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At least 19 recordsLinked to original sources

PubMed2002

A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study.

BACKGROUND: A number of prediction models have been developed in an attempt to accurately identify patients at increased risk of a first coronary heart disease event. We sought to determine the ten-year incidence of coronary heart disease events in a healthy cohort with measurable risk factors, and to compare these results with the predicted number of events by use of both the Framingham and European Society of Cardiology risk prediction models. METHODS: We compared the predicted and observed number of events in 5 risk categories in 1393 subjects aged 30 to 74 years who were enrolled in the Normative Aging Study. RESULTS: The risk prediction models reliably stratify populations with regards to relative risk of coronary heart disease events and there is reasonable agreement between the 2 models (weighted kappa = 0.46, P <.01). The Framingham model underestimated the absolute risk of coronary heart disease events in the low-risk group, and both risk prediction models overestimated the absolute risk of events in the high- or very-high-risk groups (Framingham c-statistic = 0.60, European Society of Cardiology c-statistic = 0.58). CONCLUSIONS: Despite simplification, the accuracy of the European model was not significantly different from the Framingham model. But the accuracy of absolute risk prediction, particularly at the extremes of risk, is imperfect. Refinement and validation of these risk prediction models is important because they affect the management of individual patients and the allocation of community resources.

Adult↗

PubMed2006

Prospective breast cancer risk prediction model for women undergoing screening mammography.

BACKGROUND: Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography. METHODS: There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided. RESULTS: Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women. CONCLUSION: Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.

Adult↗

PubMed2006

Deficiencies of cardiovascular risk prediction models for type 1 diabetes.

OBJECTIVE: Cardiovascular risk prediction models are available for the general population (Framingham) and for type 2 diabetes (U.K. Prospective Diabetes Study [UKPDS] Risk Engine) but may not be appropriate in type 1 diabetes, as risk factors including younger age at diabetes onset and presence of diabetes complications are not considered. Therefore, our objective was to examine the accuracy of Framingham and UKPDS models for predicting coronary heart disease (CHD) in a type 1 diabetic cohort. RESEARCH DESIGN AND METHODS: Ten-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications (EDC) study, a prospective cohort study of 658 subjects with childhood-onset type 1 diabetes diagnosed between 1950 and 1980 first seen in 1986-1988, were analyzed. EDC study data were used to calculate the 10-year probability of CHD (fatal CHD, nonfatal myocardial infarction, or Q-waves) applying to the Framingham and UKPDS equations. RESULTS: Mean age at CHD onset was 39 years. When fatal/nonfatal myocardial infarction and CHD death were modeled, both the UKPDS and Framingham models showed significant lack of calibration (P < 0.0001) but moderate discrimination (0.76 UKPDS, 0.77 Framingham men, and 0.88 Framingham women). Both the UKPDS and Framingham models underestimated probability of events in highest risk deciles. CONCLUSIONS: Currently available CHD models poorly predict events in type 1 diabetes. Future research should focus on determining the risk factors accounting for the lack of fit and developing prediction models specific to this high-risk group.

Adult↗

PubMed2006

The effect of including C-reactive protein in cardiovascular risk prediction models for women.

BACKGROUND: While high-sensitivity C-reactive protein (hsCRP) is an independent predictor of cardiovascular risk, global risk prediction models incorporating hsCRP have not been developed for clinical use. OBJECTIVE: To develop and compare global cardiovascular risk prediction models with and without hsCRP. DESIGN: Observational cohort study. SETTING: U.S. female health professionals. PARTICIPANTS: Initially healthy nondiabetic women age 45 years and older participating in the Women's Health Study and followed an average of 10 years. MEASUREMENTS: Incident cardiovascular events (myocardial infarction, stroke, coronary revascularization, and cardiovascular death). RESULTS: High-sensitivity CRP made a relative contribution to global risk at least as large as that provided by total, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol individually, but less than that provided by age, smoking, and blood pressure. All global measures of fit improved when hsCRP was included, with likelihood-based measures demonstrating strong preference for models that include hsCRP. With use of 10-year risk categories of 0% to less than 5%, 5% to less than 10%, 10% to less than 20%, and 20% or greater, risk prediction was more accurate in models that included hsCRP, particularly for risk between 5% and 20%. Among women initially classified with risks of 5% to less than 10% and 10% to less than 20% according to the Adult Treatment Panel III covariables, 21% and 19%, respectively, were reclassified into more accurate risk categories. Although addition of hsCRP had minimal effect on the c-statistic (a measure of model discrimination) once age, smoking, and blood pressure were accounted for, the effect was nonetheless greater than that of total, LDL, or HDL cholesterol, suggesting that the c-statistic may be insensitive in evaluating risk prediction models. LIMITATIONS: Data were available only for women. CONCLUSIONS: A global risk prediction model that includes hsCRP improves cardiovascular risk classification in women, particularly among those with a 10-year risk of 5% to 20%. In models that include age, blood pressure, and smoking status, hsCRP improves prediction at least as much as do lipid measures.

Age Factors↗

PubMed2026

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75 161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et al., Nanda et al., Naylor et al., and Van Leeuwen et al., each showing fair discrimination. The Teede et al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et al. and van Leeuwen et al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans↗

PubMed1999

How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts.

BACKGROUND: Previous models used to predict individual risk of death from coronary heart disease (CHD) were developed from data of 3 decades ago from the Framingham Heart Study. CHD mortality rates have declined markedly since that period as a result of improvement in both risk factor status and medical interventions. Generalization of the results from this one study to the population at large remains a matter of concern. We compared predictive functions derived from the major risk factors for CHD from Framingham and 2 more recent national cohorts, the First and Second National Health and Nutrition Examination Survey (NHANES I and NHANES II). METHODS AND RESULTS: The participants included 1846 men and 2323 women 35 to 69 years of age and free of CHD at the fourth examination (1954 to 1958) from the Framingham Study; 2753 men and 3858 women from the NHANES I (1971 to 1975); and 2655 men and 3050 women from NHANES II (1976 to 1980). The 3 cohorts were monitored for 24, 20, and 15 years, respectively. Significant heterogeneity existed among studies in the magnitude of the Cox coefficients for the individual factors (ie, age, systolic blood pressure, serum total cholesterol, and smoking status), especially among men. When risk factors were considered collectively, however, functions derived from and applied to different cohorts had a similar ability to rank individual risk. The areas under the receiver operating characteristic curves were 0. 71 to 0.76 in men and 0.76 to 0.81 in women when different risk functions were applied to their own population or to a second population. The cumulative CHD survival observed in women in the 2 national cohorts was close to what was predicted from the Framingham equation. However, Framingham overestimated the cumulative CHD mortality rates in men in NHANES I and NHANES II. CONCLUSIONS: The Framingham risk model for the prediction of CHD mortality rates provides a reasonable rank ordering of risk for individuals in the US white population for the period 1975 to 1990. However, prediction of absolute risk is less accurate.

Adolescent↗

PubMed2002

Recent innovations in intensive care unit risk-prediction models.

During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.

Artificial Intelligence↗

PubMed2000

The cardiovascular event reduction tool (CERT)--a simplified cardiac risk prediction model developed from the West of Scotland Coronary Prevention Study (WOSCOPS).

The clinical decision to treat hypercholesterolemia is premised on an awareness of patient risk, and cardiac risk prediction models offer a practical means of determining such risk. However, these models are based on observational cohorts where estimates of the treatment benefit are largely inferred. The West of Scotland Coronary Prevention Study (WOSCOPS) provides an opportunity to develop a risk-benefit prediction model from the actual observed primary event reduction seen in the trial. Five-year Cox model risk estimates were derived from all WOSCOPS subjects (n = 6,595 men, aged 45 to 64 years old at baseline) using factors previously shown to be predictive of definite fatal coronary heart disease or nonfatal myocardial infarction. Model risk factors included age, diastolic blood pressure, total cholesterol/ high-density lipoprotein ratio (TC/HDL), current smoking, diabetes, family history of fatal coronary heart disease, nitrate use or angina, and treatment (placebo/ 40-mg pravastatin). All risk factors were expressed as categorical variables to facilitate risk assessment. Risk estimates were incorporated into a simple, hand-held slide rule or risk tool. Risk estimates were identified for 5-year age bands (45 to 65 years), 4 categories of TC/HDL ratio (<5.5, 5.5 to <6.5, 6.5 to <7.5, > or = 7.5), 2 levels of diastolic blood pressure (<90, > or = 90 mm Hg), from 0 to 3 additional risk factors (current smoking, diabetes, family history of premature fatal coronary heart disease, nitrate use or angina), and pravastatin treatment. Five-year risk estimates ranged from 2% in very low-risk subjects to 61% in the very high-risk subjects. Risk reduction due to pravastatin treatment averaged 31%. Thus, the Cardiovascular Event Reduction Tool (CERT) is a risk prediction model derived from the WOSCOPS trial. Its use will help physicians identify patients who will benefit from cholesterol reduction.

Age Factors↗

PubMed1996

Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good sensitivity, specificity and predictive value, and pay attention to environmental or individual confounding factors and effect modifiers. Before such data are available, the ISO 5349 model should not be used for risk prediction. It can serve, however, as an incentive for manufacturers to produce tools that vibrate less, and for employers to implement practical measures in order to reduce the total and dose of effective exposure.

Fingers↗

PubMed2006

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

OBJECTIVE: To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. DESIGN: Prospective cohort study. SETTING: A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. PATIENTS: A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. CONCLUSIONS: Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.

APACHE↗

PubMed2003

Impact of cardiovascular risk factors in an urban sample of Portuguese adults according to the Framingham risk prediction models.

OBJECTIVES: To estimate the 10-year risk of coronary heart disease (CHD) according to the Framingham risk prediction models and to assess the impact of major risk factors in an urban sample of Portuguese adults. METHODS: We surveyed 938 non-institutionalized adults aged 40 to 74 years, recruited by random digit dialing. A fasting venous blood sample was collected to measure glucose, total cholesterol and high-density-lipoprotein (HDL) cholesterol. Blood pressure was measured and a resting 12-lead ECG registered. Sixty-eight individuals (7.2%) were excluded due to prevalent CHD, according to self-reported information and/or ECG. Data on the remaining 532 women and 338 men at risk were used for analysis. For each participant we calculated the 10-year risk using the Framingham risk tables. RESULTS: In this community-based sample, the expected 10-year cumulative incidence was estimated to be on average 19.3% for men and 9.5% for women. The factor with greatest impact was hypertension. Had this factor been altered such that individuals whose blood pressure was > or = 140/90 mmHg instead had high-normal blood pressure (130-139/85-89 mmHg), the risk of CHD would have been reduced to 15.7% in men and 7.3% in women. Thus, 18.6% and 23.2% of cases would have been avoided, respectively. The number of individuals in whom hypertension should be prevented to avoid one case of CHD was 15 among men and 25 among women. The impact of other risk factors expressed as a proportion of attributable cases was, in men and women respectively, 5.7% and 7.4% if none had diabetes; 12.4% and 3.2% if none smoked; 23.8% and 14.7% if total cholesterol were between 160 and 199 mg/dl instead of over 200 mg/dl; and 10.9% and 5.3% if HDL were above 45 mg/dl. If all participants did not smoke, did not have diabetes, had total cholesterol between 160 and 199 instead of over 200 mg/dl, HDL over 45 mg/dl and high-normal blood pressure instead of hypertension, the expected cumulative incidence would be 8.1% for men and 5.1% for women. This means that respectively 58.0% and 46.3% of cases would have been avoided. CONCLUSION: Blood pressure control in this population is the intervention with greatest impact on CHD prevention. Given the tendency for risk factors to cluster and to interact, we can expect to achieve even greater success with multifactorial interventions.

Adult↗

PubMed1992

University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models.

The purpose of this analysis is to compare three different statistical models for predicting children likely to be at risk of developing dental caries over a 3-yr period. Data are based on 4117 children who participated in the University of North Carolina Caries Risk Assessment Study, a longitudinal study conducted in the Aiken, South Carolina, and Portland, Maine areas. The three models differed with respect to either the types of variables included or the definition of disease outcome. The two "Prediction" models included both risk factor variables thought to cause dental caries and indicator variables that are associated with dental caries, but are not thought to be causal for the disease. The "Etiologic" model included only etiologic factors as variables. A dichotomous outcome measure--none or any 3-yr increment, was used in the "Any Risk Etiologic model" and the "Any Risk Prediction Model". Another outcome, based on a gradient measure of disease, was used in the "High Risk Prediction Model". The variables that are significant in these models vary across grades and sites, but are more consistent among the Etiologic model than the Predictor models. However, among the three sets of models, the Any Risk Prediction Models have the highest sensitivity and positive predictive values, whereas the High Risk Prediction Models have the highest specificity and negative predictive values. Considerations in determining model preference are discussed.

Child↗

PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗

PubMed2005

Discrimination and calibration of mortality risk prediction models in interventional cardiology.

OBJECTIVES: Using a local percutaneous coronary intervention (PCI) data repository, we sought to compare the performance of a number of local and well-known mortality models with respect to discrimination and calibration. BACKGROUND: Accurate risk prediction is important for a number of reasons including physician decision support, quality of care assessment, and patient education. Current evidence on the value of applying PCI risk models to individual cases drawn from a different population is controversial. METHODS: Data were collected from January 01, 2002 to September 30, 2004 on 5216 consecutive percutaneous coronary interventions at Brigham and Women's Hospital (Boston, MA). Logistic regression was used to create a local risk model for in-hospital mortality in these procedures, and a number of statistical methods were used to compare the discrimination and calibration of this new and old local risk models, as well as the Northern New England Cooperative Group, New York State (1992 and 1997), University of Michigan consortium, American College of Cardiology-National Cardiovascular Data Registry, and The Cleveland Clinic Foundation risk prediction models. Areas under the ROC (AUC) curves were used to evaluate discrimination, and the Hosmer-Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases. RESULTS: Multivariate risk factors included in the newly constructed local model were: age, prior intervention, diabetes, unstable angina, salvage versus elective procedure, cardiogenic shock, acute myocardial infarction (AMI), and left anterior descending artery intervention. The area under the ROC curve (AUC) was 0.929 (SE=0.017), and the p value for the Hosmer-Lemeshow (HL) goodness-of-fit was 0.473. This indicates good discrimination and calibration. Bootstrap re-sampling indicated AUC stability. Evaluation of the external models showed an AUC range from 0.82 to 0.90 indicating good discrimination across all models, but poor calibration (HL p value < or = 0.0001). CONCLUSIONS: Validation of AUC values across all models suggests that certain risk factors have remained important over the last decade. However, the lack of calibration suggests that small changes in patient populations and data collection methods quickly reduce the accuracy of patient level estimations over time. Possible solutions to this problem involve either recalibration of models using local data or development of new local models.

Angioplasty, Balloon, Coronary↗

PubMed2006

Risk prediction models for familial breast cancer.

A positive family history of breast cancer, reflecting genetic susceptibility, is one of the strongest risk factors for the disease. A number of breast cancer susceptibility genes have been identified to date, with the most important being BRCA1 and BRCA2. Risk prediction models can be used to identify individuals likely to carry BRCA1 and BRCA2 mutations and individuals at high risk of developing the disease. This information can then be used to target genetic testing, screening and interventions more effectively. In this article, the authors review the risk models that have been developed for familial breast cancer and discuss their applicability, strengths and weaknesses, and present examples of classifying women into risk categories according to the predictions by the various models. The review concludes with a discussion of the ways in which risk models could be improved in the immediate- and long-term future.

BRCA2 Protein↗

PubMed2001

Identification of patients at high cardiovascular risk: a critical appraisal of applicability of statistical risk prediction models.

Assessment of cardiovascular risk is widely proposed as a basis for taking management decisions about patients presenting with hypertension or hypercholesterolemia. Our aim was to critically assess the use of risk equations derived from epidemiological studies for the purpose of identifying high-risk patients. Risk equations were retrieved from the MEDLINE database and then applied to a data set of 118 patients. This data set was an evaluation study of the clinical value of the World Health Organization 1993 hypertension guidelines for the decision to treat mild hypertensive patients. We calculated agreement: 1) between equations and 2) between equations and the decision to treat taken by the physician. Most models were not applicable to our population, mainly because the original population had a narrow age range or comprised only males. Between-model agreement was better for the lower and upper risk quintiles than for the three other risk quintiles (0.58, 0.33, 0.34, 0.45, 0.70, from the lower to the upper risk quintile). When using an arbitrary threshold for defining high-risk patients (i.e. > 2% per year), we observed a huge variation of the proportion of patients classified at high risk (from 0 to 17%). There was a poor agreement between risk models and the decision to treat taken by the physician. These results suggest that risk-based guidelines should be validated before their diffusion.

Antihypertensive Agents↗

PubMed2000

A comparison of neonatal mortality risk prediction models in very low birth weight infants.

BACKGROUND: Risk-adjusted severity of illness is frequently used in clinical research and quality assessments. Although there are multiple methods designed for neonates, they have been infrequently compared and some have not been assessed in large samples of very low birth weight (VLBW; <1500 g) infants. OBJECTIVES: To test and compare published neonatal mortality prediction models, including Clinical Risk Index for Babies (CRIB), Score for Neonatal Acute Physiology (SNAP), SNAP-Perinatal Extension (SNAP-PE), Neonatal Therapeutic Interventions Scoring System, the National Institute of Child Health and Human Development (NICHD) network model, and other individual admission factors such as birth weight, low Apgar score (<7 at 5 minutes), and small for gestational age status in a cohort of VLBW infants from the Washington, DC area. METHODS: Data were collected on 476 VLBW infants admitted to 8 neonatal intensive care units between October 1994 and February 1997. The calibration (closeness of total observed deaths to the predicted total) of models with published coefficients (SNAP-PE, CRIB, and NICHD) was assessed using the standardized mortality ratio. Discrimination was quantified as the area under the curve (AUC) for the receiver operating characteristic curves. Calibrated models were derived for the current database using logistic regression techniques. Goodness-of-fit of predicted to observed probabilities of death was assessed with the Hosmer-Lemeshow goodness-of-fit test. RESULTS: The calibration of published algorithms applied to our data was poor. The standardized mortality ratios for the NICHD, CRIB, and SNAP-PE models were.65,.56, and.82, respectively. Discrimination of all the models was excellent (range:.863-.930). Surprisingly, birth weight performed much better than in previous analyses, with an AUC of.869. The best models using both 12- and 24-hour postadmission data, significantly outperformed the best model based on birth data only but were not significantly different from each other. The variables in the best model were birth weight, birth weight squared, low 5-minute Apgar score, and SNAP (AUC =.930). CONCLUSION: Published models for severity of illness overpredicted hospital mortality in this set of VLBW infants, indicating a need for frequent recalibration. Discrimination for these severity of illness scores remains excellent. Birth variables should be reevaluated as a method to control for severity of illness in predicting mortality.

Female↗

PubMed2005

Cancer risk prediction models: a workshop on development, evaluation, and application.

Cancer researchers, clinicians, and the public are increasingly interested in statistical models designed to predict the occurrence of cancer. As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that they are appropriately applied, correctly developed, and rigorously evaluated. On May 20-21, 2004, the National Cancer Institute sponsored a workshop in which experts identified strengths and limitations of cancer and genetic susceptibility prediction models that were currently in use and under development and explored methodologic issues related to their development, evaluation, and validation. Participants also identified research priorities and resources in the areas of 1) revising existing breast cancer risk assessment models and developing new models, 2) encouraging the development of new risk models, 3) obtaining data to develop more accurate risk models, 4) supporting validation mechanisms and resources, 5) strengthening model development efforts and encouraging coordination, and 6) promoting effective cancer risk communication and decision-making.

Breast Neoplasms↗