JMIR perioperative medicine最新文献

Background: The majority of American adults search for health and illness information on the internet. However, the quality and accuracy of this information are notoriously variable. With the advent of social media, US individuals have increasingly shared their own health and illness experiences, including those related to bariatric surgery, on social media platforms. Previous research has found that peer-to-peer requesting and giving of advice related to bariatric surgery on social media is common, that such advice is often presented in stark terms, and that the advice may not reflect patient standards of care. These previous investigations have helped to map bariatric surgery content on Facebook and YouTube.

Objective: This objective of this study was to document and compare weight loss surgery (WLS)-related content on Instagram in the months leading up to the COVID-19 pandemic and 1 year later.

Methods: We analyzed a total of 300 Instagram posts (50 posts per week for 3 consecutive weeks in late February and early March in both 2020 and 2021) uploaded using the hashtag #wls. Descriptive statistics were reported, and independent 1-tailed chi-square tests were used to determine if a post's publication year statistically affected its inclusion of a particular type of content.

Results: Overall, advice giving and personal responsibility for outcomes were emphasized by WLS posters on Instagram. However, social support was less emphasized. The safety, challenges, and risks associated with WLS were rarely discussed. The majority of posts did not contain references to facts from reputable medical sources. Posts published in 2021 were more likely to mention stress/hardships of living with WLS (45/150, 30%, vs 29/150, 19.3%; P=.03); however, those published in 2020 more often identified the importance of ongoing support for WLS success (35/150, 23.3%, vs 16/150, 10.7%; P=.004).

Conclusions: Given that bariatric patients have low rates of postoperative follow-up, yet post-operative care and yet support are associated with improved health and weight loss outcomes, and given that health content on the web is of mixed accuracy, bariatric professionals may wish to consider including an online support forum moderated by a professional as a routine part of postoperative care. Doing so may not only improve follow-up rates but may offer providers the opportunity to counter inaccuracies encountered on social media.

Background: The Enhanced Recovery After Surgery (ERAS) protocol has been recently extended to hepatopancreatobiliary (HPB) surgery, with excellent outcomes reported. Early mobilization is an essential facet of the ERAS protocol, but compliance has been reported to be poor. We recently reported our success in a 6-month clinical practice improvement program (CPIP) for early postoperative mobilization. During the COVID-19 pandemic, we experienced reduced staffing and resource availability, which can make CPIP sustainability difficult.

Objective: We report outcomes at 1 year following the implementation of our CPIP to improve postoperative mobilization in patients undergoing major HPB surgery during the COVID-19 pandemic.

Methods: We divided our study into 4 phases-phase 1: before CPIP implementation (January to April 2019); phase 2: CPIP implementation (May to September 2019); phase 3: post-CPIP implementation but prior to the COVID-19 pandemic (October 2019 to March 2020); and phase 4: post-CPIP implementation and during the pandemic (April 2020 to September 2020). Major HPB surgery was defined as any surgery on the liver, pancreas, and biliary system with a duration of >2 hours and with an anticipated blood loss of ≥500 ml. Study variables included length of hospital stay, distance ambulated on postoperative day (POD) 2, morbidity, balance measures (incidence of fall and accidental dislodgement of drains), and reasons for failure to achieve targets. Successful mobilization was defined as the ability to sit out of bed for >6 hours on POD 1 and ambulate ≥30 m on POD 2. The target mobilization rate was ≥75%.

Results: A total of 114 patients underwent major HPB surgery from phases 2 to 4 of our study, with 33 (29.0%), 45 (39.5%), and 36 (31.6%) patients in phases 2, 3, and 4, respectively. No baseline patient demographic data were collected for phase 1 (pre-CPIP implementation). The majority of the patients were male (n=79, 69.3%) and underwent hepatic surgery (n=92, 80.7%). A total of 76 (66.7%) patients underwent ON-Q PainBuster insertion intraoperatively. The median mobilization rate was 22% for phase 1, 78% for phases 2 and 3 combined, and 79% for phase 4. The mean pain score was 2.7 (SD 1.0) on POD 1 and 1.8 (SD 1.5) on POD 2. The median length of hospitalization was 6 days (IQR 5-11.8). There were no falls or accidental dislodgement of drains. Six patients (5.3%) had pneumonia, and 21 (18.4%) patients failed to ambulate ≥30 m on POD 2 from phases 2 to 4. The most common reason for failure to achieve the ambulation target was pain (6/21, 28.6%) and lethargy or giddiness (5/21, 23.8%).

Conclusions: This follow-up study demonstrates the sustainability of our CPIP in improving early postoperative mobilization rates following major HPB surgery 1 year after implementation, even during the COVID-19 pandemic. Further large-scale, multi-institu

Background: Capnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a "smart alarm" that can alert clinicians to apneic events that are predicted to be prolonged.

Objective: To determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds).

Methods: A secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds).

Results: A total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy.

Conclusions: Decision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds.

Background: The rapid spread of the novel coronavirus (COVID-19) has presented immeasurable challenges to health care workers who remain at the frontline of the pandemic. A rapidly evolving body of literature has quantitatively demonstrated significant psychological impacts of the pandemic on health care workers. However, little is known about the lived experience of the pandemic for frontline medical staff.

Objective: This study aimed to explore the qualitative experience of perioperative staff from a large trauma hospital in Melbourne, Australia.

Methods: Inductive thematic analysis using a critical realist approach was used to analyze data from 9 semistructured interviews.

Results: Four key themes were identified. Hospital preparedness related to the perceived readiness of the hospital to respond to the pandemic and encompassed key subthemes around communication of policy changes, team leadership, and resource availability. Perceptions of readiness contributed to the perceived psychological impacts of the pandemic, which were highly varied and ranged from anger to anxiety. A number of coping strategies were identified in response to psychological impacts which incorporated both internal and external coping mechanisms. Finally, adaptation with time reflected change and growth over time, and encompassed all other themes.

Conclusions: While frontline staff and hospitals have rapidly marshalled a response to managing the virus, relatively less consideration was seen regarding staff mental health in our study. Findings highlight the vulnerability of health care workers in response to the pandemic and reinforce the need for a coordinated approach to managing mental health.

Many promising telemedicine innovations fail to be accepted and used over time, and there are longstanding questions about how to best evaluate telemedicine services and other health information technologies. In response to these challenges, there is a growing interest in how to take the sociotechnical complexity of health care into account during design, implementation, and evaluation. This paper discusses the methodological implications of this complexity and how the sociotechnical context holds the key to understanding the effects and outcomes of telemedicine. Examples from a work domain analysis of a surgical setting, where a telemedicine service for remote surgical consultation was to be introduced, are used to show how abstracted functional modeling can provide a structured and rigorous means to analyze and represent the implementation context in complex health care settings.

Background: Surgical audit is an essential aspect of modern reflective surgical practice and is key to improving surgical outcomes. The surgical logbook is an important method of data collection for both personal and unit audits; however, current electronic data collection tools, especially mobile apps, lack the minimum recommended data fields.

Objective: This feasibility study details the creation of a free, effective surgical logbook tool with the iOS Shortcuts app and investigates the time investment required to maintain a surgical logbook with this tool. In addition, we investigate the potential utility of the Shortcuts app in creating medical data collection tools.

Methods: Using the iOS Shortcuts app, we created a shortcut "Operation Note," which collects surgical logbook data by using the minimum and extended audit data sets recommended by the Royal Australasian College of Surgeons. We practically assessed the feasibility of the tool, assessing the time requirement for entry, accuracy, and completeness of the entered data.

Results: The shortcut collected accurate and useful data for a surgical audit. Data entry took on average 65 seconds per case for the minimum data set, and 135 seconds per case for the extended data set, with a mean difference of 68 seconds (P<.001; 95% CI 61.6-77.7).

Conclusions: This feasibility study demonstrates the utility of the iOS Shortcuts app in the creation of a surgical logbook and the time-consuming nature of data collection for surgical audit. Our iOS Operation Note shortcut is a free, rapid, and customizable alternative to currently available logbook apps and offers surgical trainees and consultants a method for recording surgical operations, complications, and demographic data.

Background: Continuous vital sign monitoring using wearable sensors may enable early detection of patient deterioration and sepsis.

Objective: This study aimed to explore patient experiences with wearable sensor technology and carry out continuous monitoring through questionnaire and interview studies in an acute hospital setting.

Methods: Patients were recruited for a wearable sensor study and were asked to complete a 9-item questionnaire. Patients responses were evaluated using a Likert scale and with continuous variables. A subgroup of surgical patients wearing a Sensium Vital Sign Sensor was invited to participate in semistructured interviews. The Sensium wearable sensor measures the vital signs: heart rate, respiratory rate, and temperature. All interview data were subjected to thematic analysis.

Results: Out of a total of 500 patients, 453 (90.6%) completed the questionnaire. Furthermore, 427 (85.4%) patients agreed that the wearable sensor was comfortable, 429 (85.8%) patients agreed to wear the patch again when in hospital, and 398 (79.6%) patients agreed to wear the patch at home. Overall, 12 surgical patients consented to the interviews. Five main themes of interest to patients emerged from the interviews: (1) centralized monitoring, (2) enhanced feelings of patient safety, (3) impact on nursing staff, (4) comfort and usability, and (5) future use and views on technology.

Conclusions: Overall, the feedback from patients using wearable monitoring sensors was strongly positive with relatively few concerns raised. Patients felt that the wearable sensors would improve their sense of safety, relieve pressure on health care staff, and serve as a favorable aspect of future health care technology.

What does the COVID-19 false-negative exposure problem mean in the context of a local anesthesia practice? We present a customizable online calculator designed to quantify and better understand individual and aggregate provider exposure risk.

Background: Monitoring surgical recovery has traditionally been confined to metrics measurable within the hospital and clinic setting. However, commercially available mobile sensors are now capable of extending measurements into a patient's home. As these sensors were developed for nonmedical applications, their clinical role has yet to be established. The aim of this systematic review is to evaluate the relationship between data generated by mobile sensors and postoperative outcomes.

Objective: The objective of this study is to describe the current use of mobile sensors in the perioperative setting and the correlation between their data and clinical outcomes.

Methods: A systematic search of EMBASE, MEDLINE, and Cochrane Library from inception until April 2019 was performed to identify studies of surgical patients monitored with mobile sensors. Sensors were considered if they collected patient metrics such as step count, temperature, or heart rate. Studies were included if patients underwent major surgery (≥1 inpatient postoperative day), patients were monitored using mobile sensors in the perioperative period, and the study reported postoperative outcomes (ie, complications and hospital readmission). For studies including step count, a pooled analysis of the step count per postoperative day was calculated for the complication and noncomplication cohorts using mean and a random-effects linear model. The Grading of Recommendations, Assessment, Development, and Evaluation tool was used to assess study quality.

Results: From 2209 abstracts, we identified 11 studies for review. Reviewed studies consisted of either prospective observational cohorts (n=10) or randomized controlled trials (n=1). Activity monitors were the most widely used sensors (n=10), with an additional study measuring temperature, respiratory rate, and heart rate (n=1). Low step count was associated with worse postoperative outcomes. A median step count of around 1000 steps per postoperative day was associated with adverse surgical outcomes. Within the studies, there was heterogeneity between the type of surgery and type of reported postoperative outcome.

Conclusions: Despite significant heterogeneity in the type of surgery and sensors, low step count was associated with worse postoperative outcomes across surgical specialties. Further studies and standardization are needed to assess the role of mobile sensors in postoperative care, but a threshold of approximately 1000 steps per postoperative day warrants further investigation.

Background: Patients undergoing esophagectomy are at serious risk of developing postoperative complications. To support early recognition of clinical deterioration, wireless sensor technologies that enable continuous vital signs monitoring in a ward setting are emerging.

Objective: This study explored nurses' and surgeons' expectations of the potential effectiveness and impact of continuous wireless vital signs monitoring in patients admitted to the ward after esophagectomy.

Methods: Semistructured interviews were conducted at 3 esophageal cancer centers in the Netherlands. In each center, 2 nurses and 2 surgeons were interviewed regarding their expectations of continuous vital signs monitoring for early recognition of complications after esophagectomy. Historical data of patient characteristics and clinical outcomes were collected in each center and presented to the local participants to support estimations on clinical outcome.

Results: The majority of nurses and surgeons expected that continuous vital signs monitoring could contribute to the earlier recognition of deterioration and result in earlier treatment for postoperative complications, although the effective time gain would depend on patient and situational factors. Their expectations regarding the impact of potential earlier diagnosis on clinical outcomes varied. Nevertheless, most caregivers would consider implementing continuous monitoring in the surgical ward to support patient monitoring after esophagectomy.

Conclusions: Caregivers expected that wireless vital signs monitoring would provide opportunities for early detection of postoperative complications in patients undergoing esophagectomy admitted to the ward and prevent sequelae under certain circumstances. As the technology matures, clinical outcome studies will be necessary to objectify these expectations and further investigate overall effects on patient outcome.