Utilizing triage rates to improve ICU admission guidelines of elderly rib fracture patients

Goldstein, Carma; Juthani, Biren; Livingston, David H.; Glass, Nina E.; Sifri, Ziad C.

doi:10.1016/j.amjsurg.2021.07.034

The American Journal of Surgery

2022

DOI: 10.1016/j.amjsurg.2021.07.034

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Utilizing triage rates to improve ICU admission guidelines of elderly rib fracture patients

Carma Goldstein

Biren Juthani

David H. Livingston

et al.

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Cited by 2 publications

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“…We agree that a formalized definition of ICU overtriage should be developed. While ICU overtriage has been most frequently studied in trauma populations and some in medical populations, little guidance has been published despite the recognition of the inconsistency of postoperative triaging practices 15,19–22. The National Surgical Quality Improvement Program (NSQIP)23 and other programs may benefit from tracking and benchmarking this metric, similar to how Trauma Quality Improvement Program (TQIP) tracks trauma triages.…”

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“…While ICU overtriage has been most frequently studied in trauma populations and some in medical populations, little guidance has been published despite the recognition of the inconsistency of postoperative triaging practices. 15,[19][20][21][22] The National Surgical Quality Improvement Program (NSQIP) 23 and other programs may benefit from tracking and benchmarking this metric, similar to how Trauma Quality Improvement Program (TQIP) tracks trauma triages. This could allow for a broad understanding of ICU triaging practices and develop a multi-institutional dataset that can support the development of generalizable models aimed at optimizing resource allocation.…”

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Postoperative Intensive Care Unit Overtriage

Jones

Tignanelli

2022

Annals of Surgery

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T he coronavirus disease 2019 (COVID-19) pandemic has highlighted just how precious of a resource the intensive care unit (ICU) bed is across the world. 1 Hospitals, which typically functioned near capacity pre-COVID-19, were outpaced by the pandemic. 2 COVID-19 persists and systems originally built for pre-COVID capacity are forced to adapt until new units and hospital wings can be developed. 2 Given this, it is imperative, now more than ever that, we learn to optimally use our most valuable resource, the critical care ward. While other specialties such as trauma have defined, monitored, and reported definitions for both under and overtriage, we lack a consensus definition for ICU overtriage. [3][4][5][6] It is thus imperative that we as a surgical community develop such metrics for postoperative ICU overtriage.Historically, a problem such as defining and identifying ICU overtriage would have been addressed with a scoring system or rubric. However, in the modern era, big data analytics leveraging machine learning can provide a valuable and more generalizable solution to this problem. Artificial intelligence-enabled decision support is a burgeoning field, which will fundamentally reshape how healthcare is delivered and how clinical treatment decisions are made, resulting in more data-driven approaches to healthcare and more personalized care delivery. 7,8 In the past, surgery was defined by new surgical techniques, and a shift to minimally invasive interventions; however, the future of surgery will be defined by the surgical application of artificial intelligence-enabled decision support systems and procedural automation. 9,10 Loftus et al. 11 represents an excellent early example of the use of machine learning to interpret big data to investigate a clinical and operational gap (the appropriate use of ICU beds). This study uses a previously validated MySurgeryRisk tool to predict postoperative overtriage to the ICU and estimate the cost of care associated with ICU overtriage. 12,13 MySurgeryRisk is an internally validated machine learning tool that uses electronic health record (EHR) data to generate risk predictions including hospital mortality and prolonged ICU stay. In this study, it used preoperative and intraoperative EHR data to make risk predictions at the surgery end time. Of a longitudinal cohort of postoperative ICU admissions, the authors used MySurgeryRisk to determine which patients were appropriate versus overtriaged to the ICU. They identified a risk-matched cohort of postoperative ward admissions as their control. ICU admission was defined as a patient being physically admitted to an ICU bed under either ICU or intermediate status. Appropriate ICU triage was defined as the patient having at least a 2% risk of in-hospital mortality or prolonged ICU stay based on the MySurgeryRisk tool or American Society of Anesthesiologists Physical Status classification 14 or needing immediate postoperative mechanical ventilation or vasopressor support. "Overtriage" was defined as being admitted to the ICU but no...

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confidence: 99%

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Postoperative Intensive Care Unit Overtriage

Jones

Tignanelli

2022

Annals of Surgery

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Intensive care unit admission criteria: a scoping review

Soares,

Leung,

Campbell

et al. 2024

Journal of the Intensive Care Society

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Background: Effectively identifying deteriorated patients is vital to the development and validation of automated systems designed to predict clinical deterioration. Existing outcome measures used for this purpose have significant limitations. Published criteria for admission to high acuity inpatient areas may represent markers of patient deterioration and could inform the development of alternate outcome measures. Objectives: In this scoping review, we aimed to characterise published criteria for admission of adult inpatients to high acuity inpatient areas including intensive care units. A secondary aim was to identify variables that are extractable from electronic health records (EHRs). Data sources: Electronic databases PubMed and ProQuest EBook Central were searched to identify papers published from 1999 to date of search. We included publications which described prescriptive criteria for admission of adult inpatients to a clinical area with a higher level of care than a general hospital ward. Charting methods: Data was extracted from each publication using a standardised data-charting form. Admission criteria characteristics were summarised and cross-tabulated for each criterion by population group. Results: Five domains were identified: diagnosis-based criteria, clinical parameter criteria, organ-support criteria, organ-monitoring criteria and patient baseline criteria. Six clinical parameter-based criteria and five needs-based criteria were frequently proposed and represent variables extractable from EHRs. Thresholds for objective clinical parameter criteria varied across publications, and by disease subgroup, and universal cut-offs for criteria could not be elucidated. Conclusions: This study identified multiple criteria which may represent markers of deterioration. Many of the criteria are extractable from the EHR, making them potential candidates for future automated systems. Variability in admission criteria and associated thresholds across the literature suggests clinical deterioration is a heterogeneous phenomenon which may resist being defined as a single entity via a consensus-driven process.

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Utilizing triage rates to improve ICU admission guidelines of elderly rib fracture patients

Cited by 2 publications

References 22 publications

Postoperative Intensive Care Unit Overtriage

Postoperative Intensive Care Unit Overtriage

Intensive care unit admission criteria: a scoping review

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