Timing of delivery in a high-risk obstetric population: a clinical prediction model

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 identify patients at high risk of death in the emergency department

Intensive Care Medicine ◽

10.1007/s00134-015-3737-x ◽

2015 ◽

Vol 41 (6) ◽

pp. 1029-1036 ◽

Cited By ~ 16

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

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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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The high risk criteria of a clinical prediction model were specific but not sensitive for predicting ectopic pregnancy

Evidence-Based Medicine ◽

10.1136/ebm.5.3.96 ◽

2000 ◽

Vol 5 (3) ◽

pp. 96-96

Author(s):

W. M Ankum

Keyword(s):

High Risk ◽

Prediction Model ◽

Ectopic Pregnancy ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Risk Criteria

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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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