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The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method.

To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score.

From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification.

This paper helps in identifying patient' clinical status.

Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information.

This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

This paper aims to examine the association between the dietary inflammatory index, the consumption of Enteral Nutrition Supplemented with probiotics with certain serum inflammation markers and gastrointestinal complications among individuals diagnosed with COVID-19.

This cross-sectional investigation involved 100 COVID-19 patients who were admitted to intensive care units in hospitals. These patients were administered two different types of Enteral Nutrition, so the dietary inflammatory index (DII), gastrointestinal complications and some serum inflammation markers have been compared between two groups.

The mean DII scores in all patients were significantly pro-inflammatory (probiotic formula 2.81 ± 0.01 vs usual formula group 2.93 ± 0.14 p = 0.19). The probiotic formula consumption had an inverse association with High-sensitivity C-reactive Protein concentration (coef = −3.19, 95% CI −1.25, −5.14 p = 0.001) and lead to a reduction of 2.14 mm/h in the serum level of Erythrocyte sedimentation rate compared to normal formula. The incidence of diarrhea, abdominal pain and vomiting in probiotic formula patients was respectively 94%, 14% and 86% less than in usual formula patients (p = 0.05).

In this cross-sectional study for the first time, the authors found that probiotic formula consumption was inversely associated with serum inflammation markers and gastrointestinal complications incidence. The high DII leads to more gastrointestinal complications incidence and inflammation markers. More studies are needed to prove this relationship.