Background Five percent of adult patients undergoing noncardiac inpatient surgery experience a major pulmonary complication. The authors hypothesized that the choice of neuromuscular blockade reversal (neostigmine vs. sugammadex) may be associated with a lower incidence of major pulmonary complications. Methods Twelve U.S. Multicenter Perioperative Outcomes Group hospitals were included in a multicenter observational matched-cohort study of surgical cases between January 2014 and August 2018. Adult patients undergoing elective inpatient noncardiac surgical procedures with general anesthesia and endotracheal intubation receiving a nondepolarizing neuromuscular blockade agent and reversal were included. Exact matching criteria included institution, sex, age, comorbidities, obesity, surgical procedure type, and neuromuscular blockade agent (rocuronium vs. vecuronium). Other preoperative and intraoperative factors were compared and adjusted in the case of residual imbalance. The composite primary outcome was major postoperative pulmonary complications, defined as pneumonia, respiratory failure, or other pulmonary complications (including pneumonitis; pulmonary congestion; iatrogenic pulmonary embolism, infarction, or pneumothorax). Secondary outcomes focused on the components of pneumonia and respiratory failure. Results Of 30,026 patients receiving sugammadex, 22,856 were matched to 22,856 patients receiving neostigmine. Out of 45,712 patients studied, 1,892 (4.1%) were diagnosed with the composite primary outcome (3.5% sugammadex vs. 4.8% neostigmine). A total of 796 (1.7%) patients had pneumonia (1.3% vs. 2.2%), and 582 (1.3%) respiratory failure (0.8% vs. 1.7%). In multivariable analysis, sugammadex administration was associated with a 30% reduced risk of pulmonary complications (adjusted odds ratio, 0.70; 95% CI, 0.63 to 0.77), 47% reduced risk of pneumonia (adjusted odds ratio, 0.53; 95% CI, 0.44 to 0.62), and 55% reduced risk of respiratory failure (adjusted odds ratio, 0.45; 95% CI, 0.37 to 0.56), compared to neostigmine. Conclusions Among a generalizable cohort of adult patients undergoing inpatient surgery at U.S. hospitals, the use of sugammadex was associated with a clinically and statistically significant lower incidence of major pulmonary complications. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Use of the electronic health record (EHR) has become a routine part of perioperative care in the United States. Secondary use of EHR data includes research, quality, and educational initiatives. Fundamental to secondary use is a framework to ensure fidelity, transparency, and completeness of the source data. In developing this framework, competing priorities must be considered as to which data sources are used and how data are organized and incorporated into a useable format. In assembling perioperative data from diverse institutions across the United States and Europe, the Multicenter Perioperative Outcomes Group (MPOG) has developed methods to support such a framework. This special article outlines how MPOG has approached considerations of data structure, validation, and accessibility to support multicenter integration of perioperative EHRs. In this multicenter practice registry, MPOG has developed processes to extract data from the perioperative EHR; transform data into a standardized format; and validate, deidentify, and transfer data to a secure central Coordinating Center database. Participating institutions may obtain access to this central database, governed by quality and research committees, to inform clinical practice and contribute to the scientific and clinical communities. Through a rigorous and standardized approach to ensure data integrity, MPOG enables data to be usable for quality improvement and advancing scientific knowledge. As of March 2019, our collaboration of 46 hospitals has accrued 10.7 million anesthesia records with associated perioperative EHR data across heterogeneous vendors. Facilitated by MPOG, each site retains access to a local repository containing all site-specific perioperative data, distinct from source EHRs and readily available for local research, quality, and educational initiatives. Through committee approval processes, investigators at participating sites may additionally access multicenter data for similar initiatives. Emerging from this work are 4 considerations that our group has prioritized to improve data quality: (1) data should be available at the local level before Coordinating Center transfer; (2) data should be rigorously validated against standardized metrics before use; (3) data should be curated into computable phenotypes that are easily accessible; and (4) data should be collected for both research and quality improvement purposes because these complementary goals bolster the strength of each endeavor.

Background Wearable technology has rapidly entered consumer markets and has health-care potential; however, wearable device data for diverse populations are scarce. We therefore aimed to describe and compare key wearable signals (ie, heart rate, step count, and home blood pressure measurements) across age, sex, race, ethnicity, and clinical phenotypes. Methods In the Michigan Predictive Activity & ClinicalTrajectories in Health (MIPACT) prospective observational study, we enrolled participants from Michigan Medicine, Ann Abor, MI, USA, and followed them up for at least 90 days. Patients were included if they were aged 18 years or older, were fluent in English, owned an iPhone 6 or newer model with a supported iOS version, and had regular access to the internet throughout the study period. All participants were provided with an Apple Watch Series 3 or 4, an Omron Evolv Wireless Blood Pressure Monitor, and the MyDataHelps study smartphone application. Participants were asked to wear their watch for 12 h per day or longer and to do daily or weekly tasks, including home blood pressure measurements and breathing tasks. Heart rate, blood pressure, step counts, and distance walked were collected. The study was divided into two phases: an intensive 45-day collection phase (phase 1); and a 3-year longitudinal monitoring phase (phase 2). Here we report the first 90 days of data for all participants, which includes all of phase 1 and the first 45 days of phase 2. Participants' electronic health records were used to establish clinical diagnoses for analysis.