scholarly journals A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: A prospective multicentre observational study

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254366
Author(s):  
Ruud G. Nijman ◽  
Dorine H. Borensztajn ◽  
Joany M. Zachariasse ◽  
Carine Hajema ◽  
Paulo Freitas ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Vital Signs ◽  
Extended Model ◽  
Clinical Prediction ◽  
Children At Risk ◽  
Clinical Prediction Model ◽  
Clinical Model ◽  
Serious Illness ◽  
Index Visit

Background To develop a clinical prediction model to identify children at risk for revisits with serious illness to the emergency department. Methods and findings A secondary analysis of a prospective multicentre observational study in five European EDs (the TRIAGE study), including consecutive children aged <16 years who were discharged following their initial ED visit (‘index’ visit), in 2012–2015. Standardised data on patient characteristics, Manchester Triage System urgency classification, vital signs, clinical interventions and procedures were collected. The outcome measure was serious illness defined as hospital admission or PICU admission or death in ED after an unplanned revisit within 7 days of the index visit. Prediction models were developed using multivariable logistic regression using characteristics of the index visit to predict the likelihood of a revisit with a serious illness. The clinical model included day and time of presentation, season, age, gender, presenting problem, triage urgency, and vital signs. An extended model added laboratory investigations, imaging, and intravenous medications. Cross validation between the five sites was performed, and discrimination and calibration were assessed using random effects models. A digital calculator was constructed for clinical implementation. 7,891 children out of 98,561 children had a revisit to the ED (8.0%), of whom 1,026 children (1.0%) returned to the ED with a serious illness. Rates of revisits with serious illness varied between the hospitals (range 0.7–2.2%). The clinical model had a summary Area under the operating curve (AUC) of 0.70 (95% CI 0.65–0.74) and summary calibration slope of 0.83 (95% CI 0.67–0.99). 4,433 children (5%) had a risk of > = 3%, which was useful for ruling in a revisit with serious illness, with positive likelihood ratio 4.41 (95% CI 3.87–5.01) and specificity 0.96 (95% CI 0.95–0.96). 37,546 (39%) had a risk <0.5%, which was useful for ruling out a revisit with serious illness (negative likelihood ratio 0.30 (95% CI 0.25–0.35), sensitivity 0.88 (95% CI 0.86–0.90)). The extended model had an improved summary AUC of 0.71 (95% CI 0.68–0.75) and summary calibration slope of 0.84 (95% CI 0.71–0.97). As study limitations, variables on ethnicity and social deprivation could not be included, and only return visits to the original hospital and not to those of surrounding hospitals were recorded. Conclusion We developed a prediction model and a digital calculator which can aid physicians identifying those children at highest and lowest risks for developing a serious illness after initial discharge from the ED, allowing for more targeted safety netting advice and follow-up.

scholarly journals Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e041093
Author(s):  
Todd Adam Florin ◽  
Daniel Joseph Tancredi ◽  
Lilliam Ambroggio ◽  
Franz E Babl ◽  
Stuart R Dalziel ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Severe Disease ◽  
Pediatric Emergency ◽  
Global Network ◽  
Clinical Prediction ◽  
Emergency Research ◽  
Research Networks ◽  
Clinical Prediction Model ◽  
Severity Scores

IntroductionPneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysisThis study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and disseminationThis study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation.

scholarly journals A clinical prediction model to identify patients at high risk of death in the emergency department

2015 ◽  
Vol 41 (6) ◽  
pp. 1029-1036 ◽  
Author(s):  
Michael Coslovsky ◽  
Jukka Takala ◽  
Aristomenis K. Exadaktylos ◽  
Luca Martinolli ◽  
Tobias M. Merz
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Prediction Model ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Risk Of Death

scholarly journals Development of a Clinical Prediction Model for Central Line–Associated Bloodstream Infection in Children Presenting to the Emergency Department

2019 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Laura M. Figueroa-Phillips ◽  
Christopher P. Bonafide ◽  
Susan E. Coffin ◽  
Michelle E. Ross ◽  
James P. Guevara
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Bloodstream Infection ◽  
Central Line ◽  
Clinical Prediction ◽  
Clinical Prediction Model

scholarly journals Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study

BMJ ◽  
2013 ◽  
Vol 346 (apr02 1) ◽  
pp. f1706-f1706 ◽  
Author(s):  
R. G. Nijman ◽  
Y. Vergouwe ◽  
M. Thompson ◽  
M. van Veen ◽  
A. H. J. van Meurs ◽  
...  
Keyword(s):  
At Risk ◽  
Prediction Model ◽  
Bacterial Infections ◽  
Diagnostic Study ◽  
Clinical Prediction ◽  
Children At Risk ◽  
Clinical Prediction Model ◽  
Serious Bacterial Infections ◽  
Emergency Doctors

scholarly journals Derivation of a Clinical Prediction Model for the Emergency Department Diagnosis of Ectopic Pregnancy

1998 ◽  
Vol 5 (10) ◽  
pp. 951-960 ◽  
Author(s):  
Robert G. Buckley ◽  
Kerry J. King ◽  
Jeffrey D. Disney ◽  
Pamela K. Ambroz ◽  
John D. Gorman ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Ectopic Pregnancy ◽  
Clinical Prediction ◽  
Clinical Prediction Model

scholarly journals Minimum sample size for external validation of a clinical prediction model with a binary outcome

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Binary Outcome ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Minimum Sample

Develop a Clinical Prediction Model for Postoperative Cognitive Dysfunction after Major Noncardiac Surgery in Elderly Patients: A Protocol for a Prospective Observational Study

Gerontology ◽  
2021 ◽  
pp. 1-8
Author(s):  
Yang Shen ◽  
Xianchen Li ◽  
Junyan Yao
Keyword(s):  
Observational Study ◽  
Prediction Model ◽  
Cognitive Decline ◽  
Cognitive Dysfunction ◽  
Prospective Observational Study ◽  
Postoperative Cognitive Dysfunction ◽  
Noncardiac Surgery ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Postoperative Cognitive Decline

Perioperative neurocognitive disorders (PNDs) refer to cognitive decline identified in the preoperative or postoperative period. It has been reported that the incidence of postoperative neurocognitive impairment after noncardiac surgery in patients older than 65 at 1 week was 25.8∼41.4%, and at 3 months 9.9∼12.7%. PNDs will last months or even develop to permanent dementia, leading to prolonged hospital stays, reduced quality of life, and increased mortality within 1 year. Despite the high incidence and poor prognosis of PNDs in the aged population, no effective clinical prediction model has been established to predict postoperative cognitive decline preoperatively. To develop a clinical prediction model for postoperative neurocognitive dysfunction, a prospective observational study (Clinical trial registration number: ChiCTR2000036304) will be performed in the Shanghai General Hospital during January 2021 to October 2022. A sample size of 675 patients aged &#x3e;65 years old, male or female, and scheduled for elective major noncardiac surgery will be recruited. A battery of neuropsychological tests will be used to test the cognitive function of patients at 1 week, 1 month, and 3 months postoperatively. We will evaluate the associations of PNDs with a bunch of candidate predictors including general characteristics of patients, blood biomarkers, indices associated with anesthesia and surgery, retinal nerve-fiber layer thickness, and frailty index to develop the clinical prediction model by using multiple logistic regression analysis and least absolute shrinkage and the selection operator (LASSO) method. The <i>k</i>-fold cross-validation method will be utilized to validate the clinical prediction model. In conclusion, this study was aimed to develop a clinical prediction model for postoperative cognitive dysfunction of old patients. It is anticipated that the knowledge gained from this study will facilitate clinical decision-making for anesthetists and surgeons managing the aged patients undergoing noncardiac surgery.

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