The predictive value of medical history taking and Chlamydia IgG ELISA antibody testing (CAT) in the selection of subfertile women for diagnostic laparoscopy: a clinical prediction model approach

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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Estimating the risk of gestational diabetes mellitus: a clinical prediction model based on patient characteristics and medical history

BJOG An International Journal of Obstetrics & Gynaecology ◽

10.1111/j.1471-0528.2009.02425.x ◽

2009 ◽

Vol 117 (1) ◽

pp. 69-75 ◽

Cited By ~ 68

Author(s):

M van Leeuwen ◽

BC Opmeer ◽

EJK Zweers ◽

E van Ballegooie ◽

HG ter Brugge ◽

...

Keyword(s):

Diabetes Mellitus ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Prediction Model ◽

Medical History ◽

Patient Characteristics ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Model Based

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A Prediction Model of Sufficient Filter Lifespan in Anticoagulation-free CRRT Patients

10.21203/rs.3.rs-73156/v1 ◽

2020 ◽

Author(s):

Wei Zhang ◽

Ming Bai ◽

Ling Zhang ◽

Yan Yu ◽

Yangping Li ◽

...

Keyword(s):

Prediction Model ◽

Predictive Value ◽

Primary Outcome ◽

Area Under The Curve ◽

Bleeding Risk ◽

Multivariable Logistic Regression Analysis ◽

Clinical Prediction ◽

Internal Validation ◽

Clinical Prediction Model ◽

Final Model

Abstract Background: Anticoagulation-free continuous renal replacement therapy (CRRT) was recommended by the current clinical guideline for patients with increased bleeding risk and contraindications of citrate and resulted in heterogeneous filter lifespan. There was no prediction model to identify the patients would have sufficient filter lifespan when they have to accept CRRT without the use of any anticoagulation. The purpose of our present study is to develop a clinical prediction model of sufficient filter lifespan in anticoagulation-free CRRT patients.Method: Patients who underwent anticoagulation-free CRRT in our center between June 2013 and June 2019 were retrospectively included. The primary outcome was sufficient filter lifespan (≥ 24 hours). The final model was established by using multivariable logistic regression analysis. And, the prediction model was validated in an external cohort. Results: A total of 170 patients were included in the development cohort. Sufficient filter lifespan were observed in 80 patients. The probability of sufficient filter lifespan could be calculated using the following regression formula: P (%) = exp (Z)/1 + exp (Z), where Z = 0.49896-(0.08552*BMI)+(0.44107*T)+(0.03373*MAP)-(0.03389*WBC)+(1.51579*[vasopressor=1])-(0.01132*PLT)+(0.00422*ALP)-(2.66910*pH)-(0.00214*UA)+(0.05992*BUN)+(0.00400*Db)–(0.00014*D-dimer)+(0.02818*APTT). The area under the curve (AUC) of the stepwise model and internal validation model was 0.82 (95%CI [0.76-0.88]) and 0.8 (95%CI [0.74-0.87]), respectively. At the optimal cut-off value of -0.1052, the positive predictive value and the negative predictive value of the stepwise model was 0.77 and 0.79, respectively. The AUC of the external model was 0.82 (95%CI [0.69-0.96]). Conclusion: The use of a prediction model instead of an assessment based only on coagulation parameters could facilitate the identification of the patients with filter lifespan of ≥ 24 hours when they accepted anticoagulation-free CRRT.

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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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