JMIR perioperative medicine最新文献

{"title":"Patient Blood Management Adoption and Transfusion-Threshold Concordance Among Romanian Anesthesiologists: National Survey Analysis.","authors":"Mihai Gabriel Stefan, Daniela Filipescu, Dan Corneci, Cornelia Predoi, Silvius Negoita, Dorel Sandesc, Liana Valeanu, Serban Ion Bubenek Turconi, Stefan Andrei, Dana Tomescu","doi":"10.2196/106731","DOIUrl":"10.2196/106731","url":null,"abstract":"<p><strong>Background: </strong>Romania issued national patient blood management (PBM) guidelines in 2018. A 2023 national survey described the overall uptake of PBM among anesthesiologists but did not examine which clinicians and institutions adopted PBM, nor whether stated transfusion thresholds aligned with a study-defined restrictive reference.</p><p><strong>Objective: </strong>This study aimed to identify determinants of PBM adoption and to quantify transfusion-trigger concordance, using a planned, exploratory, cross-sectional secondary analysis of the 2023 survey.</p><p><strong>Methods: </strong>We analyzed 511 responses to the 38-item Romanian Society of Anaesthesia and Intensive Care (SRATI) PBM survey. Three adoption outcomes (applying PBM, a formal hospital PBM group, and routine preoperative anemia treatment) were modeled by multivariable logistic regression against physician grade, hospital category, and local PBM educational events. Eight standardized clinical vignettes (age×cardiac disease×bleeding state) were scored against a restrictive hemoglobin reference (7 g/dL general; 8 g/dL with cardiac disease). The reported timing of the preanesthetic consultation (4 frequency items) was additionally examined as a determinant of preoperative anemia treatment. This was a planned, exploratory secondary analysis; the robustness of the main findings was examined in sensitivity analyses that accounted for survey attrition and for alternative transfusion-threshold scoring rules.</p><p><strong>Results: </strong>Overall, 73.7% (373/506) reported applying PBM, 33.1% (158/477) reported a formal PBM group, and 33.5% (117/349) reported routine preoperative anemia treatment. Local PBM educational events were the strongest adjusted correlate of reported adoption (applying PBM: adjusted odds ratio [aOR] 9.78, 95% CI 4.33-22.07; <i>P</i><.001; formal group: aOR 7.72, 95% CI 4.30-13.88; <i>P</i><.001). Earlier preanesthetic consultation was associated with routine preoperative anemia treatment (aOR 1.79 per frequency level for consultation more than 7 days before surgery, 95% CI 1.36-2.35; <i>P</i><.001). Transfusion-trigger concordance was lower for older than younger patients in otherwise identical scenarios (eg, active bleeding without cardiac disease: 195/345, 56.5% at ≥65 years vs 288/347, 83% at <65 years), and 56.5% (196/347) endorsed a liberal trigger (hemoglobin ≥8 g/dL) in at least one noncardiac scenario. Self-reported PBM application was not associated with a restrictive transfusion pattern (aOR 1.17, 95% CI 0.69-1.98; <i>P</i>=.56). Findings were unchanged across scoring rules, and reported PBM application was similar among respondents who did and did not complete the demographic block (246/329, 74.8% vs 127/177, 71.8%; <i>P</i>=.53).</p><p><strong>Conclusions: </strong>In this exploratory cross-sectional analysis, reported PBM adoption was associated more strongly with local educational activity than with hospital category or physician senio","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e106731"},"PeriodicalIF":2.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208342","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":"AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.","authors":"Bhagvat J Maheta, Rachel L Ross, Isabella Raspi, Christina Keny, Marti N Okech, Selen Bozkurt, Karleen F Giannitrapani","doi":"10.2196/89936","DOIUrl":"10.2196/89936","url":null,"abstract":"<p><strong>Background: </strong>Despite the promising potential of AI in the perioperative context, the rapid pace of development and diverse implementation warrant a thorough review to consolidate existing knowledge, identify gaps, and assess the use of trustworthiness principles in AI integration into the perioperative period for patients with serious illness.</p><p><strong>Objective: </strong>The purpose of this study was to address deficiencies in the perioperative AI literature by elucidating the extent to which discussions of equity, ethics, and safety are incorporated, thereby establishing a foundation for the development of robust ethical guidelines for the safe and effective integration of AI in health care.</p><p><strong>Methods: </strong>We searched PubMed, Embase, CENTRAL (Cochrane Central Register of Controlled Trials), and Scopus for studies published from 2010 to July 2024. We included studies that reported patient functional outcomes, occurred in the perioperative period (30 d before and up to 90 d after surgery), incorporated AI integration, and included patients with serious illness (defined as malignancy, advanced organ failure, frailty, dementia or neurodegenerative disease, or stroke). To ensure reliability and minimize bias, 2 independent reviewers screened all studies at the title or abstract and full-text stages; conflicts were resolved through team consensus. The abstraction form was developed iteratively and was tested through pilot abstractions. Any discrepancies identified during data extraction were resolved through discussion and consensus among the reviewers. The ROBINS-I (Risk of Bias Tool in Nonrandomized Studies of Interventions) tool was used to assess quality. Abstraction and risk assessment were conducted through a blinded, independent dual-review process. A narrative review was compiled from the identified studies.</p><p><strong>Results: </strong>Of the 10,980 papers identified through the database searches, this review yielded 81 papers that met the inclusion criteria. Analysis of AI implementation strategies revealed foundational efforts toward equitable access, with 6 studies providing open-access tools and several more designing models with simple inputs suitable for low-resource settings (17 studies). Seven studies mentioned their commitment to transparency (eg, publishing code) to enhance safety and trust. However, significant ethical deficiencies persist, particularly regarding input data, as only 2 studies explicitly addressed racial or ethnic disparities, and concerns about lack of sample diversity (16 studies) and the omission of socially relevant features (5 studies) were frequently noted as limitations.</p><p><strong>Conclusions: </strong>Machine learning for predictive analytics and other types of AI tools for surgical outcomes offer significant potential but require adherence to trustworthiness and safety principles to be clinically viable. Future research should prioritize adherence to guidelin","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e89936"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119638","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":"In-House 3D Planning for Corrective Osteotomy of the Distal Radius: Prospective Cohort Study With 1-Year Radiological and Clinical Follow-Up.","authors":"Charlotte Stor Swinkels, Peter Axelsson, Katleen Libberecht, Per Fredrikson, Emilia Gryska, Anders Björkman","doi":"10.2196/101764","DOIUrl":"10.2196/101764","url":null,"abstract":"<p><strong>Background: </strong>Distal radius malunions occur in 5% to 17% of fractures and may lead to pain and functional impairment. While 3D surgical planning with patient-specific guides has demonstrated high accuracy and reduced fluoroscopy use compared with conventional 2D techniques, its routine clinical implementation remains limited, particularly for in-house hospital-based workflows.</p><p><strong>Objective: </strong>This study aimed to evaluate the precision of corrective osteotomy for distal radius malunions using in-house-designed 3D patient-specific surgical guides 1 year after surgery. Secondary objectives were to assess clinical and patient-reported outcomes and document intraoperative fluoroscopy use.</p><p><strong>Methods: </strong>Corrective osteotomies of extra-articular distal radius malunions were performed in 16 consecutive patients using in-house 3D surgical planning and patient-specific surgical guides. Accuracy was evaluated by comparing preoperative plans with 1-year postoperative computed tomography-based 3D models, assessing ulnar variance, volar tilt, and radial inclination. Acceptable error margins were defined as 5° or less for volar tilt and 2 mm or less for ulnar variance. Clinical outcomes were evaluated using patient-reported outcome measures and range of motion, lift, grip, and torque strength measurements.</p><p><strong>Results: </strong>The mean absolute error was 1 (SD 0.8) mm in ulnar variance, 4.9° (SD 2.7°) in volar tilt, and 1.9° (SD 3.6°) in radial inclination. The corrections were within predefined equivalence bounds of the virtual plans in terms of ulnar variance but not volar tilt. Significant improvements were observed in Patient-Rated Wrist Evaluation (PRWE) and Disabilities of the Arm, Shoulder, and Hand (DASH) scores; pain during activity; pain at rest; lift strength; wrist flexion; and ulnar deviation. In total, 12.5% (2/16) of the patients experienced complications following concomitant ulnar shortening procedures and were transferred to standard care. The median intraoperative fluoroscopy duration was 56 (IQR 26-72) seconds.</p><p><strong>Conclusions: </strong>In-house 3D planning was feasible and associated with generally favorable radiological and clinical outcomes at 1 year. However, the predefined equivalence criterion was not achieved for volar tilt, and the findings should be interpreted considering the small sample size and observed complications. In-house 3D planning supports continuous surgical refinement and quality improvement.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e101764"},"PeriodicalIF":2.4,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921972","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":"Exploring Provider Perspectives on Gaps in Trauma-Informed Care Delivery Within the Acute Care Surgery Timeline at a Federally Qualified Health Center: Human-Centered Design Study.","authors":"Angelica T McDaniel, Willow Frye, Neema Rashidi, Christiana D von Hippel, Marianna G Salvatori, Adrienne Greer, Manami Diaz Tsuzuki, Amanda Sammann","doi":"10.2196/81004","DOIUrl":"10.2196/81004","url":null,"abstract":"<p><strong>Background: </strong>Trauma-informed care (TIC) education plays a pivotal role in preparing physicians to deliver care to patients who have experienced emotional and physical trauma, empowering patients to have agency in their care. The delivery of TIC by providers is especially important at Federally Qualified Health Centers (FQHCs), as their patients disproportionately experience trauma due to the impacts of socioeconomic determinants of health, including limited health care access, systemic racism, and housing insecurity. There is limited literature on how to adapt TIC practices in acute care settings, specifically for emergency medicine and surgical providers who deliver care in high-demand, time-constrained environments.</p><p><strong>Objective: </strong>The goal of this study was to understand the shared challenges faced by providers implementing TIC within acute care settings at an urban safety-net hospital.</p><p><strong>Methods: </strong>This exploratory qualitative study applied methods from the first stage of the human-centered design process, known as the inspiration phase, to identify the factors that influence providers' decisions to provide or not to provide TIC within the acute care ecosystem. In this inspiration phase, we conducted 18 semistructured interviews with providers delivering surgical care at an FQHC in Northern California. Interviewers were purposefully sampled to represent the diversity of providers involved throughout the perioperative timeline, including physicians, nurses, social workers, psychologists, and de-escalation staff. Inductive thematic analysis of transcripts and subsequent insight statement generation were conducted to identify tensions between stakeholder needs and to elucidate design opportunities to improve their ability to deliver TIC.</p><p><strong>Results: </strong>Inductive analysis identified 21 unique themes and 7 insights that affect the effective implementation of TIC by providers in acute care settings. Time-constrained workflows, vicarious trauma, and provider burnout present significant barriers to its application. Furthermore, limitations in TIC training, hospital design, and security measures, translation challenges, and hierarchical structures all hinder its integration. Supportive measures include a strong desire to improve health outcomes for communities and patients at FQHCs.</p><p><strong>Conclusions: </strong>Through this first phase of the human-centered design methodology, we identified 7 key insights and design opportunities that lay the foundation for developing solutions to address gaps and enhance the delivery of TIC within safety-net hospitals.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e81004"},"PeriodicalIF":2.4,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921983","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":"Patient Satisfaction With Perioperative Services and Associated Factors in Ethiopia: Systematic Review and Meta-Analysis.","authors":"Melesse Abiye Munie, Tegene Atamenta Kitaw, Aregash Birhane Terefe, Alemu Birara Zemariam, Molalign Aligaz Adisu, Molla Azmeraw, Tesfaye Engdaw Habtie, Betelhem Walelgn Dagnaw, Abebe Merchaw Faris, Tilahun Wodaynew, Amsalu Baylie Taye, Gebrie Kassaw Yirga, Yabibal Asfaw Desro","doi":"10.2196/84457","DOIUrl":"10.2196/84457","url":null,"abstract":"<p><strong>Background: </strong>Patient satisfaction is a key indicator of health care quality, and it guides improvement efforts. Although many local studies have examined perioperative patient satisfaction in Ethiopia, there is no comprehensive national synthesis. This gap limits the development of targeted strategies to enhance patient care.</p><p><strong>Objective: </strong>The aim of this systematic review and meta-analysis is to determine the pooled prevalence of patient satisfaction with perioperative services in Ethiopia and identify associated factors.</p><p><strong>Methods: </strong>This study included all observational research articles on patient satisfaction with perioperative services in Ethiopia. A multidatabase search strategy, incorporating PubMed/MEDLINE, HINARI, Web of Science, Cochrane Library, African Journals Online, and Scopus, was used alongside a gray literature search to identify all Ethiopian studies on perioperative satisfaction available before January 1, 2024. The Newcastle-Ottawa Scale was used to assess the quality of the studies. To assess heterogeneity, subgroup analyses were conducted, and I² statistics were calculated. This study used funnel plots, the Egger test, and a nonparametric trim-and-fill analysis to assess publication bias. A sensitivity analysis was also used to identify any influential studies. Univariate meta-regression examined the association between study-level covariates and perioperative satisfaction.</p><p><strong>Results: </strong>This review included 21 studies comprising 6858 participants. Overall satisfaction with perioperative services was expressed by 5072 participants (73.96%, 95% CI 68.84%-79.08%; I²=96.56%). Factors significantly associated with higher satisfaction included effective postoperative pain management (adjusted odds ratio [AOR] 2.23, 95% CI 1.56-2.90), illiteracy (AOR 3.18, 95% CI 1.23-5.13), primary school education (AOR 6.55, 95% CI 3.61-9.49), local anesthesia use (AOR 2.80, 95% CI 2.03-3.57), and history of prior surgery or anesthesia (AOR 2.76, 95% CI 1.51-4.01).</p><p><strong>Conclusions: </strong>This study found that the pooled prevalence of patient satisfaction with perioperative services in Ethiopia was 73.96% (5072/6858 participants). Postoperative pain management, illiteracy, primary school, local anesthesia, and a history of surgery or anesthesia were significantly associated with patient satisfaction with perioperative services. Health care facilities should focus on providing effective postoperative pain management, clear information about perioperative services, and training for surgical and anesthesia teams to boost patient satisfaction with perioperative services in Ethiopia.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e84457"},"PeriodicalIF":2.4,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13450891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690245","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":"Machine Learning for Predicting Critical Postoperative Interventions: Proof-of-Concept Study Using the INSPIRE Dataset.","authors":"Manan Shukla, Paul Fodor, Suresh Yelika, Nicholas Ahn","doi":"10.2196/65327","DOIUrl":"10.2196/65327","url":null,"abstract":"<p><strong>Background: </strong>Postoperative complications contribute significantly to patient morbidity and mortality. Early prediction of such complications could enable the care team to intervene promptly and improve patient outcomes. Existing surgical risk scores are easy to use but lack accuracy and do not provide individualized risk or guidance for clinical decision-making.</p><p><strong>Objective: </strong>This study aimed to develop and evaluate machine learning models using the INSPIRE perioperative dataset to predict whether an individual patient will require critical postoperative interventions-ventilatory support, extracorporeal membrane oxygenation (ECMO), an intra-aortic balloon pump (IABP), or continuous renal replacement therapy (CRRT).</p><p><strong>Methods: </strong>Four artificial neural network models and 4 random forest classifiers were trained and tested using the publicly available INSPIRE dataset (131,000 surgical cases). Preoperative laboratory data, medication use, surgery type, and intraoperative vital signs were used as inputs. The dataset was randomly divided into a 70% training set (91,000 cases) and a 30% test set (38,998 cases), with a temporal split to prevent leakage. Model performance was assessed using accuracy, sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC).</p><p><strong>Results: </strong>The artificial neural network models achieved high predictive performance for ECMO (accuracy 98.9%; AUC 0.992), ventilatory support (accuracy 97.7%; AUC 0.995), IABP (accuracy 98.0%; AUC 0.992), and CRRT (accuracy 94.9%; AUC 0.983). In the test set of 38,998 patients, the neural network models correctly classified 38,569 cases for ECMO, 38,097 for ventilatory support, 38,230 for IABP, and 37,021 for CRRT, corresponding to accuracies of 98.9%, 97.7%, 98.0%, and 94.9%, respectively. The corresponding random forest classifiers achieved accuracies of 98.0%, 99.0%, 98.2%, and 96.5%, respectively, with similar AUC values. These results indicate that the models can reliably identify patients who will or will not require lifesaving postoperative interventions despite the marked class imbalance. Our work began in April 2023, with the research concept taking shape over the following 1 to 2 months. Model development spanned approximately 3 to 4 months and was followed by 1 month of testing and validation conducted by other computer scientists.</p><p><strong>Conclusions: </strong>Machine learning models trained and tested on the INSPIRE dataset can provide individualized risk estimates for critical postoperative interventions. By supplying accurate predictions before or during surgery, these models can support proactive perioperative planning (eg, intensive care unit bed allocation and device readiness).</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e65327"},"PeriodicalIF":2.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13412138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148611001","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":"Automated Identification of Surgical Site Infections From Electronic Medical Records: Retrospective Observational Predictive Modeling Study.","authors":"Arjun Chakraborty, Peter Tarczy-Hornoch, Dustin Long, Meliha Yetisgen","doi":"10.2196/87896","DOIUrl":"10.2196/87896","url":null,"abstract":"<p><strong>Background: </strong>Surgical site infections (SSIs) affect 160,000 to 300,000 patients annually, increasing postoperative mortality, causing significant complications, and incurring US $3.5 to US $10 billion in excess costs each year. Effective SSI surveillance can inform strategies to mitigate these outcomes. Traditional SSI surveillance methods, primarily manual chart reviews, are costly and labor-intensive.</p><p><strong>Objective: </strong>This study aimed to evaluate whether an automated SSI surveillance system built using newer natural language processing methods and deep learning could outperform previous approaches and whether such an approach could enable more efficient infection surveillance.</p><p><strong>Methods: </strong>Our dataset comprised approximately 30,000 surgical cases from the University of Washington Medical Center (UWMC) and Harborview Medical Center (HMC). Data from UWMC were captured for the National Surgical Quality Improvement Program, and data from HMC were captured for the National Healthcare Safety Network. Electronic health record (EHR) data for each surgical case included structured data pertaining to surgical procedure characteristics, laboratory values, and antibiotic administration, as well as clinical text notes for a surgical case from 7 days before to 90 days after surgery. Using this data, we applied a myriad of machine learning approaches to the task of SSI prediction. We reported the following performance metrics: F1-score, precision (positive predictive value), recall (sensitivity), area under the precision-recall curve, and precision at 0.9 recall for each machine learning approach.</p><p><strong>Results: </strong>In a cohort of 5996 surgical cases, incorporating multimodal EHR information-including contextual information from clinical text and temporal information from laboratory values-improved SSI prediction performance. Models using structured data and clinical text outperformed structured data alone (F1=0.68, 95% CI 0.68-0.69 vs F1=0.55, 95% CI 0.54-0.56; P<.001). Adding temporal features further improved performance (F1=0.70, 95% CI 0.69-0.71; P<.001). Deep learning approaches leveraging large language models also outperformed the state-of-the-art rule-based system (F1=0.70, 95% CI 0.69-0.71 vs F1=0.43; P<.001). The optimal approach combined foundation models for text summarization with deep learning methods for clinical text and temporal data processing. This system achieved a precision of 0.38 at 0.9 recall, demonstrating its potential for efficient, data-driven SSI surveillance.</p><p><strong>Conclusions: </strong>Automated surveillance approaches-particularly deep learning approaches-in combination with voluminous, multimodal data from the EHR, can enable more efficient infection surveillance processes. This has the potential to increase the quantity of SSI surveillance data available to guide interventions aimed at reducing SSI rates.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e87896"},"PeriodicalIF":2.4,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148341242","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":"Comparing the Quality of Patient Recovery After Open Appendectomy Under General Versus Spinal Anesthesia: Prospective Cohort Study.","authors":"Negesse Zurbachew Gobezie, Temesgen Birlie Asmare, Kumlachew Geta Belete, Gezahagn Demsu Gedefaw, Biruk Demissie, Abere Gebru Abuhay, Belayneh Jejaw Abate, Birhanu Mengist Munie, Wubet Tazeb Wondie, Habtie Bantider Wubet","doi":"10.2196/86273","DOIUrl":"10.2196/86273","url":null,"abstract":"<p><strong>Background: </strong>Open appendectomy is commonly performed under either general or spinal anesthesia. Postoperative quality of recovery, a multidimensional patient-centered measure of outcomes after surgery, is affected by the choice of anesthesia technique.</p><p><strong>Objective: </strong>The objective of our study was to compare the effects of general and spinal anesthesia on the immediate postoperative quality of recovery in adults undergoing open appendectomy.</p><p><strong>Methods: </strong>In this prospective cohort study, 74 patients were assigned to either the general (n=37) or the spinal anesthesia (n=37) groups. The primary outcome was the total Quality of Recovery-15 score measured 24 hours postoperatively. Secondary outcomes included postoperative pain scores, analgesic consumption, incidence of postoperative nausea and vomiting, time to first oral intake, time to first ambulation, and length of hospital stay. Intergroup comparisons were performed using the chi-square test, Fisher exact test, independent t test, or Mann-Whitney U test, as appropriate.</p><p><strong>Results: </strong>Patients in the spinal anesthesia group had significantly higher Quality of Recovery-15 scores than those in the general anesthesia group (P=.001). They also exhibited lower pain scores at 1, 2, 6, and 12 hours postoperatively (P<.001), reduced consumption of diclofenac and tramadol, and a decreased incidence of postoperative nausea and vomiting (P<.001). The time to first mobilization, time to first oral intake, and length of hospital stay were also significantly lower in the spinal anesthesia group (P<.001).</p><p><strong>Conclusions: </strong>Spinal anesthesia is the preferred anesthesia technique for patients undergoing open appendectomy. It may provide improved postoperative recovery, with lower pain scores and improved analgesia. It may also reduce the need for additional analgesics, decrease the incidence of nausea and vomiting, and improve intrahospital patient recovery trajectories.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e86273"},"PeriodicalIF":2.4,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13299004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148335050","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":"Association Between Complications and Death Within 30 Days After Orthopedic Surgery: Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION) Substudy.","authors":"Lily J Park, P J Devereaux, Ameen Patel, Vikas Tandon, Diane Heels-Ansdell, Lehana Thabane, Pablo E Serrano, Matthew T V Chan, Wojciech Szczeklik, Sadeesh Srinathan, Ignacio Garutti, Gerard Urrutia, Ernesto Guerra-Farfan, Hassaan Abdel Khalik, Emmanuelle Duceppe, Sandra Ofori, Maura Marcucci, David Conen, Michael K Wang, Jessica Spence, Daniel Tushinski, Kamal Bali, Anthony Adili, Vickas Khanna, Ana Claudia Tonelli, Francesca Mulazzani, Wenjun Jiang, Olufemi R Ayeni, Gerard Slobegean, Theodore Miclau, Mohit Bhandari, Flavia K Borges","doi":"10.2196/90823","DOIUrl":"10.2196/90823","url":null,"abstract":"<p><strong>Background: </strong>The contemporary causes of postoperative mortality in orthopedic surgery are not well characterized.</p><p><strong>Objective: </strong>This study aimed to describe the epidemiology of postoperative complications among adult patients who underwent orthopedic surgery and inform their relationships with 30-day mortality.</p><p><strong>Methods: </strong>Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION) was a prospective cohort study involving 40,004 adult patients who underwent noncardiac surgery across 28 centers in 14 countries. For the subset of patients who underwent orthopedic surgery, a Cox proportional hazards model was used to determine time-dependent associations between various surgical complications and 30-day postoperative mortality. Analyses were adjusted for preoperative and surgical variables.</p><p><strong>Results: </strong>Among 8385 patients who underwent an orthopedic surgery in VISION, 1.6% (n=132) died within 30 days of surgery. Of the 132 deaths, 63.6% (n=84) occurred in hospital during the index hospitalization, while 36.4% (n=48) occurred after discharge. The incidence of death across the subcategories of orthopedic surgery was above-knee amputation (30/221, 13.6%), internal fixation of femur (29/750, 3.9%), lower leg amputation (9/252, 3.6%), major hip or pelvic surgery (49/2898, 1.7%), major spine surgery (8/1405, 0.6%), and knee arthroplasty (7/2876, 0.2%). A total of 6 postoperative complications (myocardial injury after noncardiac surgery [MINS], major bleeding, infection without sepsis, sepsis, stroke, and atrial fibrillation) were associated with death in adjusted analyses. The greatest attributable fractions of postoperative mortality (ie, proportion of deaths in the cohort that can be attributed to each complication, if causality were established) were from MINS (1454/8385, 17.3%; hazard ratio [HR] 2.08, 95% CI 1.38-3.14; P<.001; attributable fraction=20.6%), major bleeding (2422/8385, 28.9%; HR 1.95, 95% CI 1.34-2.85; P<.001; attributable fraction=16.5%), and sepsis (318/8385, 3.8%; HR 6.24, 95% CI 3.85-10.12; P<.001; attributable fraction=9.7%).</p><p><strong>Conclusions: </strong>The complications most attributable to 30-day mortality following orthopedic surgery were MINS, major bleeding, and sepsis. These findings highlight areas for further study to mitigate perioperative mortality in orthopedic surgery. MINS demonstrated the highest attributable fraction for mortality (20.6%), emphasizing the importance of appropriate MINS screening, diagnosis, and management.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e90823"},"PeriodicalIF":2.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13249062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213566","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":"AI-Generated Avatar Videos for Postoperative Patient Education Among Health Care Workers: Pilot Randomized Controlled Trial.","authors":"Syed Ali Haider, Srinivasagam Prabha, Cesar Abraham Gomez-Cabello, Ariana Genovese, Bernardo G Collaco, Nadia Wood, James London, Sanjay Bagaria, Mark A Lifson, Cui Tao, Antonio Jorge Forte","doi":"10.2196/89277","DOIUrl":"10.2196/89277","url":null,"abstract":"<p><strong>Background: </strong>Effective postoperative communication is vital for patient recovery, yet traditional text-based discharge instructions often lead to poor comprehension and adherence, particularly among patients with limited health literacy. Although educational videos improve understanding and retention, their widespread use has been hampered by high production costs. Generative artificial intelligence (AI) offers a scalable solution for creating engaging video content.</p><p><strong>Objective: </strong>The primary objective of this pilot study was to assess the feasibility of creating and deploying AI-generated, avatar-led videos for postoperative instruction delivery. Secondary objectives included comparing knowledge retention, engagement, perceived clarity, and user experience between AI-generated video and traditional text-based handout formats among health care workers.</p><p><strong>Methods: </strong>In this randomized pilot study, 38 health care worker volunteers were recruited as a convenience sample to pilot-test the intervention before patient implementation. Participants were assigned to either a text handout group (n=19, 50%) or an AI-generated video group (n=19, 50%). Both groups received information on 10 common postoperative topics. The primary outcome was objective knowledge, assessed via a 10-item quiz. Secondary outcomes, measured through surveys with 5-point Likert scales, included engagement time, subjective engagement, perceived clarity, usefulness, confidence in understanding, and information retention. Qualitative feedback was also collected.</p><p><strong>Results: </strong>Objective knowledge quiz scores did not differ significantly between groups (mean 8.89, SD 1.20 for the AI-generated video group vs mean 8.21, SD 1.78 for the text handout group; P=.17; Cohen d=0.45). Participants in the AI-generated video group demonstrated significantly higher engagement time (mean 15.11, SD 7.78 minutes vs mean 8.84, SD 4.03 minutes; P=.004; Cohen d=1.04). They also rated instructions as significantly clearer (mean 4.63, SD 0.50 vs mean 4.00, SD 0.82; P=.007; Cohen d=0.93), more engaging (mean 4.05, SD 0.78 vs mean 3.32, SD 1.00; P=.02; Cohen d=0.81), and more effective for retention (mean 4.42, SD 0.84 vs mean 3.37, SD 0.68; P<.001; Cohen d=1.38). Qualitative feedback highlighted the engaging nature of AI-generated videos but noted areas for avatar refinement.</p><p><strong>Conclusions: </strong>In this pilot study with health care workers, AI-generated avatar videos did not improve objective knowledge scores but significantly enhanced engagement, perceived retention and perceived clarity (Cohen d=0.81-1.38). Future studies in actual patient populations with diverse health literacy levels are needed to determine whether these engagement advantages translate into improved knowledge outcomes.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"9 ","pages":"e89277"},"PeriodicalIF":2.4,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13215632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044942","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}