Eilon Gabel

A Systematic Approach to Creation of a Perioperative Data Warehouse.

Extraction of data from the electronic medical record is becoming increasingly important for quality improvement initiatives such as the American Society of Anesthesiologists Perioperative Surgical Home. To meet this need, the authors have built a robust and scalable data mart based on their implementation of EPIC containing data from across the perioperative period. The data mart is structured in such a way so as to first simplify the overall EPIC reporting structure into a series of Base Tables and then create several Reporting Schemas each around a specific concept (operating room cases, obstetrics, hospital admission, etc.), which contain all of the data required for reporting on various metrics. This structure allows centralized definitions with simplified reporting by a large number of individuals who access only the Reporting Schemas. In creating the database, the authors were able to significantly reduce the number of required table identifiers from >10 to 3, as well as to correct errors in linkages affecting up to 18.4% of cases. In addition, the data mart greatly simplified the code required to extract data, making the data accessible to individuals who lacked a strong coding background. Overall, this infrastructure represents a scalable way to successfully report on perioperative EPIC data while standardizing the definitions and improving access for end users.

Successful Use of Extracorporeal Membrane Oxygenation in an Adult Patient With Toxic Shock-Induced Heart Failure:

Cardiomyopathy secondary to toxic shock syndrome (TSS) is an uncommon but potentially life-threatening problem. We report the case of a 51-year-old male who presented with profound cardiogenic shock and multiorgan failure that could not be managed by conventional therapy with intravenous fluids, vasopressors and inotropes. Venoarterial extracorporeal membrane oxygenation (VA ECMO) was instituted as a bridge to recovery. After administration of antibiotics and intravenous immunoglobulin, the patient’s condition improved and he was successfully weaned off ECMO after 6 days. The patient recovered from multiorgan failure, and left ventricular ejection fraction improved from <10% pre-ECMO to 65% 8 months after discharge. This case supports the view that VA ECMO can be used successfully to support vital organ perfusion in patients with profound but reversible cardiomyopathy attributed to TSS.

Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems.

With increasing adoption of anesthesia information management systems (AIMS), there is growing interest in utilizing AIMS data for intraoperative clinical decision support (CDS). CDS for anesthesia has the potential for improving quality of care, patient safety, billing, and compliance. Intraoperative CDS can range from passive and post hoc systems to active real-time systems that can detect ongoing clinical issues and deviations from best practice care. Real-time CDS holds the most promise because real-time alerts and guidance can drive provider behavior toward evidence-based standardized care during the ongoing case. In this review, we describe the different types of intraoperative CDS systems with specific emphasis on real-time systems. The technical considerations in developing and implementing real-time CDS are systematically covered. This includes the functional modules of a CDS system, development and execution of decision rules, and modalities to alert anesthesia providers concerning clinical issues. We also describe the regulatory aspects that affect development, implementation, and use of intraoperative CDS. Methods and measures to assess the effectiveness of intraoperative CDS are discussed. Last, we outline areas of future development of intraoperative CDS, particularly the possibility of providing predictive and prescriptive decision support.

Advancing Perioperative Medicine and Anesthesia Practices into the Era of Digital Quality Improvement.

1740 www.anesthesia-analgesia.org June 2016 • Volume 122 • Number 6 Copyright

Preoperative risk stratification of critically ill patients

STUDY OBJECTIVE Risk assessment historically emphasized cardiac morbidity and mortality in elective, outpatient, non-cardiac surgery. However, critically ill patients increasingly present for therapeutic interventions. Our study investigated the relationship of American Society of Anesthesiologists (ASA) class, revised cardiac risk index (RCRI), and sequential organ failure assessment (SOFA) score with survival to discharge in critically ill patients with respiratory failure. DESIGN Retrospective cohort analysis over a 21-month period. SETTING Five adult intensive care units (ICUs) at a single tertiary medical center. PATIENTS Three hundred fifty ICU patients in respiratory failure, who underwent 501 procedures with general anesthesia. MEASUREMENTS Demographic, clinical, and surgical variables were collected from the pre-anesthesia evaluation forms and preoperative ICU charts. The primary outcome was survival to discharge. MAIN RESULTS Ninety-six patients (27%) did not survive to discharge. There were significant differences between survivors and non-survivors for ASA (3.7 vs. 3.9, p=0.001), RCRI (1.6 vs. 2.0, p=0.003), and SOFA score (8.1 vs. 11.2, p<0.001). Based on the area under the receiver operating characteristic curve for these relationships, there was only modest discrimination between the groups, ranging from the most useful SOFA (0.68) to less useful RCRI (0.60) and ASA (0.59). CONCLUSIONS This single center retrospective study quantified a high perioperative risk for critically ill patients with advanced airways: one in four did not survive to discharge. Preoperative ASA score, RCRI, and SOFA score only partially delineated survivors and non-survivors. Given the existing limitations, future research may identify assessment tools more relevant to discriminating survival outcomes for critically ill patients in the perioperative environment.

Preoperative predictions of in-hospital mortality using electronic medical record data

Background Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient’s medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores. Methods Data from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC). Results We found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time. Conclusions Features easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time. Author summary Rapid, preoperative identification of those patients at highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level, or utilize the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. In this manuscript we report on using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery. This score has a higher AUC than both the ASA physical status score and the Charlson comorbidity score. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.