scholarly journals Validation and update of the thoracic surgery scoring system (Thoracoscore) risk model

2020 ◽  
Vol 58 (2) ◽  
pp. 350-356
Author(s):  
Julien Die Loucou ◽  
Pierre-Benoit Pagès ◽  
Pierre-Emmanuel Falcoz ◽  
Pascal-Alexandre Thomas ◽  
Caroline Rivera ◽  
...  
Keyword(s):  
Thoracic Surgery ◽  
Prediction Model ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Risk Model ◽  
Age Distribution ◽  
Original Data ◽  
Validation Group ◽  
Goodness Of Fit Test ◽  
Validation Data

Abstract OBJECTIVES The performance of prediction models tends to deteriorate over time. The purpose of this study was to update the Thoracoscore risk prediction model with recent data from the Epithor nationwide thoracic surgery database. METHODS From January 2016 to December 2017, a total of 56 279 patients were operated on for mediastinal, pleural, chest wall or lung disease. We used 3 recommended methods to update the Thoracoscore prediction model and then proceeded to develop a new risk model. Thirty-day hospital mortality included patients who died within the first 30 days of the operation and those who died later during the same hospital stay. RESULTS We compared the baseline patient characteristics in the original data used to develop the Thoracoscore prediction model and the validation data. The age distribution was different, with specifically more patients older than 65 years in the validation group. Video-assisted thoracoscopy accounted for 47% of surgeries in the validation group compared but only 18% in the original data. The calibration curve used to update the Thoracoscore confirmed the overfitting of the 3 methods. The Hosmer–Lemeshow goodness-of-fit test was significant for the 3 updated models. Some coefficients were overfitted (American Society of Anesthesiologists score, performance status and procedure class) in the validation data. The new risk model has a correct calibration as indicated by the Hosmer–Lemeshow goodness-of-fit test, which was non-significant. The C-index was strong for the new risk model (0.84), confirming the ability of the new risk model to differentiate patients with and without the outcome. Internal validation shows no overfitting for the new model CONCLUSIONS The new Thoracoscore risk model has improved performance and good calibration, making it appropriate for use in current clinical practice.

scholarly journals Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

2021 ◽  
Vol 11 ◽  
Author(s):  
Jin-feng Pan ◽  
Rui Su ◽  
Jian-zhou Cao ◽  
Zhen-ya Zhao ◽  
Da-wei Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Elevated Serum ◽  
Training Group ◽  
Validation Group ◽  
Clinical Indicators

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

scholarly journals Tracking Pandemic Severity Using Data on the Age Structure of Mortality: Lessons From the 1918 Influenza Pandemic in Michigan

2021 ◽  
Vol 111 (S2) ◽  
pp. S149-S155
Author(s):  
Siddharth Chandra ◽  
Julia Christensen
Keyword(s):  
Public Health ◽  
Age Structure ◽  
Goodness Of Fit ◽  
Influenza Pandemic ◽  
Age Distribution ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
1918 Influenza ◽  
Death Data ◽  
Excess Deaths

Objectives. To test whether distortions in the age structure of mortality during the 1918 influenza pandemic in Michigan tracked the severity of the pandemic. Methods. We calculated monthly excess deaths during the period of 1918 to 1920 by using monthly data on all-cause deaths for the period of 1912 to 1920 in Michigan. Next, we measured distortions in the age distribution of deaths by using the Kuiper goodness-of-fit test statistic comparing the monthly distribution of deaths by age in 1918 to 1920 with the baseline distribution for the corresponding month for 1912 to 1917. Results. Monthly distortions in the age distribution of deaths were correlated with excess deaths for the period of 1918 to 1920 in Michigan (r = 0.83; P < .001). Conclusions. Distortions in the age distribution of deaths tracked variations in the severity of the 1918 influenza pandemic. Public Health Implications. It may be possible to track the severity of pandemic activity with age-at-death data by identifying distortions in the age distribution of deaths. Public health authorities should explore the application of this approach to tracking the COVID-19 pandemic in the absence of complete data coverage or accurate cause-of-death data.

scholarly journals The role of triiodothyronine (T3) and T3/free thyroxine (fT4) on glucose metabolism during pregnancy: the Ma’anshan Birth Cohort Study

2021 ◽  
Author(s):  
Beibei Zhu ◽  
Yan Han ◽  
Fen Deng ◽  
Kun Huang ◽  
Shuangqin Yan ◽  
...  
Keyword(s):  
Cohort Study ◽  
Glucose Metabolism ◽  
Birth Cohort ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
First Trimester ◽  
Repeated Measurements ◽  
Free Thyroxine ◽  
Birth Cohort Study ◽  
Goodness Of Fit Test

Objectives: Compared with other thyroid markers, fewer studies explored the associations between triiodothyronine (T3) and T3/free thyroxine (fT4) and glucose abnormality during pregnancy. Thus, we aimed to: (1) examine the associations of T3 and T3/fT4 with glucose metabolism indicators; and (2) evaluate, in the first trimester, the performance of the two markers as predictors of gestational diabetes mellitus (GDM) risk. Methods: Longitudinal data from 2723 individuals, consisting of three repeated measurements of T3 and fT4, from the Man’anshan birth cohort study (MABC), China, were analyzed using a time-specific generalized estimating equation (GEE). The receiver operating characteristic curve (ROC) - area under the curve (AUC) and Hosmer-Lemeshow goodness of fit test were used to assess the discrimination and calibration of prediction models. Results: T3 and T3/fT4 presented stable associations with the level of fasting glucose, glucose at 1h/2h across pregnancy. T3 and T3/fT4 in both the first and second trimesters were positively associated with the risk of GDM, with the larger magnitude of association observed in the second trimester (Odds ratio (OR) = 2.50, 95%CI = 1.95, 3.21 for T3; OR = 1.09, 95%CI = 1.07, 1.12 for T3/fT4). T3 ((AUC) = 0.726, 95%CI = 0.698, 0.754) and T3/fT4 (AUC = 0.724, 95%CI = 0.696, 0.753) in the first trimester could improve the performance of the predicting model; however, the overall performance is not good. Conclusion: Significant and stable associations of T3, T3/fT4 and glucose metabolism indicators were documented. Both T3 and T3/fT4 improve the performance of the GDM predictive model.

scholarly journals Age Distribution Model For GCC Countries

2011 ◽  
Vol 8 (11) ◽  
Author(s):  
Shafiqur Rahman
Keyword(s):  
Goodness Of Fit ◽  
Census Data ◽  
Age Distribution ◽  
Developed Countries ◽  
Distribution Model ◽  
School Age Children ◽  
Population Census ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Gcc Countries

Efficient and reliable estimates of the proportions of population at different age levels are essential for making quality budget of any developing or developed nation. These estimates are obtained from the best-fitted age distribution model and can be used to find the number of school age children, number of pensioners etc. Past population census data of GCC countries are analyzed to find the best-fitted age distribution model applying chi-square goodness of fit test and model selection criteria and observed that the age distribution of most of the GCC countries is exponential. A comparative study of the age distributions of six GCC countries with some developed countries is also provided.

scholarly journals A transformation of oxygen saturation (the saturation virtual shunt) to improve clinical prediction model calibration and interpretation

2018 ◽  
Author(s):  
Guohai Zhou ◽  
Walter Karlen ◽  
Rollin Brant ◽  
Matthew Wiens ◽  
Niranjan Kissoon ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Disease Severity ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Approximation Formula ◽  
P Value ◽  
Clinical Prediction ◽  
Goodness Of Fit Test ◽  
Peripheral Oxygen Saturation

ABSTRACTBackgroundThe relationship between peripheral oxygen saturation (SpO2) and the inspired oxygen concentration is non-linear. SpO2 is frequently used as a dichotomized predictor, to manage this non-linearity. We propose the saturation virtual shunt (VS) as a transformation of SpO2 to a continuous linear variable to improve interpretation of disease severity within clinical prediction models.MethodWe calculate the saturation VS based on an empirically derived approximation formula between physiological VS and SpO2. We evaluated the utility of the saturation VS in a clinical study predicting the need for facility admission in children in a low resource health-care setting.ResultsThe transformation was saturation VS = 68.864*log10(103.711 − SpO2) −52.110. The ability to predict hospital admission based on a dichotomized SpO2 produced an area under the receiver operating characteristic curve of 0.57, compared to 0.71 based on the untransformed SpO2 and saturation VS. However, the untransformed SpO2 demonstrated a lack of fit compared to the saturation VS (goodness-of-fit test p-value <0.0001 versus 0.098). The observed admission rates varied non-linearly with the untransformed SpO2 but varied linearly with the saturation VS.ConclusionThe saturation VS estimates a continuous linearly interpretable disease severity based on SpO2 and improves clinical prediction.

scholarly journals Prediction Model for Severe Mycoplasma Pneumoniae Pneumonia in Pediatric Patients by Admission Laboratory Indicators

2021 ◽  
Author(s):  
Qing Chang ◽  
Hong-Lin Chen ◽  
Neng-Shun Wu ◽  
Yan-Min Gao ◽  
Rong Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Mycoplasma Pneumoniae ◽  
Eosinophil Count ◽  
Goodness Of Fit ◽  
Pediatric Patients ◽  
Community Acquired Pneumonia ◽  
Goodness Of Fit Test ◽  
Independent Risk Factors ◽  
Mycoplasma Pneumoniae Pneumonia ◽  
Actual Incidence

Abstract Objective The purpose of this study was to develop a model for predicting severe mycoplasma pneumoniae pneumonia (SMMP) in pediatric patients with MMP on admission by laboratory indicators. Methods Pediatric patients with MPP from January 2019 to December 2020 in our hospital were enrolled in this study. SMMP was diagnosed according to guideline for diagnosis and treatment of community acquired pneumonia in children (2019 version). Prediction model was developed according to the admission laboratory indicators. ROC curve and Goodness of fit test were analyzed for the predictive value. Results A total of 233 MMP patients were included in the study, with 121 males and 112 females, aged 4.541 (1–14) years. Among them, 84 (36.1%, 95% CI 29.9%-42.6%) pediatric patients were diagnosed as SMPP. Some admission laboratory indicators (IgM, eosinophil proportion, eosinophil count, hemoglobin, ESR, total protein, albumin and prealbumin) were found statistically different (P < 0.05) between non-SMMP group and SMMP group. Logistic regress analysis showed IgM, eosinophil proportion, eosinophil count, ESR, and prealbumin were independent risk factors for SMMP. According to these five admission laboratory indicators, Nomograph prediction model was developed. The AUC of the Nomograph prediction model was 0.777, and the goodness of fit test showed that the predicted incidence of the model was consistent with the actual incidence (χ2 = 244.51, P = 0.203). Conclusion We developed a model for predicting SMMP in pediatric patients by admission laboratory indicators. This model has good discrimination and calibration, which provides a basis for the early identification SMMP on admission.

84 Development of a Risk Prediction Model and Comparison with Clinical Severity Scores in Burn Patients

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S54-S55
Author(s):  
Dohern Kym
Keyword(s):  
Prediction Model ◽  
Goodness Of Fit ◽  
Scoring Systems ◽  
Clinical Severity ◽  
Brier Score ◽  
P Value ◽  
Burn Patients ◽  
Goodness Of Fit Test ◽  
Prediction Of Mortality ◽  
Severity Scores

Abstract Introduction The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients. Methods This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis. Results The new prediction model showed good calibration with the HL test (χ2=8.785, p=0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value &lt; 0.001) when compared with FLAMES, respectively. Conclusions This model reflects the current risk factors of mortality among adult burn patients. Furthermore, it was a highly discriminatory and well-calibrated model for the prediction of mortality in this cohort. Applicability of Research to Practice There are many severity scoring systems widely used in the ICU to predict outcomes and characterize the severity of the disease. All of these scoring systems have been developed for the mixed population in the ICU. Their accuracy among subgroups, such as burn patients, is questionable and therefore, burn-specific scoring systems are required for accurate prediction.

scholarly journals Lymphocyte–monocyte–neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Yingjie Qi ◽  
Jian-an Jia ◽  
Huiming Li ◽  
Nagen Wan ◽  
Shuqin Zhang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Disease Severity ◽  
Prediction Models ◽  
Principal Component ◽  
Component Analysis ◽  
Sparse Principal Component Analysis ◽  
Validation Data ◽  
Severity Prediction ◽  
Prediction Efficiency

Abstract Background It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed. Methods Clinical indicators of COVID-19 patients from two independent cohorts (Training data: Hefei Cohort, 82 patients; Validation data: Nanchang Cohort, 169 patients) were retrospected. Sparse principal component analysis (SPCA) using Hefei Cohort was performed and prediction models were deduced. Prediction results were evaluated by receiver operator characteristic curve and decision curve analysis (DCA) in above two cohorts. Results SPCA using Hefei Cohort revealed that the first 13 principal components (PCs) account for 80.8% of the total variance of original data. The PC1 and PC12 were significantly associated with disease severity with odds ratio of 4.049 and 3.318, respectively. They were used to construct prediction model, named Model-A. In disease severity prediction, Model-A gave the best prediction efficiency with area under curve (AUC) of 0.867 and 0.835 in Hefei and Nanchang Cohort, respectively. Model-A’s simplified version, named as LMN index, gave comparable prediction efficiency as classical clinical markers with AUC of 0.837 and 0.800 in training and validation cohort, respectively. According to DCA, Model-A gave slightly better performance than others and LMN index showed similar performance as albumin or neutrophil-to-lymphocyte ratio. Conclusions Prediction models produced by SPCA showed robust disease severity prediction efficiency for COVID-19 patients and have the potential for clinical application.

Validity of the Framingham Risk Model Applied to Japanese Men

2002 ◽  
Vol 41 (03) ◽  
pp. 213-215 ◽  
Author(s):  
H. Sugimori ◽  
K. Yoshida ◽  
M. Suka
Keyword(s):  
Goodness Of Fit ◽  
Risk Model ◽  
Area Under The Curve ◽  
Health Examination ◽  
Characteristic Analysis ◽  
Goodness Of Fit Test ◽  
Japanese Men ◽  
Discriminatory Accuracy ◽  
Framingham Risk ◽  
Chd Risk

Summary Objectives: To examine whether the Framingham Risk Model can appropriately predict coronary heart disease (CHD) events detected by electrocardiography (ECG) in Japanese men. Methods: Using the annual health examination database of a Japanese company 5611 male workers, between the ages of 30 to 59, who were free of cardiovascular disease, were followed up to observe the occurrence of CHD events detected by ECG over a period of five to seven years. The probability of CHD was calculated for each individual from the equations of the Framingham risk model (with total cholesterol). Results: The incidence of CHD increased with the estimated CHD risk. The Hosmer-Lemeshow goodness of fit test showed an adequate fit of the risk model to the data of the study subjects. In the receiver operating characteristic analysis, the area under the curve reached 0.67 which indicated an acceptable discriminatory accuracy of the risk model. Conclusions: The Framingham risk model provides useful information on future CHD events in Japanese men.

scholarly journals Comparison of in-hospital mortality risk prediction models from COVID-19

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244629
Author(s):  
Ali A. El-Solh ◽  
Yolanda Lawson ◽  
Michael Carter ◽  
Daniel A. El-Solh ◽  
Kari A. Mergenhagen
Keyword(s):  
Hospital Mortality ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
External Validation ◽  
Prognostic Models ◽  
Study Cohort ◽  
Goodness Of Fit Test ◽  
Risk Prediction Models

Objective Our objective is to compare the predictive accuracy of four recently established outcome models of patients hospitalized with coronavirus disease 2019 (COVID-19) published between January 1st and May 1st 2020. Methods We used data obtained from the Veterans Affairs Corporate Data Warehouse (CDW) between January 1st, 2020, and May 1st 2020 as an external validation cohort. The outcome measure was hospital mortality. Areas under the ROC (AUC) curves were used to evaluate discrimination of the four predictive models. The Hosmer–Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases. Results During the study period, 1634 unique patients were identified. The mean age of the study cohort was 68.8±13.4 years. Hypertension, hyperlipidemia, and heart disease were the most common comorbidities. The crude hospital mortality was 29% (95% confidence interval [CI] 0.27–0.31). Evaluation of the predictive models showed an AUC range from 0.63 (95% CI 0.60–0.66) to 0.72 (95% CI 0.69–0.74) indicating fair to poor discrimination across all models. There were no significant differences among the AUC values of the four prognostic systems. All models calibrated poorly by either overestimated or underestimated hospital mortality. Conclusions All the four prognostic models examined in this study portend high-risk bias. The performance of these scores needs to be interpreted with caution in hospitalized patients with COVID-19.

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