Ira S. Hofer

Impact of Enhanced Recovery After Surgery and Opioid-Free Anesthesia on Opioid Prescriptions at Discharge From the Hospital: A Historical-Prospective Study.

BACKGROUND: The United States is in the midst of an opioid epidemic, and opioid use disorder often begins with a prescription for acute pain. The perioperative period represents an important opportunity to prevent chronic opioid use, and recently there has been a paradigm shift toward implementation of enhanced recovery after surgery (ERAS) protocols that promote opioid-free and multimodal analgesia. The objective of this study was to assess the impact of an ERAS intervention for colorectal surgery on discharge opioid prescribing practices. METHODS: We conducted a historical-prospective quality improvement study of an ERAS protocol implemented for patients undergoing colorectal surgery with a focus on the opioid-free and multimodal analgesia components of the pathway. We compared patients undergoing colorectal surgery 1 year before implementation (June 15, 2015, to June 14, 2016) and 1 year after implementation (June 15, 2016, to June 14, 2017). RESULTS: Before the ERAS intervention, opioids at discharge were not significantly increasing (1% per month; 95% confidence interval [CI], −1% to 3%; P = .199). Immediately after the ERAS intervention, opioid prescriptions were not significantly lower (13%; 95% CI, −30% to 3%; P = .110). After the intervention, the rate of opioid prescriptions at discharge did not decrease significantly 1% (95% CI, −3% to 1%) compared to the pre-period rate (P = .399). Subgroup analysis showed that in patients with a combination of low discharge pain scores, no preoperative opioid use, and low morphine milligram equivalents consumption before discharge, the rate of discharge opioid prescription was 72% (95% CI, 61%–83%). CONCLUSIONS: This study is the first to report discharge opioid prescribing practices in an ERAS setting. Although an ERAS intervention for colorectal surgery led to an increase in opioid-free anesthesia and multimodal analgesia, we did not observe an impact on discharge opioid prescribing practices. The majority of patients were discharged with an opioid prescription, including those with a combination of low discharge pain scores, no preoperative opioid use, and low morphine milligram equivalents consumption before discharge. This observation in the setting of an ERAS pathway that promotes multimodal analgesia suggests that our findings are very likely to also be observed in non-ERAS settings and offers an opportunity to modify opioid prescribing practices on discharge after surgery. For opioid-free anesthesia and multimodal analgesia to influence the opioid epidemic, the dose and quantity of the opioids prescribed should be modified based on the information gathered by in-hospital pain scores and opioid use as well as pain history before admission.

Relationship among surgical volume, repair quality, and perioperative outcomes for repair of mitral insufficiency in a mitral valve reference center

OBJECTIVE Although it has been demonstrated that the repair rates and quality of the repair of mitral insufficiency are superior in mitral valve reference centers, it has not been studied whether an advantage exists for perioperative morbidity and mortality. We report 1 surgeons evolution over 7 years, specifically considering the changes in perioperative morbidity and mortality. METHODS We performed a retrospective review of 1054 patients who had undergone elective, day-of-surgery-admission mitral valve repair by a single surgeon (D.H.A.) at our institution from April 2005 to June 2012. The outcome variables studied were operative mortality (30-day or in-hospital mortality, if longer), length of stay, low cardiac output state after cardiopulmonary bypass, and major morbidity. RESULTS The overall operative mortality was 0.58%. Of the 1054 patients, 31% developed a low cardiac output state postoperatively and 6.52% experienced at least 1 of the composite morbidity events. Increased aortic crossclamp times were significantly and independently associated with a low cardiac output state, length of stay, and morbidity. When divided by service year, a statistically and clinically significant decrease was found in the aortic crossclamp time, despite an increase in the complexity of cases. The morbidity decreased concurrently with the decreases in crossclamp times. CONCLUSIONS As the number of mitral valve repairs performed each year by a single surgeon at a single institution increased, morbidity, including postoperative heart function and length of stay, decreased. This was demonstrated to occur in large part from a reduction in the aortic crossclamp times, despite an increase in the complexity of the procedures. This further demonstrates the value of reference centers for mitral valve surgery.

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.

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.

Operationalizing Precision Cardiovascular Medicine: Three Innovations

For precision medicine to become a reality, we propose 3 changes. First, healthcare deliverables must be prioritized, enabling translation of knowledge to the clinic. Second, physicians and patients must be convinced to participate, requiring additional infrastructure in health systems. Third, discovery science must evolve to shift the preclinical landscape for innovation. We propose a change in the fundamental relationship between basic and clinical science: rather than 2 distinct entities between which concepts must be translated, we envision a natural hybrid of these approaches, wherein discovery science and clinical trials coincide in the same health systems and patient populations. Even the generally progressive physician can be unsure and perhaps even skeptical about how precision medicine will fit into daily practice. In our view, precision medicine is personalized clinical care informed (primarily) by measurements of the genome, epigenome, proteome, transcriptome, metabolome, and microbiome. Precision medicine should result in a more healthy life with less disease burden, and it should be delivered on an individualized basis. Many therapies fail when they are tested in large animal models1,2 or when transitioning from phase I to phase II clinical trials,3,4 leading to a decrease in the number of new drugs making it to market.5 Despite improvements based on guideline adherence, cardiovascular disease remains the leading killer and a tremendous financial burden on the nation. Implementation of precision medicine will require collaboration across the spectrum of research and healthcare delivery, including funding agencies, insurers, academic medical centers, private hospitals and consumer ‘omics providers, and the potentially underappreciated relationship between patient and physician. Where to begin? How to convince patients and families to enroll? How to persuade clinicians to participate? How to add value to the health system? Who should pay? In answering these questions, we considered a new model …

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

Perioperative Temperature Measurement Considerations Relevant to Reporting Requirements for National Quality Programs Using Data From Anesthesia Information Management Systems

BACKGROUND: Perioperative hypothermia may increase the incidences of wound infection, blood loss, transfusion, and cardiac morbidity. US national quality programs for perioperative normothermia specify the presence of at least 1 “body temperature” ≥35.5°C during the interval from 30 minutes before to 15 minutes after the anesthesia end time. Using data from 4 academic hospitals, we evaluated timing and measurement considerations relevant to the current requirements to guide hospitals wishing to report perioperative temperature measures using electronic data sources. METHODS: Anesthesia information management system databases from 4 hospitals were queried to obtain intraoperative temperatures and intervals to the anesthesia end time from discontinuation of temperature monitoring, end of surgery, and extubation. Inclusion criteria included age >16 years, use of a tracheal tube or supraglottic airway, and case duration ≥60 minutes. The end-of-case temperature was determined as the maximum intraoperative temperature recorded within 30 minutes before the anesthesia end time (ie, the temperature that would be used for reporting purposes). The fractions of cases with intervals >30 minutes between the last intraoperative temperature and the anesthesia end time were determined. RESULTS: Among the hospitals, averages (binned by quarters) of 34.5% to 59.5% of cases had intraoperative temperature monitoring discontinued >30 minutes before the anesthesia end time. Even if temperature measurement had been continued until extubation, averages of 5.9% to 20.8% of cases would have exceeded the allowed 30-minute window. Averages of 8.9% to 21.3% of cases had end-of-case intraoperative temperatures <35.5°C (ie, a quality measure failure). CONCLUSIONS: Because of timing considerations, a substantial fraction of cases would have been ineligible to use the end-of-case intraoperative temperature for national quality program reporting. Thus, retrieval of postanesthesia care unit temperatures would have been necessary. A substantive percentage of cases had end-of-case intraoperative temperatures below the 35.5°C threshold, also requiring postoperative measurement to determine whether the quality measure was satisfied. Institutions considering reporting national quality measures for perioperative normothermia should consider the technical and logistical issues identified to achieve a high level of compliance based on the specified regulatory language.

A patient who received clopidogrel with an indwelling epidural catheter.

A patient with a drug-eluting stent placed 18 months earlier received a thoracic epidural for perioperative analgesic control as part of her thoracotomy. Postoperatively, the patient was started on clopidogrel for secondary prevention. After consultation with the Hematology service and a platelet function assay, the patient was transfused two pools of platelets and the epidural catheter was removed on postoperative day 4. The patient then underwent hourly neurologic checks for 24 hours and was discharged several days later without any negative sequelae. If neuraxial techniques and the need for clopidogrel prophylaxis come into direct conflict, vigilance is necessary for warning signs of epidural hematoma and platelet transfusion should be considered to reverse the effects of the drug.

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.