Validation of a Clinical Prediction Model for Early Admission to the Intensive Care Unit of Patients With Pneumonia

IntroductionCurrently, we are unable to accurately predict mortality or neurological morbidity following resuscitation after paediatric out of hospital (OHCA) or in-hospital (IHCA) cardiac arrest. A clinical prediction model may improve communication with parents and families and risk stratification of patients for appropriate postcardiac arrest care. This study aims to the derive and validate a clinical prediction model to predict, within 1 hour of admission to the paediatric intensive care unit (PICU), neurodevelopmental outcome at 3 months after paediatric cardiac arrest.Methods and analysisA prospective study of children (age: >24 hours and <16 years), admitted to 1 of the 24 participating PICUs in the UK and Ireland, following an OHCA or IHCA. Patients are included if requiring more than 1 min of cardiopulmonary resuscitation and mechanical ventilation at PICU admission Children who had cardiac arrests in PICU or neonatal intensive care unit will be excluded. Candidate variables will be identified from data submitted to the Paediatric Intensive Care Audit Network registry. Primary outcome is neurodevelopmental status, assessed at 3 months by telephone interview using the Vineland Adaptive Behavioural Score II questionnaire. A clinical prediction model will be derived using logistic regression with model performance and accuracy assessment. External validation will be performed using the Therapeutic Hypothermia After Paediatric Cardiac Arrest trial dataset. We aim to identify 370 patients, with successful consent and follow-up of 150 patients. Patient inclusion started 1 January 2018 and inclusion will continue over 18 months.Ethics and disseminationEthical review of this protocol was completed by 27 September 2017 at the Wales Research Ethics Committee 5, 17/WA/0306. The results of this study will be published in peer-reviewed journals and presented in conferences.Trial registration numberNCT03574025.

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Failure to Validate a Multivariable Clinical Prediction Model to Identify Pediatric Intensive Care Unit Patients at High Risk for Candidemia

Journal of the Pediatric Infectious Diseases Society ◽

10.1093/jpids/piv024 ◽

2015 ◽

Vol 5 (4) ◽

pp. 458-461 ◽

Cited By ~ 8

Author(s):

Brian T. Fisher ◽

Rachael K. Ross ◽

Emmanuel Roilides ◽

Debra L. Palazzi ◽

Mark J. Abzug ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Pediatric Intensive Care Unit ◽

Case Control Study ◽

Prediction Rule ◽

Pediatric Intensive Care ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Control Study

Abstract We attempted to validate a previously derived clinical prediction rule for candidemia in the pediatric intensive care unit. This multicenter case control study did not identify significant association of candidemia with most of the previously identified predictors. Additional study in larger cohorts with other predictor variables is needed.

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Localized Osteosarcoma in Upper Egypt: A Clinical Prediction Model

Case Medical Research ◽

10.31525/ct1-nct03839095 ◽

2000 ◽

Author(s):

Keyword(s):

Prediction Model ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Upper Egypt

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

Statistics in Medicine ◽

10.1002/sim.9025 ◽

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

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

Gerontology ◽

10.1159/000517511 ◽

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

BMJ Open ◽

10.1136/bmjopen-2020-041093 ◽

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.

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