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

Linear Discriminant Trees

Univariate decision trees at each decision node consider the value of only one feature leading to axis-aligned splits. In a multivariate decision tree, each decision node divides the input space into two with an arbitrary hyperplane leading to oblique splits. In this paper, we propose linear discriminant trees where each node implements Fisher's linear discriminant analysis (LDA) to make a binary split. On twenty data sets from the UCI repository, we compare the linear discriminant trees with the univariate decision tree method ID3, multivariate decision tree methods CART, neural trees and linear machine decision trees (LMDT). Our results indicate that linear discriminant trees learn faster than other multivariate methods with binary splits, generalize well and construct small trees

Ordering and finding the best of K>2 supervised learning algorithms

Abstract—Given a data set and a number of supervised learning algorithms, we would like to find the algorithm with the smallest expected error. Existing pairwise tests allow a comparison of two algorithms only; range tests and ANOVA check whether multiple algorithms have the same expected error and cannot be used for finding the smallest. We propose a methodology, the MultiTest algorithm, whereby we order supervised learning algorithms taking into account 1) the result of pairwise statistical tests on expected error (what the data tells us), and 2) our prior preferences, e.g., due to complexity. We define the problem in graph-theoretic terms and propose an algorithm to find the “best ” learning algorithm in terms of these two criteria, or in the more general case, order learning algorithms in terms of their “goodness. ” Simulation results using five classification algorithms on 30 data sets indicate the utility of the method. Our proposed method can be generalized to regression and other loss functions by using a suitable pairwise test. Index Terms—Machine learning, classifier design and evaluation, experimental design. æ

Intraventricular hemorrhage in preterm newborns: Risk factors and results from a University Hospital in Istanbul, 8 years after

Background: In this prospective study, the authors aimed to show intraventricular hemorrhage (IVH) incidence of premature newborns in the Neonatal Intensive Care Unit of Cerrahpasa Faculty of Medicine, Istanbul, Turkey, and its risk factors, and they tried to compare these results with those they reported 8 years ago

Energy investment planning at a private company: a mathematical programming-based model and its application in Turkey

Semra Ağralı (MEF Author)We consider a mid-sized private electricity generating company that plans to enter the market that is partially regulated. There is a cap and trade system in operation in the industry. There are nine possible power plant types that the company considers to invest on through a planning horizon. Some of these plants may include a carbon capture and storage technology. We develop a stochastic mixed-integer linear program for this problem where the objective is to maximize the expected net present value of the profit obtained. We include restrictions on the maximum and minimum possible amount of investment for every type of investment option. We also enforce market share conditions such that the percentage of the total investments of the company over the total installed capacity of the country stay between upper and lower bounds. Moreover, in order to distribute the investment risk, the percentage of each type of power plant investment is restricted by some upper bound. We tested the model for a hypothetical company operating in Turkey. The results show that the model is suitable to be used for determining the investment strategy of the company.WOS:000413247800003Scopus - Affiliation ID: 60105072Science Citation Index Expanded - Social Sciences Citation IndexQ1ArticleUluslararası işbirliği ile yapılmayan - HAYIRKasım2017YÖK - 2017-1