of 47 hospitals had too few nonobese patients to permit fine balance for the nominal variable with 47 levels representing the hospital, but our new algorithm came very close to fine balance. Moreover, in that example, there was a shortage of nonobese diabetic patients, and incorporation of an additional constraint forced the match to include all of these nonobese diabetic patients, thereby coming as close as possible to balance for this important but recalcitrant covariate

Large, Sparse Optimal Matching with Refined Covariate Balance in an Observational Study of the Health Outcomes Produced by New Surgeons

Every newly trained surgeon performs her first unsupervised operation. How do the health outcomes of her patients compare with the patients of experienced surgeons? Using data from 498 hospitals, we compare 1252 pairs comprised of a new surgeon and an experienced surgeon working at the same hospital. We introduce a new form of matching that matches patients of each new surgeon to patients of an otherwise similar experienced surgeon at the same hospital, perfectly balancing 176 surgical procedures and closely balancing a total of 2.9 million categories of patients; additionally, the individual patient pairs are as close as possible. A new goal for matching is introduced, called refined covariate balance, in which a sequence of nested, ever more refined, nominal covariates is balanced as closely as possible, emphasizing the first or coarsest covariate in that sequence. A new algorithm for matching is proposed and the main new results prove that the algorithm finds the closest match in terms of the total within-pair covariate distances among all matches that achieve refined covariate balance. Unlike previous approaches to forcing balance on covariates, the new algorithm creates multiple paths to a match in a network, where paths that introduce imbalances are penalized and hence avoided to the extent possible. The algorithm exploits a sparse network to quickly optimize a match that is about two orders of magnitude larger than is typical in statistical matching problems, thereby permitting much more extensive use of fine and near-fine balance constraints. The match was constructed in a few minutes using a network optimization algorithm implemented in R. An R package called rcbalance implementing the method is available from CRAN

Anesthesia Technique, Mortality, and Length of Stay After Hip Fracture Surgery

Importance: More than 300 000 hip fractures occur each year in the United States. Recent practice guidelines have advocated greater use of regional anesthesia for hip fracture surgery. Objective: To test the association of regional (ie, spinal or epidural) anesthesia vs general anesthesia with 30-day mortality and hospital length of stay after hip fracture

Racial Differences in Surgeons and Hospitals for Endometrial Cancer Treatment

PURPOSE: To determine whether (1) black and white women with endometrial cancer were treated by different surgical specialties and in different types of hospitals and (2) differences in specialty and hospital type contributed to racial differences in survival. METHODS: Retrospective cohort study of 12,307 women aged 65 years and older who underwent surgical treatment of endometrial cancer between 1991 and 1999 in the 11 Surveillance Epidemiology and End Results registries. RESULTS: Black women were more likely to have a gynecologic oncologist to perform their surgery and to be treated at hospitals that were higher volume, larger, teaching, National Cancer Institute centers, urban, and where a greater proportion of the surgeries were performed by a gynecologic oncologist. In unadjusted models, black women were over twice as likely as white women who died because of cancer (hazards ratio [HR]: 2.33), but nearly all of the initial racial difference in survival was explained by differences in cancer stage, and grade as well as age and comorbidities at presentation (adjusted HR: 1.10). Surgical specialty was not associated with survival and, of the hospital characteristics studied, only surgical volume was associated with survival (P \u3c 0.005). Adjusting for hospital characteristics did not change the racial difference in survival (HR: 1.10). Adjustment for the specific hospital where the woman was treated eliminated the association between race and surgeon specialty and slightly widened the residual racial difference in survival (HR: 1.23 vs. 1.10). CONCLUSIONS: In contrast to several studies suggesting that blacks with breast cancer, colon cancer, or cardiovascular disease are treated in hospitals with lower quality indicators, black women diagnosed with endometrial cancer in Surveillance Epidemiology and End Results regions between 1991 and 1999 were more likely to be treated by physicians with advanced training and in high volume, large, urban, teaching hospitals. However, except for a modest association with hospital surgical volume, these provider and hospital characteristics were largely unrelated to survival for women with endometrial cancer. The great majority of the difference in survival was explained by differences in tumor and clinical characteristics at presentation