scholarly journals A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation

2021 ◽  
Author(s):  
Lihong Lai
Keyword(s):  
Risk Factors ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Information Criterion ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Net Reclassification Improvement ◽  
Artery Disease ◽  
Integrated Discrimination Improvement

Objective: To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease (CAD).Methods and Results: A total of 1,025 patients referred for coronary angiography were included in a retrospective, single-center study. Randomly, 720 patients (70%) were selected as the development group and the other patients were selected as the validation group. Multivariate logistic regression analysis showed that the seven risk factors age, sex, systolic blood pressure, lipoprotein-associated phospholipase A2, type of angina, hypertension, and diabetes were significant for diagnosis of CAD, from which we established model A. We established model B with the risk factors age, sex, height, systolic blood pressure, low-density lipoprotein cholesterol, lipoprotein-associated phospholipase A2, type of angina, hypertension, and diabetes via the Akaike information criterion. The risk factors from the original Framingham Risk Score were used for model C. From comparison of the areas under the receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement of models A, B, and C, we chose model B to develop the nomogram because of its fitness in discrimination, calibration, and clinical efficiency. The nomogram for diagnosis of CAD could be used easily and conveniently. Conclusion: An individualized clinical prediction model for patients with CAD allowed an accurate estimation in Chinese populations. The Akaike information criterion is a better method in screening risk factors. The net reclassification improvement and integrated discrimination improvement are better than the area under the receiver operating characteristic curve in discrimination. Decision curve analysis can be used to evaluate the efficiency of clinical prediction models.

scholarly journals External validation of six existing multivariable clinical prediction models for short-term mortality in patients undergoing lung resection

2020 ◽  
Author(s):  
Marcus Taylor ◽  
Bartłomiej Szafron ◽  
Glen P Martin ◽  
Udo Abah ◽  
Matthew Smith ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Lung Resection ◽  
External Validation ◽  
Clinical Prediction ◽  
National Guidelines ◽  
Clinical Prediction Models ◽  
Receiver Operating

Abstract OBJECTIVES National guidelines advocate the use of clinical prediction models to estimate perioperative mortality for patients undergoing lung resection. Several models have been developed that may potentially be useful but contemporary external validation studies are lacking. The aim of this study was to validate existing models in a multicentre patient cohort. METHODS The Thoracoscore, Modified Thoracoscore, Eurolung, Modified Eurolung, European Society Objective Score and Brunelli models were validated using a database of 6600 patients who underwent lung resection between 2012 and 2018. Models were validated for in-hospital or 30-day mortality (depending on intended outcome of each model) and also for 90-day mortality. Model calibration (calibration intercept, calibration slope, observed to expected ratio and calibration plots) and discrimination (area under receiver operating characteristic curve) were assessed as measures of model performance. RESULTS Mean age was 66.8 years (±10.9 years) and 49.7% (n = 3281) of patients were male. In-hospital, 30-day, perioperative (in-hospital or 30-day) and 90-day mortality were 1.5% (n = 99), 1.4% (n = 93), 1.8% (n = 121) and 3.1% (n = 204), respectively. Model area under the receiver operating characteristic curves ranged from 0.67 to 0.73. Calibration was inadequate in five models and mortality was significantly overestimated in five models. No model was able to adequately predict 90-day mortality. CONCLUSIONS Five of the validated models were poorly calibrated and had inadequate discriminatory ability. The modified Eurolung model demonstrated adequate statistical performance but lacked clinical validity. Development of accurate models that can be used to estimate the contemporary risk of lung resection is required.

scholarly journals External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

2021 ◽  
Author(s):  
Benjamin S. Wessler ◽  
Jason Nelson ◽  
Jinny G. Park ◽  
Hannah McGinnes ◽  
Gaurav Gulati ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
External Validation ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Operating Characteristic Curve ◽  
Performance Heterogeneity

Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0–94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th–75th percentile [interquartile range (IQR)], 0.66–0.79), representing a median percent decrease in discrimination of −11.1% (IQR, −32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was −3.7% (IQR, −13.2 to 3.1) for closely related validations (n=123), −9.0 (IQR, −27.6 to 3.9) for related validations (n=862), and −17.2% (IQR, −42.3 to 0) for distantly related validations (n=717; P <0.001). Conclusions: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

scholarly journals Traditional Cardiovascular Risk Factors and Individual Prediction of Cardiovascular Events in Childhood Cancer Survivors

2019 ◽  
Vol 112 (3) ◽  
pp. 256-265 ◽  
Author(s):  
Yan Chen ◽  
Eric J Chow ◽  
Kevin C Oeffinger ◽  
William L Border ◽  
Wendy M Leisenring ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Cardiovascular Risk ◽  
Childhood Cancer ◽  
Cardiovascular Risk Factors ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Childhood Cancer Survivors ◽  
Operating Characteristic Curve

Abstract Background Childhood cancer survivors have an increased risk of heart failure, ischemic heart disease, and stroke. They may benefit from prediction models that account for cardiotoxic cancer treatment exposures combined with information on traditional cardiovascular risk factors such as hypertension, dyslipidemia, and diabetes. Methods Childhood Cancer Survivor Study participants (n = 22 643) were followed through age 50 years for incident heart failure, ischemic heart disease, and stroke. Siblings (n = 5056) served as a comparator. Participants were assessed longitudinally for hypertension, dyslipidemia, and diabetes based on self-reported prescription medication use. Half the cohort was used for discovery; the remainder for replication. Models for each outcome were created for survivors ages 20, 25, 30, and 35 years at the time of prediction (n = 12 models). Results For discovery, risk scores based on demographic, cancer treatment, hypertension, dyslipidemia, and diabetes information achieved areas under the receiver operating characteristic curve and concordance statistics 0.70 or greater in 9 and 10 of the 12 models, respectively. For replication, achieved areas under the receiver operating characteristic curve and concordance statistics 0.70 or greater were observed in 7 and 9 of the models, respectively. Across outcomes, the most influential exposures were anthracycline chemotherapy, radiotherapy, diabetes, and hypertension. Survivors were then assigned to statistically distinct risk groups corresponding to cumulative incidences at age 50 years of each target outcome of less than 3% (moderate-risk) or approximately 10% or greater (high-risk). Cumulative incidence of all outcomes was 1% or less among siblings. Conclusions Traditional cardiovascular risk factors remain important for predicting risk of cardiovascular disease among adult-age survivors of childhood cancer. These prediction models provide a framework on which to base future surveillance strategies and interventions.

scholarly journals A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Joeri W van Straalen ◽  
Gabriella Giancane ◽  
Yasmine Amazrhar ◽  
Nikolay Tzaribachev ◽  
Calin Lazar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Protective Factor ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Information Criterion ◽  
Logistic Regression Models ◽  
Adjusted Risk ◽  
Independent Risk Factors

Abstract Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

Cancer-associated thrombosis

Hematology ◽  
2013 ◽  
Vol 2013 (1) ◽  
pp. 684-691 ◽  
Author(s):  
Gregory C. Connolly ◽  
Charles W. Francis
Keyword(s):  
Risk Factors ◽  
Clinical Trials ◽  
Cancer Patients ◽  
Prediction Models ◽  
Risk Model ◽  
Clinical Prediction ◽  
Consensus Guidelines ◽  
Clinical Factors ◽  
Clinical Prediction Models ◽  
Cancer Associated Thrombosis

AbstractCancer-associated thrombosis accounts for almost one-fifth of all cases of venous thromboembolism (VTE) and is a leading cause of death, morbidity, delays in care, and increased costs. Our understanding of risk factors for cancer-associated thrombosis has expanded in recent years, and investigators have begun to use biomarkers and clinical prediction models to identify those cancer patients at greatest risk for VTE. The Khorana Risk Model, which is based on easily obtained biomarkers and clinical factors, has now been validated in several studies. Recent clinical trials of prophylaxis and treatment of VTE in cancer patients are reviewed here. In addition, consensus guidelines and expert opinion regarding management of VTE in specific challenging situations are presented.

Abstract 11009: Serum Cholesterol Efflux Capacity is an Independent Predictor of All-cause and Cardiovascular Mortality in Patients With Coronary Artery Disease

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Chaoqun Liu ◽  
Yuan Zhang ◽  
Ding Ding ◽  
Dongliang Wang ◽  
Xinrui Li ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Serum Cholesterol ◽  
Cardiovascular Mortality ◽  
Cholesterol Efflux ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Cholesterol Efflux Capacity ◽  
Net Reclassification Improvement ◽  
Artery Disease

Objective To investigate the association between serum cholesterol efflux capacity and all-cause and cardiovascular mortality in patients with coronary artery disease. Design Prospective cohort study. Setting Guangdong Coronary Artery Disease Cohort, established in 2008-2011. Participants 1 765 patients with coronary artery disease were followed-up for a median of 3.9 years. Main outcome measures Primary outcome was the association of baseline serum cholesterol efflux capacity with the risks of all-cause and cardiovascular mortality. Results: During 6 778 person-years of follow-up, 170 deaths were registered, 126 of which were caused by cardiovascular diseases. After multivariate adjustment for factors related to cardiovascular diseases, the hazard ratios (95% confidence intervals) across quartiles of serum cholesterol efflux capacity were 1.00, 0.75 (0.51-1.10), 0.51 (0.33-0.81) and 0.43 (0.25-0.73) for all-cause mortality ( P = 0.003), and 1.00, 0.76 (0.49-1.18), 0.37 (0.21-0.65), and 0.25 (0.12-0.52) for cardiovascular mortality ( P < 0.001). Adding serum cholesterol efflux capacity to a model containing traditional cardiovascular risk factors significantly increases its discriminatory power and predictive ability for all-cause (area under receiver operating characteristic curve 0.68 versus 0.61, P < 0.001; net reclassification improvement 14.5%, P = 0.001) and cardiovascular (area under receiver operating characteristic curve 0.71 versus 0.63, P < 0.001; net reclassification improvement 18.4%, P < 0.001) death, respectively. Conclusions: Serum cholesterol efflux capacity may serve as an independent measure for predicting all-cause and cardiovascular mortality in patients with coronary artery disease.

scholarly journals Comparison of emergency department trauma triage performance of clinicians and clinical prediction models: a cohort study in India

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e032900 ◽  
Author(s):  
Ludvig Wärnberg Gerdin ◽  
Monty Khajanchi ◽  
Vineet Kumar ◽  
Nobhojit Roy ◽  
Makhan Lal Saha ◽  
...  
Keyword(s):  
Emergency Department ◽  
Cohort Study ◽  
Prediction Models ◽  
Adult Patients ◽  
Clinical Prediction ◽  
Operating Characteristics ◽  
Clinical Prediction Models ◽  
Net Reclassification Improvement ◽  
Trauma Triage ◽  
Trauma Score

ObjectiveThe aim of this study was to evaluate and compare the abilities of clinicians and clinical prediction models to accurately triage emergency department (ED) trauma patients. We compared the decisions made by clinicians with the Revised Trauma Score (RTS), the Glasgow Coma Scale, Age and Systolic Blood Pressure (GAP) score, the Kampala Trauma Score (KTS) and the Gerdin et al model.DesignProspective cohort study.SettingThree hospitals in urban India.ParticipantsIn total, 7697 adult patients who presented to participating hospitals with a history of trauma were approached for enrolment. The final study sample included 5155 patients. The majority (4023, 78.0%) were male.Main outcome measureThe patient outcome was mortality within 30 days of arrival at the participating hospital. A grid search was used to identify model cut-off values. Clinicians and categorised models were evaluated and compared using the area under the receiver operating characteristics curve (AUROCC) and net reclassification improvement in non-survivors (NRI+) and survivors (NRI−) separately.ResultsThe differences in AUROCC between each categorised model and the clinicians were 0.016 (95% CI −0.014 to 0.045) for RTS, 0.019 (95% CI −0.007 to 0.058) for GAP, 0.054 (95% CI 0.033 to 0.077) for KTS and −0.007 (95% CI −0.035 to 0.03) for Gerdin et al. The NRI+ for each model were −0.235 (−0.37 to −0.116), 0.17 (−0.042 to 0.405), 0.55 (0.47 to 0.65) and 0.22 (0.11 to 0.717), respectively. The NRI− were 0.385 (0.348 to 0.4), −0.059 (−0.476 to −0.005), −0.162 (−0.18 to −0.146) and 0.039 (−0.229 to 0.06), respectively.ConclusionThe findings of this study suggest that there are no substantial differences in discrimination and net reclassification improvement between clinicians and all four clinical prediction models when using 30-day mortality as the outcome of ED trauma triage in adult patients.Trial registration numberClinicalTrials.gov Registry (NCT02838459).

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