Perioperative Beta-Blocker Therapy and Mortality after Major Noncardiac Surgery

Lindenauer, Peter K.; Pekow, Penelope S.; Wang, Kaijun; Mamidi, Dheeresh K.; Gutiérrez, Benjamín; Benjamin, Evan M.

doi:10.1056/nejmoa041895

Cited by 790 publications

(461 citation statements)

References 24 publications

(9 reference statements)

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“…Participating hospitals are drawn from all regions of the United States, with greater representation from urban and southern hospitals. PDW has been used extensively for research purposes 5, 6, 7, 8, 9. Because the data are proprietary, we are not able to make the data set or study materials available to other researchers.…”

Section: Methodsmentioning

confidence: 99%

“…A recent innovation that will facilitate severity adjustment in claims data is the development of multihospital databases (eg, Premier, University HealthSystem Consortium) that standardize highly detailed billing data across hospitals, providing time‐ or date‐stamped information about all tests and services provided to individual patients 5, 6, 7, 8, 9. Using one of these data sets, we sought to develop and validate a model that could be used to compare hospitals' performance in the care of HF patients.…”

Section: Introductionmentioning

confidence: 99%

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Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Lagu

Pekow

Stefan

et al. 2018

JAHA

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BackgroundComparing heart failure (HF) outcomes across hospitals requires adequate risk adjustment. We aimed to develop and validate a model that can be used to compare quality of HF care across hospitals.Methods and ResultsWe included patients with HF aged ≥18 years admitted to one of 433 hospitals that participated in the Premier Inc Data Warehouse. This model (Premier) contained patient demographics, comorbidities, and acute conditions present on admission, derived from administrative and billing records. In a separate data set derived from electronic health records, we validated the Premier model by comparing hospital risk‐standardized mortality rates calculated with the Premier model to those calculated with a validated clinical model containing laboratory data (LAPS [Laboratory‐Based Acute Physiology Score]). Among the 200 832 admissions in the Premier Inc Data Warehouse, inpatient mortality was 4.0%. The model showed acceptable discrimination in the warehouse data (C statistic 0.75; 95% confidence interval, 0.74–0.76). In the validation data set, both the Premier model and the LAPS models showed acceptable discrimination (C statistic: Premier: 0.76 [95% confidence interval, 0.74–0.77]; LAPS: 0.78 [95% confidence interval, 0.76–0.80]). Risk‐standardized mortality rates for both models ranged from 2% to 7%. A linear regression equation describing the association between Premier‐ and LAPS‐specific mortality rates revealed a regression line with a slope of 0.71 (SE: 0.07). The correlation coefficient of the standardized mortality rates from the 2 models was 0.82.ConclusionsCompared with a validated model derived from clinical data, an HF mortality model derived from administrative data showed highly correlated risk‐standardized mortality rate estimates, suggesting it could be used to identify high‐ and low‐performing hospitals for HF care.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Lagu

Pekow

Stefan

et al. 2018

JAHA

Self Cite

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“…To examine the clinical characteristics of the study population, we extracted demographic variables, diagnostic and surgical procedure codes, socioeconomic information (including monthly income and urbanization level [4 levels, 1=urban and 4=rural]), number of outpatient visits in the past year, Charlson Comorbidity Index,19 revised cardiac risk index (including 6 variables: high‐risk surgery, cerebrovascular disease, ischemic heart disease, congestive heart failure, DM, and renal insufficiency),20, 21 and adapted Diabetes Complications Severity Index for the severity of DM 22, 23, 24. We also identified other comorbidities related to general health and treatment with concomitant medications, including antidiabetic drugs, alpha‐blockers, angiotensin‐converting‐enzyme inhibitors, angiotensin II receptor blockers, calcium channel blockers, diuretics, other antihypertensive drugs, aspirin, clopidogrel, ticlopidine, warfarin, dipyridamole, nitrates, and statins.…”

Section: Methodsmentioning

confidence: 99%

Impact of Beta‐Blocker Initiation Timing on Mortality Risk in Patients With Diabetes Mellitus Undergoing Noncardiac Surgery: A Nationwide Population‐Based Cohort Study

Chen

Chu

Tsai

2017

JAHA

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BackgroundRelevant clinical studies have been small and have not convincingly demonstrated whether the perioperative initiation of beta‐blockers should be considered in patients with diabetes mellitus undergoing noncardiac surgery.Methods and ResultsIn this nationwide propensity score–matched study, we included patients with diabetes mellitus undergoing noncardiac surgery between 2000 and 2011 from Taiwan's National Health Insurance Research Database. Patients were classified as beta‐blocker and non–beta‐blocker cohorts. We further stratified beta‐blocker users into cardioprotective beta‐blocker (atenolol, bisoprolol, metoprolol, or carvedilol) and other beta‐blocker users. To investigate time of initiation of beta‐blocker use, initiation time was stratified into 2 periods (>30 and ≤30 days preoperatively). The outcomes of interest were in‐hospital and 30‐day mortality. After propensity score matching, we identified 50 952 beta‐blocker users and 50 952 matched controls. Compared with non–beta‐blocker users, cardioprotective beta‐blocker users were associated with lower risks of in‐hospital (odds ratio 0.75, 95% CI 0.68–0.82) and 30‐day (odds ratio 0.75, 95% CI 0.70–0.81) mortality. Among initiation times, only the use of cardioprotective beta‐blockers for >30 days was associated with decreased risk of in‐hospital (odds ratio 0.72, 95% CI 0.65–0.78) and 30‐day (odds ratio 0.72, 95% CI 0.66–0.78) mortality. Of note, use of other beta‐blockers for ≤30 days before surgery was associated with increased risk of both in‐hospital and 30‐day mortality.ConclusionsThe use of cardioprotective beta‐blockers for >30 days before surgery was associated with reduced mortality risk, whereas short‐term use of beta‐blockers was not associated with differences in mortality in patients with diabetes mellitus.

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“…The database vendor assures a high level of database integrity by performing numerous quality and data validation checks before the data are released for analysis. Numerous earlier publications have relied on this data source for retrospective studies in a variety of clinical subspecialties [19,22,25]. Our group utilized the same database previously for various projects focusing exclusively on comparative perioperative outcomes and ICU utilization in patients undergoing lower-extremity joint arthroplasty [21,29].…”

Section: Institutional Review Board Approval and Data Sourcementioning

confidence: 99%

Peripheral Nerve Blocks in Shoulder Arthroplasty: How Do They Influence Complications and Length of Stay?

Stundner

Rasul

Chiu

et al. 2014

Clinical Orthopaedics &Amp; Related Research

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Background Regional anesthesia has proven to be a highly effective technique for pain control after total shoulder arthroplasty. However, concerns have been raised about the safety of upper-extremity nerve blocks, particularly with respect to the incidence of perioperative respiratory and neurologic complications, and little is known about their influence, if any, on length of stay after surgery. Questions/purposes Using a large national cohort, we asked: (1) How frequently are upper-extremity peripheral nerve blocks added to general anesthesia in patients undergoing total shoulder arthroplasty? (2) Are there differences in the incidence of and adjusted risk for major perioperative complications and mortality between patients receiving general anesthesia with and without nerve blocks? And (3) does resource utilization (blood product transfusion, intensive care unit admission, length of stay) differ between groups? Methods We searched a nationwide discharge database for patients undergoing total shoulder arthroplasty under general anesthesia with or without addition of a nerve Each author certifies that he or she, or a member of his or her immediate family, has no commercial associations (eg, consultancies, stock ownership, equity interest, patent/licensing arrangements, etc) that might pose a conflict of interest in connection with the submitted article. The institution of one of the authors (SGM) has received, during the study, funding from the Anna-Maria and Stephen Kellen Physician-Scientist Career Development Award provided by the Hospital for Special Surgery. One or more of the authors (RR, YLC, XS, MM) has received, during the study,

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Perioperative Beta-Blocker Therapy and Mortality after Major Noncardiac Surgery

Cited by 790 publications

References 24 publications

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Impact of Beta‐Blocker Initiation Timing on Mortality Risk in Patients With Diabetes Mellitus Undergoing Noncardiac Surgery: A Nationwide Population‐Based Cohort Study

Peripheral Nerve Blocks in Shoulder Arthroplasty: How Do They Influence Complications and Length of Stay?

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