Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Numerical discretization of stationary random processes

The increasing interest of the research community to the probabilistic analysis concerning the civil structures with space-variant properties points out the problem of achieving a reliable discretization of random processes (or random fields in a multi-dimensional domain). Given a discretization method, a continuous random process is approximated by a finite set of random variables. Its dimension affects significantly the accuracy of the approximation, in terms of the relevant properties of the continuous random process under investigation. The paper presents a discretization procedure based on the truncated Karhunen - Loève series expansion and the finite element method. The objective is to link in a rational way the number of random variables involved in the approximation to a quantitative measure of the discretization accuracy. The finite element method is applied to evaluate the terms of the series expansion when a closed form expression is not available. An iterative refinement of the finite element mesh is proposed in this paper, leading to an accurate random process discretization. The technique is tested with respect to the exponential covariance function, that enables a comparison with analytical expressions of the approximated properties of the random process. Then, the procedure is applied to the square exponential covariance functions, which is one of the most used covariance models in the structural engineering field. The comparison of the adaptive refinement of the discretization with a non-adaptive procedure and with the wavelet Galerkin approach allows to demonstrate the computational efficiency of the proposal within the framework of the Karhunen-Loève series expansion. A comparison with the Expansion Optimal Linear Estimation (EOLE) method is performed in terms of efficiency of thediscretization strateg

Discretization of 2D random fields: A genetic algorithm approach

The random fields are widely used in the literature to include the random spatial variability of material properties, geometrical dimensions and loads into the mathematical models describing the behaviour of the engineering structures. In practice, a discretization procedure is necessary to reduce a continuous random field to a finite set of random variables. It is clear that the accuracy of the discretization is a key point for any subsequent probabilistic investigation of the structural performance. Indeed it is meaningful to formulate a discretization error estimator to quantify the accuracy of the discretization and to require that the approximated random field fulfils a prescribed target accuracy. The discretization of a random field can be formulated as an optimization problem, where the optimization variable is the finite number of random variables involved in the representation of the random field and the objective function depends on the discretization error estimator and on the target accuracy. The advantage of the formulation of the optimization problem depends on the availability of a simple, accurate and versatile numerical approach. A genetic algorithm is proposed in this paper to achieve an optimal discretization of two-dimensional (2D) homogeneous random fields represented by the Karhunen Loeve series expansion. The numerical procedure is applied to the discretization of 2D random fields describing the random spatial fluctuations of the concrete properties in a bridge dec

An efficient coupling of FORM and Karhunen-Loeve series expansion

The topic of this paper is the solution of reliability problems where failure is influenced by the spatial random fluctuations of loads and material properties. Homogeneous random fields are used to model this kind of uncertainty. The first step of the investigation is the random field discretization, which transforms a random field into a finite set of random variables. The second step is the reliability analysis, which is performed using the FORM in this paper. A parametric analysis of the reliability index is usually performed with respect to the random field discretization accuracy. This approach requires several independent reliability analyses. A new and efficient approach is proposed in this paper. The Karhunen-Loève series expansion is combined with the FEM for the discretization of the random fields. An efficient solution of the reliability problem is proposed to predict the reliability index as the discretization accuracy increases