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

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.

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Development and internal validation of a clinical prediction model for acute adjacent vertebral fracture after vertebral augmentation

The Bone & Joint Journal ◽

10.1302/0301-620x.104b1.bjj-2021-0151.r3 ◽

2022 ◽

Vol 104-B (1) ◽

pp. 97-102

Author(s):

Yasukazu Hijikata ◽

Tsukasa Kamitani ◽

Masayuki Nakahara ◽

Shinji Kumamoto ◽

Tsubasa Sakai ◽

...

Keyword(s):

Decision Making ◽

Vertebral Fracture ◽

High Risk ◽

Prediction Model ◽

Vertebral Augmentation ◽

Clinical Prediction ◽

Internal Validation ◽

Duration Of Symptoms ◽

Clinical Prediction Model ◽

Adjacent Vertebral Fracture

Aims To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. Methods In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism. Results Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity. Conclusion In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97–102.

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A Clinical Prediction Model to Estimate Risk for 30-Day Adverse Events in Emergency Department Patients With Symptomatic Atrial Fibrillation

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2010.05.031 ◽

2011 ◽

Vol 57 (1) ◽

pp. 1-12 ◽

Cited By ~ 31

Author(s):

Tyler W. Barrett ◽

Amy R. Martin ◽

Alan B. Storrow ◽

Cathy A. Jenkins ◽

Frank E. Harrell ◽

...

Keyword(s):

Atrial Fibrillation ◽

Emergency Department ◽

Adverse Events ◽

Prediction Model ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Emergency Department Patients

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A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit

Critical Care ◽

10.1186/s13054-017-1874-z ◽

2017 ◽

Vol 21 (1) ◽

Cited By ~ 7

Author(s):

Cristhian Potes ◽

Bryan Conroy ◽

Minnan Xu-Wilson ◽

Christopher Newth ◽

David Inwald ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

High Risk ◽

Prediction Model ◽

Pediatric Intensive Care Unit ◽

Pediatric Intensive Care ◽

Hemodynamic Instability ◽

Clinical Prediction ◽

Clinical Prediction Model

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Risk of Gestational Diabetes Mellitus (GDM) Using Clinical Prediction Model Based on Maternal Characteristics

Journal of Armed Forces Medical College Bangladesh ◽

10.3329/jafmc.v11i1.30675 ◽

2016 ◽

Vol 11 (1) ◽

pp. 64-68 ◽

Cited By ~ 1

Author(s):

Sharmin Jahan ◽

Mohammad Ali

Keyword(s):

Diabetes Mellitus ◽

High Risk ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Prediction Model ◽

Predictive Value ◽

Area Under Curve ◽

Risk Group ◽

Clinical Prediction ◽

Clinical Prediction Model

Introduction: The healthcare delivery challenges in Bangladesh are phenomenal. Improving maternal and child health, reducing the high maternal and infant mortality & morbidity are challenging. Arrangement of additional expenditure for GDM screening is again challenging. The efficiency of screening could be enhanced by considering womens risks of gestational diabetes on the basis of their clinical characteristics.Objectives: To find out the use of the clinical prediction model of gestational diabetes mellitus (GDM) is valid for Bangladeshi pregnant women and to assess the risk of gestational diabetes by using clinical prediction model based on maternal characteristics.Materials and Methods: A cross sectional study was carried out from July 2011 to June 2012 among purposively selected 217 pregnant women of ?24 weeks of gestation in the Gynae and Obstetric outpatient department of Combined Military Hospital, Dhaka. Data were collected by face to face interview, anthropometric measurement and record review. Two step oral glucose tests were done for diagnosis of GDM.Results: According to Chadakaran clinical prediction model 84 (38.7%) respondents were at high risk, 92 (42.4%) were at intermediate risk and 41(18.9%) found at low risk of gestational diabetes but only 24(11.05%) developed gestational diabetes. Highest occurrence of gestational diabetes was found in high risk group 17 (20.2%) with zero occurrence in low risk group. Risk score performance at the level of ?380, sensitivity was 100% and specificity 21.8%, 13.6% positive predictive value, 100% negative predictive value and area under curve was 0.385. At the level of 460 score the sensitivity and specificity was found closest (70.8% and 65.3%, respectively) and area under curve was highest 0.657. The receiver operating characteristics curve of the risk score in the study sample for predicting women with glucose tolerance test demonstrated an area 0.763 (95%, 0.682 0.845).Conclusion: The use of clinical prediction model is a simple, non invasive, cost effective useful method to identify women at increased risk of gestational diabetes mellitus and could be short listed for further testing.Journal of Armed Forces Medical College Bangladesh Vol.11(1) 2015: 64-68

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

Pediatric Emergency Care ◽

10.1097/pec.0000000000001835 ◽

2019 ◽

Vol Publish Ahead of Print ◽

Cited By ~ 1

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

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1: A Clinical Prediction Model to Estimate Risk for 30-Day Adverse Events in Emergency Department Patients With Symptomatic Atrial Fibrillation

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2009.06.021 ◽

2009 ◽

Vol 54 (3) ◽

pp. S1-S2

Author(s):

T.W. Barrett ◽

A.R. Martin ◽

A.B. Storrow ◽

C.A. Jenkins ◽

S. Russ ◽

...

Keyword(s):

Atrial Fibrillation ◽

Emergency Department ◽

Adverse Events ◽

Prediction Model ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Emergency Department Patients

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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 ◽

10.1371/journal.pone.0254366 ◽

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.

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