Carlo Ricciardi

and 5 more

In literature, several organizational solutions are proposed to determine the probability of patients’ voluntary discharge from the emergency department. Here, the issue of self-discharge is analyzed by modeling through the Markov theory, an innovative approach recently applied to the healthcare field. The aim of the work is to propose a new method to calculate the rate of voluntary discharge by defining a generic model to describe the process of first aid using the “behavioral” Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then applied on MatLab and validated to a real case study at the hospital “A. Cardarelli” of Naples. It was found that most of the risk of self-discharge is during the wait time before the patient is seen and for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and adaptable to each hospital facility by changing only the transition probabilities between states.Version of record at "doi: 10.3934/mbe.2021013":Carlo Ricciardi, Alfonso Maria Ponsiglione, Giuseppe Converso, Ida Santalucia, Maria Triassi, Giovanni Improta. Implementation and validation of a new method to model voluntary departures from emergency departments. Mathematical Biosciences and Engineering, 2021, 18(1): 253-273. doi: 10.3934/mbe.2021013

fabiana rubba

and 10 more

Abstract Aims In the era of personalized therapy liquid biopsy is considered an important diagnostic tool in the clinical management of cancer patients. Tissue specimen represents “gold standard” for molecular evaluation of specific gene targets alterations that lead cancer patients to benefit of a “tailed therapy” based on molecular features of the tumor. This innovative source of nucleic acids was introduced in clinical setting only for NSCLC patients to test Epidermal Grow Factor Receptor (EGFR) mutations when tissue is not available or to monitor acquired resistance mutation after a first line of treatment. The study aimed at assessing the diagnostic potential of liquid biopsy in balanced tertiary screening modeling. Methods From 2014 to 2019 molecular diagnostics activity performed on liquid biopsy specimens in the Predictive Diagnostic laboratory of AOU Federico II were reviewed. Laboratory data were collected in SPSS. Non parametric analysis were performed in order to test the differences between patients WILD TYPE or not. A multivariate logistic model was performed in order to assess the effect of mutation, age and sex, on the tumor progression. The results of the revision concern 515 total cases (almost of all plasma or peripheral blood) allowed to evaluate the liquid biopsies for women and men. The average age of the Patients is 66.3 years, and the 25 percentile is 59 years. Results The cases are 221 basal and 294 by progression after first line TKIs treatment. The cases with mutation, as expected, have an OR 4,15 compared to the basal to have a tumor progression (95% IC: 2,7 - 6,3) regardless of sex and age. The mutations detected were 131 from different types of lung carcinomas.Conclusions Working on case data, specifying the characteristics of the Patients with mutations will drive a further estimate in tertiary prevention screening designs