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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michelle Louise Gatt ◽  
Maria Cassar ◽  
Sandra C. Buttigieg

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.


2022 ◽  
Vol 32 (1) ◽  
Author(s):  
Sheng-Han Tsai ◽  
Chia-Yin Shih ◽  
Chin-Wei Kuo ◽  
Xin-Min Liao ◽  
Peng-Chan Lin ◽  
...  

AbstractThe primary barrier to initiating palliative care for advanced COPD patients is the unpredictable course of the disease. We enroll 752 COPD patients into the study and validate the prediction tools for 1-year mortality using the current guidelines for palliative care. We also develop a composite prediction index for 1-year mortality and validate it in another cohort of 342 patients. Using the current prognostic models for recent mortality in palliative care, the best area under the curve (AUC) for predicting mortality is 0.68. Using the Modified Medical Research Council dyspnea score and oxygen saturation to define the combined dyspnea and oxygenation (DO) index, we find that the AUC of the DO index is 0.84 for predicting mortality in the validated cohort. Predictions of 1-year mortality based on the current palliative care guideline for COPD patients are poor. The DO index exhibits better predictive ability than other models in the study.


2022 ◽  
pp. 10-18
Author(s):  
Tariq Shaheed ◽  
Jake Martinez ◽  
Amanda Frugoli ◽  
Weldon Zane Smith ◽  
Ian Cahatol ◽  
...  

Atrial fibrillation is the most common postoperative arrhythmia and is associated with increased length of stay, cost, morbidity and mortality. The incidence of postoperative atrial fibrillation for noncardiac, nonthoracic surgeries ranges from 0.4% to 26%. The incidence increases to 20%–50% in cardiac surgery, occurring in approximately 30% of isolated coronary artery bypass grafting (CABG), approximately 40% of isolated valve surgeries and up to 50% of CABG plus valve surgeries. Our aim was to identify risk factors that may predispose patients to postoperative atrial fibrillation and compare the efficacy of previously developed prediction tools to a new bedside prediction tool. We sought to develop a bedside screening tool using 4 easily identifiable variables: body mass index, age, congestive heart failure and hypertension (BACH). We predicted that our model would compare similarly to previously developed and validated prediction models but would be easier to use.


Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1909
Author(s):  
Jennifer D. Kaminker ◽  
Alexander V. Timoshenko

Galectins comprise a family of soluble β-galactoside-binding proteins, which regulate a variety of key biological processes including cell growth, differentiation, survival, and death. This paper aims to address the current knowledge on the unique properties, regulation, and expression of the galectin-16 gene (LGALS16) in human cells and tissues. To date, there are limited studies on this galectin, with most focusing on its tissue specificity to the placenta. Here, we report the expression and 8-Br-cAMP-induced upregulation of LGALS16 in two placental cell lines (BeWo and JEG-3) in the context of trophoblastic differentiation. In addition, we provide the results of a bioinformatics search for LGALS16 using datasets available at GEO, Human Protein Atlas, and prediction tools for relevant transcription factors and miRNAs. Our findings indicate that LGALS16 is detected by microarrays in diverse human cells/tissues and alters expression in association with cancer, diabetes, and brain diseases. Molecular mechanisms of the transcriptional and post-transcriptional regulation of LGALS16 are also discussed based on the available bioinformatics resources.


2021 ◽  
Author(s):  
Laizhi Zhang ◽  
Xuanwen Wang ◽  
Lin Zhang ◽  
Yanzheng Meng ◽  
Yu Chen ◽  
...  

As a recently-reported post-translational modification, S-itaconation plays an important role in inflammation suppression. In order to understand its regulatory mechanism in many life activities, the essential step is the recognition of S-itaconation. However, it is difficult to identify S-itaconation in the proteome for the high cost, which limits further investigation. In this study, we constructed an ensemble algorithm based on Soft Voting Classifier. The area under the ROC curve (AUC) value 0.73 for ensemble model. Accordingly, we constructed the on-line prediction tool dubbed SBP-SITA for easily identifying Cystine sites. SBP-SITA is available at http://www.bioinfogo.org/sbp-sita.


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