José R. Zubizarreta

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. DESIGN, SETTING, AND PATIENTS We conducted a matched retrospective cohort study involving patients 50 years or older who were undergoing surgery for hip fracture at general acute care hospitals in New York State between July 1, 2004, and December 31, 2011. Our main analysis was a near-far instrumental variable match that paired patients who lived at different distances from hospitals that specialized in regional or general anesthesia. Supplementary analyses included a within-hospital match that paired patients within the same hospital and an across-hospital match that paired patients at different hospitals. EXPOSURES Spinal or epidural anesthesia; general anesthesia. MAIN OUTCOMES AND MEASURES Thirty-day mortality and hospital length of stay. Because the distribution of length of stay had long tails, we characterized this outcome using the Huber M estimate with Huber weights, a robust estimator similar to a trimmed mean. RESULTS Of 56,729 patients, 15,904 (28%) received regional anesthesia and 40,825 (72%) received general anesthesia. Overall, 3032 patients (5.3%) died. The M estimate of the length of stay was 6.2 days (95% CI, 6.2 to 6.2). The near-far matched analysis showed no significant difference in 30-day mortality by anesthesia type among the 21,514 patients included in this match: 583 of 10,757 matched patients (5.4%) who lived near a regional anesthesia-specialized hospital died vs 629 of 10,757 matched patients (5.8%) who lived near a general anesthesia-specialized hospital (instrumental variable estimate of risk difference, -1.1%; 95% CI, -2.8 to 0.5; P = .20). Supplementary analyses of within and across hospital patient matches yielded mortality findings to be similar to the main analysis. In the near-far match, regional anesthesia was associated with a 0.6-day shorter length of stay than general anesthesia (95% CI, -0.8 to -0.4, P < .001). Supplementary analyses also showed regional anesthesia to be associated with shorter length of stay, although the observed association was smaller in magnitude than in the main analysis. CONCLUSIONS AND RELEVANCE Among adults in acute care hospitals in New York State undergoing hip repair, the use of regional anesthesia compared with general anesthesia was not associated with lower 30-day mortality but was associated with a modestly shorter length of stay. These findings do not support a mortality benefit for regional anesthesia in this setting.

Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery

This article presents a new method for optimal matching in observational studies based on mixed integer programming. Unlike widely used matching methods based on network algorithms, which attempt to achieve covariate balance by minimizing the total sum of distances between treated units and matched controls, this new method achieves covariate balance directly, either by minimizing both the total sum of distances and a weighted sum of specific measures of covariate imbalance, or by minimizing the total sum of distances while constraining the measures of imbalance to be less than or equal to certain tolerances. The inclusion of these extra terms in the objective function or the use of these additional constraints explicitly optimizes or constrains the criteria that will be used to evaluate the quality of the match. For example, the method minimizes or constrains differences in univariate moments, such as means, variances, and skewness; differences in multivariate moments, such as correlations between covariates; differences in quantiles; and differences in statistics, such as the Kolmogorov–Smirnov statistic, to minimize the differences in both location and shape of the empirical distributions of the treated units and matched controls. While balancing several of these measures, it is also possible to impose constraints for exact and near-exact matching, and fine and near-fine balance for more than one nominal covariate, whereas network algorithms can finely or near-finely balance only a single nominal covariate. From a practical standpoint, this method eliminates the guesswork involved in current optimal matching methods, and offers a controlled and systematic way of improving covariate balance by focusing the matching efforts on certain measures of covariate imbalance and their corresponding weights or tolerances. A matched case–control study of acute kidney injury after surgery among Medicare patients illustrates these features in detail. A new R package called mipmatch implements the method.

Effect of the 2010 Chilean earthquake on posttraumatic stress: reducing sensitivity to unmeasured bias through study design.

In 2010, a magnitude 8.8 earthquake hit Chile, devastating parts of the country. Having just completed its national socioeconomic survey, the Chilean government reinterviewed a subsample of respondents, creating unusual longitudinal data about the same persons before and after a major disaster. The follow-up evaluated posttraumatic stress symptoms (PTSS) using Davidson’s Trauma Scale. We use these data with two goals in mind. Most studies of PTSS after disasters rely on recall to characterize the state of affairs before the disaster. We are able to use prospective data on preexposure conditions, free of recall bias, to study the effects of the earthquake. Second, we illustrate recent developments in statistical methodology for the design and analysis of observational studies. In particular, we use new and recent methods for multivariate matching to control 46 covariates that describe demographic variables, housing quality, wealth, health, and health insurance before the earthquake. We use the statistical theory of design sensitivity to select a study design with findings expected to be insensitive to small or moderate biases from failure to control some unmeasured covariate. PTSS were dramatically but unevenly elevated among residents of strongly shaken areas of Chile when compared with similar persons in largely untouched parts of the country. In 96% of exposed-control pairs exhibiting substantial PTSS, it was the exposed person who experienced stronger symptoms (95% confidence interval = 0.91–1.00).

Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data

Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by the difficulty of adequately adjusting for observed covariates in some settings. To address these limitations, this article presents a new weighting method that finds the weights of minimum variance that adjust or balance the empirical distribution of the observed covariates up to levels prespecified by the researcher. This method allows the researcher to balance very precisely the means of the observed covariates and other features of their marginal and joint distributions, such as variances and correlations and also, for example, the quantiles of interactions of pairs and triples of observed covariates, thus, balancing entire two- and three-way marginals. Since the weighting method is based on a well-defined convex optimization problem, duality theory provides insight into the behavior of the variance of the optimal weights in relation to the level of covariate balance adjustment, answering the question, how much does tightening a balance constraint increases the variance of the weights? Also, the weighting method runs in polynomial time so relatively large datasets can be handled quickly. An implementation of the method is provided in the new package sbw for R. This article shows some theoretical properties of the resulting weights and illustrates their use by analyzing both a dataset from the 2010 Chilean earthquake and a simulated example.

Acute Kidney Injury, Renal Function, and the Elderly Obese Surgical Patient: A Matched Case-Control Study

Objective: To investigate the association between obesity and perioperative acute kidney injury (AKI), controlling for preoperative kidney dysfunction. Background: More than 30% of patients older than 60 years are obese and, therefore, at risk for kidney disease. Postoperative AKI is a significant problem. Methods: We performed a matched case-control study of patients enrolled in the Obesity and Surgical Outcomes Study, using data of Medicare claims enriched with detailed chart review. Each AKI patient was matched with a non-AKI control similar in procedure type, age, sex, race, emergency status, transfer status, baseline estimated glomerular filtration rate, admission APACHE score, and the risk of death score with fine balance on hospitals. Results: We identified 514 AKI cases and 694 control patients. Of the cases, 180 (35%) followed orthopedic procedures and 334 (65%) followed colon or thoracic surgery. After matching, obese patients undergoing a surgical procedure demonstrated a 65% increase in odds of AKI within 30 days from admission (odds ratio = 1.65, P < 0.005) when compared with the nonobese patients. After adjustment for potential confounders, the odds of postoperative AKI remained elevated in the elderly obese (odds ratio = 1.68, P = 0.01.) Conclusions: Obesity is an independent risk factor for postoperative AKI in patients older than 65 years. Efforts to optimize kidney function preoperatively should be employed in this at-risk population along with keen monitoring and maintenance of intraoperative hemodynamics. When subtle reductions in urine output or a rising creatinine are observed postoperatively, timely clinical investigation is warranted to maximize renal recovery.

Obesity and readmission in elderly surgical patients.

BACKGROUND Reducing readmissions has become a focus in efforts by Medicare to improve health care quality and reduce costs. This study aimed to determine whether causes for readmission differed between obese and nonobese patients, possibly allowing for targeted interventions. METHODS A matched case control study of Medicare patients admitted between 2002 and 2006 who were readmitted after hip or knee surgery, colectomy, or thoracotomy was performed. Patients were matched exactly for procedure, while also balancing on hospital, age, and sex. Conditional logistic regression was used to study the odds of readmission for very obese cases (body mass index >35 kg/m2) versus normal weight patients (body mass index of 20-30 kg/m2) after also controlling for race, transfer-in and emergency status, and comorbidities. RESULTS Among 15,914 patient admissions, we identified 1,380 readmitted patients and 2,760 controls. The risk of readmission was increased for obese compared to nonobese patients both before and after controlling for comorbidities (before: odds ratio, 1.35; P = .003; after: odds ratio, 1.25; P = .04). Reasons for readmission varied by procedure but were not different by body mass index category. CONCLUSION Obese patients have an increased risk of readmission, yet the reasons for readmission in obese patients appear to be similar to those for nonobese patients, suggesting that improved postdischarge management for the obese cannot focus on a few specific causes of readmission but must instead provide a broad range of interventions.

Matching for Several Sparse Nominal Variables in a Case-Control Study of Readmission Following Surgery.

Matching for several nominal covariates with many levels has usually been thought to be difficult because these covariates combine to form an enormous number of interaction categories with few if any people in most such categories. Moreover, because nominal variables are not ordered, there is often no notion of a “close substitute” when an exact match is unavailable. In a case-control study of the risk factors for readmission within 30 days of surgery in the Medicare population, we wished to match for 47 hospitals, 15 surgical procedures grouped or nested within 5 procedure groups, two genders, or 47 × 15 × 2 = 1410 categories. In addition, we wished to match as closely as possible for the continuous variable age (65–80 years). There were 1380 readmitted patients or cases. A fractional factorial experiment may balance main effects and low-order interactions without achieving balance for high-order interactions. In an analogous fashion, we balance certain main effects and low-order interactions among the covariates; moreover, we use as many exactly matched pairs as possible. This is done by creating a match that is exact for several variables, with a close match for age, and both a “near-exact match” and a “finely balanced match” for another nominal variable, in this case a 47 × 5 = 235 category variable representing the interaction of the 47 hospitals and the five surgical procedure groups. The method is easily implemented in R.

Stronger instruments via integer programming in an observational study of late preterm birth outcomes

In an optimal nonbipartite match, a single population is divided into matched pairs to minimize a total distance within matched pairs. Nonbipartite matching has been used to strengthen instrumental variables in observational studies of treatment effects, essentially by forming pairs that are similar in terms of covariates but very different in the strength of encouragement to accept the treatment. Optimal nonbipartite matching is typically done using network optimization techniques that can be quick, running in polynomial time, but these techniques limit the tools available for matching. Instead, we use integer programming techniques, thereby obtaining a wealth of new tools not previously available for nonbipartite matching, including fine and near-fine balance for several nominal variables, forced near balance on means and optimal subsetting. We illustrate the methods in our on-going study of outcomes of late-preterm births in California, that is, births of 34 to 36 weeks of gestation. Would lengthening the time in the hospital for such births reduce the frequency of rapid readmissions? A straightforward comparison of babies who stay for a shorter or longer time would be severely biased, because the principal reason for a long stay is some serious health problem. We need an instrument, something inconsequential and haphazard that encourages a shorter or a longer stay in the hospital. It turns out that babies born at certain times of day tend to stay overnight once with a shorter length of stay, whereas babies born at other times of day tend to stay overnight twice with a longer length of stay, and there is nothing particularly special about a baby who is born at 11:00 pm.

MATCHING FOR BALANCE, PAIRING FOR HETEROGENEITY IN AN OBSERVATIONAL STUDY OF THE EFFECTIVENESS OF FOR-PROFIT AND NOT-FOR-PROFIT HIGH SCHOOLS IN CHILE

Conventionally, the construction of a pair-matched sample selects treated and control units and pairs them in a single step with a view to balancing observed covariates

Dissonant Conclusions When Testing the Validity of an Instrumental Variable

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