A cardiotoxicity dataset for breast cancer patients.

This dataset is a result of the collaboration between the University of A Coruña and the University Hospital of A Coruña. It contains information about 531 women diagnosed with HER2+ breast cancer, treated with potentially cardiotoxic oncologic therapies. These treatments can cause cardiovascular adverse events, including cardiac systolic dysfunction, the development of which has important clinical and prognostic implications. The availability of good predictors may enable early detection of these cardiac problems. Variables such as age, weight and height are available for each patient, as well as some measures obtained from echocardiography, a technique used prior and during the treatment to check the structure and function of the heart. Among them, there is a functional variable that measures the myocardial velocity during the cardiac cycle. For patients that experienced cancer therapy-related cardiac dysfunction during the treatment period, time until its appearance is known. This dataset aims to enable the scientific community in conducting new research on this cardiovascular side effect.

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

Cure models to estimate time until hospitalization due to COVID-19: A case study in Galicia (NW Spain).

A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included. The main nonparametric and semiparametric approaches were applied to a real time dataset of COVID-19 patients from the first weeks of the epidemic in Galicia (NW Spain). The aim is to model the elapsed time from diagnosis to hospital admission. The main conclusions, as well as the limitations of both the cure models and the dataset, are presented, illustrating the usefulness of cure models in this kind of studies, where the influence of age and sex on the time to hospital admission is shown.

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients' hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.

How do early socioeconomic circumstances impact inflammatory trajectories? Findings from Generation XXI.

BACKGROUND: The association between socioeconomic position and markers of inflammation in adults, including C-reactive protein (CRP), is well-established. We hypothesized that children from families of less-advantaged socioeconomic circumstances may be at higher inflammatory risk during childhood and, consequently, throughout their life course. Thus, we aimed to investigate whether early socioeconomic circumstances impact CRP trajectories using repeated measures of data from a population-based birth cohort. METHODS: Data from 2510 participants of Generation XXI, a prospective Portuguese population-based birth cohort, were included in this study. Early socioeconomic circumstances comprised maternal education and occupation, paternal education and occupation, and household income at the child's birth. Venous blood samples were collected from the children at ages four, seven, and ten years, and high-sensitivity CRP (Hs-CRP) was quantified. Hs-CRP trajectories were computed using a linear mixed-model approach. RESULTS: Participants from less-advantaged socioeconomic circumstances presented higher levels of Hs-CRP by age of ten years. The higher the mother´s education and disposable household income, the lower the minimum value of the log Hs-CRP observed throughout childhood. Further, the age at which that minimum log Hs-CRP value was reached occurs later, meaning that children born in more-advantaged socioeconomic circumstances had lower levels of log Hs-CRP compared with children from less-advantaged families. CONCLUSIONS: Poor socioeconomic circumstances early in life are associated with increased inflammation levels throughout the first decade of life. This study demonstrates that social inequalities may impact population health beginning at very early ages.

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models.

In lifetime data, like cancer studies, there may be long term survivors, which lead to heavy censoring at the end of the follow-up period. Since a standard survival model is not appropriate to handle these data, a cure model is needed. In the literature, covariate hypothesis tests for cure models are limited to parametric and semiparametric methods. We fill this important gap by proposing a nonparametric covariate hypothesis test for the probability of cure in mixture cure models. A bootstrap method is proposed to approximate the null distribution of the test statistic. The procedure can be applied to any type of covariate, and could be extended to the multivariate setting. Its efficiency is evaluated in a Monte Carlo simulation study. Finally, the method is applied to a colorectal cancer dataset.