Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study

Background In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new “Trigger Tool” represents a sensitive predictor of adverse events in general surgery. Methods An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described “Trigger Tool” based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis. Results The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The “Trigger Tool” had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the “Trigger Tool”. Conclusions The “Trigger Tool” has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning

Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms

Lamb Behaviors Analysis Using a Predictive CNN Model and a Single Camera

Object tracking is the process of estimating in time N the location of one or more moving element through an agent (camera, sensor, or other perceptive device). An important application in object tracking is the analysis of animal behavior to estimate their health. Traditionally, experts in the field have performed this task. However, this approach requires a high level of knowledge in the area and sufficient employees to ensure monitoring quality. Another alternative is the application of sensors (inertial and thermal), which provides precise information to the user, such as location and temperature, among other data. Nevertheless, this type of analysis results in high infrastructure costs and constant maintenance. Another option to overcome these problems is to analyze an RGB image to obtain information from animal tracking. This alternative eliminates the reliance on experts and different sensors, yet it adds the challenge of interpreting image ambiguity correctly. Taking into consideration the aforementioned, this article proposes a methodology to analyze lamb behavior from an approach based on a predictive model and deep learning, using a single RGB camera. This method consists of two stages. First, an architecture for lamb tracking was designed and implemented using CNN. Second, a predictive model was designed for the recognition of animal behavior. The results obtained in this research indicate that the proposed methodology is feasible and promising. In this sense, according to the experimental results on the used dataset, the accuracy was 99.85% for detecting lamb activities with YOLOV4, and for the proposed predictive model, a mean accuracy was 83.52% for detecting abnormal states. These results suggest that the proposed methodology can be useful in precision agriculture in order to take preventive actions and to diagnose possible diseases or health problems

How do women living with HIV experience menopause? Menopausal symptoms, anxiety and depression according to reproductive age in a multicenter cohort

CatedresBackground: To estimate the prevalence and severity of menopausal symptoms and anxiety/depression and to assess the differences according to menopausal status among women living with HIV aged 45-60 years from the cohort of Spanish HIV/AIDS Research Network (CoRIS). Methods: Women were interviewed by phone between September 2017 and December 2018 to determine whether they had experienced menopausal symptoms and anxiety/depression. The Menopause Rating Scale was used to evaluate the prevalence and severity of symptoms related to menopause in three subscales: somatic, psychologic and urogenital; and the 4-item Patient Health Questionnaire was used for anxiety/depression. Logistic regression models were used to estimate odds ratios (ORs) of association between menopausal status, and other potential risk factors, the presence and severity of somatic, psychological and urogenital symptoms and of anxiety/depression. Results: Of 251 women included, 137 (54.6%) were post-, 70 (27.9%) peri- and 44 (17.5%) pre-menopausal, respectively. Median age of onset menopause was 48 years (IQR 45-50). The proportions of pre-, peri- and post-menopausal women who had experienced any menopausal symptoms were 45.5%, 60.0% and 66.4%, respectively. Both peri- and post-menopause were associated with a higher likelihood of having somatic symptoms (aOR 3.01; 95% CI 1.38-6.55 and 2.63; 1.44-4.81, respectively), while post-menopause increased the likelihood of having psychological (2.16; 1.13-4.14) and urogenital symptoms (2.54; 1.42-4.85). By other hand, post-menopausal women had a statistically significant five-fold increase in the likelihood of presenting severe urogenital symptoms than pre-menopausal women (4.90; 1.74-13.84). No significant differences by menopausal status were found for anxiety/depression. Joint/muscle problems, exhaustion and sleeping disorders were the most commonly reported symptoms among all women. Differences in the prevalences of vaginal dryness (p = 0.002), joint/muscle complaints (p = 0.032), and sweating/flush (p = 0.032) were found among the three groups. Conclusions: Women living with HIV experienced a wide variety of menopausal symptoms, some of them initiated before women had any menstrual irregularity. We found a higher likelihood of somatic symptoms in peri- and post-menopausal women, while a higher likelihood of psychological and urogenital symptoms was found in post-menopausal women. Most somatic symptoms were of low or moderate severity, probably due to the good clinical and immunological situation of these women

COVID-19 in hospitalized HIV-positive and HIV-negative patients : A matched study

CatedresObjectives: We compared the characteristics and clinical outcomes of hospitalized individuals with COVID-19 with [people with HIV (PWH)] and without (non-PWH) HIV co-infection in Spain during the first wave of the pandemic. Methods: This was a retrospective matched cohort study. People with HIV were identified by reviewing clinical records and laboratory registries of 10 922 patients in active-follow-up within the Spanish HIV Research Network (CoRIS) up to 30 June 2020. Each hospitalized PWH was matched with five non-PWH of the same age and sex randomly selected from COVID-19@Spain, a multicentre cohort of 4035 patients hospitalized with confirmed COVID-19. The main outcome was all-cause in-hospital mortality. Results: Forty-five PWH with PCR-confirmed COVID-19 were identified in CoRIS, 21 of whom were hospitalized. A total of 105 age/sex-matched controls were selected from the COVID-19@Spain cohort. The median age in both groups was 53 (Q1-Q3, 46-56) years, and 90.5% were men. In PWH, 19.1% were injecting drug users, 95.2% were on antiretroviral therapy, 94.4% had HIV-RNA < 50 copies/mL, and the median (Q1-Q3) CD4 count was 595 (349-798) cells/μL. No statistically significant differences were found between PWH and non-PWH in number of comorbidities, presenting signs and symptoms, laboratory parameters, radiology findings and severity scores on admission. Corticosteroids were administered to 33.3% and 27.4% of PWH and non-PWH, respectively (P = 0.580). Deaths during admission were documented in two (9.5%) PWH and 12 (11.4%) non-PWH (P = 0.800). Conclusions: Our findings suggest that well-controlled HIV infection does not modify the clinical presentation or worsen clinical outcomes of COVID-19 hospitalization

Revista Temas Agrarios Volumen 26; Suplemento 1 de 2021

1st International and 2nd National Symposium of Agronomic Sciences: The rebirth of the scientific discussion space for the Colombian Agro.1 Simposio Intenacional y 2 Nacional de Ciencias Agronómicas: El renacer del espacio de discusión científica para el Agro colombiano