Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Unimodal‐Bio‐GAN: Keyless biometric salting scheme based on generative adversarial network

Abstract Cancellable biometrics enabled us to develop robust authentication systems by replacing the storage of the original biometric template with another secured version. A technique called biometric salting uses a parameter (key) and an invertible function to transform the human biometrics features into a secured format that can be protected and stored securely in a biometric database system. The salting key plays a main role in the success of this transformation, which makes it robust or vulnerable to many security attacks. One of the main challenges that faces biometrics' researchers currently is how to design and protect such a salting key considering two basic measures: security and recognition accuracy. In this article, we propose unimodal‐Bio‐GAN, a reliable keyless biometric salting technique based on standard generative adversarial network (GAN). In unimodal‐Bio‐GAN, a random permuted version of the human biometric data is implicitly considered as a salting key and required only during the enrolment stage, which increases the system reliability to overcome different security attacks. The experimental results of unimodal‐Bio‐GAN using the CASIA Iris‐V3‐Internal database outperform the previous methods and its security efficiency is analysed using different attack types

Ant Lion Optimization algorithm for kidney exchanges.

The kidney exchange programs bring new insights in the field of organ transplantation. They make the previously not allowed surgery of incompatible patient-donor pairs easier to be performed on a large scale. Mathematically, the kidney exchange is an optimization problem for the number of possible exchanges among the incompatible pairs in a given pool. Also, the optimization modeling should consider the expected quality-adjusted life of transplant candidates and the shortage of computational and operational hospital resources. In this article, we introduce a bio-inspired stochastic-based Ant Lion Optimization, ALO, algorithm to the kidney exchange space to maximize the number of feasible cycles and chains among the pool pairs. Ant Lion Optimizer-based program achieves comparable kidney exchange results to the deterministic-based approaches like integer programming. Also, ALO outperforms other stochastic-based methods such as Genetic Algorithm in terms of the efficient usage of computational resources and the quantity of resulting exchanges. Ant Lion Optimization algorithm can be adopted easily for on-line exchanges and the integration of weights for hard-to-match patients, which will improve the future decisions of kidney exchange programs. A reference implementation for ALO algorithm for kidney exchanges is written in MATLAB and is GPL licensed. It is available as free open-source software from: https://github.com/SaraEl-Metwally/ALO_algorithm_for_Kidney_Exchanges

Compatibility information with respect to the pool size, n.

<p>Compatibility information with respect to the pool size, n.</p

The composition of a resulting optimal solution.

<p>(a) IP-KPD, (b) Proposed, (c) GA-KPD.</p

Elite individual’s fitness per iteration.

<p>Elite individual’s fitness per iteration.</p

Two-cycle exchange.

<p> blood type is incompatible with his donor , the incompatibility is represented by dotted lines. The two incompatible pairs can swap their kidneys as illustrated by solid lines.</p

Transplants returned by all applied methods.

<p>(a) Pool size = 30, (b) Pool size = 40, (c) Pool size = 50, (d) Pool size = 75, (e) Pool size = 100, (f) Pool size = 200.</p

Number of transplants produced by methods(GA-KPD, Proposed, IP-KPD) for various KPD pool sizes.

<p>M denotes total number of extracted exchanges from KPD pool.</p