Low Food Consumption Interferes with the Nutritional Status of Surgical Patients with Neoplasia of the Gastrointestinal Tract

Nutritional support strongly influence the nutritional status of the surgical neoplastic patients. This study aimed to evaluate the influence of food consumption on the perioperative nutritional status of hospitalized patients with neoplasia of the upper (UGIT) and lower (LGIT) gastrointestinal tract. Methods: Observational, longitudinal, and prospective study. Data collected: food consumption, Subjective Global Assessment, anthropometry, laboratory tests. Results: Eighty patients were followed up: 43 (54%) in the UGIT and 37 (46%) in the LGIT. The consumption in the perioperative period was lower than the usual consumption in the UGIT and LGIT groups, respectively, of energy (14.2 ± 6.5; 22.8 ± 11.2 Kcal/kg/d, p < 0.001; 13.6 ± 1.2; 19.0 ± 2.0 Kcal/kg/d; p = 0.014), protein (1.1 ± 0.7; 0.6 ± 0.3 g/kg/d, p < 0.001; 0.8 ± 0.1; 0.5 ± 0.1 g/kg/d; p = 0.058), selenium, zinc and copper. Most patients presented in the UGIT and LGIT groups, respectively, worsening malnutrition and muscle depletion according to the Subjective Global Assessment (61.9%; 51.4%) and hypoalbuminemia, mainly in the UGIT in the postoperative. Conclusion: Low food consumption during the perioperative period associated with prolongation of the postoperative fasting period worsens the nutritional status of patients undergoing surgery of the gastrointestinal tract for neoplasia, especially in the UGIT group.

A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records

We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually imposed and the regularization technique is popularly used. Motivated by the fact that the missingness mechanism, albeit usually treated as a nuisance, is difficult to specify correctly, we adopt the conditional likelihood approach so that the nuisance can be completely ignored throughout our procedure. We establish the asymptotic theory of the proposed estimator and develop an easy-to-implement algorithm via some data manipulation strategy. In particular, under the high-dimensional setting where regularization is needed, we propose a data perturbation method for the post-selection inference. The proposed methodology is especially appealing when the true missingness mechanism tends to be missing not at random, e.g., patient reported outcomes or real world data such as electronic health records. The performance of the proposed method is evaluated by comprehensive simulation experiments as well as a study of the albumin level in the MIMIC-III database.