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BACKGROUND The incentive spirometer is a basic and common medical device from which electronic healthcare data cannot be directly collected. As a result, despite numerous studies investigating its utility in clinical care, there remains little consensus on optimal device utility and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications. OBJECTIVE Develop a medical device to capture and use incentive spirometry data. METHODS We designed, built, and tested an add-on device using reflexive optical sensors to identify the real time location of the volume and flow pistons of the incentive spirometer. Data was simultaneously stored internally on the device and transmitted for collection and analysis. An algorithm was developed to determine breath quality to improve validity of data analysis. The device and algorithm were tested for data capture and labeling accuracy, respectively. Subsequently, we developed a cross platform video game for use with this device and tested using the Apple iPad. RESULTS The add-on device featured sensors mounted on the back of the incentive spirometer with an adjoining base containing electronics. Volume and flow piston locations were captured at 99% accuracy (+/- 1.4% SD) for volume and 100% accuracy for flow, while the breath algorithm displayed a 100% sensitivity and a 99% specificity. A custom video game operated in real time with Apple iPad devices. CONCLUSIONS Incentive spirometers are routinely used in hospitals, especially the postoperative period, but use recommendations lack thorough investigation partially due to a lack of data. We created and tested an effective and reusable add-on device for the incentive spirometer, allowing collection of previously inaccessible incentive spirometer data and demonstrating internet-of-things utility on a common hospital device. Spirometer data can facilitate research to improve adoption, guide clinical care, and incentivize adherence, to further investigate the clinical effectiveness of the incentive spirometer.

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 Heart failure with reduced ejection fraction (HFrEF) is a condition imposing significant health care burden. Given its syndromic nature and often insidious onset, the diagnosis may not be made until clinical manifestations prompt further evaluation. Detecting HFrEF in precursor stages could allow for early initiation of treatments to modify disease progression. Granular data collected during the perioperative period may represent an underutilized method for improving the diagnosis of HFrEF. We hypothesized that patients ultimately diagnosed with HFrEF following surgery can be identified via machine-learning approaches using pre- and intraoperative data. Methods Perioperative data were reviewed from adult patients undergoing general anesthesia for major surgical procedures at an academic quaternary care center between 2010 and 2016. Patients with known HFrEF, heart failure with preserved ejection fraction, preoperative critical illness, or undergoing cardiac, cardiology, or electrophysiologic procedures were excluded. Patients were classified as healthy controls or undiagnosed HFrEF. Undiagnosed HFrEF was defined as lacking a HFrEF diagnosis preoperatively but establishing a diagnosis within 730 days postoperatively. Undiagnosed HFrEF patients were adjudicated by expert clinician review, excluding cases for which HFrEF was secondary to a perioperative triggering event, or any event not associated with HFrEF natural disease progression. Machine-learning models, including L1 regularized logistic regression, random forest, and extreme gradient boosting were developed to detect undiagnosed HFrEF, using perioperative data including 628 preoperative and 1195 intraoperative features. Training/validation and test datasets were used with parameter tuning. Test set model performance was evaluated using area under the receiver operating characteristic curve (AUROC), positive predictive value, and other standard metrics. Results Among 67,697 cases analyzed, 279 (0.41%) patients had undiagnosed HFrEF. The AUROC for the logistic regression model was 0.869 (95% confidence interval, 0.829-0.911), 0.872 (0.836-0.909) for the random forest model, and 0.873 (0.833-0.913) for the extreme gradient boosting model. The corresponding positive predictive values were 1.69% (1.06%-2.32%), 1.42% (0.85%-1.98%), and 1.78% (1.15%-2.40%), respectively. Conclusions Machine-learning models leveraging perioperative data can detect undiagnosed HFrEF with good performance. However, the low prevalence of the disease results in a low positive predictive value, and for clinically meaningful sensitivity thresholds to be actionable, confirmatory testing with high specificity (eg, echocardiography or cardiac biomarkers) would be required following model detection. Future studies are necessary to externally validate algorithm performance at additional centers and explore the feasibility of embedding algorithms into the perioperative electronic health record for clinician use in real time.

Background Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. Methods Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. Results Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. Conclusions Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

BackgroundDespite the significant healthcare impact of acute kidney injury, little is known regarding prevention. Single-center data have implicated hypotension in developing postoperative acute kidney injury. The generalizability of this finding and the interaction between hypotension and baseline patient disease burden remain unknown. The authors sought to determine whether the association between intraoperative hypotension and acute kidney injury varies by preoperative risk.MethodsMajor noncardiac surgical procedures performed on adult patients across eight hospitals between 2008 and 2015 were reviewed. Derivation and validation cohorts were used, and cases were stratified into preoperative risk quartiles based upon comorbidities and surgical procedure. After preoperative risk stratification, associations between intraoperative hypotension and acute kidney injury were analyzed. Hypotension was defined as the lowest mean arterial pressure range achieved for more than 10 min; ranges were defined as absolute (mmHg) or relative (percentage of decrease from baseline).ResultsAmong 138,021 cases reviewed, 12,431 (9.0%) developed postoperative acute kidney injury. Major risk factors included anemia, estimated glomerular filtration rate, surgery type, American Society of Anesthesiologists Physical Status, and expected anesthesia duration. Using such factors and others for risk stratification, patients with low baseline risk demonstrated no associations between intraoperative hypotension and acute kidney injury. Patients with medium risk demonstrated associations between severe-range intraoperative hypotension (mean arterial pressure less than 50 mmHg) and acute kidney injury (adjusted odds ratio, 2.62; 95% CI, 1.65 to 4.16 in validation cohort). In patients with the highest risk, mild hypotension ranges (mean arterial pressure 55 to 59 mmHg) were associated with acute kidney injury (adjusted odds ratio, 1.34; 95% CI, 1.16 to 1.56). Compared with absolute hypotension, relative hypotension demonstrated weak associations with acute kidney injury not replicable in the validation cohort.ConclusionsAdult patients undergoing noncardiac surgery demonstrate varying associations with distinct levels of hypotension when stratified by preoperative risk factors. Specific levels of absolute hypotension, but not relative hypotension, are an important independent risk factor for acute kidney injury.Editor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is New

Recent studies have investigated the effect of overlapping surgeon responsibilities or nurse to patient staffing ratios on patient outcomes, but the association of overlapping anesthesiologist responsibilities with patient outcomes remains unexplored to our knowledge.To examine the association between different levels of anesthesiologist staffing ratios and surgical patient morbidity and mortality.A retrospective, matched cohort study consisting of major noncardiac inpatient surgical procedures performed from January 1, 2010, to October 31, 2017, was conducted in 23 US academic and private hospitals. A total of 866 453 adult patients (aged ≥18 years) undergoing major inpatient surgery within the Multicenter Perioperative Outcomes Group electronic health record registry were included. Anesthesiologist sign-in and sign-out times were used to calculate a continuous time-weighted average staffing ratio variable for each operation. Propensity score-matching methods were applied to create balanced sample groups with respect to patient-, operative-, and hospital-level confounders and resulted in 4 groups based on anesthesiologist staffing ratio. Groups consisted of patients receiving care from an anesthesiologist covering 1 operation (group 1), more than 1 to no more than 2 overlapping operations (group 1-2), more than 2 to no more than 3 overlapping operations (group 2-3), and more than 3 to no more than 4 overlapping operations (group 3-4). Data analysis was performed from October 2019 to October 2021.Undergoing a major inpatient surgical operation that involved an anesthesiologist providing care for up to 4 overlapping operations.The primary composite outcome was 30-day mortality and 6 major surgical morbidities (cardiac, respiratory, gastrointestinal, urinary, bleeding, and infectious complications) derived from International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision discharge diagnosis codes.In all, 578 815 adult patients (mean [SD] age, 55.7 [16.2] years; 55.1% female) were analyzed. After matching operations according to anesthesiologist staffing ratio, 48 555 patients were in group 1; 247 057, group 1-2; 216 193, group 2-3; and 67 010, group 3-4. Increasing anesthesiologist coverage responsibilities was associated with an increase in risk-adjusted surgical patient morbidity and mortality. Compared with patients in group 1-2, those in group 2-3 had a 4% relative increase in risk-adjusted mortality and morbidity (5.06% vs 5.25%; adjusted odds ratio [AOR], 1.04; 95% CI, 1.01-1.08; P = .02) and those in group 3-4 had a 14% increase in risk-adjusted mortality and morbidity (5.06% vs 5.75%; AOR, 1.15; 95% CI, 1.09-1.21; P .001).This study's findings suggest that increasing overlapping coverage by anesthesiologists is associated with increased surgical patient morbidity and mortality. Therefore, the potential effects of staffing ratios in perioperative team models should be considered in clinical coverage efforts.

Background Administrative costs for billing and insurance-related activities in the United States are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, developing advanced models to predict hospital and professional billing codes has become feasible. These models can be used for administrative cost reduction and billing process improvements. Objective In this study, we aim to develop an automated anesthesiology current procedural terminology (CPT) prediction system that translates manually entered surgical procedure text into standard forms using neural machine translation (NMT) techniques. The standard forms are calculated using similarity scores to predict the most appropriate CPT codes. Although this system aims to enhance medical billing coding accuracy to reduce administrative costs, we compare its performance with that of previously developed machine learning algorithms. Methods We collected and analyzed all operative procedures performed at Michigan Medicine between January 2017 and June 2019 (2.5 years). The first 2 years of data were used to train and validate the existing models and compare the results from the NMT-based model. Data from 2019 (6-month follow-up period) were then used to measure the accuracy of the CPT code prediction. Three experimental settings were designed with different data types to evaluate the models. Experiment 1 used the surgical procedure text entered manually in the electronic health record. Experiment 2 used preprocessing of the procedure text. Experiment 3 used preprocessing of the combined procedure text and preoperative diagnoses. The NMT-based model was compared with the support vector machine (SVM) and long short-term memory (LSTM) models. Results The NMT model yielded the highest top-1 accuracy in experiments 1 and 2 at 81.64% and 81.71% compared with the SVM model (81.19% and 81.27%, respectively) and the LSTM model (80.96% and 81.07%, respectively). The SVM model yielded the highest top-1 accuracy of 84.30% in experiment 3, followed by the LSTM model (83.70%) and the NMT model (82.80%). In experiment 3, the addition of preoperative diagnoses showed 3.7%, 3.2%, and 1.3% increases in the SVM, LSTM, and NMT models in top-1 accuracy over those in experiment 2, respectively. For top-3 accuracy, the SVM, LSTM, and NMT models achieved 95.64%, 95.72%, and 95.60% for experiment 1, 95.75%, 95.67%, and 95.69% for experiment 2, and 95.88%, 95.93%, and 95.06% for experiment 3, respectively. Conclusions This study demonstrates the feasibility of creating an automated anesthesiology CPT classification system based on NMT techniques using surgical procedure text and preoperative diagnosis. Our results show that the performance of the NMT-based CPT prediction system is equivalent to that of the SVM and LSTM prediction models. Importantly, we found that including preoperative diagnoses improved the accuracy of using the procedure text alone.

Intravenous (IV) fluid administration is critical for all patients undergoing care in a hospital setting. In-patient hospital practice, surgeries, and emergency care require functional IVs for fluid replacement and medication administration. Proper placement of IVs is vital to providing medical services. The ease of placement of an IV catheter, however, depends not only on the size of the catheter but also on provider experience and patient demographics such as age, body mass index, hydration status, and medical comorbidities present challenges to successful IV placement. Smaller diameter IV placement can improve success and there are instances where multiple small diameter catheters are placed for patient care when larger bore access is unattainable. Smaller inner-diameter catheters for anesthesia have functional constraints. Ideally, there would be a smaller catheter for placement that could function as a larger catheter for patient care. One solution is the idea of functionally responsive catheters. Here, we evaluated tubular-shaped hydrogels as potential functional catheters that can increase in inner diameter through fluid swelling using cross-linked homopolymers of polyacrylamide, PAM (10–40% w/w), and their copolymers with 0–8% w/w Poly-(Ethylene Glycol)-Diacrylate, PEGDA. For the PAM gels, the water transport mechanism was shown to be concentration-dependent Fickian diffusion, with the less concentrated gels exhibiting increasingly anomalous modes. Increasing the PEGDA content in the network yielded an initial high rate of water uptake, characterized by Case II transport. The swelling kinetics depended strongly on the sample geometry and boundary conditions. Initially, in a submerged swelling, the annulus expands symmetrically in both outward and inward directions (it thickens), reducing the internal diameter by up to 70%. After 1 h, however, the inner diameter increases steadily so that at equilibrium, there is a net (>100%) increase in all the dimensions of the tube. The amount of linear swelling at equilibrium depended only on the polymer volume fraction as made, while the rate of inner diameter expansion depended on the hydrophilicity of the matrix and the kinetics of sorption. This study serves as proof of concept to identify key parameters for the successful design of hydrogel-based catheter devices with expanding inner-diameters for applications in medical care.

Manuscript Type: Conceptual Research Question/Issue: Board reforms around the globe, corporate governance failures, and equivocal research findings provide the impetus for studying boards from a perspective different from the current emphasis on control and service roles. A board's strategic role is conceptualized and the impact of board diversity and leadership structure on the speed and breadth of top management team (TMT) strategic capabilities is explicated. Research Findings/Insights: Four board configurations are identified by examining the joint impact of diversity and leadership. Each configuration differentially influences TMT strategic action capabilities. We then propose that the nature of the competitive environment (dynamism and complexity) impacts the effectiveness of a particular configuration. Testable propositions applicable in communitarian, Anglo-Saxon, and emerging markets are developed in a manner that leverages current theoretical insights and empirical findings. Theoretical/Academic Implications: Theorizing that board strategic control manifests in multiple forms and influences TMT strategic capabilities in nuanced ways highlights how particular board configurations differentially impact firm processes and outcomes. This approach links the board, TMT, and competitive rivalry literatures, providing a potential explanation for mixed research findings, and suggests directions for future research. Practitioner/Policy Implications: Universally prescribed board configurations may be harmful to firm and industry performance as well as national economies. Specific board configurations differentially impact TMT capabilities and should be properly aligned with environmental contexts. Those charged with governance design and board configuration should be cognizant of how the diversity and leadership of strategically involved boards influences the speed and breadth of TMT strategic action capabilities.

Brain derived neurotrophic factor (BDNF) is a promising therapeutic candidate for a variety of neurological diseases. However, it is difficult to produce as a recombinant protein. In its native mammalian context, BDNF is first produced as a pro-protein with subsequent proteolytic removal of the pro-region to yield mature BDNF protein. Therefore, in an attempt to improve yeast as a host for heterologous BDNF production, the BDNF pro-region was first evaluated for its effects on BDNF surface display and secretion. Addition of the wild-type pro-region to yeast BDNF production constructs improved BDNF folding both as a surface-displayed and secreted protein in terms of binding its natural receptors TrkB and p75, but titers remained low. Looking to further enhance the chaperone-like functions provided by the pro-region, two rounds of directed evolution were performed, yielding mutated pro-regions that further improved the display and secretion properties of BDNF. Subsequent optimization of the protease recognition site was used to control whether the produced protein was in pro- or mature BDNF forms. Taken together, we have demonstrated an effective strategy for improving BDNF compatibility with yeast protein engineering and secretion platforms.

The chemical behavior of aromatic carboxylic acids in water at elevated temperatures has implications for industrial chemistry, fuel processing and waste treatment processes. Therefore, the hydrothermal reactions of six different aromatic carboxylic acids were investigated from 250–410 °C at times up to 240 min. Benzoic acid was the most stable of the six, showing negligible degradation after 1 h of hydrothermal treatment at 350 °C. Terephthalic acid, 2,6-naphthalene dicarboxylic acid and isophthalic acid were stable after 1 h at 300 °C, but they decarboxylated to form monoacids in 10–15% yields at 350 °C. Trimellitic anhydride decomposed completely after 30 min at 350 °C, but showed no appreciable decomposition after 30 min at 250 °C. Terephthalic acid and isophthalic acid were the main degradation products, but o-phthalic acid was also formed in small amounts at 350 °C. The o-phthalic acid conversion to benzoic acid was 73% after 60 min at 300 °C, but the diacid remained stable at 250 °C for 1 h. An autocatalytic kinetics model provided a good description of the trimellitic anhydride and terephthalic acid decomposition at 300 and 410 °C, respectively.

Brain-derived neurotrophic factor (BDNF) plays an important role in nervous system function and has therapeutic potential. Microbial production of BDNF has resulted in a low-fidelity protein product, often in the form of large, insoluble aggregates incapable of binding to cognate TrkB or p75 receptors. In this study, employing Saccharomyces cerevisiae display and secretion systems, it was found that BDNF was poorly expressed and partially inactive on the yeast surface and that BDNF was secreted at low levels in the form of disulfide-bonded aggregates. Thus, for the purpose of increasing the compatibility of yeast as an expression host for BDNF, directed-evolution approaches were employed to improve BDNF folding and expression levels. Yeast surface display was combined with two rounds of directed evolution employing random mutagenesis and shuffling to identify BDNF mutants that had 5-fold improvements in expression, 4-fold increases in specific TrkB binding activity, and restored p75 binding activity, both as displayed proteins and as secreted proteins. Secreted BDNF mutants were found largely in the form of soluble homodimers that could stimulate TrkB phosphorylation in transfected PC12 cells. Site-directed mutagenesis studies indicated that a particularly important mutational class involved the introduction of cysteines proximal to the native cysteines that participate in the BDNF cysteine knot architecture. Taken together, these findings show that yeast is now a viable alternative for both the production and the engineering of BDNF.

Survival rates at military and civilian trauma sites decrease dramatically if medical treatment is delayed for more than the golden hour. Remote gathering of advance wound information is possible with medical smart textile, an innovation by Science, Math & Engineering, Inc. Medical smart textile is an electrically conductive fabric possessing a cross hatched network of conducting paths etched into the textile. Open wound information would register in the textile as holes resulting from penetrating projectiles breaching the fabric. Interrogation for open wound information could occur passively, as a wear-and-forget fabric, or actively, as a wear-and-activate fabric. The passive mode of the textile is remotely interrogated by a radar for wound holes whereas the active mode of the textile senses wound holes as changes in conductor network resistance. Detection of holes by radar in the passive mode was successfully demonstrated at the MIT Lincoln Laboratory indoor radar range.

We report a spectroscopic study of electronic transitions from the ground state to several excited states in the potential well lying outside the free surface of liquid helium formed by the long-range attractive-image potential and the short-range electron-atom repulsive potential. These electric dipole transitions were observed at frequencies from 130 to 220 GHz by measuring the microwave absorption derivative at fixed frequency as the splittings between states were tuned to resonance by an applied electric field $\mathcal{E}$. Transitions were observed between the ground state and the first through seventh excited states, and detailed measurements were made of the frequency versus $\mathcal{E}$ relation for transitions to the first and second excited states. Extrapolation of the respective data to $\mathcal{E}=0$ yields splittings of 125.9 \ifmmode\pm\else\textpm\fi{} 0.2 and 148.6 \ifmmode\pm\else\textpm\fi{} 0.3 GHz and initial Stark-tuning rates of 2.3 \ifmmode\pm\else\textpm\fi{} 0.1 and 5.9 \ifmmode\pm\else\textpm\fi{} 0.4 GHz/(V/cm). The data can be fit satisfactorily by variational calculations based on a simple model potential which takes the origin of the image potential to lie 1.04 \AA{} inside the liquid-helium surface. These calculations show the wave functions to be significantly compressed by the Stark-tuning fields; e.g., the second excited state is compressed at our highest fields to 1/2 of its original size. Measurements of the linewidth of the lowest transition as a function of the helium vapor density are reported. The peak-to-peak line width is 1.0 GHz at a vapor density of 5 \ifmmode\times\else\texttimes\fi{} ${10}^{18}$ atoms/${\mathrm{cm}}^{3}$ and increases approximately linearly with increasing vapor density. A phenomenological theory of several mechanisms contributing to the linewidth is discussed.