https://periop.jmir.org/issue/feedJMIR Perioperative Medicine2023-01-12T09:30:04-05:00JMIR Publicationseditor@jmir.orgOpen Journal Systems Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in the Journal of Medical Internet Research...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. Technology and data science for interdisciplinary innovation to improve care delivery and surgical patient outcomes. https://periop.jmir.org/2024/1/e57012/ Blood Management: A Current Opportunity in Perioperative Medicine2024-03-08T09:15:03-05:00Moises AuronThe purpose of this viewpoint is to provide awareness of the current opportunities to enhance a high-value care approach to blood product transfusion. It provides a historical context to the evolution of blood management, as well as of the patient safety and high-value care movement. Leveraging current technology for enhanced education, as well as clinical decision support, is also discussed.2024-03-08T09:15:03-05:00 https://periop.jmir.org/2024/1/e45126/ Comparing Anesthesia and Surgery Controlled Time for Primary Total Knee and Hip Arthroplasty Between an Academic Medical Center and a Community Hospital: Retrospective Cohort Study2024-02-26T09:15:04-05:00Thy B NguyenNathaen WeitzelCraig HoganRachel M KacmarKayla M WilliamsonJack PatteeVesna Jevtovic-TodorovicColby G SimmonsAdeel Ahmad Faruki<strong>Background:</strong> Osteoarthritis is a significant cause of disability, resulting in increased joint replacement surgeries and health care costs. Establishing benchmarks that more accurately predict surgical duration could help to decrease costs, maximize efficiency, and improve patient experience. We compared the anesthesia-controlled time (ACT) and surgery-controlled time (SCT) of primary total knee (TKA) and total hip arthroplasties (THA) between an academic medical center (AMC) and a community hospital (CH) for 2 orthopedic surgeons. <strong>Objective:</strong> This study aims to validate and compare benchmarking times for ACT and SCT in a single patient population at both an AMC and a CH. <strong>Methods:</strong> This retrospective 2-center observational cohort study was conducted at the University of Colorado Hospital (AMC) and UCHealth Broomfield Hospital (CH). Cases with current procedural terminology codes for THA and TKA between January 1, 2019, and December 31, 2020, were assessed. Cases with missing data were excluded. The primary outcomes were ACT and SCT. Primary outcomes were tested for association with covariates of interest. The primary covariate of interest was the location of the procedure (CH vs AMC); secondary covariates of interest included the American Society of Anesthesiologists (ASA) classification and anesthetic type. Linear regression models were used to assess the relationships. <strong>Results:</strong> Two surgeons performed 1256 cases at the AMC and CH. A total of 10 THA cases and 12 TKA cases were excluded due to missing data. After controlling for surgeon, the ACT was greater at the AMC for THA by 3.77 minutes and for TKA by 3.58 minutes (<i>P</i><.001). SCT was greater at the AMC for THA by 11.14 minutes and for TKA by 14.04 minutes (<i>P</i><.001). ASA III/IV classification increased ACT for THA by 3.76 minutes (<i>P</i><.001) and increased SCT for THA by 6.33 minutes after controlling for surgeon and location (<i>P</i>=.008). General anesthesia use was higher at the AMC for both THA (29.2% vs 7.3%) and TKA (23.8% vs 4.2%). No statistically significant association was observed between either ACT or SCT and anesthetic type (neuraxial or general) after adjusting for surgeon and location (all <i>P</i>>.05). <strong>Conclusions:</strong> We observed lower ACT and SCT at the CH for both TKA and THA after controlling for the surgeon of record and ASA classification. These findings underscore the efficiency advantages of performing primary joint replacements at the CH, showcasing an average reduction of 16 minutes in SCT and 4 minutes in ACT per case. Overall, establishing more accurate benchmarks to improve the prediction of surgical duration for THA and TKA in different perioperative environments can increase the reliability of surgical duration predictions and optimize scheduling. Future studies with study populations at multiple community hospitals and academic medical centers are needed before extrapolating these findings. 2024-02-26T09:15:04-05:00 https://periop.jmir.org/2023/1/e49186/ Survey of the Impact of Decision Support in Preoperative Management of Anemia (i-Anemia): Survey Study2023-12-01T09:30:05-05:00Gaëtan MignanelliRichard BoyerNicolas BonifasEmmanuel RineauYassine MoussaliMorgan Le Guen<strong>Background:</strong> Major surgery on patients with anemia has demonstrated an increased risk of perioperative blood transfusions and postoperative morbidity and mortality. Recent studies have shown that integrating preoperative anemia treatment as a component of perioperative blood management may reduce blood product utilization and improve outcomes in both cardiac and noncardiac surgery. However, outpatient management of anemia falls outside of daily practice for most anesthesiologists and is probably weakly understood. <strong>Objective:</strong> We conducted a simulated case survey with anesthesiologists to accomplish the following aims: (1) evaluate the baseline knowledge of the preoperative optimization of anemia and (2) determine the impact of real-time clinical decision support on anemia management. <strong>Methods:</strong> We sent a digital survey (i-Anemia) to members of the French Society of Anaesthesia and Critical Care. The i-Anemia survey contained 7 simulated case vignettes, each describing a patient’s brief clinical history and containing up to 3 multiple-choice questions related to preoperative anemia management (12 questions in total). The cases concerned potential situations of preoperative anemia and were created and validated with a committee of patient blood management experts. Correct answers were determined by the current guidelines or by expert consensus. Eligible participants were randomly assigned to control or decision support groups. In the decision support group, the primary outcome measured was the correct response rate. <strong>Results:</strong> Overall, 1123 participants were enrolled and randomly divided into control (n=568) and decision support (n=555) groups. Among them, 763 participants fully responded to the survey. We obtained a complete response rate of 65.6% (n=364) in the group receiving cognitive aid and 70.2% (n=399) in the group without assistance. The mean duration of response was 10.2 (SD 6.8) minutes versus 7.8 (SD 5) minutes for the decision support and control groups, respectively (<i>P</i><.001). The score significantly improved with cognitive aid (mean 10.3 out of 12, SD 2.1) in comparison to standard care (mean 6.2 out of 12, SD 2.1; <i>P</i><.001). <strong>Conclusions:</strong> Management strategies to optimize preoperative anemia are not fully known and applied by anesthesiologists in daily practice despite their clinical importance. However, adding a decision support tool can significantly improve patient care by reminding practitioners of current recommendations. 2023-12-01T09:30:05-05:00 https://periop.jmir.org/2023/1/e54344/ JMIR Perioperative Medicine: A Global Journal for Publishing Interdisciplinary Innovations, Research, and Perspectives2023-11-21T09:00:30-05:00Nidhi Rohatgi<i>JMIR Perioperative Medicine</i> supports the dissemination of technological and data science–driven innovative research conducted by interdisciplinary teams in perioperative medicine. We invite contributions on a broad range of topics from clinicians, scientists, and allied health professionals from across the globe.2023-11-21T09:00:30-05:00 https://periop.jmir.org/2023/1/e50212/ Teaching Basic Surgical Skills Using a More Frugal, Near-Peer, and Environmentally Sustainable Way: Mixed Methods Study2023-11-15T09:45:37-05:00Ben SmithChristopher PatonPrashanth Ramaraj<strong>Background:</strong> The Royal College of Surgeons Basic Surgical Skills (BSS) course is ubiquitous among UK surgical trainees but is geographically limited and costly. The COVID-19 pandemic has reduced training quality. Surveys illustrate reduced logbook completion and increased trainee attrition. Local, peer-led teaching has been shown to be effective at increasing confidence in surgical skills in a cost-effective manner. Qualitative data on trainee well-being, recruitment, and retention are lacking. <strong>Objective:</strong> This study aims to evaluate the impact of a novel program of weekly, lunchtime BSS sessions on both quantitative and qualitative factors. <strong>Methods:</strong> A weekly, lunchtime BSS course was designed to achieve the outcomes of the Royal College of Surgeons BSS course over a 16-week period overlapping with 1 foundation doctor rotation. All health care workers at the study center were eligible to participate. The study was advertised via the weekly, trust-wide information email. Course sessions included knot tying, suturing, abscess incision and drainage, fracture fixation with application of plaster of Paris, joint aspirations and reductions, abdominal wall closure, and basic laparoscopic skills. The hospital canteen sourced unwanted pig skin from the local butcher for suturing sessions and pork belly for abscess and abdominal wall closure sessions. Out-of-date surgical equipment was used. This concurrent, nested, mixed methods study involved descriptive analysis of perceived improvement scores in each surgical skill before and after each session, over 4 iterations of the course (May 2021 to August 2022). After the sessions, students completed a voluntary web-based feedback form scoring presession and postsession confidence levels on a 5-point Likert scale. Qualitative thematic analysis of voluntary semistructured student interview transcripts was also performed to understand the impact of a free-to-attend, local, weekly, near-peer teaching course on perceived well-being, quality of training, and interest in a surgical career. Students consented to the use of feedback and interview data for this study. Ethics approval was requested but deemed not necessary by the study center’s ethics committee. <strong>Results:</strong> There were 64 responses. Confidence was significantly improved from 47% to 73% (95% CI 15%-27%; <i>P</i><.001; <i>t</i><sub>13</sub>=5.3117) across all surgical skills over 4 iterations. Among the 7 semistructured interviews, 100% (7/7) of the participants reported improved perceived well-being, value added to training, and positivity toward near-peer teaching and 71% (5/7) preferred local weekly teaching. Interest in a surgical career was unchanged. <strong>Conclusions:</strong> This course was feasible around clinical workloads, resourced locally at next to no cost, environmentally sustainable, and free to attend. The course offered junior doctors not only a weekly opportunity to learn but also to teach. Peer-led, decentralized surgical education increases confidence and has a positive effect on perceptions about well-being and training. We hope to disseminate this course, leading to reproduction in other centers, refinement, and wide implementation. 2023-11-15T09:45:37-05:00 https://periop.jmir.org/2023/1/e50188/ A New Index for the Quantitative Evaluation of Surgical Invasiveness Based on Perioperative Patients’ Behavior Patterns: Machine Learning Approach Using Triaxial Acceleration2023-11-14T09:45:27-05:00Kozo NakanishiHidenori Goto<strong>Background:</strong> The minimally invasive nature of thoracoscopic surgery is well recognized; however, the absence of a reliable evaluation method remains challenging. We hypothesized that the postoperative recovery speed is closely linked to surgical invasiveness, where recovery signifies the patient’s behavior transition back to their preoperative state during the perioperative period. <strong>Objective:</strong> This study aims to determine whether machine learning using triaxial acceleration data can effectively capture perioperative behavior changes and establish a quantitative index for quantifying variations in surgical invasiveness. <strong>Methods:</strong> We trained 7 distinct machine learning models using a publicly available human acceleration data set as supervised data. The 3 top-performing models were selected to predict patient actions, as determined by the Matthews correlation coefficient scores. Two patients who underwent different levels of invasive thoracoscopic surgery were selected as participants. Acceleration data were collected via chest sensors for 8 hours during the preoperative and postoperative hospitalization days. These data were categorized into 4 actions (walking, standing, sitting, and lying down) using the selected models. The actions predicted by the model with intermediate results were adopted as the actions of the participants. The daily appearance probability was calculated for each action. The 2 differences between 2 appearance probabilities (sitting vs standing and lying down vs walking) were calculated using 2 coordinates on the x- and y-axes. A 2D vector composed of coordinate values was defined as the index of behavior pattern (iBP) for the day. All daily iBPs were graphed, and the enclosed area and distance between points were calculated and compared between participants to assess the relationship between changes in the indices and invasiveness. <strong>Results:</strong> Patients 1 and 2 underwent lung lobectomy and incisional tumor biopsy, respectively. The selected predictive model was a light-gradient boosting model (mean Matthews correlation coefficient 0.98, SD 0.0027; accuracy: 0.98). The acceleration data yielded 548,466 points for patient 1 and 466,407 points for patient 2. The iBPs of patient 1 were [(0.32, 0.19), (–0.098, 0.46), (–0.15, 0.13), (–0.049, 0.22)] and those of patient 2 were [(0.55, 0.30), (0.77, 0.21), (0.60, 0.25), (0.61, 0.31)]. The enclosed areas were 0.077 and 0.0036 for patients 1 and 2, respectively. Notably, the distances for patient 1 were greater than those for patient 2 ({0.44, 0.46, 0.37, 0.26} vs {0.23, 0.0065, 0.059}; <i>P</i>=.03 [Mann-Whitney <i>U</i> test]). <strong>Conclusions:</strong> The selected machine learning model effectively predicted the actions of the surgical patients with high accuracy. The temporal distribution of action times revealed changes in behavior patterns during the perioperative phase. The proposed index may facilitate the recognition and visualization of perioperative changes in patients and differences in surgical invasiveness. 2023-11-14T09:45:27-05:00 https://periop.jmir.org/2023/1/e44139/ Efficacy of Electronic Reminders in Increasing the Enhanced Recovery After Surgery Protocol Use During Major Breast Surgery: Prospective Cohort Study2023-11-03T09:30:05-04:00Sumeet GopwaniEhab BahrunTanvee SinghDaniel PopovskyJoseph CramerXue Geng<strong>Background:</strong> 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. <strong>Objective:</strong> 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. <strong>Methods:</strong> 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. <strong>Results:</strong> 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; <i>P</i><.001), preoperative administration of oral gabapentin (600 mg) increased from 13% (n=11) to 43% (n=43; <i>P</i><.001), and oral celebrex (400 mg) use increased from 16% (n=14) to 35% (n=35; <i>P</i>=.006). There were no statistically significant differences in the use of scopolamine transdermal patch (<i>P</i>=.05), ketamine (<i>P</i>=.35), and oral acetaminophen (<i>P</i>=.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. <strong>Conclusions:</strong> 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. 2023-11-03T09:30:05-04:00 https://periop.jmir.org/2023/1/e47714/ Description of the Content and Quality of Publicly Available Information on the Internet About Inhaled Volatile Anesthesia and Total Intravenous Anesthesia: Descriptive Study2023-11-02T09:15:24-04:00Xinwen HuBethany R Tellor PenningtonMichael S AvidanSachin KheterpalNastassjia G deBourbonMary C Politi<strong>Background:</strong> More than 300 million patients undergo surgical procedures requiring anesthesia worldwide annually. There are 2 standard-of-care general anesthesia administration options: inhaled volatile anesthesia (INVA) and total intravenous anesthesia (TIVA). There is limited evidence comparing these methods and their impact on patient experiences and outcomes. Patients often seek this information from sources such as the internet. However, the majority of websites on anesthesia-related topics are not comprehensive, updated, and fully accurate. The quality and availability of web-based patient information about INVA and TIVA have not been sufficiently examined. <strong>Objective:</strong> This study aimed to (1) assess information on the internet about INVA and TIVA for availability, readability, accuracy, and quality and (2) identify high-quality websites that can be recommended to patients to assist in their anesthesia information-seeking and decision-making. <strong>Methods:</strong> Web-based searches were conducted using Google from April 2022 to November 2022. Websites were coded using a coding instrument developed based on the International Patient Decision Aids Standards criteria and adapted to be appropriate for assessing websites describing INVA and TIVA. Readability was calculated with the Flesch-Kincaid (F-K) grade level and the simple measure of Gobbledygook (SMOG) readability formula. <strong>Results:</strong> A total of 67 websites containing 201 individual web pages were included for coding and analysis. Most of the websites provided a basic definition of general anesthesia (unconsciousness, n=57, 85%; analgesia, n=47, 70%). Around half of the websites described common side effects of general anesthesia, while fewer described the rare but serious adverse events, such as intraoperative awareness (n=31, 46%), allergic reactions or anaphylaxis (n=29, 43%), and malignant hyperthermia (n=18, 27%). Of the 67 websites, the median F-K grade level was 11.3 (IQR 9.5-12.8) and the median SMOG score was 13.5 (IQR 12.2-14.4), both far above the American Medical Association (AMA) recommended reading level of sixth grade. A total of 51 (76%) websites distinguished INVA versus TIVA as general anesthesia options. A total of 12 of the 51 (24%) websites explicitly stated that there is a decision to be considered about receiving INVA versus TIVA for general anesthesia. Only 10 (20%) websites made any direct comparisons between INVA and TIVA, discussing their positive and negative features. A total of 12 (24%) websites addressed the concept of shared decision-making in planning anesthesia care, but none specifically asked patients to think about which features of INVA and TIVA matter the most to them. <strong>Conclusions:</strong> While the majority of websites described INVA and TIVA, few provided comparisons. There is a need for high-quality patient education and decision support about the choice of INVA versus TIVA to provide accurate and more comprehensive information in a format conducive to patient understanding. 2023-11-02T09:15:24-04:00 https://periop.jmir.org/2023/1/e50895/ Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study2023-10-26T09:30:04-04:00Koutarou MatsumotoYasunobu NoharaMikako SakaguchiYohei TakayamaSyota FukushigeHidehisa SoejimaNaoki NakashimaMasahiro Kamouchi<strong>Background:</strong> Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. <strong>Objective:</strong> The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. <strong>Methods:</strong> The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. <strong>Results:</strong> A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept –0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. <strong>Conclusions:</strong> The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium. <strong>Trial Registration:</strong> 2023-10-26T09:30:04-04:00 https://periop.jmir.org/2023/1/e50116/ A Mobile App for Postoperative Pain Management Among Older Veterans Undergoing Total Knee Arthroplasty: Mixed Methods Feasibility and Acceptability Pilot Study2023-10-18T09:15:06-04:00Jessica Kelley MorganCaitlin R RawlinsSteven K WaltherAndrew HarveyAnnmarie O'DonnellMarla GreeneTroy G Schmidt<strong>Background:</strong> Prescription opioid misuse risk is disproportionate among veterans; military veterans wounded in combat misuse prescription opioids at an even higher rate (46.2%). Opioid misuse is costly in terms of morbidity, mortality, and humanitarian and economic burden and costs the Civilian Health and Medical Program of the Department of Veterans Affairs more than US $1.13 billion annually. Preventing opioid misuse at the time of prescription is a critical component in the response to the opioid crisis. The CPMRx mobile app has been shown to decrease the odds of opioid misuse during the postoperative period. <strong>Objective:</strong> The overarching purpose of this feasibility pilot study was to explore whether deploying a mobile app (CPMRx) to track postoperative pain and medication use is feasible in a Department of Veterans Affairs medical center. In support of this goal, we had four complementary specific aims: (1) determine the technological and logistical feasibility of the mobile app, (2) assess the acceptability of the mobile app to participants, (3) measure demand for and engagement with the mobile app, and (4) explore the potential use of the mobile app to patients and providers. <strong>Methods:</strong> Participants (N=10) were veterans undergoing total knee arthroplasty within the Veterans Health Administration provided with the CPMRx app to self-manage their pain during their 7-day at-home recovery following surgery. CPMRx uses scientifically validated tools to help clinicians understand how a patient can use the least amount of medication while getting the most benefit. The suite of software includes a mobile app for patients that includes a behavioral health intervention and a clinical decision support tool for health care providers that provides feedback about pain and medication use trends. Patients filled out paper questionnaires regarding acceptability at their postoperative follow-up appointment. <strong>Results:</strong> Overall, quantitative measures of acceptability were high. The average rating for the amount of time required to use the app was 4.9 of 5 (5=“very little”), and the average rating for ease of use was 4.4 of 5 (5=“very easy”). Open-ended questions also revealed that most participants found ease of use to be high. Demand and engagement were high as well with a mean number of mobile app entries of 34.1 (SD 20.1) during the postoperative period. There were no reported technological or logistical issues with the mobile app. Participants took an average of 25.13 (SD 14.37) opioid tablets to manage their postoperative pain. <strong>Conclusions:</strong> Results of this study revealed that the use of a mobile app for pain and medication management during postoperative recovery was both feasible and acceptable in older veterans undergoing total knee arthroplasty within the Veterans Health Administration. The wide variation in opioid consumption across participants revealed the potential use of the mobile app to provide actionable insights to clinicians if adopted more widely. 2023-10-18T09:15:06-04:00