Research

Open AccessArticle

Kidney Transplant: Survival Analysis and Prognostic Factors after 10 Years of Follow-Up

by

Álvaro Beviá-Romero

,

Francisco Quereda-Flores

,

Javier Díaz-Carnicero

,

Francisco Gómez-Palomo

,

María Ramos-Cebrián

,

Joaquín Espinosa-Vañó

,

Dario J. Castillo-Antón

,

Enrique Broseta-Rico

,

David Vivas-Consuelo

and

Alberto Budía-Alba

Mathematics 2023, 11(7), 1640; https://doi.org/10.3390/math11071640 - 28 Mar 2023

Viewed by 587

Abstract

The aim of this work is to analyse recipient and graft survival after kidney transplant in a three-year cohort and to identify predictive factors with up to 10 years of follow-up. Methods: retrospective consecutive cohort study of 250 kidney transplant recipients operated between [...] Read more.

The aim of this work is to analyse recipient and graft survival after kidney transplant in a three-year cohort and to identify predictive factors with up to 10 years of follow-up. Methods: retrospective consecutive cohort study of 250 kidney transplant recipients operated between 2010 and 2012. Multiorganic transplants and both dead-donor and living-donor transplants were included. Data were collected from electronic health records. A survival analysis was conducted using the Kaplan-Meier method and a Cox proportional-hazards multivariate model. Results: mean follow-up was 8.1 ± 3.2 years. Graft survival at 2, 5 and 10 years was 89.0%, 85.1% and 78.4% respectively. The multivariate model identified the following risk factors for graft loss: diabetic nephropathy (HR 3.2 CI95% [1.1–9.4]), delayed graft function (3.8 [2.0–7.4]), chronic kidney rejection (3.7 [1.2–11.4]), and early surgical complications (2.6 [1.4–5.1]). Conversely, combined transplant was found to be a protective factor for graft loss (0.1 [0.0–0.5]). Recipient patient survival was 94.3%, 90.0% and 76.6% at 2, 5 and 10 years respectively. The model identified the following mortality risk factors: older recipient age (1.1 [1.1–1.2]), combined transplant (7.6 [1.7–34.5]) and opportunistic infections (2.6 [1.3–5.0]). Conclusions: 10-year recipient and graft survival were 76.6% and 78.4% respectively. Main mortality risk factors were older recipient age, opportunistic infections and multiorganic transplant. Main graft loss risk factors were diabetic nephropathy, delayed graft function, chronic kidney rejection and early surgical complications. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

A Novel Strategy to Classify Chronic Patients at Risk: A Hybrid Machine Learning Approach

by

Fabián Silva-Aravena

,

Hugo Núñez Delafuente

and

César A. Astudillo

Mathematics 2022, 10(17), 3053; https://doi.org/10.3390/math10173053 - 24 Aug 2022

Cited by 2 | Viewed by 964

Abstract

Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently [...] Read more.

Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently monitored by the health team.. In fact, and according to the Chilean Ministry of Health (MINSAL), 7 out of 10 chronic patients have suspended their medical check-ups, generating critical situations, such as a more significant number of visits to emergency units, expired prescriptions, and a higher incidence in hospitalization rates. For this problem, health services in Chile have had to reschedule their scarce medical resources to provide care in all health processes. One element that has been considered is caring through telemedicine and patient prioritization. In the latter case, the aim was to provide timely care to those critical patients with high severity and who require immediate clinical attention. For this reason, in this work, we present the following methodological contributions: first, an unsupervised algorithm that analyzes information from anonymous patients to classify them according to priority levels; and second, rules that allow health teams to understand which variable(s) determine the classification of patients. The results of the proposed methodology allow classifying new patients with 99.96% certainty using a three-level decision tree and five classification rules. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

Validation of a New Telenursing Questionnaire: Testing the Test

by

Julio Emilio Marco-Franco

,

Margarida Reis-Santos

,

Isabel Barrachina-Martínez

,

Silvia González-de-Julián

and

Ramón Camaño-Puig

Mathematics 2022, 10(14), 2463; https://doi.org/10.3390/math10142463 - 15 Jul 2022

Viewed by 867

Abstract

Background: Existing surveys on telenursing refer to specific areas of nursing after the implementation of a programme, but telenursing in general has not been fully evaluated from a prospective approach. Aim: Design and statistical validation of a telenursing questionnaire. Methods: A new questionnaire [...] Read more.

Background: Existing surveys on telenursing refer to specific areas of nursing after the implementation of a programme, but telenursing in general has not been fully evaluated from a prospective approach. Aim: Design and statistical validation of a telenursing questionnaire. Methods: A new questionnaire was designed with 18 paired (to avoid leading) questions (Likert-5) plus three dichotomous questions (randomly ordered, inspired by existing validated tests) to analyse the dimensions of: acceptance, usefulness and appropriateness of telenursing from the nursing point of view (7 min test). The questionnaire was validated by classical tests and item response tests (Rasch) using six computer-generated databases with different response profiles (tendency to be positioned against, neutral and positioned in favour) with two degrees of agreement between each pair of responses for each option. Results: Classical testing: Cronbach’s alphas (from 0.8 to 0.95), Kaiser–Meyer–Olkin (KMO) (0.93 to 0.95) and a significant p < 0.0001 for Bartlett’s test of sphericity were obtained. Rasch analysis: Reliability coefficients (0.94). Warm’s mean weighted likelihood estimates (0.94). Extreme infit-t and outfit-t values (+1.61 to −1.98). Conclusions: Both the classical test and the Rasch approaches confirm the usefulness of the new test for assessing nurses’ positioning in relation to telenursing. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

Modelling Deprivation Level and Multimorbidity in a Health District

by

María Pilar Botija Yagüe

,

Sofía Sorbet-Santiago

,

Javier Díaz-Carnicero

,

Silvia González-de-Julián

and

Ruth Usó-Talamantes

Mathematics 2022, 10(4), 659; https://doi.org/10.3390/math10040659 - 20 Feb 2022

Viewed by 1083

Abstract

Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the [...] Read more.

Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the subsequent use of healthcare resources in one health district, using the socioeconomic, clinical and geographical data from its administrative health records. Eight socioeconomic indicators were identified and weighted using the methodology of two-phase principal component analysis, providing an index that allowed each census section to be classified into seven deprivation groups. Secondly, the possible relation between the IPID and the variables for multimorbidity and healthcare resources was analysed using the theory of multiple comparisons. It was observed that places with a greater proportion of healthy people presented lower values of deprivation and that, at lower levels of deprivation, there were fewer hospital admissions. The results show that living in an area with a higher deprivation index is associated with greater consumption of healthcare resources and disease burden. Identifying areas of sociosanitary vulnerability can help to identify health inequalities and allow intervention by clinical practices and healthcare management to reduce them. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

Effective Optimisation of the Patient Circuits of an Oncology Day Hospital: Mathematical Programming Models and Case Study

by

Adrián González-Maestro

,

,

,

,

and

Francisco Reyes-Santias

Mathematics 2022, 10(1), 62; https://doi.org/10.3390/math10010062 - 25 Dec 2021

Viewed by 1718

Abstract

In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model [...] Read more.

In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model in such a way that we minimise the duration of the waiting room stays of the total set of patients and taking into account the restrictions of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach. This model will explicitly consider the existing uncertainty in terms of the different times involved in the circuit, and this model also allows the reorganisation of the schedules of medical appointments with oncologists. The models are complemented by a tool that solves the problem of assigning nurses to patients. The work is motivated by the particular characteristics of a real hospital and the models are used and compared with data from this case. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic

by

Francisco Gabriel Morillas-Jurado

,

María Caballer-Tarazona

and

Vicent Caballer-Tarazona

Mathematics 2022, 10(1), 46; https://doi.org/10.3390/math10010046 - 23 Dec 2021

Cited by 5 | Viewed by 1843

Abstract

In Spain, the COVID-19 pandemic has impacted the various regions of the country differently. The availability of reliable and up-to-date information has proved to be fundamental for the management of this health crisis. However, especially during the first wave of the pandemic (February–August [...] Read more.

In Spain, the COVID-19 pandemic has impacted the various regions of the country differently. The availability of reliable and up-to-date information has proved to be fundamental for the management of this health crisis. However, especially during the first wave of the pandemic (February–August 2020), the disparity in the recording criteria and in the timing of providing these figures to the central government created controversy and confusion regarding the real dimension of the pandemic. It is therefore necessary to have objective and homogeneous criteria at the national level to guide health managers in the correct recording and evaluation of the magnitude of the pandemic. Within this context, we propose using Benford’s Law as an auditing tool to monitor the reliability of the number of daily COVID-related deaths to identify possible deviations from the expected trend. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

Markov Models for Economic Evaluation in Osteoporosis Treatment

by

Marta Osca-Guadalajara

,

Javier Díaz-Carnicero

,

Silvia González-de-Julián

and

David Vivas-Consuelo

Mathematics 2021, 9(18), 2331; https://doi.org/10.3390/math9182331 - 20 Sep 2021

Viewed by 1323

Abstract

Osteoporosis is frequent in elderly people, causing bone fractures and lowering their quality of life. The costs incurred by these fractures constitute a problem for public health. Markov chains were used to carry out an incremental cost-utility analysis of the four main drugs [...] Read more.

Osteoporosis is frequent in elderly people, causing bone fractures and lowering their quality of life. The costs incurred by these fractures constitute a problem for public health. Markov chains were used to carry out an incremental cost-utility analysis of the four main drugs used in Spain to treat osteoporosis (alendronate, risedronate, denosumab and teriparatide). We considered 14 clinical transition states, from starting osteoporotic treatment at the age of 50 until death or the age of 100. Cost-effectiveness was measured by quality adjusted life years (QALYs). The values used in the Markov model were obtained from the literature. Teriparatide is the cost-effective alternative in the treatment of osteoporosis in patients with fractures from the age of 50, establishing a payment threshold of 20,000 EUR/QALY. However, it is the most expensive therapy, not appearing cost-effective in cases that do not present fracture and in ages over 80 years with fracture. Alendronate and denosumab therapies are presented as cost-effective osteoporosis treatment alternatives depending on the age of onset and duration of treatment. From the perspective of cost-effectiveness, establishing a payment threshold of 20,000 EUR/QALY, teriparatide is the cost-effective alternative in patients with fracture from the age of 50 to 70 years old in Spain. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

► Show Figures

Figure 1

Open AccessArticle

Predicting the Reputation of Pharmaceutical Firms with Financing and Geographical Location Data

by

Mª Ángeles Alcaide

,

Elena de la Poza

and

Mª Natividad Guadalajara

Mathematics 2021, 9(16), 1893; https://doi.org/10.3390/math9161893 - 09 Aug 2021

Cited by 2 | Viewed by 1303

Abstract

Reputation is a strategic asset for firms, but has been poorly studied in the pharmaceutical industry, particularly in relation to their financial and stock-market performance. This work aimed to predict the probability of a firm being included in a pharmaceutical reputation index (Merco [...] Read more.

Reputation is a strategic asset for firms, but has been poorly studied in the pharmaceutical industry, particularly in relation to their financial and stock-market performance. This work aimed to predict the probability of a firm being included in a pharmaceutical reputation index (Merco and PatientView), and the position it occupies, according to its economic–financial and stock-market outcomes and its geographical location. Fifty firms with excellent sales in 2019 and their rankings in 2017–2019 were employed. The methodology followed was logistic regression. Their research and development (R&D) expenditures and dividends strongly influenced them being included in both rankings. Non-Asian pharmaceutical companies were more likely to belong to the two reputation indices than Asian ones, and to occupy the best positions in the Merco ranking. Although no large differences appeared in the firms in both indices, differences were found in the position that pharmaceutical companies occupied in rankings and in the variables that contribute to them occupying these positions. Being in PatientView influenced dividends, sales, and income, while appearing in Merco showed accounting aspects like value in books and debt ratio. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

Open AccessArticle

Simplified Mathematical Modelling of Uncertainty: Cost-Effectiveness of COVID-19 Vaccines in Spain

by

Julio Emilio Marco-Franco

,

Pedro Pita-Barros

,

Silvia González-de-Julián

,

Iryna Sabat

and

David Vivas-Consuelo

Mathematics 2021, 9(5), 566; https://doi.org/10.3390/math9050566 - 06 Mar 2021

Cited by 10 | Viewed by 2630

Abstract

When exceptional situations, such as the COVID-19 pandemic, arise and reliable data is not available at decision-making times, estimation using mathematical models can provide a reasonable reckoning for health planning. We present a simplified model (static but with two-time references) for estimating the [...] Read more.

When exceptional situations, such as the COVID-19 pandemic, arise and reliable data is not available at decision-making times, estimation using mathematical models can provide a reasonable reckoning for health planning. We present a simplified model (static but with two-time references) for estimating the cost-effectiveness of the COVID-19 vaccine. A simplified model provides a quick assessment of the upper bound of cost-effectiveness, as we illustrate with data from Spain, and allows for easy comparisons between countries. It may also provide useful comparisons among different vaccines at the marketplace, from the perspective of the buyer. From the analysis of this information, key epidemiological figures, and costs of the disease for Spain have been estimated, based on mortality. The fatality rate is robust data that can alternatively be obtained from death registers, funeral homes, cemeteries, and crematoria. Our model estimates the incremental cost-effectiveness ratio (ICER) to be 5132 € (4926–5276) as of 17 February 2021, based on the following assumptions/inputs: An estimated cost of 30 euros per dose (plus transport, storing, and administration), two doses per person, efficacy of 70% and coverage of 70% of the population. Even considering the possibility of some bias, this simplified model provides confirmation that vaccination against COVID-19 is highly cost-effective. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

Journal Menu

Journal Browser

Special Issue "Mathematical Prediction Models Applied to Health Management"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI