The effectiveness and acceptability of formal versus informal mindfulness among university students with and without recent nonsuicidal self-injury: Protocol for an online, parallel-group, randomized controlled trial.

BACKGROUND: Mindfulness-based programming comprising both formal (FM) and informal (IM) mindfulness practice is increasingly offered to university students. However, difficulties with emotion regulation, self-criticism, and a potentially complex relationship with their body may undermine the benefits of FM for students with a history of nonsuicidal self-injury (NSSI), given its requirement of sustained attention on thoughts, emotions, and bodily sensations. IM may be better tolerated by these students. This protocol describes a randomized controlled trial comparing a brief FM practice, a brief IM practice, and an active control task in university students with and without recent NSSI. METHODS: Recruitment began in November 2022. An online, parallel-group, single-blind, randomized controlled trial will compare a 10-min, single-session FM practice, a 10-min, single session IM practice, and a 10-min, single session active control task in university students with and without recent NSSI. Outcomes will be assessed immediately pre-intervention and within five minutes post-intervention. The primary outcome will be state mindfulness. Secondary outcomes will be state stress, state well-being, and acceptability. Students with a history of NSSI are expected to report (i) greater improvements in state mindfulness, stress, and well-being, and (ii) greater acceptability in the IM condition, relative to the FM and control conditions. This distinction is not expected to occur in the no-NSSI comparison group. CONCLUSION: This trial is the first to parse out the impacts of FM versus IM practice among university students with and without a history of self-injury. Findings will be relevant to academic and clinical audiences within university settings.

The Fast Cognitive Evaluation (FaCE): a screening tool to detect cognitive impairment in patients with cancer.

Cancer-related cognitive impairment (CRCI) is one of the most concerning conditions experienced by patients living with cancer and has a major impact on their quality of life. Available cognitive assessment tools are too time consuming for day-to-day clinical setting assessments. Importantly, although shorter, screening tools such as the Montreal Cognitive Assessment or the Mini-Mental State Evaluation have demonstrated a ceiling effect in persons with cancer, and thus fail to detect subtle cognitive changes expected in patients with CRCI. This study addresses this lack of cognitive screening tools by developing a novel tool, the Fast Cognitive Evaluation (FaCE).A population of 245 patients with 11 types of cancer at different illness and treatment time-points was enrolled for the analysis. FaCE was developed using Rasch Measurement Theory, a model that establishes the conditions for a measurement tool to be considered a rating scale.FaCE shows excellent psychometric properties. The population size was large enough to test the set of items (item-reliability-index=0.96). Person-reliability (0.65) and person-separation (1.37) indexes indicate excellent internal consistency. FaCE's scale is accurate (reliable) with high discriminant ability between cognitive levels. Within the average testing time of five minutes, FaCE assesses the main cognitive domains affected in CRCI.FaCE is a rapid, reliable, and sensitive tool for detecting even minimal cognitive changes over time. This can contribute to early and appropriate interventions for better quality of life in patients with CRCI. In addition, FaCE could be used as a measurement tool in research exploring cognitive disorders in cancer survivors.

The new robotic platform Hugo™ RAS for lateral transabdominal adrenalectomy: a first world report of a series of five cases.

Robotic assisted surgery is the most rapidly developing field of minimally invasive surgery. Its wide diffusion has led to the development and standardization of robotic-assisted approaches also for adrenalectomy. In this study, we present the first five robotic-assisted lateral transabdominal adrenalectomies performed with the new Hugo RAS™ system (Medtronic, Minneapolis, MN, USA). After an official training course of the surgical team, five consecutive patients scheduled for unilateral adrenalectomy, underwent robotic-assisted operations in our institution. Patients that were candidates for partial adrenalectomy were excluded. A description of the operating theatre, robotic arms and docking setup is provided. Four female and one male patient underwent lateral transabdominal adrenalectomy, three for lesions on the left side and two on the right. Median lesion size was 3.9 cm (range: 30-90) and preoperative diagnosis was Cushing's syndrome in three patients, an adrenal cystic lesion and a pheochromocytoma. The median docking time was 5 min (range: 5-8) and the median console time was 55 min (range: 29-108). Procedures were performed without intraoperative complications and no conversions or additional ports were needed. System's function and docking were uneventful. Based on our initial experience, adrenalectomy with the Hugo™ system is feasible. This study provides technical notes for other centres that wish to perform robotic-assisted adrenalectomies with the Hugo™ RAS as well as general information and our preliminary insights on this new platform.

Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices.

BACKGROUND: Intraoperative hypotension is associated with increased perioperative complications, hospital length of stay (LOS) and healthcare expenditure in gynecologic surgery. We tested the hypothesis that the adoption of a machine learning-based warning algorithm (hypotension prediction index-HPI) might yield an economic advantage, with a reduction in adverse outcomes that outweighs the costs for its implementation as a medical device. METHODS: A retrospective-matched cohort cost-benefit Italian study in gynecologic surgery was conducted. Sixty-six female patients treated with standard goal-directed therapy (GDT) were matched in a 2:1 ratio with thirty-three patients treated with HPI based on ASA status, diagnosis, procedure, surgical duration and age. RESULTS: The most relevant contributor to medical costs was operating room occupation (46%), followed by hospital stay (30%) and medical devices (15%). Patients in the HPI group had EURO 300 greater outlay for medical devices without major differences in total costs (GDT 5425 (3505, 8127), HPI 5227 (4201, 7023) p = 0.697). A pre-specified subgroup analysis of 50% of patients undergoing laparotomic surgery showed similar medical device costs and total costs, with a non-significant saving of EUR 1000 in the HPI group (GDT 8005 (5961, 9679), HPI 7023 (5227, 11,438), p = 0.945). The hospital LOS and intensive care unit stay were similar in the cohorts and subgroups. CONCLUSIONS: Implementation of HPI is associated with a scenario of cost neutrality, with possible economic advantage in high-risk settings.

The value of surgical admissions for malignant uterine cancer. A comparative analysis of robotic, laparoscopic, and laparotomy surgery in a university hospital.

Background: Robotic surgery for malignant uterine cancer raises issue of economic sustainability for providers. The objective of this study was to assess the value of surgical admissions for malignant uterine cancer in a University Hospital through an analysis of their costs and outcomes by comparing three different surgical approaches (laparotomy, laparoscopic, and robotic surgery). Methods: Hospitalizations between 1 January 2019 and 31 October 2021 for malignant uterine cancer surgery were selected and stratified. For each surgical approach, mean values (with 95% confidence intervals, CI) were calculated for cost items. Moreover, 30-day readmission frequency was calculated for the three approaches compared to each other. ANOVA and Student's t-test and relative risk (RR) were used for statistical analysis. A break-even analysis was carried out by evaluating the volume of robotic and non-robotic surgical admissions. Results: A total of 1,336 hospitalizations were included in the study, 366 with robotic, 591 with laparoscopic, and 379 with laparotomy surgery. Robotic surgery, compared to laparoscopic and laparotomy ones, showed a statistically significant difference (p < 0.001) in the economic margin, which was largely negative (-1069.18 €; 95%CI:-1240.44--897.92 €) mainly due to devices cost, and a lower percentage of 30-day readmissions (1.4%; 95%CI: 0.2-2.6%), with a statistically significant difference only vs. laparotomy (p = 0.029). Laparoscopic compared to laparotomy surgery showed a significantly (p < 0,001) more profitable economic margin (1692.21 €; 95%CI: 1531.75 €-1852.66 €) without a significant difference for 30-day readmissions. Break-even analysis showed that, on average, for each malignant uterine cancer elective surgery performed laparoscopically, 1.58 elective robotic surgeries are sustainable for the hospital (95% CI: 1.23-2.06). Conclusion: Break-even analysis could be a useful tool to support hospital management in planning and governance of malignant uterine cancer surgery. Systematic application of this tool will allow defining over time right distribution of robotic, laparoscopic, and laparotomy surgeries' volumes to perform to ensure both quality and economic-financial balance and therefore value of uterine oncological surgery. Concerning research, this study paves the way for a multicentric study, the extension of outcomes of malignant uterine surgery to be considered and assessed, and the future inclusion of other therapeutic interventions in the analysis.

Introducing the New Surgical Robot HUGO™ RAS: System Description and Docking Settings for Gynecological Surgery.

This study provides a detailed description of the new HUGO™ RAS System and suggests docking settings for gynecological surgery. The system is composed of an "open" surgical console with an HD-3D passive display, a system tower, and four arm carts. Each arm has an extremely wide range of adaptability resulting from the numerous joints. The human cadaver labs were performed at the ORSI Academy between August and December 2021. All procedures were performed by two surgical teams, each composed of a high-volume surgeon experienced in robotic surgery, gynecologic oncology, and pelvic sidewall surgery, and one bedside assistant. Three main gynecological surgical scenarios were identified: standard pelvic surgery, pelvic sidewall surgery, and para-aortic/upper abdominal surgery. Concerning the port placement, the chosen options were called "straight" and "bridge"; instead, the so-called "compact" and "butterfly" configurations were identified for the arm cart positioning. Four cadavers were used to perform total hysterectomy, radical hysterectomy, pelvic exenteration, pelvic and para-aortic lymphadenectomy, and omentectomy. We performed several tests, identifying the best system configurations to draw the proper efficiency from the flexibility of the system in all gynecological surgical scenarios. The straight port placement seems to be adequate for standard pelvic surgery. The bridge trocar position is best to reach the deeper and lateral anatomical regions of the female pelvis. The compact and butterfly arm cart allocations are adequate for both straight and bridge port placement. When deep pelvic surgery was performed, the bedside assistant became more proficient by working with a standard laparoscopic instrument from an ancillary port placed in the left iliac fossa. The arm carts needed to be moved in an open manner, like for the proposed butterfly configuration. On the contrary, the compact disposition left enough space to assist from Palmer's point port. Several basic and advanced gynecological surgical procedures were performed and completed successfully without encountering any technical or surgical issue, the results obtained were judged sufficient to proceed with the clinical experience in daily practice. The HUGO™ RAS system is flexible and highly performative in various surgical scenarios.

A real-time integrated framework to support clinical decision making for covid-19 patients.

BACKGROUND: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS: The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS: The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION: The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

Post-thyroidectomy hypocalcemia: Is a routine preferable over a selective supplementation?

BACKGROUND: Comparative studies among protocols for the management of post-total thyroidectomy (TT) hypocalcemia are lacking. We compared the effectiveness of PTH-driven selective supplementation (PD-SS) and routine calcium and calcitriol supplementation with preoperative calcitriol administration in preventing symptomatic hypocalcemia (SH) and readmission. METHODS: Three-hundred consecutive patients undergoing TT were assigned to 3 groups: the PD-SS group, the high-dose routine supplementation (HD-RS) group and the low-dose routine supplementation (LD-RS) group. RESULTS: Mean post-operative stay was shorter in HD-RS patients when compared to PD-SS and LD-RS (p < 0.001). Significantly more patients in the PD-SS group experienced SH (p = 0.042). The rate of post-operative hypocalcemia was not significantly different among the groups (p = 0.063). No readmission for SH or hypercalcemia occurred. CONCLUSIONS: HD-RS emerged as the most effective treatment to prevent SH, without increasing the risk of readmission for calcitriol-related hypercalcemia. Basing on the present results, HD-RS should be recommended as the preferable protocol.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Robotic adrenalectomy: evaluation of cost-effectiveness.

Laparoscopic adrenalectomy (LA) is the preferred treatment option for adrenal lesions, considering the improved patients outcomes, due to the reduced postoperative morbidity and postoperative pain, the faster recovery and the shorter length of hospital stay. The widespread diffusion of robotic technology led to the development and standardization of robot-assisted approach to adrenalectomy. However, to date, no clear benefit from the use of the robot-assisted approach has been found. The higher costs remain an important drawback and limit the implementation of robot-assisted adrenalectomy (RA) programs. This review summarizes the current available data regarding RA including its operative outcomes, advantages and drawbacks in comparison with conventional LA, evaluating its cost-effectiveness.

Integration of Personalized Healthcare Pathways in an ICT Platform for Diabetes Managements: A Small-Scale Exploratory Study.

The availability of new tools able to support patient monitoring and personalized care may substantially improve the quality of chronic disease management. A personalized healthcare pathway (PHP) has been developed for diabetes disease management and integrated into an information and communication technology system to accomplish a shift from organization-centered care to patient-centered care. A small-scale exploratory study was conducted to test the platform. Preliminary results are presented that shed light on how the PHP influences system usage and performance outcomes.

Exploring the measurement properties of the Montreal Cognitive Assessment in a population of people with cancer.

BACKGROUND: Cancer and cancer-related treatments are associated with a constellation of physical and psychological changes. Treatments associated with noncentral nervous system neoplasms can have short- and long-term effects on cognition, affecting quality of life in people with cancer. Clinical measurement tools specific to cancer-related mild cognitive impairment (MCI) are lacking. The Montreal Cognitive Assessment (MoCA) has been validated in a geriatric population and used in studies assessing MCI in persons with cancer, but no studies have yet shown its psychometric properties when used with this population. PURPOSE: The purpose of this study is to explore the psychometric properties of the MoCA within a population of persons with noncentral nervous system cancer. METHODS: A total of 74 participants were included from persons attending a Cancer Nutrition-Rehabilitation Program at the McGill University Health Centre. Rasch analyses were conducted. RESULTS: The MoCA data fit all the properties of the Rasch model with a person separation index of 1.04 and person reliability of 0.52. The MoCA items were found to measure a unidimensional construct and spanned 6.57 logits, with item difficulty levels between 2.49 and -4.08 logits. However, the MoCA presented a lack of items of higher difficulty, as person cognitive ability levels ranged from -0.51 to 5.17 logits. CONCLUSION: Within the limits of a small sample size, the results of this exploratory study suggest the possibility that the MoCA, when used within a population of persons with cancer, may meet criteria for unidimensionality and adequate item fit but may present weaknesses when used with participants of higher cognitive abilities.