Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Glass, George; Hartka, Thomas; Keim‐Malpass, Jessica; Enfield, Kyle B.; Clark, Matthew

doi:10.1007/s10877-020-00500-3

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…10,11,13,15 To accept new tools like predictive analytics, particularly those that derive information from the physiologic waveforms, clinicians need to understand what is happening inside. 11,13,16,17 We presented the published evidence base 6,[18][19][20][21][22] to provide transparency into the algorithms' underpinnings and to emphasize the strengths of the scientific foundation. 16 We focused other sessions on current patients to give clinicians a more detailed examination of how data elements interacted within the algorithm to produce a risk score.…”

Section: Frameworkmentioning

confidence: 99%

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Moorman¹

2021

Appl Clin Inform

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A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.

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Section: Frameworkmentioning

confidence: 99%

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Moorman¹

2021

Appl Clin Inform

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“…The timing of transfer to the ICU is a considerable determinant of patient outcomes 8 . Early risk stratification of patients with acute poisoning is essential to identify patients at higher risk of ICU admission.…”

Section: Introductionmentioning

confidence: 99%

Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit

Elgazzar

Afifi

Shama

et al. 2021

Basic Clin Pharma Tox

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Early risk stratification of acutely poisoned patients is essential to identify patients at high risk of intensive care unit (ICU) admission. We aimed to develop a prognostic model and risk‐stratification nomogram based on the readily accessible clinical and laboratory predictors on admission for the probability of ICU admission in acutely poisoned patients. This retrospective cohort study included adult patients with acute toxic exposure to a drug or a chemical substance. Patients' demographic, toxicologic, clinical and laboratory data were collected. Among the 1260 eligible patients, 180 (14.3%) were admitted to the ICU. We developed a generalized prognostic model for predicting ICU admission in patients with acute poisoning. The predictors included the Glasgow coma scale, oxygen saturation, diastolic blood pressure, respiratory rate and blood bicarbonate concentration. The model displayed excellent discrimination and calibration (optimistic‐adjusted area under the curve = 0.924 and optimistic‐adjusted Hosmer and Lemeshow test = 0.922, respectively) when internally validated. Additionally, we developed prognostic models that determine ICU admission in patients with specific poisonings. Furthermore, we constructed risk‐stratification nomograms that rank the probability of ICU admission in these patients. The developed risk‐stratification nomograms help decision‐making regarding ICU admission in acute poisonings. Future external validation in independent cohorts is necessary before clinical application.

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“…Further, the majority of children demonstrated clinical deterioration within 48 hours of arrival to the acute care floor. This finding emphasizes the known challenges with prognostication, defining clinical acuity, and determining the appropriate level of care [ 26 , 27 ]. We found many clinical reasons for deterioration, supporting the notion that there is unlikely to be a single early warning score that adequately captures all types of decompensation [ 15 ].…”

Section: Discussionmentioning

confidence: 81%

Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis

Kowalski

Lee

Spaeder

et al. 2021

JMIR Pediatr Parent

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Background Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. Objective The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. Methods We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. Results During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. Conclusions This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.

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Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Cited by 9 publications

References 38 publications

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit

Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis

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