Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Long‐term care facilities' response to the COVID ‐19 pandemic: An international, cross‐sectional survey

Aims To (i) assess the adherence of long‐term care (LTC) facilities to the COVID‐19 prevention and control recommendations, (ii) identify predictors of this adherence and (iii) examine the association between the adherence level and the impact of the pandemic on selected unfavourable conditions. Design Cross‐sectional survey. Methods Managers (n = 212) and staff (n = 2143) of LTC facilities (n = 223) in 13 countries/regions (Brazil, Egypt, England, Hong Kong, Indonesia, Japan, Norway, Portugal, Saudi Arabia, South Korea, Spain, Thailand and Turkey) evaluated the adherence of LTC facilities to COVID‐19 prevention and control recommendations and the impact of the pandemic on unfavourable conditions related to staff, residents and residents' families. The characteristics of participants and LTC facilities were also gathered. Data were collected from April to October 2021. The study was reported following the STROBE guidelines. Results The adherence was significantly higher among facilities with more pre‐pandemic in‐service education on infection control and easier access to information early in the pandemic. Residents' feelings of loneliness and feeling down were the most affected conditions by the pandemic. More psychological support to residents was associated with fewer residents' aggressive behaviours, and more psychological support to staff was associated with less work–life imbalance. Conclusions Pre‐pandemic preparedness significantly shaped LTC facilities' response to the pandemic. Adequate psychological support to residents and staff might help mitigate the negative impacts of infection outbreaks. Impact This is the first study to comprehensively examine the adherence of LTC facilities to COVID‐19 prevention and control recommendations. The results demonstrated that the adherence level was significantly related to pre‐pandemic preparedness and that adequate psychological support to staff and residents was significantly associated with less negative impacts of the pandemic on LTC facilities' staff and residents. The results would help LTC facilities prepare for and respond to future infection outbreaks. Patient or public contribution No Patient or Public Contribution

Responding of Long Green Pepper Plants to Different Sources of Foliar Potassium Fertiliser

The aim of this study was to explore the efficiency of foliar potassium fertiliser relative to morphological, physiological and biochemical properties of hybrid long pepper (Capsicum annuum). Pepper plants were grown in a greenhouse and supplied with three sources of foliar potassium fertilisers, i.e., potassium-humate (1 g/L = 1,250 g/ha), potassium sulfate (1 g/L = 1,250 g/ha), and potassium chloride (1 g/L = 1,250 g/ha). Water served as control. The impacts of these treatments on the phytosynthetic parameters (photosynthetic rate, stomatal conductance, intercellular CO2, leaf carotenoids) and chlorophyll a and b, metabolic compounds and nitrogen, phosphorus and potassium were measured. The phytosynthetic parameters significantly improved by different foliar potassium application and the highest level of photosynthetic activity was noted in plants supplied with potassium sulfate, followed by potassium-humate and potassium chloride. Plant biomass accumulation, cholorophyll (a and b), and total yield showed larger increases in plants fertilised with potassium sulfate than those fertilised with potassium-humate; smallest increases occurred with potassium chloride. Concentrations of total sugars, carotenoids, chlorophyll (a and b), and endogenous level of nitrogen, phosphorus, and potassium contents in plants and fruits were possitively influenced by varying sources of potassium. The fruit color parameters and total soluble solid were also significantly increased with all foliar potassium treatments compared with control. Foliar application of potassium sulfate recorded the highest values and significantly increase all anatomical characters for leaf, stem and fruit of pepper plant

Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes

Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity