No abstract

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

Telomeres cap the ends of eukaryotic chromosomes and distinguish them from broken DNA ends to suppress DNA damage response, cell cycle arrest and genomic instability. Telomeres are elongated by telomerase to compensate for incomplete replication and nuclease degradation and to extend the proliferation potential of germ and stem cells and most cancers. However, telomeres in somatic cells gradually shorten with age, ultimately leading to cellular senescence. Hoyeraal-Hreidarsson syndrome (HHS) is characterized by accelerated telomere shortening and diverse symptoms including bone marrow failure, immunodeficiency, and neurodevelopmental defects. HHS is caused by germline mutations in telomerase subunits, factors essential for its biogenesis and recruitment to telomeres, and in the helicase RTEL1. While diverse phenotypes were associated with RTEL1 deficiency, the telomeric role of RTEL1 affected in HHS is yet unknown. Inducible ectopic expression of wild-type RTEL1 in patient fibroblasts rescued the cells, enabled telomerase-dependent telomere elongation and suppressed the abnormal cellular phenotypes, while silencing its expression resulted in gradual telomere shortening. Our observations reveal an essential role of the RTEL1 C-terminus in facilitating telomerase action at the telomeric 3′ overhang. Thus, the common etiology for HHS is the compromised telomerase action, resulting in telomere shortening and reduced lifespan of telomerase positive cells.

Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.