Thoracic epidural analgesia as part of an enhanced recovery program in gynecologic oncology: a prospective cohort study.

OBJECTIVE: To evaluate the safety and the effectiveness of thoracic epidural analgesia as part of the enhanced recovery after surgery (ERAS) multimodal analgesic protocol in patients with gynecologic oncology who have undergone laparotomy for suspected or confirmed malignancy. METHODS: We conducted a prospective cohort study, following an enhanced recovery after surgery pathway, among patients who had undergone laparotomy for confirmed or suspected gynecological malignancy between January 2020 and September 2021. All patients who underwent laparotomy at the gynecologic oncology department for the aforementioned reason during that time were considered eligible. Patients (n=217) were divided into two groups: epidural (n=118) and non-epidural (n=99) group. Both groups were treated with the standard ERAS departmental analgesic protocol. The primary outcomes were length of hospital stay, complications, and readmission rates. RESULTS: Data from 217 patients (epidural group, n=118 vs non-epidural group, n=99) with median age of 61 years (IQR 53-68) were analyzed. The most common type of cancer was of ovarian origin (85/217, 39.2%, p=0.055) and median (Aletti) surgical complexity score was 3 (p=0.42). No differences were observed in the patients' demographics, clinical, and surgical characteristics. Primarily, median length of stay was 4 days in both groups with statistically significant lower IQR in the epidural group (3-5 vs 4-5, p=0.021). Complication rates were more common in the non-epidural group (38/99, 38.3% vs 36/118, 30.5%, p<0.001) with similar rates of grade III (p=0.51) and IV (0%) complications and readmission rates (p=0.51) between the two groups. Secondarily, the epidural group showed lower pain scores (p<0.001) on the day of surgery and in the first post-operative day (p<0.001), higher mobilization rates on the day of surgery (94.1% vs 57.6%, p<0.001), faster removal of urinary catheter (p<0.001), shorter time to flatus (p<0.001), and less nausea on the day of surgery (p<0.001). CONCLUSION: In this study we showed that thoracic epidural analgesia, when used as part of an ERAS protocol, is safe and offers more favorable pain relief along with a number of additional benefits, improving the peri-operative experience of patients with gynecologic cancer.

Enhanced Recovery After Surgery Protocols in Obese Gynecological Oncology Patients: A Single-Center Experience.

Objective The aim of this study is to present our experience and evaluate the safety and outcomes of the implementation of Enhanced Recovery After Surgery (ERAS) protocols in obese patients who underwent surgery for suspected or confirmed gynecological malignancies. Method From January 2020 to September 2021, 217 patients underwent laparotomy for a confirmed or suspected gynecological malignancy following a 19-element ERAS pathway. The patients were divided into two groups: obese (BMI ≥ 30 kg/m2, n = 104) and non-obese (BMI < 30, n = 113). Both groups were treated with a 19-element ERAS protocol. Results After dividing the 217 patients into two groups, significantly more comorbidities were observed in the obese group (diabetes mellitus: 23% vs. 8%, p = 0.004; ASA score grade 3: 25.0% vs. 6.2%, p < 0.001), as well as higher rates of endometrial cancer (51.9% vs. 17.7%, p < 0.001) compared to the non-obese group. The overall ERAS compliance rates when matched element by element were similar. Postoperatively, complication rates of all grades were significantly higher in the obese group (46.1% vs. 27.4%, p < 0.001) without differences in the length of stay, readmission, and reoperation rates. Conclusion In this retrospective study, we showed that obese gynecological oncology patients can be safely managed with ERAS protocols perioperatively while potentially minimizing the adverse outcomes in these otherwise high-risk patients.

Should patients with diabetes be encouraged to integrate social media into their care plan?

AIM: To evaluate the use of social media of individuals with diabetes mellitus (DM). MATERIALS & METHODS: Both web-based and in-clinic surveys were collected from individuals with DM. Descriptive and correlation analyses were employed to evaluate respondents' diabetes-specific social networking site behaviors. RESULTS: Forty-five patients with DM completed the web-based survey and 167, the clinic-based survey, of whom only 40 visited diabetes-specific social networking sites. Analysis of online survey data indicated that self-reported adherence to lifestyle recommendations was significantly correlated (p < 0.01) with visiting the sites. Clinic-based survey data found that patients who reported using DM-specific web sites monitored home glucose values more often and had better compliance with insulin administration (both p < 0.05) compared with nonusers. CONCLUSION: This study provides insight into why individuals visit DM-specific social networking sites. Certain self-management behaviors may improve as a result of visiting these sites. Further work is needed to explore how to leverage social media technology to assist patients with the management of DM.

Strength in Numbers: Using Big Data to Simplify Sentiment Classification.

Sentiment classification, the task of assigning a positive or negative label to a text segment, is a key component of mainstream applications such as reputation monitoring, sentiment summarization, and item recommendation. Even though the performance of sentiment classification methods has steadily improved over time, their ever-increasing complexity renders them comprehensible by only a shrinking minority of expert practitioners. For all others, such highly complex methods are black-box predictors that are hard to tune and even harder to justify to decision makers. Motivated by these shortcomings, we introduce BigCounter: a new algorithm for sentiment classification that substitutes algorithmic complexity with Big Data. Our algorithm combines standard data structures with statistical testing to deliver accurate and interpretable predictions. It is also parameter free and suitable for use virtually "out of the box," which makes it appealing for organizations wanting to leverage their troves of unstructured data without incurring the significant expense of creating in-house teams of data scientists. Finally, BigCounter's efficient and parallelizable design makes it applicable to very large data sets. We apply our method on such data sets toward a study on the limits of Big Data for sentiment classification. Our study finds that, after a certain point, predictive performance tends to converge and additional data have little benefit. Our algorithmic design and findings provide the foundations for future research on the data-over-computation paradigm for classification problems.

Economic impact and policy implications from urban shared transportation: The case of Pittsburgh's shared bike system.

During the last years the number of cities that have installed and started operating shared bike systems has significantly increased. These systems provide an alternative and sustainable mean of transportation to the city dwellers. Apart from the energy sustainability benefits, shared bike systems can have a positive effect on residents' health, air quality and the overall condition of the currently crumbling road network infrastructure. Anecdotal stories and survey studies have also identified that bike lanes have a positive impact on local businesses. In this study, driven by the rapid adoption of shared bike systems by city governments and their potential positive effects on a number of urban life facets we opt to study and quantify the value of these systems. We focus on a specific aspect of this value and use evidence from the real estate market in the city of Pittsburgh to analyze the effect on dwellers' properties of the shared bike system installed in the city in June 2015. We use quasi-experimental techniques and find that the shared bike system led to an increase in the housing prices (both sales and rental prices) in the zip codes where shared bike stations were installed. We further bring into the light potential negative consequences of this impact (i.e., gentrification) and discuss/propose two public policies that can exploit the impact of the system for the benefit of both the local government as well as the city dwellers.

Levosimendan Improves Neurological Outcome in a Swine Model of Asphyxial Cardiac Arrest.

BACKGROUND: In asphyxial cardiac arrest, the severe hypoxic stress complicates the resuscitation efforts and results in poor neurological outcomes. Our aim was to assess the effects of levosimendan on a swine model of asphyxial cardiac arrest. METHODS: Asphyxial cardiac arrest was induced in 20 Landrace/Large White piglets, which were subsequently left untreated for four minutes. The animals were randomised to receive adrenaline alone (n=10, Group A) and adrenaline plus levosimendan (n=10, Group B). All animals were resuscitated according to the 2010 European Resuscitation Council guidelines. Haemodynamic variables were measured before arrest, during arrest and resuscitation, and during the first 30 minutes after return of spontaneous circulation (ROSC), while survival and neurologic alertness score were measured 24 hours later. RESULTS: Return of spontaneous circulation was achieved in six animals (60%) from Group A and nine animals (90%) from Group B (p=0.303). During the first minute of cardiopulmonary resuscitation, coronary perfusion pressure was significantly higher in Group B (p=0.046), but there was no significant difference at subsequent time points until ROSC. Although six animals (60%) from each group survived after 24 hours (p=1.000), neurologic examination was significantly better in the animals of Group B (p<0.01). CONCLUSIONS: The addition of levosimendan to adrenaline improved coronary perfusion pressure immediately after the onset of cardiopulmonary resuscitation and resulted in better 24-hour neurological outcome.