Development and validation of an admission prediction tool for emergency departments in the Netherlands

Kraaijvanger, Nicole; Rijpsma, Douwe; Roovers, L.; Leeuwen, Henk van; Kaasjager, Karin A H; Brand, Lillian van den; Horstink, Laura; Edwards, Michael J.

doi:10.1136/emermed-2017-206673

Cited by 24 publications

(43 citation statements)

References 23 publications

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“…(Frailty and comorbidities are directly incorporated into START+. Medical history was not found to be a significant predictor by Kraaijvanger et al 2 and was not examined by Lucke et al 3). Does a clinician really need a rule to understand this?…”

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confidence: 91%

“…The authors then develop an extended tool (START+) to identify patients expected to have lengths of stay of under 48 hours, that is, discharged directly from the ED or from short-stay units. Also in this issue, Kraaijvanger et al report on the development and validation of another admission prediction rule 2. In a previous issue of EMJ , Lucke et al proposed separate rules for patients older and younger than 70 years to predict admissions 3…”

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confidence: 99%

“…Of note, the rule does not distinguish between short stay and discharge, calling into question its utility in streamlining patients to short-stay units. In the study from Ebker-White, using optimal cut-offs, 2.4% of patients at the community hospitals and 6.6% at the academic hospital were ‘wrongly admitted’, while 14.1% and 19.7% respectively could be identified early for admission 2. Lucke et al show a positive predictive value of 0.71 for the ‘top 10% most likely to be admitted patients’ under age 70, and 0.87 for those 70 and older.…”

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confidence: 99%

“…Nearly universally, a young healthy patient presenting with a upper respiratory infection (URI) will not benefit from an admission, while an elderly dialysis patient with sepsis will. In fact, in deriving Ebker-White et al ’s rule, the authors excluded these ‘obvious’ cases from analysis as unlikely to require application of a rule 2. However, for patients with moderate illness, comorbidities and social issues, admission decisions are based on a complex web of factors, which vary locally and individually.…”

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confidence: 99%

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Admission prediction rules: some limited promise, but far from proven

Rabin

2018

Emerg Med J

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confidence: 91%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Admission prediction rules: some limited promise, but far from proven

Rabin

2018

Emerg Med J

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“…One exception of direct EHR model implementation is included in a recent study from the Netherlands, where investigators present a model developed and prospectively validated to determine the likelihood of admission from the emergency department. This was subsequently integrated into the EHR to assist with future work in triage [29]. Our work differs with the novel feature that we integrated our models into the EHR and undertook prospective validation, so that the entire system, EHR and model together, were evaluated.…”

Section: Plos Onementioning

confidence: 99%

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

et al. 2020

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Background Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. Objective We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. Materials & methods We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. Results Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.

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Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department

2022

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Objectives This study aims to develop and internally validate a low‐dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post‐triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low‐dimensional model with fewer variables may be easier to implement. Methods This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtained from the model with the highest area under the curve (AUC) and used to create a low‐dimensional model. Results Eligible attendances totaled 72,229 (15% admission rate). The AUC was 0.853 (95% confidence interval [CI], 0.846–0.859) for GBM, 0.845 (95% CI, 0.838–0.852) for logistic regression and 0.813 (95% CI, 0.806–0.821) for naïve Bayes. Important predictors in the GBM model used to create a low‐dimensional model were presenting complaint, triage category, referral source, registration month, location type (resuscitation/other), distance traveled, admission history, and weekday (AUC 0.835 [95% CI, 0.829‐0.842]). Conclusions Admission and discharge probability can be predicted early in a pediatric ED using 8 variables. Future work could analyze the false positives and false negatives to gain an understanding of the implementation of these predictions.

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Development and validation of an admission prediction tool for emergency departments in the Netherlands

Abstract: Admission probability for ED patients can be calculated using a prediction tool. Further research must show whether using this tool can improve patient flow in the ED.

Cited by 24 publications

References 23 publications

Admission prediction rules: some limited promise, but far from proven

Admission prediction rules: some limited promise, but far from proven

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department

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