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

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.

Development and internal validation of a clinical prediction model for external cephalic version

2018 ◽  
Vol 228 ◽  
pp. 137-142 ◽  
Author(s):  
Joost Velzel ◽  
Ewoud Schuit ◽  
Floortje Vlemmix ◽  
Jan F.M. Molkenboer ◽  
Joris A.M. Van der Post ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Cephalic Version ◽  
Clinical Prediction ◽  
Internal Validation ◽  
Clinical Prediction Model

scholarly journals A stillbirth calculator: Development and internal validation of a clinical prediction model to quantify stillbirth risk

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173461 ◽  
Author(s):  
Amanda S. Trudell ◽  
Methodius G. Tuuli ◽  
Graham A. Colditz ◽  
George A. Macones ◽  
Anthony O. Odibo
Keyword(s):  
Prediction Model ◽  
Clinical Prediction ◽  
Internal Validation ◽  
Clinical Prediction Model

scholarly journals Development and internal validation of a clinical prediction model for non-recovery in older adults with back pain

2020 ◽  
Vol 28 ◽  
pp. S254-S255
Author(s):  
W.H. van der Gaag ◽  
A. Chiarotto ◽  
M.W. Heymans ◽  
W.T. Enthoven ◽  
P.A. Luijsterburg ◽  
...  
Keyword(s):  
Older Adults ◽  
Back Pain ◽  
Prediction Model ◽  
Clinical Prediction ◽  
Internal Validation ◽  
Clinical Prediction Model

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

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.

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

2016 ◽  
Vol 11 (1) ◽  
pp. 64-68 ◽  
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 women’s 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

scholarly journals Development and Internal Validation of a Multivariable Prediction Model for Adrenocortical-Carcinoma-Specific Mortality

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2720
Author(s):  
Madeleine H. T. Ettaieb ◽  
Sander M. J. van Kuijk ◽  
Annelies de Wit-Pastoors ◽  
Richard A. Feelders ◽  
Eleonora P. M. Corssmit ◽  
...  
Keyword(s):  
Prediction Model ◽  
Adrenocortical Carcinoma ◽  
Cox Proportional Hazards ◽  
Adrenal Tumors ◽  
Mortality Data ◽  
Clinical Prediction ◽  
Discriminative Ability ◽  
Internal Validation ◽  
Clinical Prediction Model ◽  
Specific Mortality

Adrenocortical carcinoma (ACC) has an incidence of about 1.0 per million per year. In general, survival of patients with ACC is limited. Predicting survival outcome at time of diagnosis is a clinical challenge. The aim of this study was to develop and internally validate a clinical prediction model for ACC-specific mortality. Data for this retrospective cohort study were obtained from the nine centers of the Dutch Adrenal Network (DAN). Patients who presented with ACC between 1 January 2004 and 31 October 2013 were included. We used multivariable Cox proportional hazards regression to compute the coefficients for the prediction model. Backward stepwise elimination was performed to derive a more parsimonious model. The performance of the initial prediction model was quantified by measures of model fit, discriminative ability, and calibration. We undertook an internal validation step to counteract the possible overfitting of our model. A total of 160 patients were included in the cohort. The median survival time was 35 months, and interquartile range (IQR) 50.7 months. The multivariable modeling yielded a prediction model that included age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. The c-statistic was 0.77 (95% Confidence Interval: 0.72, 0.81), indicating good predictive performance. We developed a clinical prediction model for ACC-specific mortality. ACC mortality can be estimated using a relatively simple clinical prediction model with good discriminative ability and calibration.

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