Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study

Background

Recently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found to be a significant indicator of ED bottlenecks. The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care processes and results in increased mortality and health expenditure. Therefore, it is critical to understand the major factors influencing the ED-LOS through forecasting tools enabling early improvements.

Methods

The purpose of this work is to use a limited set of features impacting ED-LOS, both related to patient characteristics and to ED workflow, to predict it. Different factors were chosen (age, gender, triage level, time of admission, arrival mode) and analysed. Then, machine learning (ML) algorithms were employed to foresee ED-LOS. ML procedures were implemented taking into consideration a dataset of patients obtained from the ED database of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital (Salerno, Italy) from the period 2014-2019.

Results

For the years considered, 496,172 admissions were evaluated and 143,641 of them (28.9%) revealed a prolonged ED-LOS. Considering the complete data (48.1% female vs. 51.9% male), 51.7% patients with prolonged ED-LOS were male and 47.3% were female. Regarding the age groups, the patients that were most affected by prolonged ED-LOS were over 64 years. The evaluation metrics of Random Forest algorithm proved to be the best; indeed, it achieved the highest accuracy (74.8%), precision (72.8%), and recall (74.8%) in predicting ED-LOS.

Conclusions

Different variables, referring to patients' personal and clinical attributes and to the ED process, have a direct impact on the value of ED-LOS. The suggested prediction model has encouraging results; thus, it may be applied to anticipate and manage ED-LOS, preventing crowding and optimising effectiveness and efficiency of the ED.

A case study to investigate the impact of overcrowding indices in emergency departments

Background

Emergency department (ED) overcrowding is widespread in hospitals in many countries, causing severe consequences to patient outcomes, staff work and the system, with an overall increase in costs. Therefore, health managers are constantly looking for new preventive and corrective measures to counter this phenomenon. To do this, however, it is necessary to be able to characterize the problem objectively. For this reason, various indices are used in the literature to assess ED crowding. In this work, we explore the use of two of the most widespread crowding indices in an ED of an Italian national hospital, investigate their relationships and discuss their effectiveness.

Methods

In this study, two of the most widely used indices in the literature, the National Emergency Department Overcrowding Scale (NEDOCS) and the Emergency Department Working Index (EDWIN), were analysed to characterize overcrowding in the ED of A.O.R.N. “A. Cardarelli” of Naples, which included 1678 clinical cases. The measurement was taken every 15 minutes for a period of 7 days.

Results

The results showed consistency in the use of EDWIN and NEDOCS indices as measures of overcrowding, especially in severe overcrowding conditions. Indeed, in the examined case study, both EDWIN and NEDOCS showed very low rates of occurrence of severe overcrowding (2–3%). In contrast, regarding differences in the estimation of busy to overcrowded ED rates, the EDWIN index proved to be less sensitive in distinguishing these variations in the occupancy of the ED. Furthermore, within the target week considered in the study, the results show that, according to both EDWIN and NEDOCS, higher overcrowding rates occurred during the middle week rather than during the weekend. Finally, a low degree of correlation between the two indices was found.

Conclusions

The effectiveness of both EDWIN and NEDOCS in measuring ED crowding and overcrowding was investigated, and the main differences and relationships in the use of the indices are highlighted. While both indices are useful ED performance metrics, they are not always interchangeable, and their combined use could provide more details in understanding ED dynamics and possibly predicting future critical conditions, thus enhancing ED management.

Case Report: A Novel Homozygous Missense Variant of FBN3 Supporting It Is a New Candidate Gene Causative of a Bardet–Biedl Syndrome–Like Phenotype

Fibrillin proteins are extracellular matrix glycoproteins assembling into microfibrils. FBN1, FBN2, and FBN3 encode the human fibrillins and mutations in FBN1 and FBN2 cause connective tissue disorders called fibrillinopathies, affecting cardiovascular, dermal, skeletal, and ocular tissues. Recently, mutations of the less characterized fibrillin family member, FBN3, have been associated in a single family with Bardet–Biedl syndrome (BBS). Here, we report on a patient born from two first cousins and affected by developmental delay, cognitive impairment, obesity, dental and genital anomalies, and brachydactyly/syndactyly. His phenotype was very similar to that reported in the previous FBN3-mutated family and fulfilled BBS clinical diagnostic criteria, although lacking polydactyly, the most recurrent clinical feature, as the previous siblings described. A familial SNP-array and proband’s WES were performed prioritizing candidate variants on the sole patient’s runs of homozygosity. This analysis disclosed a novel homozygous missense variant in FBN3 (NM_032447:c.5434A>G; NP_115823:p.Ile1812Val; rs115948457), inherited from the heterozygous parents. This study further supports that FBN3 is a candidate gene for a BBS-like syndrome characterized by developmental delay, cognitive impairment, obesity, dental, genital, and skeletal anomalies. Anyway, additional studies are necessary to investigate the exact role of the gene and possible interactions between FBN3 and BBS proteins.

Physiologic Range of Myocardial Mechano-Energetic Efficiency among Healthy Subjects: Impact of Gender and Age

BACKGROUND

Myocardial mechano-energetic efficiency (MEE) is the capability of the left ventricle (LV) to convert the chemical energy obtained from the cardiac oxidative metabolism into mechanical work. The aim of present study was to establish normal non-invasive MEE and MEEi reference values.

METHODS

In total, 1168 healthy subjects underwent physical examinations, clinical assessment, and standardized transthoracic echocardiographic (TTE) examination. MEE was obtained by TTE as the ratio between stroke volume (SV) and heart rate (HR): MEE = SV/HR [HR expressed in seconds (HR/60)]. Because MEE is highly related to left ventricular mass (LVM), MEE was then divided by LVM with the purpose of obtaining an estimate of energetic expenditure per unit of myocardial mass (i.e., indexed MEE, MEEi, mL/s/g).

RESULTS

The mean values of MEE and MEEi in the overall population were 61.09 ± 18.19 mL/s; 0.45 ± 0.14, respectively. In a multivariable analysis, gender, body surface area (BSA), diastolic blood pressure, left atrial volume indexed to BSA, E/e' and tricuspid annular plane systolic excursion (TAPSE) were the independent variables associated with MEE, while age, gender, BSA and TAPSE were the independent variables associated with MEEi.

CONCLUSIONS

The knowledge of age- and gender-based MEE and MEEi normal values may improve the global assessment of LV cardiac mechanics and serve as a reference to identify phenotypes at high risk of cardiovascular events.

Successful surgical repair of left ventricular pseudoaneurysm in a patient with subacute ST-elevation myocardial infarction

We report a successful surgical repair of left ventricular pseudoaneurysm in a patient with subacute ST-elevation myocardial infarction (STEMI). In the case of expansion of the infarct related ventricular wall, early (within 24 hours) or late (3-5 days after STEMI) cardiac rupture should be always borne in mind in order to proceed to life saving prompt surgical repair.