scholarly journals Evaluation and Validation of a Prediction Model for Extubation Success in Very Preterm Infants

2021 ◽  
Author(s):  
Rebecca Dryer ◽  
Anand Salem ◽  
Vivek Saroha ◽  
Rachel Greenberg ◽  
Matthew Rysavy ◽  
...  
Keyword(s):  
Prediction Model ◽  
Clinical Decision Making ◽  
Model Performance ◽  
Clinical Decision ◽  
Success Prediction ◽  
Receiver Operating Curve ◽  
Potential Utility ◽  
Single Center ◽  
Very Preterm Infants ◽  
Very Preterm

Abstract ObjectiveTo evaluate the performance of a publicly available model predicting extubation success in very preterm infants.Study DesignRetrospective study of infants born < 1250 g at a single center. Model performance evaluated using the area under the receiver operating curve (AUROC) and comparing observed and expected probabilities of extubation success, defined as survival ≥ 5 d without an endotracheal tube.ResultsOf 177 infants, 120 (68%) were extubated successfully. The median (IQR) gestational age was 27 weeks (25–28) and weight at extubation was 915 g (755–1050). The model had acceptable discrimination (AUROC 0.72 [95% CI 0.65–0.80]) and adequate calibration (calibration slope 0.96, intercept − 0.06, mean observed-to-expected difference in probability of extubation success − 0.08 [95% CI -0.01, -0.15]).ConclusionsThe extubation success prediction model has acceptable performance in an external cohort, supporting its potential utility in clinical decision-making. Additional studies are needed to determine if its use can improve outcomes.

scholarly journals Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e040361
Author(s):  
Amanda Klinger ◽  
Ariel Mueller ◽  
Tori Sutherland ◽  
Christophe Mpirimbanyi ◽  
Elie Nziyomaze ◽  
...  
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Tertiary Hospital ◽  
Early Warning Score ◽  
Receiver Operating Curve ◽  
Predictive Capacity ◽  
Suspected Infection ◽  
Significant Difference

RationaleMortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.ObjectiveTo determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.Design, setting, participants and outcome measuresWe prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.ResultsWe screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.ConclusionThree scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

A Prediction Model for Positive Infant Meconium and Urine Drug Tests

2020 ◽  
Author(s):  
Elizabeth A. Simpson ◽  
David A. Skoglund ◽  
Sarah E. Stone ◽  
Ashley K. Sherman
Keyword(s):  
Prediction Model ◽  
Drug Testing ◽  
Clinical Decision Making ◽  
Drugs Of Abuse ◽  
Clinical Decision ◽  
P Value ◽  
Test Characteristics ◽  
Drug Test ◽  
Urine Drug ◽  
Urine Drug Test

Objective This study aimed to determine the factors associated with positive infant drug screen and create a shortened screen and a prediction model. Study Design This is a retrospective cohort study of all infants who were tested for drugs of abuse from May 2012 through May 2014. The primary outcome was positive infant urine or meconium drug test. Multivariable logistic regression was used to identify independent risk factors. A combined screen was created, and test characteristics were analyzed. Results Among the 3,861 live births, a total of 804 infants underwent drug tests. Variables associated with having a positive infant test were (1) positive maternal urine test, (2) substance use during pregnancy, (3) ≤ one prenatal visit, and (4) remote substance abuse; each p-value was less than 0.0001. A model with an indicator for having at least one of these four predictors had a sensitivity of 94% and a specificity of 69%. Application of this screen to our population would have decreased drug testing by 57%. No infants had a positive urine drug test when their mother's urine drug test was negative. Conclusion This simplified screen can guide clinical decision making for determining which infants should undergo drug testing. Infant urine drug tests may not be needed when a maternal drug test result is negative. Key Points

scholarly journals Bronchopulmonary dysplasia in very preterm infants: Outcome up to preschool age, in a single center of Austria

2019 ◽  
Vol 61 (4) ◽  
pp. 381-387 ◽  
Author(s):  
Friedrich Reiterer ◽  
Anna Scheuchenegger ◽  
Bernhard Resch ◽  
Ute Maurer‐Fellbaum ◽  
Alexander Avian ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Bronchopulmonary Dysplasia ◽  
Preschool Age ◽  
Single Center ◽  
Very Preterm Infants ◽  
Very Preterm

scholarly journals Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population

2021 ◽  
Author(s):  
Yaqian Mao ◽  
Lizhen Xu ◽  
Ting Xue ◽  
Jixing Liang ◽  
Wei Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Calibration Curve ◽  
Model Performance ◽  
Clinical Decision ◽  
Lasso Regression ◽  
Threshold Probability ◽  
Male Population ◽  
Incidence Risk ◽  
Chinese Male

Objective: To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40years) based on data mining technology. Materials and methods: A total of 1,834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model. Results: The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95%CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1% and 100%, the nomogram had good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870. Conclusions: This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population.

Dynamic logistic state‐space prediction model for clinical decision making

Biometrics ◽  
2021 ◽  
Author(s):  
Jiakun Jiang ◽  
Wei Yang ◽  
Erin M. Schnellinger ◽  
Stephen E. Kimmel ◽  
Wensheng Guo
Keyword(s):  
Decision Making ◽  
Prediction Model ◽  
State Space ◽  
Clinical Decision Making ◽  
Clinical Decision

Predicting poor outcome in patients with suspected COVID-19 presenting to the Emergency Department (COVERED) – Development, internal and external validation of a prediction model

2021 ◽  
Vol 20 (1) ◽  
pp. 4-14
Author(s):  
K. Azijli ◽  
◽  
A.W.E. Lieveld ◽  
S.F.B. van der Horst ◽  
N. de Graaf ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Brier Score ◽  
Multi Centre Study ◽  
Poor Outcome

Background: A recent systematic review recommends against the use of any of the current COVID-19 prediction models in clinical practice. To enable clinicians to appropriately profile and treat suspected COVID-19 patients at the emergency department (ED), externally validated models that predict poor outcome are desperately needed. Objective: Our aims were to identify predictors of poor outcome, defined as mortality or ICU admission within 30 days, in patients presenting to the ED with a clinical suspicion of COVID-19, and to develop and externally validate a prediction model for poor outcome. Methods: In this prospective, multi-centre study, we enrolled suspected COVID-19 patients presenting at the EDs of two hospitals in the Netherlands. We used backward logistic regression to develop a prediction model. We used the area under the curve (AUC), Brier score and pseudo-R2 to assess model performance. The model was externally validated in an Italian cohort. Results: We included 1193 patients between March 12 and May 27 2020, of whom 196 (16.4%) had a poor outcome. We identified 10 predictors of poor outcome: current malignancy (OR 2.774; 95%CI 1.682-4.576), systolic blood pressure (OR 0.981; 95%CI 0.964-0.998), heart rate (OR 1.001; 95%CI 0.97-1.028), respiratory rate (OR 1.078; 95%CI 1.046-1.111), oxygen saturation (OR 0.899; 95%CI 0.850-0.952), body temperature (OR 0.505; 95%CI 0.359-0.710), serum urea (OR 1.404; 95%CI 1.198-1.645), C-reactive protein (OR 1.013; 95%CI 1.001-1.024), lactate dehydrogenase (OR 1.007; 95%CI 1.002-1.013) and SARS-CoV-2 PCR result (OR 2.456; 95%CI 1.526-3.953). The AUC was 0.86 (95%CI 0.83-0.89), with a Brier score of 0.32 and, and R2 of 0.41. The AUC in the external validation in 500 patients was 0.70 (95%CI 0.65-0.75). Conclusion: The COVERED risk score showed excellent discriminatory ability, also in external validation. It may aid clinical decision making, and improve triage at the ED in health care environments with high patient throughputs.

scholarly journals Roles and Clinical Applications of OPG and TRAIL as Biomarkers in Cardiovascular Disease

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Stella Bernardi ◽  
Fleur Bossi ◽  
Barbara Toffoli ◽  
Bruno Fabris
Keyword(s):  
Cardiovascular Disease ◽  
Decision Making ◽  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Clinical Applications ◽  
Nuclear Factor ◽  
Potential Utility ◽  
Receptor Activator

Cardiovascular diseases (CVD) remain the major cause of death and premature disability in Western societies. Assessing the risk of CVD is an important aspect in clinical decision-making. Among the growing number of molecules that are studied for their potential utility as CVD biomarkers, a lot of attention has been focused on osteoprotegerin (OPG) and its ligands, which are receptor activator of nuclear factorκB ligand (RANKL) and TNF-related apoptosis-inducing ligand. Based on the existing literature and on our experience in this field, here we review what the possible roles of OPG and TRAIL in CVD are and their potential utility as CVD biomarkers.

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