Intraoperative blood glucose management: impact of a real-time decision support system on adherence to institutional protocol

Anesthesia decision analysis using a cloud-based big data platform

Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.

Efficacy of Electronic Reminders in Increasing the Enhanced Recovery After Surgery Protocol Use During Major Breast Surgery: Prospective Cohort Study

BACKGROUND

Enhanced recovery after surgery (ERAS) protocols are patient-centered, evidence-based guidelines for peri-, intra-, and postoperative management of surgical candidates that aim to decrease operative complications and facilitate recovery after surgery. Anesthesia providers can use these protocols to guide decision-making and standardize aspects of their anesthetic plan in the operating room.

OBJECTIVE

Research across multiple disciplines has demonstrated that clinical decision support systems have the potential to improve protocol adherence by reminding providers about departmental policies and protocols via notifications. There remains a gap in the literature about whether clinical decision support systems can improve patient outcomes by improving anesthesia providers' adherence to protocols. Our hypothesis is that the implementation of an electronic notification system to anesthesia providers the day prior to scheduled breast surgeries will increase the use of the already existing but underused ERAS protocols.

METHODS

This was a single-center prospective cohort study conducted between October 2017 and August 2018 at an urban academic medical center. After obtaining approval from the institutional review board, anesthesia providers assigned to major breast surgery cases were identified. Patient data were collected pre- and postimplementation of an electronic notification system that sent the anesthesia providers an email reminder of the ERAS breast protocol the night before scheduled surgeries. Each patient's record was then reviewed to assess the frequency of adherence to the various ERAS protocol elements.

RESULTS

Implementation of an electronic notification significantly improved overall protocol adherence and several preoperative markers of ERAS protocol adherence. Protocol adherence increased from 16% (n=14) to 44% (n=44; P<.001), preoperative administration of oral gabapentin (600 mg) increased from 13% (n=11) to 43% (n=43; P<.001), and oral celebrex (400 mg) use increased from 16% (n=14) to 35% (n=35; P=.006). There were no statistically significant differences in the use of scopolamine transdermal patch (P=.05), ketamine (P=.35), and oral acetaminophen (P=.31) between the groups. Secondary outcomes such as intraoperative and postoperative morphine equivalent administered, postanesthesia care unit length of stay, postoperative pain scores, and incidence of postoperative nausea and vomiting did not show statistical significance.

CONCLUSIONS

This study examines whether sending automated notifications to anesthesia providers increases the use of ERAS protocols in a single academic medical center. Our analysis exhibited statistically significant increases in overall protocol adherence but failed to show significant differences in secondary outcome measures. Despite the lack of a statistically significant difference in secondary postoperative outcomes, our analysis contributes to the limited literature on the relationship between using push notifications and clinical decision support in guiding perioperative decision-making. A variety of techniques can be implemented, including technological solutions such as automated notifications to providers, to improve awareness and adherence to ERAS protocols.

Feasibility of intraoperative continuous glucose monitoring: An observational study in general surgery patients

BACKGROUND

Perioperative hyperglycemia is associated with adverse outcomes in surgical patients, and major societies recommend intraoperative monitoring and treatment targeting glucose <180-200 mg/dL. However, compliance with these recommendations is poor, in part due to fear of unrecognized hypoglycemia. Continuous Glucose Monitors (CGMs) measure interstitial glucose with a subcutaneous electrode and can display the results on a receiver or smartphone. Historically CGMs have not been utilized for surgical patients. We investigated the use of CGM in the perioperative setting compared to current standard practices.

METHOD

This study evaluated the use of Abbott Freestyle Libre 2.0 and/or Dexcom G6 CGMs in a prospective cohort of 94 participants with diabetes mellitus undergoing surgery of ≥3 h duration. CGMs were placed preoperatively and compared to point of care (POC) BG checks obtained by capillary samples analyzed with a NOVA glucometer. Frequency of intraoperative blood glucose measurement was at the discretion of the anesthesia care team, with a recommendation of once per hour targeting BG of 140-180 mg/dL. Of those consented, 18 were excluded due to lost sensor data, surgery cancellation, or rescheduling to a satellite campus resulting in 76 enrolled subjects. There were zero occurrences of failure with sensor application. Paired POC BG and contemporaneous CGM readings were compared with Pearson product-moment correlation coefficients, and Bland-Altman plots.

RESULTS

Data for use of CGM in perioperative period was analyzed for 50 participants with Freestyle Libre 2.0, 20 participants with Dexcom G6, and 6 participants with both devices worn simultaneously. Lost sensor data occurred in 3 participants (15%) wearing Dexcom G6, 10 participants wearing Freestyle Libre 2.0 (20%) and 2 of the participants wearing both devices simultaneously. The overall agreement of the two CGM's utilized had a Pearson correlation coefficient of 0.731 in combined groups with 0.573 in Dexcom arm evaluating 84 matched pairs and 0.771 in Libre arm with 239 matched pairs. Modified Bland-Altman plot of the difference of CGM and POC BG indicated for the overall dataset a bias of -18.27 (SD 32.10).

CONCLUSIONS

Both Dexcom G6 and Freestyle Libre 2.0 CGMs were able to be utilized and functioned well if no sensor error occurred at time of initial warmup. CGM provided more glycemic data and further characterized glycemic trends more than individual BG readings. Required time of CGM warm up was a barrier for intraoperative use as well as unexplained sensor failure. CGMs had a fixed warm of time, 1 h for Libre 2.0 and 2 h for Dexcom G6 CGM, before glycemic data obtainable. Sensor application issues did not occur. It is anticipated that this technology could be used to improve glycemic control in the perioperative setting. Additional studies are needed to evaluate use intraoperatively and assess further if any interference from electrocautery or grounding devices may contribute to initial sensor failure. It may be beneficial in future studies to place CGM during preoperative clinic evaluation the week prior to surgery. Use of CGMs in these settings is feasible and warrants further evaluation of this technology on perioperative glycemic management.

Implementation of App-Based Diabetes Medication Management: Outpatient and Perioperative Clinical Decision Support

PURPOSE OF REVIEW

Outpatient and perioperative therapeutic decision making for patients with diabetes involves increasingly complex medical-decision making due to rapid advances in knowledge and treatment modalities. We sought to review mobile decision support tools available to clinicians for this essential and increasingly difficult task, and to highlight the development and implementation of novel mobile applications for these purposes.

RECENT FINDINGS

We found 211 mobile applications related to diabetes from the search, but only five were found to provide clinical decision support for outpatient diabetes management and none for perioperative decision support. We found a dearth of tools for clinicians to navigate these tasks. We highlight key aspects for effective development of future diabetes decision support. These include just-in-time availability, respect for the five rights of clinical decision support, and integration with clinical workflows including the electronic medical record.

Gradually Increasing Surgical Site Infection Prevention Bundle with Monitoring of Potentially Preventable Infections Resulting in Decreasing Overall Surgical Site Infection Rate

Objective: Reduction of surgical site infection. Methods: Retrospective evaluation of a surgical infection prevention program consisting of the gradual introduction of specific infection prevention methods and a surveillance system identifying and reporting on potentially preventable surgical site infections as defined by the omission of a preventive method. Setting: A university tertiary referral medical center. Results: The sequential introduction of infection prevention elements in the bundle resulted in a fluctuating rate of potentially preventable surgical site infections simultaneously with a slow, gradual reduction of the clean wound SSI rate. Conclusions: Change in a complex, multidisciplinary environment such as an inpatient surgical unit happens gradually and requires focused attention and input from all involved professionals.

The Impact of an Intraoperative Clinical Decision Support Tool to Optimize Perioperative Glycemic Management

With the transition from Vanderbilt's Perioperative Information Management System (VPIMS) to Epic's Best Practice Advisory (BPA) framework, a replacement intraoperative glucose clinical decision support (CDS) system was designed. We examined changes in the frequency of intraoperative glucose monitoring, hyper- and hypoglycemia rates in the post-anesthesia care unit (PACU), to determine the impact of the changes on glucose management. Data were collected into three phases: 1) VPIMS CDS, 2) No CDS, and 3) BPA CDS. One-way ANOVA was conducted to test the significance of changes in the frequency of glucose monitoring and abnormal glucose across phases. Interrupted time series segmented analysis was performed to assess the autocorrelation and trend over times. A total of 3706 cases were analyzed. The monitoring rate fell from 84.5% in VPIMS CDS to 67.6% in No CDS (p < .001) and increased to 83.1% in BPA CDS (p < .001). The PACU hyperglycemia rate increased from VPIMS CDS to No CDS (5.2% to 10.4%, p < .001) and decreased from No CDS to BPA CDS (10.4% to 7.2%, p = 0.031). The segmented analysis demonstrated immediate changes in the intraoperative monitoring frequency (p < .001) and postoperative hyperglycemia rate (p = 0.002) with the replacement of CDS. The temporary removal of CDS was associated with a significant reduction in intraoperative glucose monitoring and increased hyperglycemia in the PACU. Implementation of the BPA CDS led to a significant improvement in the intraoperative glucose monitoring and glucose management in the PACU.

Process Optimization and Digital Quality Improvement to Enhance Timely Initiation of Epidural Infusions and Postoperative Pain Control

BACKGROUND

Although intraoperative epidural analgesia improves postoperative pain control, a recent quality improvement project demonstrated that only 59% of epidural infusions are started in the operating room before patient arrival in the postanesthesia care unit. We evaluated the combined effect of process and digital quality improvement efforts on provider compliance with starting continuous epidural infusions during surgery.

METHODS

In October 2014, we instituted 2 process improvement initiatives: (1) an electronic order queue to assist the operating room pharmacy with infusate preparation; and (2) a designated workspace for the storage of equipment related to epidural catheter placement and drug infusion delivery. In addition, we implemented a digital quality improvement initiative, an Anesthesia Information Management System-mediated clinical decision support, to prompt anesthesia providers to start and document epidural infusions in pertinent patients. We assessed anesthesia provider compliance with epidural infusion initiation in the operating room and postoperative pain-related outcomes before (PRE: October 1, 2012 to September 31, 2014) and after (POST: January 1, 2015 to December 31, 2016) implementation of the quality improvement initiatives.

RESULTS

Compliance with starting intraoperative epidural infusions was 59% in the PRE group and 85% in the POST group. After adjustment for confounders and preintervention time trends, segmented regression analysis demonstrated a statistically significant increase in compliance with the intervention in the POST phase (odds ratio, 2.78; 95% confidence interval, 1.73-4.49; P < .001). In the PRE and POST groups, cumulative postoperative intravenous opioid use (geometric mean) was 62 and 34 mg oral morphine equivalents, respectively. A segmented regression analysis did not demonstrate a statistically significant difference (P = .38) after adjustment for preintervention time trends.

CONCLUSIONS

Process workflow optimization along with Anesthesia Information Management System-mediated digital quality improvement efforts increased compliance to intraoperative epidural infusion initiation. Adjusted for preintervention time trends, these findings coincided with a statistically insignificant decrease in postoperative opioid use in the postanesthesia care unit during the POST phase.

Intraoperative glycemic control in patients undergoing Orthotopic liver transplant: a single center prospective randomized study

Background

Perioperative hyperglycemia is associated with poor outcomes yet evidence to guide intraoperative goals and treatment modalities during non-cardiac surgery are lacking. End-stage liver disease is associated with altered glucose homeostasis; patients undergoing liver transplantation display huge fluctuations in blood glucose (BG) and represent a population of great interest. Here, we conduct a randomized trial to compare the effects of strict versus conventional glycemic control during orthotopic liver transplant (OLT).

Methods

Following approval by the Institutional Review Board of the University of Michigan Medical School and informed consent, 100 adult patients undergoing OLT were recruited. Patients were randomized to either strict (target BG 80–120 mg/dL) or conventional (target BG 180–200 mg/dL) BG control with block randomization for diabetic and nondiabetic patients. The primary outcomes measured were 1-year patient and graft survival assessed on an intention to treat basis. Graft survival is defined as death or needing re-transplant (www.unos.org). Three and 5-year patient and graft survival, infectious and biliary complications were measured as secondary outcomes. Data were examined using univariate methods and Kaplan-Meir survival analysis. A sensitivity analysis was performed to compare patients with a mean BG of ≤120 mg/dL and those > 120 mg/dL regardless of treatment group.

Results

There was no statistically significant difference in patient survival between conventional and strict control respectively;1 year, 88% vs 88% (p-0.99), 3 years, 86% vs 84% (p- 0.77), 5 years, 82% vs 78. % (p-0.36). Graft survival was not different between conventional and strict control groups at 1 year, 88% vs 84% (p-0.56), 3 years 82% vs 76% (p-0.46), 5 years 78% vs 70% (p-0.362).

Conclusion

There was no difference in patient or graft survival between intraoperative strict and conventional glycemic control during OLT.

Trial registration

Clinical trial number and registry: www.clinicaltrials.gov NCT00780026. This trial was retrospectively registered on 10/22/2008.

Development and Implementation of the Portable Operating Room Tracker App With Vital Signs Streaming Infrastructure: Operational Feasibility Study

Background

In the perioperative environment, a multidisciplinary clinical team continually observes and evaluates patient information. However, data availability may be restricted to certain locations, cognitive workload may be high, and team communication may be constrained by availability and priorities. We developed the remote Portable Operating Room Tracker app (the telePORT app) to improve information exchange and communication between anesthesia team members. The telePORT app combines a real-time feed of waveforms and vital signs from the operating rooms with messaging, help request, and reminder features.

Objective

The aim of this paper is to describe the development of the app and the back-end infrastructure required to extract monitoring data, facilitate data exchange and ensure privacy and safety, which includes results from clinical feasibility testing.

Methods

telePORT’s client user interface was developed using user-centered design principles and workflow observations. The server architecture involves network-based data extraction and data processing. Baseline user workload was assessed using step counters and communication logs. Clinical feasibility testing analyzed device usage over 11 months.

Results

telePORT was more commonly used for help requests (approximately 4.5/day) than messaging between team members (approximately 1/day). Passive operating room monitoring was frequently utilized (34% of screen visits). Intermittent loss of wireless connectivity was a major barrier to adoption (decline of 0.3%/day).

Conclusions

The underlying server infrastructure was repurposed for real-time streaming of vital signs and their collection for research and quality improvement. Day-to-day activities of the anesthesia team can be supported by a mobile app that integrates real-time data from all operating rooms.

Feasibility and indicator outcomes using computerized clinical decision support in pediatric traumatic brain injury anesthesia care

BACKGROUND

Traumatic brain injury anesthesia care is complex. The use of clinical decision support to improve pediatric trauma care has not been examined.

AIMS

The aim of this study was to examine feasibility, reliability, and key performance indicators for traumatic brain injury anesthesia care using clinical decision support.

METHODS

Clinical decision support was activated for patients under 19 years undergoing craniotomy for suspected traumatic brain injury. Anesthesia providers were prompted to adhere to process measures via on-screen alerts and notified in real time of abnormal monitor data or laboratory results (unwanted key performance indicator events). Process measures pertained to arterial line placement and blood gas draws, neuromuscular blockade, hypotension, anemia, coagulopathy, hyperglycemia, and intracranial hypertension. Unwanted key performance indicators were: hypotension, hypoxia, hypocarbia, hypercarbia, hypothermia, hyperthermia, anesthetic agent overdose; hypoxemia, coagulopathy, anemia, and hyperglycemia. Anesthesia records, vital signs, and alert logs were reviewed for 39 anesthetic cases (19 without clinical decision support and 20 with clinical decision support).

RESULTS

Data from 35 patients aged 11 months to 17 years and 77% males were examined. Clinical decision support reliably identified 39/46 eligible anesthetic cases, with 85% sensitivity and 100% specificity, and was highly sensitive, detecting 89% of monitor key performance indicator events and 100% of reported lab key performance indicator events. There were no false positive alerts. Median event duration was lower in the "with clinical decision support" group for 4/7 key performance indicators. Second insult duration was lower for duration of hypocarbia (by 44%), hypotension (29%), hypothermia (12%), and hyperthermia (15%).

CONCLUSION

Use of clinical decision support in pediatric traumatic brain injury anesthesia care is feasible, reliable, and may have the potential to improve key performance indicator outcomes. This observational study suggests the possibility of clinical decision support as a strategy to reduce second insults and improve traumatic brain injury guideline adherence during pediatric anesthesia care.

What we can learn from Big Data about factors influencing perioperative outcome

PURPOSE OF REVIEW

This narrative review will discuss what value Big Data has to offer anesthesiology and aims to highlight recently published articles of large databases exploring factors influencing perioperative outcome. Additionally, the future perspectives of Big Data and its major pitfalls will be discussed.

RECENT FINDINGS

The potential of Big Data has given an incentive to create nationwide and anesthesia-initiated registries like the MPOG and NACOR. These large databases have contributed in elucidating some of the rare perioperative complications, such as declined cognition after exposure to general anesthesia and epidural hematomas in parturients. Additionally, they are useful in finding patterns such as similar outcome in subtypes of beta-blockers and lower incidence of pneumonia in preoperative influenza vaccinations in the elderly.

SUMMARY

Big Data is becoming increasingly popular with the collaborative collection of registries offering anesthesia a way to explore rare perioperative complications and outcome to encourage further hypotheses testing. Although Big Data has its flaws in security, lack of expertise and methodological concerns, the future potential of analytics combined with genomics, machine learning and real-time decision support looks promising.

Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension

Editor’s Perspective

What We Already Know about This Topic

What This Article Tells Us That Is New

Background

Hypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.

Methods

Data was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.

Results

Out of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).

Conclusions

The success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

Study protocol for the Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine

Background: Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods: This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination: The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov ( NCT02830126). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials.

Study protocol for the Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine

Background: Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods: This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination: The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov ( NCT02830126). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials.

Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review

Objectives

To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care.

Materials and Methods

Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well.

Results

Seventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non-life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P < .001). Six of 24 disease entity settings showed high effect scores with medium or low risk of bias: blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis. Most of the implemented algorithms (72%) were rule-based. Reported input variables are shared as standardized models on a metadata repository.

Discussion and Conclusion

Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems

Anesthesia information management systems (AIMS) are sophisticated hardware and software technology solutions that can provide electronic feedback to anesthesia providers. This feedback can be tailored to provide clinical decision support (CDS) to aid clinicians with patient care processes, documentation compliance, and resource utilization. We conducted a systematic review of peer-reviewed articles on near real-time and point-of-care CDS within AIMS using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Studies were identified by searches of the electronic databases Medline and EMBASE. Two reviewers screened studies based on title, abstract, and full text. Studies that were similar in intervention and desired outcome were grouped into CDS categories. Three reviewers graded the evidence within each category. The final analysis included 25 articles on CDS as implemented within AIMS. CDS categories included perioperative antibiotic prophylaxis, post-operative nausea and vomiting prophylaxis, vital sign monitors and alarms, glucose management, blood pressure management, ventilator management, clinical documentation, and resource utilization. Of these categories, the reviewers graded perioperative antibiotic prophylaxis and clinical documentation as having strong evidence per the peer reviewed literature. There is strong evidence for the inclusion of near real-time and point-of-care CDS in AIMS to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation. Additional research is needed in many other areas of AIMS-based CDS.

Continuous intraoperative epidural infusions affect recovery room length of stay and analgesic requirements: a single-center observational study

PURPOSE

Continuous intraoperative epidural analgesia may improve post-operative pain control and decrease opioid requirements. We investigate the effect of epidural infusion initiation before or after arrival in the post-anesthesia care unit on recovery room duration and post-operative opioid use.

METHODS

We performed a retrospective chart review of abdominal, thoracic and orthopedic surgeries where an epidural catheter was placed prior to surgery at the University of Washington Medical Center during a 24 month period.

RESULTS

Patients whose epidural infusions were started prior to PACU arrival (Group 2: n = 540) exhibited a shorter PACU length of stay (p = .004) and were less likely to receive intravenous opioids in the recovery room (34 vs. 48%; p < .001) compared to patients whose infusions were started after surgery (Group 1: n = 374). Although the highest patient-reported pain scores were lower in Group 2 (5.3 vs. 6.0; p = .030), no differences in the pain scores prior to PACU discharge were observed.

CONCLUSION

Intraoperative continuous epidural infusions decrease PACU LOS as discharge criteria for patient-reported NRS pain scores are met earlier.