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2021-09-28by logicPublications

The Limitations Of Standardized Mortality Ratios For Coronavirus Disease 2019 ICU Patients

Quintairos A, Zampieri FG, Souza-Dantas VC, Salluh JIF.

Crit Care Med. 2021 Jul 29.

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by: The LOGIC Team

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2021-09-28by logicPublications

Time To Revisit Treatment Limitations In Critical Care Benchmarking

Beane A, Dongelmans DA, Fernandez AL, Guidet B, Haniffa R, Arias Lopez MP, Pilcher D, Salluh J, Vijayaraghavan BKT; Linking of Global Intensive Care Collaboration (LOGIC).
Crit Care Med. 2021 Apr 1;49(4):e472-e473. doi: 10.1097/CCM.0000000000004834.

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by: The LOGIC Team
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2021-09-28by logicPublications

Data-Driven ICU Management: Using Big Data And Algorithms To Improve Outcomes

Carra G, Salluh JIF, da Silva Ramos FJ, Meyfroidt G. J Crit Care. 2020 Dec;60:300-304. doi: 10.1016/j.jcrc.2020.09.002.

Abstract

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term “Big Data” can be used to refer to the analysis of these datasets that collect enormous amount of data of different origin and format. Complexity and variety define the value of Big Data. In fact, the retrospective analysis of these datasets allows to generate new knowledge, with consequent potential improvements in the clinical practice. Despite the promising start of Big Data analysis in medical research, which has seen a rising number of peer-reviewed articles, very limited applications have been used in ICU clinical practice. A close future effort should be done to validate the knowledge extracted from clinical Big Data and implement it in the clinic.

In this article, we provide an introduction to Big Data in the ICU, from data collection and data analysis, to the main successful examples of prognostic, predictive and classification models based on ICU data. In addition, we focus on the main challenges that these models face to reach the bedside and effectively improve ICU care.

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by: The LOGIC Team
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2021-09-28by logicPublications

Future Perspectives For Clinical Quality Registries In Critical Care

Haniffa R; Linking of Global Intensive Care (LOGIC) Consortium.
J Crit Care. 2021 Jun;63:279. doi: 10.1016/j.jcrc.2020.12.004.

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by: The LOGIC Team

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2021-09-28by logicPublications

A Scoping Review Of Registry Captured Indicators For Evaluating Quality Of Critical Care In ICU

Jawad I, Rashan S, Sigera C, Salluh J, Dondorp AM, Haniffa R, Beane A. J Intensive Care. 2021 Aug 5;9(1):48. doi: 10.1186/s40560-021-00556-6.

Abstract:

Background
Excess morbidity and mortality following critical illness is increasingly attributed to potentially avoidable complications occurring as a result of complex ICU management (Berenholtz et al., J Crit Care 17:1-2, 2002; De Vos et al., J Crit Care 22:267-74, 2007; Zimmerman J Crit Care 1:12-5, 2002). Routine measurement of quality indicators (QIs) through an Electronic Health Record (EHR) or registries are increasingly used to benchmark care and evaluate improvement interventions. However, existing indicators of quality for intensive care are derived almost exclusively from relatively narrow subsets of ICU patients from high-income healthcare systems. The aim of this scoping review is to systematically review the literature on QIs for evaluating critical care, identify QIs, map their definitions, evidence base, and describe the variances in measurement, and both the reported advantages and challenges of implementation.
Method
We searched MEDLINE, EMBASE, CINAHL, and the Cochrane libraries from the earliest available date through to January 2019. To increase the sensitivity of the search, grey literature and reference lists were reviewed. Minimum inclusion criteria were a description of one or more QIs designed to evaluate care for patients in ICU captured through a registry platform or EHR adapted for quality of care surveillance.
Results
The search identified 4780 citations. Review of abstracts led to retrieval of 276 full-text articles, of which 123 articles were accepted. Fifty-one unique QIs in ICU were classified using the three components of health care quality proposed by the High Quality Health Systems (HQSS) framework. Adverse events including hospital acquired infections (13.7%), hospital processes (54.9%), and outcomes (31.4%) were the most common QIs identified. Patient reported outcome QIs accounted for less than 6%. Barriers to the implementation of QIs were described in 35.7% of articles and divided into operational barriers (51%) and acceptability barriers (49%).
Conclusions

Despite the complexity and risk associated with ICU care, there are only a small number of operational indicators used. Future selection of QIs would benefit from a stakeholder-driven approach, whereby the values of patients and communities and the priorities for actionable improvement as perceived by healthcare providers are prioritized and include greater focus on measuring discriminable processes of care.

Link jintensivecare
by: The LOGIC Team
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