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 Predictors of disease severity in children presenting from the community with febrile illnesses: a systematic review of prognostic studies

2021 ◽  
Vol 6 (1) ◽  
pp. e003451
Author(s):  
Arjun Chandna ◽  
Rainer Tan ◽  
Michael Carter ◽  
Ann Van Den Bruel ◽  
Jan Verbakel ◽  
...  
Keyword(s):  
At Risk ◽  
Prognostic Factors ◽  
Prediction Model ◽  
Disease Severity ◽  
Prediction Models ◽  
Severe Disease ◽  
Risk Of Bias ◽  
Clinical Prediction ◽  
Children At Risk ◽  
Clinical Prediction Models

IntroductionEarly identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.MethodsWe systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.ResultsOf 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56–11.13), hypoxia (2.10–8.11), altered consciousness (1.24–14.02), and markers of acidosis (1.36–7.71) and poor peripheral perfusion (1.78–17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49–0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.ConclusionsFew studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level.PROSPERO registration numberCRD42019140542.

scholarly journals Update of a clinical prediction model for serious bacterial infections in preschool children by adding a host-protein-based assay: a diagnostic study

2019 ◽  
Vol 3 (1) ◽  
pp. e000416
Author(s):  
Chantal van Houten ◽  
Josephine Sophia van de Maat ◽  
Christiana Naaktgeboren ◽  
Louis Bont ◽  
R Oostenbrink
Keyword(s):  
Prediction Model ◽  
Bacterial Infections ◽  
Viral Infections ◽  
Interferon Γ ◽  
Host Protein ◽  
Diagnostic Study ◽  
Double Blind ◽  
C Statistic ◽  
Serious Bacterial Infections ◽  
Tract Infections

ObjectiveTo determine whether updating a diagnostic prediction model by adding a combination assay (tumour necrosis factor-related apoptosis-inducing ligand, interferon γ induced protein-10 and C reactive protein (CRP)) can accurately identify children with pneumonia or other serious bacterial infections (SBIs).DesignObservational double-blind diagnostic study.SettingTwo hospitals in Israel and four hospitals in the Netherlands.Patients591 children, aged 1–60 months, presenting with lower respiratory tract infections or fever without source. 96 of them had SBIs. The original Feverkidstool, a polytomous logistic regression model including clinical variables and CRP, was recalibrated and thereafter updated by using the assay.Main outcome measuresPneumonia, other SBIs or no SBI.ResultsThe recalibrated original Feverkidstool discriminated well between SBIs and viral infections, with a c-statistic for pneumonia of 0.84 (95% CI 0.77 to 0.92) and 0.82 (95% CI 0.77 to 0.86) for other SBIs. The discriminatory ability increased when CRP was replaced by the combination assay; c-statistic for pneumonia increased to 0.89 (95% CI 0.82 to 0.96) and for other SBIs to 0.91 (95% CI 0.87 to 0.94). This updated Feverkidstool improved diagnosis of SBIs mainly in children with low–moderate risk estimates of SBIs.ConclusionWe improved the diagnostic accuracy of the Feverkidstool by replacing CRP with a combination assay to predict pneumonia or other SBIs in febrile children. The updated Feverkidstool has the largest potential to rule out bacterial infections and thus to decrease unnecessary antibiotic prescription in children with low-to-moderate predicted risk of SBIs.

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.

552: Development of a clinical prediction model to identify pregnancies at risk for stillbirth

2014 ◽  
Vol 210 (1) ◽  
pp. S272-S273
Author(s):  
Amanda Trudell ◽  
Methodius Tuuli ◽  
Ryan Longman ◽  
Alison Cahill ◽  
George Macones ◽  
...  
Keyword(s):  
At Risk ◽  
Prediction Model ◽  
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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