JMIR perioperative medicine最新文献

{"title":"i-Anemia: The impact of decision support in preoperative management of anemia (Preprint)","authors":"Gaëtan Mignanelli, Richard Boyer, Nicolas Bonifas, Emmanuel Rineau, Yassine Moussali, Morgan Le Guen","doi":"10.2196/49186","DOIUrl":"https://doi.org/10.2196/49186","url":null,"abstract":"BACKGROUND\u0000Major 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.\u0000\u0000\u0000OBJECTIVE\u0000We 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.\u0000\u0000\u0000METHODS\u0000We 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.\u0000\u0000\u0000RESULTS\u0000Overall, 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 (P<.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; P<.001).\u0000\u0000\u0000CONCLUSIONS\u0000Management 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.","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135542300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Home Monitoring of Continuous Vital Sign Measurements by Wearables in Patients Discharged After Colorectal Surgery: Observational Feasibility Study.","authors":"Jobbe P L Leenen,&nbsp;Vera Ardesch,&nbsp;Gijsbert Patijn","doi":"10.2196/45113","DOIUrl":"https://doi.org/10.2196/45113","url":null,"abstract":"<p><strong>Background: </strong>Hospital stays after colorectal surgery are increasingly being reduced by enhanced recovery and early discharge protocols. As a result, postoperative complications may frequently manifest after discharge in the home setting, potentially leading to emergency room presentations and readmissions. Virtual care interventions after hospital discharge may capture clinical deterioration at an early stage and hold promise for the prevention of readmissions and overall better outcomes. Recent technological advances have enabled continuous vital sign monitoring by wearable wireless sensor devices. However, the potential of these devices for virtual care interventions for patients discharged after colorectal surgery is currently unknown.</p><p><strong>Objective: </strong>We aimed to determine the feasibility of a virtual care intervention consisting of continuous vital sign monitoring with wearable wireless sensors and teleconsultations for patients discharged after colorectal surgery.</p><p><strong>Methods: </strong>In a single-center observational cohort study, patients were monitored at home for 5 consecutive days after discharge. Daily vital sign trend assessments and telephone consultations were performed by a remote patient-monitoring department. Intervention performance was evaluated by analyzing vital sign trend assessments and telephone consultation reports. Outcomes were categorized as \"no concern,\" \"slight concern,\" or \"serious concern.\" Serious concern prompted contact with the surgeon on call. In addition, the quality of the vital sign data was determined, and the patient experience was evaluated.</p><p><strong>Results: </strong>Among 21 patients who participated in this study, 104 of 105 (99%) measurements of vital sign trends were successful. Of these 104 vital sign trend assessments, 68% (n=71) did not raise any concern, 16% (n=17) were unable to be assessed because of data loss, and none led to contacting the surgeon. Of 62 of 63 (98%) successfully performed telephone consultations, 53 (86%) did not raise any concerns and only 1 resulted in contacting the surgeon. A 68% agreement was found between vital sign trend assessments and telephone consultations. Overall completeness of the 2347 hours of vital sign trend data was 46.3% (range 5%-100%). Patient satisfaction score was 8 (IQR 7-9) of 10.</p><p><strong>Conclusions: </strong>A home monitoring intervention of patients discharged after colorectal surgery was found to be feasible, given its high performance and high patient acceptability. However, the intervention design needs further optimization before the true value of remote monitoring for early discharge protocols, prevention of readmissions, and overall patient outcomes can be adequately determined.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e45113"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9866400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons.","authors":"Sabrina Grant,&nbsp;Emma Tonkin,&nbsp;Ian Craddock,&nbsp;Ashley Blom,&nbsp;Michael Holmes,&nbsp;Andrew Judge,&nbsp;Alessandro Masullo,&nbsp;Miquel Perello Nieto,&nbsp;Hao Song,&nbsp;Michael Whitehouse,&nbsp;Peter Flach,&nbsp;Rachael Gooberman-Hill","doi":"10.2196/36172","DOIUrl":"https://doi.org/10.2196/36172","url":null,"abstract":"<p><strong>Background: </strong>The current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis.</p><p><strong>Objective: </strong>This study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making.</p><p><strong>Methods: </strong>Orthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach.</p><p><strong>Results: </strong>Each data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set.</p><p><strong>Conclusions: </strong>The limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants' background, skills, and experience were considered heterogeneous. Passively collected home monitoring data offered a real opportunity to more objectively characterize patients' recovery from surgery. However, orthopedic surgeons highlighted the considerable difficulty in navigating large amounts of complex data within short medical consultations with patients. Orthopedic surgeons thought that a proposed dashboard presenting information and decision support alerts would fit best with existing clinical workflows. From this, the following guidelines for system design were developed: minimize the risk of misinterpreting data, express a level of confidence in the data, support clinicians in developing relevant skills as time-series data are often unfamiliar, and consider the impact of patient engagement with data in the future.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.1136/bmjopen-2018-021862.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e36172"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9496155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Association Between Borderline Dysnatremia and Perioperative Morbidity and Mortality: Retrospective Cohort Study of the American College of Surgeons National Surgical Quality Improvement Program Database.","authors":"Jacob H Cole,&nbsp;Krista B Highland,&nbsp;Scott B Hughey,&nbsp;Brendan J O'Shea,&nbsp;Thomas Hauert,&nbsp;Ashton H Goldman,&nbsp;George C Balazs,&nbsp;Gregory J Booth","doi":"10.2196/38462","DOIUrl":"https://doi.org/10.2196/38462","url":null,"abstract":"<p><strong>Background: </strong>Hyponatremia and hypernatremia, as conventionally defined (<135 mEq/L and >145 mEq/L, respectively), are associated with increased perioperative morbidity and mortality. However, the effects of subtle deviations in serum sodium concentration within the normal range are not well-characterized.</p><p><strong>Objective: </strong>The purpose of this analysis is to determine the association between borderline hyponatremia (135-137 mEq/L) and hypernatremia (143-145 mEq/L) on perioperative morbidity and mortality.</p><p><strong>Methods: </strong>A retrospective cohort study was performed using data from the American College of Surgeons National Surgical Quality Improvement Program database. This database is a repository of surgical outcome data collected from over 600 hospitals across the United States. The National Surgical Quality Improvement Program database was queried to extract all patients undergoing elective, noncardiac surgery from 2015 to 2019. The primary predictor variable was preoperative serum sodium concentration, measured less than 5 days before the index surgery. The 2 primary outcomes were the odds of morbidity and mortality occurring within 30 days of surgery. The risk of both outcomes in relation to preoperative serum sodium concentration was modeled using weighted generalized additive models to minimize the effect of selection bias while controlling for covariates.</p><p><strong>Results: </strong>In the overall cohort, 1,003,956 of 4,551,726 available patients had a serum sodium concentration drawn within 5 days of their index surgery. The odds of morbidity and mortality across sodium levels of 130-150 mEq/L relative to a sodium level of 140 mEq/L followed a nonnormally distributed U-shaped curve. The mean serum sodium concentration in the study population was 139 mEq/L. All continuous covariates were significantly associated with both morbidity and mortality (P<.001). Preoperative serum sodium concentrations of less than 139 mEq/L and those greater than 144 mEq/L were independently associated with increased morbidity probabilities. Serum sodium concentrations of less than 138 mEq/L and those greater than 142 mEq/L were associated with increased mortality probabilities. Hypernatremia was associated with higher odds of both morbidity and mortality than corresponding degrees of hyponatremia.</p><p><strong>Conclusions: </strong>Among patients undergoing elective, noncardiac surgery, this retrospective analysis found that preoperative serum sodium levels less than 138 mEq/L and those greater than 142 mEq/L are associated with increased morbidity and mortality, even within currently accepted \"normal\" ranges. The retrospective nature of this investigation limits the ability to make causal determinations for these findings. Given the U-shaped distribution of risk, past investigations that assume a linear relationship between serum sodium concentration and surgical outcomes may need to be revisited. Likewise,","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e38462"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9412633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study.","authors":"Jonna A van der Stam,&nbsp;Eveline H J Mestrom,&nbsp;Jai Scheerhoorn,&nbsp;Fleur E N B Jacobs,&nbsp;Simon Nienhuijs,&nbsp;Arjen-Kars Boer,&nbsp;Natal A W van Riel,&nbsp;Helma M de Morree,&nbsp;Alberto G Bonomi,&nbsp;Volkher Scharnhorst,&nbsp;R Arthur Bouwman","doi":"10.2196/40474","DOIUrl":"https://doi.org/10.2196/40474","url":null,"abstract":"<p><strong>Background: </strong>Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established.</p><p><strong>Objective: </strong>This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients.</p><p><strong>Methods: </strong>The accuracy of the wrist-worn PPG sensor was assessed in 62 post-abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m²). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy.</p><p><strong>Results: </strong>Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis.</p><p><strong>Conclusions: </strong>The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e40474"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9078169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Neural Network Model Using Pain Score Patterns to Predict the Need for Outpatient Opioid Refills Following Ambulatory Surgery: Algorithm Development and Validation.","authors":"Rodney Allanigue Gabriel,&nbsp;Sierra Simpson,&nbsp;William Zhong,&nbsp;Brittany Nicole Burton,&nbsp;Soraya Mehdipour,&nbsp;Engy Tadros Said","doi":"10.2196/40455","DOIUrl":"https://doi.org/10.2196/40455","url":null,"abstract":"<p><strong>Background: </strong>Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery.</p><p><strong>Objective: </strong>The objective of this study was to develop machine learning algorithms incorporating pain and opioid features to predict the need for outpatient opioid refills following ambulatory surgery.</p><p><strong>Methods: </strong>Neural networks, regression, random forest, and a support vector machine were used to evaluate the data set. For each model, oversampling and undersampling techniques were implemented to balance the data set. Hyperparameter tuning based on k-fold cross-validation was performed, and feature importance was ranked based on a Shapley Additive Explanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), F1-score, sensitivity, and specificity for each model.</p><p><strong>Results: </strong>There were 1333 patients, of whom 144 (10.8%) refilled their opioid prescription within 2 weeks after outpatient surgery. The average AUC calculated from k-fold cross-validation was 0.71 for the neural network model. When the model was validated on the test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, postanesthesia care unit maximum pain score, postanesthesia care unit median pain score, active smoking history, and total perioperative opioid consumption.</p><p><strong>Conclusions: </strong>Applying machine learning algorithms allows providers to better predict outcomes that require specialized health care resources such as transitional pain clinics. This model can aid as a clinical decision support for early identification of at-risk patients who may benefit from transitional pain clinic care perioperatively in ambulatory surgery.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e40455"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10760314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.","authors":"Eric Mlodzinski, Gabriel Wardi, Clare Viglione, Shamim Nemati, Laura Crotty Alexander, Atul Malhotra","doi":"10.2196/41056","DOIUrl":"10.2196/41056","url":null,"abstract":"<p><strong>Background: </strong>Although there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited.</p><p><strong>Objective: </strong>We sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health care in general.</p><p><strong>Methods: </strong>We developed 2 anonymous surveys in Qualtrics, 1 single-center survey distributed to 99 critical care physicians via email, and 1 social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and nonproviders. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with SD were reported from 1 to 5. We used student t tests to examine the differences between groups. In addition, Likert scale responses were converted into 3 categories, and percentage values were reported in order to demonstrate the distribution of responses. Qualitative free-text responses were reviewed by a member of the study team to determine validity, and content analysis was performed to determine common themes in responses.</p><p><strong>Results: </strong>Out of 99 critical care physicians, 47 (48%) completed the single-center survey. Perceived knowledge of ML was low with a mean Likert score of 2.4 out of 5 (SD 0.96), with 7.5% of respondents rating their knowledge as a 4 or 5. The willingness to use the ML-based algorithm was 3.32 out of 5 (SD 0.95), with 75% of respondents answering 3 out of 5. The social media survey had 770 total responses with 605 (79%) providers and 165 (21%) nonproviders. We found no difference in providers' perceived knowledge based on level of experience in either survey. We found that nonproviders had significantly less perceived knowledge of ML (mean 3.04 out of 5, SD 1.53 vs mean 3.43, SD 0.941; P<.001) and comfort with ML (mean 3.28 out of 5, SD 1.02 vs mean 3.53, SD 0.935; P=.004) than providers. Free-text responses revealed multiple shared concerns, including accuracy/reliability, data bias, patient safety, and privacy/security risks.</p><p><strong>Conclusions: </strong>These data suggest that providers and nonproviders have positive perceptions of ML-based tools, and that a tool to predict the need for intubation would be of interest to critical care providers. There were many shared concerns about ML/AI in health care elucidated by the surveys. These results provide a baseline evaluation of implementation barriers and determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting and health care in gene","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e41056"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9367308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.","authors":"Rodney Allanigue Gabriel,&nbsp;Bhavya Harjai,&nbsp;Sierra Simpson,&nbsp;Austin Liu Du,&nbsp;Jeffrey Logan Tully,&nbsp;Olivier George,&nbsp;Ruth Waterman","doi":"10.2196/39650","DOIUrl":"https://doi.org/10.2196/39650","url":null,"abstract":"<p><strong>Background: </strong>Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration.</p><p><strong>Objective: </strong>The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration.</p><p><strong>Methods: </strong>We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R², root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance.</p><p><strong>Results: </strong>A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R²=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R² of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R² of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model.</p><p><strong>Conclusions: </strong>Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e39650"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10750721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perioperative Risk Assessment of Patients Using the MyRISK Digital Score Completed Before the Preanesthetic Consultation: Prospective Observational Study.","authors":"Fabrice Ferré,&nbsp;Rodolphe Laurent,&nbsp;Philippine Furelau,&nbsp;Emmanuel Doumard,&nbsp;Anne Ferrier,&nbsp;Laetitia Bosch,&nbsp;Cyndie Ba,&nbsp;Rémi Menut,&nbsp;Matt Kurrek,&nbsp;Thomas Geeraerts,&nbsp;Antoine Piau,&nbsp;Vincent Minville","doi":"10.2196/39044","DOIUrl":"https://doi.org/10.2196/39044","url":null,"abstract":"<p><strong>Background: </strong>The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context.</p><p><strong>Objective: </strong>Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications.</p><p><strong>Methods: </strong>This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed.</p><p><strong>Results: </strong>Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively.</p><p><strong>Conclusions: </strong>The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e39044"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10591888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Controlled Trial.","authors":"Carissa A Low,&nbsp;Michaela Danko,&nbsp;Krina C Durica,&nbsp;Julio Vega,&nbsp;Meng Li,&nbsp;Abhineeth Reddy Kunta,&nbsp;Raghu Mulukutla,&nbsp;Yiyi Ren,&nbsp;Susan M Sereika,&nbsp;David L Bartlett,&nbsp;Dana H Bovbjerg,&nbsp;Anind K Dey,&nbsp;John M Jakicic","doi":"10.2196/41425","DOIUrl":"https://doi.org/10.2196/41425","url":null,"abstract":"<p><strong>Background: </strong>Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and preliminary effects of a real-time mobile intervention that detects and disrupts prolonged SB before and after cancer surgery, relative to a monitoring-only control condition.</p><p><strong>Objective: </strong>Our aim was to evaluate the feasibility and preliminary effects of a perioperative SB intervention on objective activity behavior, patient-reported quality of life and symptoms, and 30-day readmissions.</p><p><strong>Methods: </strong>Patients scheduled for surgery for metastatic gastrointestinal cancer (n=26) were enrolled and randomized to receive either the SB intervention or activity monitoring only. Both groups used a Fitbit smartwatch and companion smartphone app to rate daily symptoms and collect continuous objective activity behavior data starting from at least 10 days before surgery through 30 days post discharge. Participants in the intervention group also received prompts to walk after any SB bout that exceeded a prespecified threshold, with less frequent prompts on days that patients reported more severe symptoms. Participants completed end-of-study ratings of acceptability, and we also examined adherence to assessments and to walking prompts. In addition, we examined effects of the intervention on objective SB and step counts, patient-reported quality of life and depressive and physical symptoms, as well as readmissions.</p><p><strong>Results: </strong>Accrual (74%), retention (88%), and acceptability ratings (mean overall satisfaction 88.5/100, SD 9.1) were relatively high. However, adherence to assessments and engagement with the SB intervention decreased significantly after surgery and did not recover to preoperative levels after postoperative discharge. All participants exhibited significant increases in SB and symptoms and decreases in steps and quality of life after surgery, and participants randomized to the SB intervention unexpectedly had longer maximum SB bouts relative to the control group. No significant benefits of the intervention with regard to activity, quality of life, symptoms, or readmission were observed.</p><p><strong>Conclusions: </strong>Perioperative patients with metastatic gastrointestinal cancer were interested in a real-time SB intervention and rated the intervention as highly acceptable, but engagement with the intervention and with daily symptom and activity monitoring decreased significantly after surgery. There were no significant effects of the intervention on step counts, patient-reported quality of life or symptoms, and postoperative readmissions, and there was an apparent adverse effect on maximum SB. Results highlight the need for additional work to modify the intervention to make reducing SB and engaging with mobile health technology after","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e41425"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9402252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}