Intermediate care units and their role in medical wards

Intermediate Care Units in Internal Medicine

BACKGROUND

Intermediate Care Units (ImCU) have been historically described as an intermediate level of care between standard wards and intensive care units (ICU), and general medical ImCUs have evolved as specifically addressed to high care medical patients. The objective of this study is to explore designs, appropriateness criteria, and quality of care of general medical ImCUs.

METHODS

a comprehensive literature search was performed in electronic database (PubMed/Medline, Embase, Cochrane and Web of Science) up to July 30th 2024 and data about general medical ImCU denominations, settings, processes and outcomes were extracted.

RESULTS

34 studies were included in systematic analyses, the more used nomenclature was ImCU (70.6 %), followed by High Dependency Unit (20.6 %). The median number of beds was 8 [4-11], the nurse-to-patients ratio 1:3.1, and internists involved in comanagement in 40.0 %. Either a step-up from standard wards or a step-down from ICUs role were reported, with a median of 50.8 % [26.2-71.0] of patients directly admitted from Emergency Departments. The main distinctive activities were continuous monitoring and non-invasive ventilation. The median ICU transfer rate was 8.0 % [5.6-12.3], while in-ImCU and in-hospital mortality were 6.2 % [3.6-8.3] and 14.0 % [8.7-19.1], respectively.

CONCLUSIONS

general medical ImCUs are being increasingly recognized as the appropriate setting for high care medical patients but present to date a wide variability of formats. Activity-based admission criteria tailored on each hospital reality could be a process model for adequate patient flow, and quality of care key indicators should consider the functional general medical ImCU role in hospital macro-systems.

The Role of Intermediate Care in Supporting Critically Ill Patients and Critical Care Infrastructure

Intermediate care (IC) is used for patients who do not require the human and technological support of the intensive care unit (ICU) yet require more care and monitoring than can be provided on general wards. Though prevalent in many countries, there is marked variability in models of organization and staffing, as well as monitoring and interventions provided. In this article, the authors will discuss the historical background of IC, review the impact of IC on ICU and IC patient outcomes, and highlight where future studies can shed light on how to optimize IC organization and outcomes.

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

BACKGROUND

Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.

METHODS

This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.

RESULTS

A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO2. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753-0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590-0.739]; p = 0.002 by Delong's test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669-0.817], p = 0.130 by Delong's test).

CONCLUSIONS

The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.