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.

Mortality risk scoring in emergency general surgery: Are we using the best tool?

2020 ◽  
pp. 175045892092013
Author(s):  
Azeem Thahir ◽  
Rui Pinto-Lopes ◽  
Stavroula Madenlidou ◽  
Laura Daby ◽  
Chandima Halahakoon
Keyword(s):  
Mortality Rate ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Emergency Laparotomy ◽  
Receiver Operator Characteristic ◽  
Characteristic Analysis ◽  
Goodness Of Fit Test ◽  
Risk Of Death ◽  
Possum Score ◽  
Operative Severity Score

Background It is imperative that an accurate assessment of risk of death is undertaken preoperatively on all patients undergoing an emergency laparotomy. Portsmouth-Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity (P-POSSUM) is one of the most widely used scores. National Emergency Laparotomy Audit (NELA) presents a novel, validated score, but no direct comparison with P-POSSUM exists. We aimed to determine which would be the best predictor of mortality. Methods We analysed all the entries on the online NELA database over a four-and-a-half-year period. The Hosmer–Lemeshow goodness of fit test was performed to assess model calibration. For the outcome of death and for each scoring system, a non-parametric receiver operator characteristic analysis was done. The sensitivity, specificity, area under receiver operator characteristic curve and their standard errors were calculated. Results Data pertaining to 650 patients were included. There were 59 deaths, giving an overall observed mortality rate of 9.1%. Predicted mortality rate for the P-POSSUM score and NELA score were 15.2% and 7.8%, respectively. The discriminative power for mortality was highest for the NELA score (C-index = 0.818, CI: 0.769–0.867, p < 0.001), when compared to P-POSSUM (C-index = 0.769, CI: 0.712–0.827, p < 0.001). Conclusions The NELA score showed good discrimination in predicting mortality in the entire cohort. The P-POSSUM over-predicted observed mortality and the NELA score under-predicted observed mortality.

Is the EuroSCORE II reliable in surgical mitral valve repair? A single-centre validation study

2020 ◽  
Author(s):  
Davide Carino ◽  
Paolo Denti ◽  
Guido Ascione ◽  
Benedetto Del Forno ◽  
Elisabetta Lapenna ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Mitral Valve Repair ◽  
Goodness Of Fit ◽  
Area Under The Curve ◽  
High Volume ◽  
Brier Score ◽  
Calibration Plot ◽  
Valve Repair ◽  
Goodness Of Fit Test ◽  
High Level

Abstract OBJECTIVES The EuroSCORE II is widely used to predict 30-day mortality in patients undergoing open and transcatheter cardiac surgery. The aim of this study is to evaluate the discriminatory ability of the EuroSCORE II in predicting 30-day mortality in a large cohort of patients undergoing surgical mitral valve repair in a high-volume centre. METHODS A retrospective review of our institutional database was carried on to find all patients who underwent mitral valve repair in our department from January 2012 to December 2019. Discrimination of the EuroSCORE II was assessed using receiver operating characteristic curves. The maximum Youden’s Index was employed to define the optimal cut-point. Calibration was assessed by generating calibration plot that visually compares the predicted mortality with the observed mortality. Calibration was also tested with the Hosmer–Lemeshow goodness-of-fit test. Finally, the accuracy of the models was tested calculating the Brier score. RESULTS A total of 2645 patients were identified, and the median EuroSCORE II was 1.3% (0.6–2.0%). In patients with degenerative mitral regurgitation (MR), the EuroSCORE II showed low discrimination (area under the curve 0.68), low accuracy (Brier score 0.27) and low calibration with overestimation of the 30-day mortality. In patients with secondary MR, the EuroSCORE II showed a good overall performance estimating the 30-day mortality with good discrimination (area under the curve 0.88), good accuracy (Brier score 0.003) and good calibration. CONCLUSIONS In patients with degenerative MR operated on in a high-volume centre with a high level of expertise in mitral valve repair, the EuroSCORE II significantly overestimates the 30-day mortality.

scholarly journals Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Enrique Ibarra-Zapata ◽  
Darío Gaytán-Hernández ◽  
Verónica Gallegos-García ◽  
Claudia Elena González-Acevedo ◽  
Thuluz Meza-Menchaca ◽  
...  
Keyword(s):  
At Risk ◽  
Influenza A ◽  
Risk Model ◽  
Geographical Area ◽  
Area Under The Curve ◽  
Type A ◽  
Moran’S I ◽  
Characteristic Analysis ◽  
Moran's I ◽  
Influenza Type

The aim of this study was to estimate the territory at risk of establishment of influenza type A (EOITA) in Mexico, using geospatial models. A spatial database of 1973 outbreaks of influenza worldwide was used to develop risk models accounting for natural (natural threat), anthropic (man-made) and environmental (combination of the above) transmission. Then, a virus establishment risk model; an introduction model of influenza A developed in another study; and the three models mentioned were utilized using multi-criteria spatial evaluation supported by geographically weighted regression (GWR), receiver operating characteristic analysis and Moran’s I. The results show that environmental risk was concentrated along the Gulf and Pacific coasts, the Yucatan Peninsula and southern Baja California. The identified risk for EOITA in Mexico were: 15.6% and 4.8%, by natural and anthropic risk, respectively, while 18.5% presented simultaneous environmental, natural and anthropic risk. Overall, 28.1% of localities in Mexico presented a High/High risk for the establishment of influenza type A (area under the curve=0.923, P<0.001; GWR, r2=0.840, P<0.001; Moran’s I =0.79, P<0.001). Hence, these geospatial models were able to robustly estimate those areas susceptible to EOITA, where the results obtained show the relation between the geographical area and the different effects on health. The information obtained should help devising and directing strategies leading to efficient prevention and sound administration of both human and financial resources.

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.

The Braden Scale for Predicting the Outcome and Prognosis of Pressure Injuries in Older Inpatients: A Multicenter, Retrospective Cohort Study

2021 ◽  
Vol 33 (5) ◽  
pp. 127-135
Author(s):  
Yi-Ping Song ◽  
Man-Li Zha ◽  
Hong-Wu Shen ◽  
Yang Li ◽  
Lin Du ◽  
...  
Keyword(s):  
Respiratory Diseases ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Healing Rate ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Goodness Of Fit Test ◽  
Braden Scale ◽  
Older Inpatients ◽  
Digestive System Diseases

Introduction. The Braden scale is used to assess the risk of patients with pressure injuries (PIs), but there are limitations to the prediction of PI healing. There is a lack of tools for evaluating PI healing and outcome in clinical practice. Objective. The purpose of this study was to examine the ability of the Braden scale to predict the outcome and prognosis of PIs in older patients. Materials and Methods. Outcome indicator was the wound healing rate of patients with PIs at discharge. The receiver operating characteristic (ROC) and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the discrimination and calibration. Results. Completed data were available for 309 patients, 181 of whom (58.6%) were male. The Braden scale had poor discrimination to predict the outcome and prognosis of PIs with an area under the curve (AUC) of 0.63 (95% CI, 0.56–0.70; P = .01). Subgroup analyses showed the Braden scale had low diagnostic value for patients aged over 90 years (AUCROC = 0.56; 95% CI, 0.17–0.96; P = .738), patients with respiratory diseases (AUCROC = 0.51; 95% CI, 0.37–0.65; P = .908), and digestive system diseases (AUCROC = 0.59; 95% CI, 0.42–0.75; P = .342). The level of calibration ability by Hosmer-Lemeshow goodness-of-fit test was acceptable, defined as P >.200 (χ2 = 6.59; P = .473). In patients aged more than 90 years (χ2 = 4.88; P = .431) and female patients (χ2 = 7.03; P = .425), the Braden scale was also fitting. It was not suitable for patients with respiratory diseases (χ2 = 11.35; P = .078). Conclusions. The Braden scale had low discrimination for predicting the outcome and prognosis of PIs in older inpatients. The development of a new tool is needed to predict healing in patients with preexisting PIs.

Estimation of cardiovascular risk: a comparison between the Framingham and the SCORE model in people under 60 years of age

2008 ◽  
Vol 15 (5) ◽  
pp. 562-566 ◽  
Author(s):  
Tjarda Scheltens ◽  
W.M. Monique Verschuren ◽  
Hendriek C. Boshuizen ◽  
Arno W. Hoes ◽  
Nicolaas P. Zuithoff ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cardiovascular Risk ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Treatment Guidelines ◽  
Risk Function ◽  
Area Under The Curve ◽  
Risk Models ◽  
Discriminative Ability ◽  
Score Model

Background The Framingham Heart Study risk model has been used in the majority of cardiovascular risk management guidelines. Recently, a new model based on the SCORE system has been proposed. We compared both risk models with regard to their ability to predict cardiovascular mortality in the Netherlands. Design Cohort study. Methods In a Dutch cohort study of 39 719 persons, three properties of the risk models were investigated: discriminating ability (ranking persons in order of risks, expressed in area under the curve); calibrating ability (prediction of events compared with actual events expressed in goodness of fit); and the number of persons assigned to treatment according to the guideline. Results The discriminative ability of both models was similar: the area under the curve of Framingham was 0.86 and of SCORE 0.85. Calibration of both functions was inadequate. The goodness of fit of the SCORE model was 35 and of the Framingham model 64, whereas a goodness of fit less than 20 is considered acceptable. Using the Dutch guideline treatment threshold of 10% mortality risk, the SCORE risk function assigned 0.4% of the population to drug treatment where the Framingham function assigned 0.7%. Conclusion The findings of this study show that both the SCORE and the Framingham model function have a good discriminative ability but are insufficient in predicting absolute risks. SCORE assigned fewer participants to treatment than Framingham. If a new risk model is implemented in treatment guidelines, comparison with the model in use and evaluation of calibrating features is needed.

scholarly journals Predictive value of Score for Neonatal Acute Physiology and Perinatal Extension II for neonatal mortality in Sanglah Hospital, Denpasar, Indonesia

2017 ◽  
Vol 56 (5) ◽  
pp. 257
Author(s):  
I Gede Ketut Aryana ◽  
I Made Kardana ◽  
I Nyoman Adipura
Keyword(s):  
Intensive Care ◽  
Neonatal Mortality ◽  
Goodness Of Fit ◽  
Assessment Tool ◽  
Area Under The Curve ◽  
Severe Infection ◽  
Scoring Systems ◽  
World Health ◽  
Severe Illness ◽  
Goodness Of Fit Test

Background Neonatal mortality, which is largely caused by severe illness, is the biggest contributor to overall infant mortality. The World Health Organization (WHO) estimated that 4 million neonates die yearly worldwide, often due to severe infection and organ system immaturity. Neonates with severe illness require treatment in the neonatal intensive care unit (NICU), in which a reliable assessment tool for illness severity is needed to guide intensive care requirements and prognosis. Neonatal disease severity scoring systems have been developed, including Score for Neonatal Acute Physiology and Perinatal Extension II  (SNAPPE II), but it has never been validated in our setting.ObjectiveTo study the prognostic value of SNAPPE II as a predictor of neonatal mortality in Sanglah Hospital, Denpasar, Indonesia.Methods This prospective cohort study was conducted in the NICU of Sanglah Hospital, Denpasar from November 2014 to February 2015. All neonates, except those with congenital anomaly, were observed during the first 12 hours of admission and their outcomes upon discharge from the NICU was recorded. We assessed the SNAPPE II cut-off point to predict neonatal mortality. The calibration of SNAPPE II was done using the Hosmer-Lemeshow goodness-of-fit test, and discrimination of SNAPPE II was determined from the receiver-operator characteristic (ROC) curve and area under the curve (AUC) value calculation.ResultsDuring the period of study, 63 children were eligible, but 5 were excluded because of major congenital abnormalities. The SNAPPE II optimum cut-off point of 37 gave a high probability of mortality and the ROC showed an AUC of 0.92 (95%CI 0.85 to 0.99). The Hosmer-Lemeshow goodness-of-fit test showed a good calibration with P = 1.0Conclusion The SNAPPE II  has a good predictive ability for neonatal mortality in Sanglah Hospital, Denpasar, Indonesia.

scholarly journals Development and Validation of a 90-day Mortality Prediction Nomogram for AMI Patients: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Rui Yang ◽  
Wen Ma ◽  
Tao Huang ◽  
Lu-Ming Zhang ◽  
Di-Di Han ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Random Forest ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Mortality Prediction ◽  
Multiple Logistic Regression ◽  
Goodness Of Fit Test ◽  
Standard Curve ◽  
Mimic Iii

Abstract Background: The purpose of this study was to identify the factors influencing the 90-day mortality of acute myocardial infarction(AMI) patients, and to establish a prognostic model for these patients based on the MIMIC-III database.Methods: Retrospective study methods were used to collect AMI patient data that met the inclusion criteria from the MIMIC-III database. Variable importance selection was determined using the random forest algorithm. Multiple logistic regression was used to determine AMI-related risk factors, with the results represented as a nomogram.Results: The baseline scores for the training and validation groups were very flat, and indicators for developing risk-model nomograms were obtained after random forest and multiple logistic regression. The AUC of the risk model was the highest (0.826 and 0.818 in the training and validation groups, respectively) . The Hosmer-Lemeshow goodness-of-fit test and standard curve both produced very consistent results. Both the NRI and IDI values indicated that the risk model had significant predictive power, and DCA results indicated that the risk model had good net benefits for clinical application.Conclusions: The results of this study indicated that age, troponinT, VT, VFI, MI_his, APS-III, bypass, and PCI were risk factors for 90-day mortality in AMI patients. Interactive nomograms could provide intuitive and concise personalized 90-day mortality predictions for AMI patients.

scholarly journals Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rahmet Guner ◽  
Bircan Kayaaslan ◽  
Imran Hasanoglu ◽  
Adalet Aypak ◽  
Hurrem Bodur ◽  
...  
Keyword(s):  
Intensive Care ◽  
Goodness Of Fit ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Severe Illness ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Logistic Regression Models ◽  
Predictive Parameters

Abstract Background Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. Methods Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer–Lemeshow Goodness-of-fit test, and calibration curve analysis. Results Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902–0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899–0.947). Hosmer–Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). Conclusion We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.

scholarly journals New PELOD-2 cut-off score for predicting death in children with sepsis

2021 ◽  
Vol 61 (1) ◽  
pp. 39-45
Author(s):  
Ni Made Rini Suari ◽  
Abdul Latief ◽  
Antonius H. Pudjiadi
Keyword(s):  
Organ Dysfunction ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Organ Damage ◽  
Goodness Of Fit Test ◽  
Logistic Organ Dysfunction ◽  
Life Threatening ◽  
Definition Of ◽  
Pediatric Sepsis

Background According to the most recent Sepsis-3 Consensus, the definition of sepsis is life-threatening organ dysfunction caused by dysregulated immune system against infection. Currently, one of the most commonly used prognostic scoring system is pediatric logistic organ damage-2 (PELOD-2) score. Objective To determine and validate the pediatric logistic organ dysfunction-2 (PELOD-2) cut-off score to predict mortality in pediatric sepsis patients. Methods A prospective cohort study was conducted in the intensive care units of Cipto Mangunkusumo Hospital, Jakarta. We assessed subjects with PELOD-2 and calculated the predicted death rate (PDR) using SFAR software. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate calibration and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) to estimate discrimination. Results Of 2,735 children admitted to the emergency department, 52 met the inclusion criteria. Patients had various types of organ dysfunction: 53.8% respiratory, 28.8% neurological, 15.4% cardiovascular, 1.9% hematological. The mortality rate in this study was 38.5%. Mean PELOD-2 score was higher in patients who died than in those who survived [13.9 (SD 4.564) vs. 7.59 (SD 3.025), respectively, P=0.000]. The discrimination of PELOD-2 score with the lactate component had an AUC of 85.5% (95%CI 74.5 to 96.5), while PELOD-2 without lactate had an AUC of 85.4% (95%CI 74.5 to 96.3%). We propose a new PELOD-2 cut-off score to predict organ dysfunction and death of 10, with 75% sensitivity, 72% specificity, 62.5% PPV, and 82% NPV. PELOD-2 score > 10 had a moderate, statistically significant correlation to mortality (r=0.599; P<0.001). Conclusion A PELOD-2 score > 10 is valid for predicting life-threatening organ dysfunction in pediatric patients with sepsis.

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