Gary S. Collins

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

In medicine, numerous decisions are made by care providers, often in shared decision making, on the basis of an estimated probability that a specific disease or condition is present (diagnostic setting) or a specific event will occur in the future (prognostic setting) in an individual. In the diagnostic setting, the probability that a particular disease is present can be used, for example, to inform the referral of patients for further testing, to initiate treatment directly, or to reassure patients that a serious cause for their symptoms is unlikely. In the prognostic context, predictions can be used for planning lifestyle or therapeutic decisions on the basis of the risk for developing a particular outcome or state of health within a specific period (13). Such estimates of risk can also be used to risk-stratify participants in therapeutic intervention trials (47). In both the diagnostic and prognostic setting, probability estimates are commonly based on combining information from multiple predictors observed or measured from an individual (1, 2, 810). Information from a single predictor is often insufficient to provide reliable estimates of diagnostic or prognostic probabilities or risks (8, 11). In virtually all medical domains, diagnostic and prognostic multivariable (risk) prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making. A multivariable prediction model is a mathematical equation that relates multiple predictors for a particular individual to the probability of or risk for the presence (diagnosis) or future occurrence (prognosis) of a particular outcome (10, 12). Other names for a prediction model include risk prediction model, predictive model, prognostic (or prediction) index or rule, and risk score (9). Predictors are also referred to as covariates, risk indicators, prognostic factors, determinants, test results, ormore statisticallyindependent variables. They may range from demographic characteristics (for example, age and sex), medical historytaking, and physical examination results to results from imaging, electrophysiology, blood and urine measurements, pathologic examinations, and disease stages or characteristics, or results from genomics, proteomics, transcriptomics, pharmacogenomics, metabolomics, and other new biological measurement platforms that continuously emerge. Diagnostic and Prognostic Prediction Models Multivariable prediction models fall into 2 broad categories: diagnostic and prognostic prediction models (Box A). In a diagnostic model, multiplethat is, 2 or morepredictors (often referred to as diagnostic test results) are combined to estimate the probability that a certain condition or disease is present (or absent) at the moment of prediction (Box B). They are developed from and to be used for individuals suspected of having that condition. Box A. Schematic representation of diagnostic and prognostic prediction modeling studies. The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present (or absent) within an individual, at this point in timethat is, the moment of prediction (T= 0). In prognosis, the prediction is about whether an individual will experience a specific event or outcome within a certain time period. In other words, in diagnostic prediction the interest is in principle a cross-sectional relationship, whereas prognostic prediction involves a longitudinal relationship. Nevertheless, in diagnostic modeling studies, for logistical reasons, a time window between predictor (index test) measurement and the reference standard is often necessary. Ideally, this interval should be as short as possible without starting any treatment within this period. Box B. Similarities and differences between diagnostic and prognostic prediction models. In a prognostic model, multiple predictors are combined to estimate the probability of a particular outcome or event (for example, mortality, disease recurrence, complication, or therapy response) occurring in a certain period in the future. This period may range from hours (for example, predicting postoperative complications [13]) to weeks or months (for example, predicting 30-day mortality after cardiac surgery [14]) or years (for example, predicting the 5-year risk for developing type 2 diabetes [15]). Prognostic models are developed and are to be used in individuals at risk for developing that outcome. They may be models for either ill or healthy individuals. For example, prognostic models include models to predict recurrence, complications, or death in a certain period after being diagnosed with a particular disease. But they may also include models for predicting the occurrence of an outcome in a certain period in individuals without a specific disease: for example, models to predict the risk for developing type 2 diabetes (16) or cardiovascular events in middle-aged nondiseased individuals (17), or the risk for preeclampsia in pregnant women (18). We thus use prognostic in the broad sense, referring to the prediction of an outcome in the future in individuals at risk for that outcome, rather than the narrower definition of predicting the course of patients who have a particular disease with or without treatment (1). The main difference between a diagnostic and prognostic prediction model is the concept of time. Diagnostic modeling studies are usually cross-sectional, whereas prognostic modeling studies are usually longitudinal. In this document, we refer to both diagnostic and prognostic prediction models as prediction models, highlighting issues that are specific to either type of model. Development, Validation, and Updating of Prediction Models Prediction model studies may address the development of a new prediction model (10), a model evaluation (often referred to as model validation) with or without updating of the model [1921]), or a combination of these (Box C and Figure 1). Box C. Types of prediction model studies. Figure 1. Types of prediction model studies covered by the TRIPOD statement. D = development data; V = validation data. Model development studies aim to derive a prediction model by selecting predictors and combining them into a multivariable model. Logistic regression is commonly used for cross-sectional (diagnostic) and short-term (for example 30-day mortality) prognostic outcomes and Cox regression for long-term (for example, 10-year risk) prognostic outcomes. Studies may also focus on quantifying the incremental or added predictive value of a specific predictor (for example, newly discovered) (22) to a prediction model. Quantifying the predictive ability of a model on the same data from which the model was developed (often referred to as apparent performance [Figure 1]) will tend to give an optimistic estimate of performance, owing to overfitting (too few outcome events relative to the number of candidate predictors) and the use of predictor selection strategies (2325). Studies developing new prediction models should therefore always include some form of internal validation to quantify any optimism in the predictive performance (for example, calibration and discrimination) of the developed model and adjust the model for overfitting. Internal validation techniques use only the original study sample and include such methods as bootstrapping or cross-validation. Internal validation is a necessary part of model development (2). After developing a prediction model, it is strongly recommended to evaluate the performance of the model in other participant data than was used for the model development. External validation (Box C and Figure 1) (20, 26) requires that for each individual in the new participant data set, outcome predictions are made using the original model (that is, the published model or regression formula) and compared with the observed outcomes. External validation may use participant data collected by the same investigators, typically using the same predictor and outcome definitions and measurements, but sampled from a later period (temporal or narrow validation); by other investigators in another hospital or country (though disappointingly rare [27]), sometimes using different definitions and measurements (geographic or broad validation); in similar participants, but from an intentionally different setting (for example, a model developed in secondary care and assessed in similar participants, but selected from primary care); or even in other types of participants (for example, model developed in adults and assessed in children, or developed for predicting fatal events and assessed for predicting nonfatal events) (19, 20, 26, 2830). In case of poor performance (for example, systematic miscalibration), when evaluated in an external validation data set, the model can be updated or adjusted (for example, recalibrating or adding a new predictor) on the basis of the validation data set (Box C) (2, 20, 21, 31). Randomly splitting a single data set into model development and model validation data sets is frequently done to develop and validate a prediction model; this is often, yet erroneously, believed to be a form of external validation. However, this approach is a weak and inefficient form of internal validation, because not all available data are used to develop the model (23, 32). If the available development data set is sufficiently large, splitting by time and developing a model using data from one period and evaluating its performance using the data from the other period (temporal validation) is a stronger approach. With a single data set, temporal splitting and model validation can be considered intermediate between internal and external validation. Incomplete and Inaccurate Reporting Prediction models are becoming increasingly abundant in the medical literature (9, 33, 34), and policymakers are incre

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

CONTEXT Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. OBJECTIVE The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. EVIDENCE ACQUISITION This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. EVIDENCE SYNTHESIS The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. CONCLUSIONS To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PATIENT SUMMARY The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes.

Selection of single blastocysts for fresh transfer via standard morphology assessment alone and with array CGH for good prognosis IVF patients: results from a randomized pilot study

BackgroundSingle embryo transfer (SET) remains underutilized as a strategy to reduce multiple gestation risk in IVF, and its overall lower pregnancy rate underscores the need for improved techniques to select one embryo for fresh transfer. This study explored use of comprehensive chromosomal screening by array CGH (aCGH) to provide this advantage and improve pregnancy rate from SET.MethodsFirst-time IVF patients with a good prognosis (age <35, no prior miscarriage) and normal karyotype seeking elective SET were prospectively randomized into two groups: In Group A, embryos were selected on the basis of morphology and comprehensive chromosomal screening via aCGH (from d5 trophectoderm biopsy) while Group B embryos were assessed by morphology only. All patients had a single fresh blastocyst transferred on d6. Laboratory parameters and clinical pregnancy rates were compared between the two groups.ResultsFor patients in Group A (n = 55), 425 blastocysts were biopsied and analyzed via aCGH (7.7 blastocysts/patient). Aneuploidy was detected in 191/425 (44.9%) of blastocysts in this group. For patients in Group B (n = 48), 389 blastocysts were microscopically examined (8.1 blastocysts/patient). Clinical pregnancy rate was significantly higher in the morphology + aCGH group compared to the morphology-only group (70.9 and 45.8%, respectively; p = 0.017); ongoing pregnancy rate for Groups A and B were 69.1 vs. 41.7%, respectively (p = 0.009). There were no twin pregnancies.ConclusionAlthough aCGH followed by frozen embryo transfer has been used to screen at risk embryos (e.g., known parental chromosomal translocation or history of recurrent pregnancy loss), this is the first description of aCGH fully integrated with a clinical IVF program to select single blastocysts for fresh SET in good prognosis patients. The observed aneuploidy rate (44.9%) among biopsied blastocysts highlights the inherent imprecision of SET when conventional morphology is used alone. Embryos randomized to the aCGH group implanted with greater efficiency, resulted in clinical pregnancy more often, and yielded a lower miscarriage rate than those selected without aCGH. Additional studies are needed to verify our pilot data and confirm a role for on-site, rapid aCGH for IVF patients contemplating fresh SET.

Body Weight Change, All-Cause Mortality, and Cause-specific Mortality in the Multiple Risk Factor Intervention Trial

Weight loss and regain is a common pattern in modern society. Weight gain occurs frequently, as shown by the prevalence of obesity, which is at an all-time high in the United States [1, 2]. Weight loss is common, as suggested by the rates of dieting. It has been estimated that approximately 50% of women and 27% of men are dieting at any given time [3]. The rates are even higher among young women [4], and several recent case reports have described infants who failed to thrive because they were placed on diets to prevent obesity [5]. Dieting and weight loss are not confined to obese persons. Approximately 25% of adult American women are clinically overweight, but twice as many are dieting [13]. Because few diets are successful [6], weight loss, weight regain, and repeated weight fluctuation (weight cycling or yo-yo dieting) occur in many people. If weight variability is associated with negative health outcomes, the public health impact could be substantial because of the number of people affected. The few studies to examine weight variability report consistent associations between weight change and negative health outcomes [79]. Weight variability was not associated with death, however, in the Baltimore Longitudinal Study of Aging [10]. One weakness of earlier studies has been the infrequent measurement of weight. In the Framingham Heart Study, for example, weights were measured every 2 years. Our study was designed to test the hypothesis that weight change is associated with an increased risk for all-cause and cause-specific mortality in a group of men participating in a longitudinal study for whom frequent weights were available. Data from the Multiple Risk Factor Intervention Trial (MRFIT) permitted a more sensitive measure of weight variability than had been available in previous studies. Methods Study Sample The MRFIT was a randomized, multicenter, primary prevention trial designed to test whether intensive intervention would result in decreased mortality rates from coronary heart disease. Men 35 to 57 years old were screened from 1973 to 1976 at 22 clinical centers in the United States. A total of 361 662 men were screened, and 12 866 who were in the upper 10% to 15% of risk for coronary heart disease (but without clinical evidence of coronary heart disease) were selected for the trial. Men who weighed more than 1.5 times their ideal weight were excluded. Participants were assigned randomly to either a special intervention (SI) or usual care (UC) group. The protocols for selection, randomization, and intervention have been described previously [11, 12]. Of those men who were alive at the seventh anniversary of their randomization date (SI group, 6164; UC group, 6171), further exclusions were made if a study participant missed both his sixth and seventh annual visits (SI group, 432; UC group, 526), if during the trial he had been diagnosed with present or suspected cancer (SI group, 287; UC group, 328), if he had three or fewer recorded weights (SI, 67; UC, 78), or if an error was suspected in a recorded weight (exclusions were based on a recorded weight of 1.96 standard deviations greater or less than the average weight calculated from all other visits and on a recorded weight 15% above or below the weights of the preceding and following weights) (28 men in the SI group and 60 in the UC group). After these exclusions, 10 529 men (5350 in the SI group and 5179 in the UC group) remained for analysis. Design This study was designed to provide a chronologic separation of body weight measurement and disease end points. Measured body weights were used for the 6 to 7 years that men were in the intervention phase of the trial. Men in the UC group had weights measured at annual visits, and men in the SI group were weighed every 4 months. The analysis included deaths that occurred after a seventh annual visit if a participant had attended that visit; otherwise, deaths occurring after the seventh anniversary of randomization were used. Follow-up continued through 31 December 1985 (average, 3.8 years). This procedure separated the temporal sequence of weight change and death. Because disease could cause weight to change and because weight change and disease could be coincidental, this separation was used to avoid confounding created by a causal link between disease and weight change. For this reason, we excluded men who were diagnosed as having cancer. Weights were measured each time the participant attended a regularly scheduled clinic visit. Procedures for the visits, including methods for measuring weight and ascertaining levels of risk factors, have been described in detail previously [13, 14]. Weight change during the intervention phase of the trial was measured in two ways. The first was the participants standard deviation of the weight measurements taken at each visit, called the intrapersonal standard deviation of weight (ISD). The ISD is a continuous measure of general weight variability, which increases as the differences in weights across all visits increase. For example, a participant whose weight was 90 kg at the first visit and whose weight declined by 2 kg at each of the next six visits (so that at his last visit his weight was 80 kg) would have an ISD of 3.74. If this participant lost 10 kg between the first and second visits and subsequently gained 2 kg at each of the next five visits, his ISD would be the same. This example shows that the ISD reflects magnitude of variability, not the type of weight change. For this reason, a second method was developed to measure the type of weight change within one of five categories: 1) No change was defined as a change of less than 5% from baseline at all visits; 2) steady loss, as a loss 5% of a previous weight that was not regained; 3) steady gain, as a gain 5% of a previous weight that was not subsequently lost; 4) cycle with last change a loss, as a loss 5% of weight that previously had been gained; and 5) cycle with last change a gain, as a gain of 5% or more of weight that previously had been lost. In both cycle categories, multiple cycles were possible; however, multiple cycles were difficult to detect when only 6 or 7 annual measurements were observed. Many more weight measurements were available for men in the SI group because these participants were invited to attend the clinic (and have their weights measured) at 4-month intervals throughout the trial. To compare results using more frequent measures of weight, some analyses were repeated only for men in the SI group using the weights from all visits to calculate ISD of weight and to note the type of weight change. Ascertainment of Death Ascertainment of death was achieved through the U.S. Social Security Administration and from the National Death Index of the National Center for Health Statistics. Death certificates were obtained and coded for underlying cause of death by two trained nosologists with the use of the International Classification of Diseases, Ninth Revision (ICD-9); differences were adjudicated by a third nosologist. Among the 10 529 men included in this analysis, all-cause mortality resulted in 380 deaths, and cardiovascular disease resulted in 228 deaths (ICD-9 390-459). Statistical Analysis The association between weight variability and death was evaluated using Cox proportional-hazards models, adjusting for baseline covariates that were associated with variability in weight [15]. The baseline covariates included age, race, study group, physical activity, body mass index (BMI), diastolic blood pressure, number of cigarettes smoked per day, serum cholesterol level, reported number of alcoholic drinks per week, and use of antihypertensive medications. We also evaluated other models in which change in weight and change in other risk factors (blood pressure, serum cholesterol, cigarettes per day, physical activity, alcohol intake, and antihypertensive drugs) over the intervention phase of the study were added. These findings did not materially change the results, and only data from the former models are presented here. To reduce further the possibility that any association between weight variability and death could be explained by pre-existing disease, analyses were done with the exclusion of men who experienced nonfatal events during the trial. Because results were unchanged compared with analyses using all men, only the results from the latter analyses are reported. (Nonfatal events included nonfatal myocardial infarction, significant serial electrocardiographic changes suggestive of myocardial infarction, stroke, congestive heart failure, renal failure, and digitalis use.) Because cigarette smoking is associated with both weight and death, analyses were stratified by smoking behavior during the trial to determine whether observed associations between weight variability and mortality could be attributed to changes in smoking behavior. Three smoking groups were created: continuous smokers (those who at every annual visit either reported smoking or had a serum thiocyanate value 17.2 mmol/L); never smokers (those who at every annual visit both reported not smoking and had a serum thiocyanate value < 17.2 mmol/L); and intermittent smokers (those who would be classified as smokers at some visits and nonsmokers at others, thus including all those whose smoking behavior changed during the trial). Both the continuous smokers and the never smokers had smoking behaviors that did not change during the trial. Results After exclusions, 10 529 men remained for analysis. Descriptive information on selected baseline variables and weight change during the trial for these men, stratified by smoking behavior during the trial, is shown in Table 1. The distribution of types of weight change differed by smoking status during the trial. Compared with those whose smoking behavior never changed, intermittent smokers were most likely to gain weight without losing it and were least likely to have no change in we

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting.

BackgroundThe World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.MethodsWe conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.ResultsThirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).ConclusionsWe found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.

External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

BackgroundBefore considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.MethodsWe conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures.Results11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models.ConclusionsThe vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data.

An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study

Objective To evaluate the performance of the QRISK2 score for predicting 10-year cardiovascular disease in an independent UK cohort of patients from general practice records and to compare it with the NICE version of the Framingham equation and QRISK1. Design Prospective cohort study to validate a cardiovascular risk score. Setting 365 practices from United Kingdom contributing to The Health Improvement Network (THIN) database. Participants 1.58 million patients registered with a general practice between 1 January 1993 and 20 June 2008, aged 35-74 years (9.4 million person years) with 71 465 cardiovascular events. Main outcome measures First diagnosis of cardiovascular disease (myocardial infarction, angina, coronary heart disease, stroke, and transient ischaemic stroke) recorded in general practice records. Results QRISK2 offered improved prediction of a patient’s 10-year risk of cardiovascular disease over the NICE version of the Framingham equation. Discrimination and calibration statistics were better with QRISK2. QRISK2 explained 33% of the variation in men and 40% for women, compared with 29% and 34% respectively for the NICE Framingham and 32% and 38% respectively for QRISK1. The incidence rate of cardiovascular events (per 1000 person years) among men in the high risk group was 27.8 (95% CI 27.4 to 28.2) with QRISK2, 21.9 (21.6 to 22.2) with NICE Framingham, and 24.8 (22.8 to 26.9) with QRISK1. Similarly, the incidence rate of cardiovascular events (per 1000 person years) among women in the high risk group was 24.3 (23.8 to 24.9) with QRISK2, 20.6 (20.1 to 21.0) with NICE Framingham, and 21.8 (18.9 to 24.6) with QRISK1. Conclusions QRISK2 is more accurate in identifying a high risk population for cardiovascular disease in the United Kingdom than the NICE version of the Framingham equation. Differences in performance between QRISK2 and QRISK1 were marginal.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

AbstractPrediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). Editors’ note: In order to encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, British Medical Journal, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying Explanation and Elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article.

An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study.

Objective To independently evaluate the performance of the QRISK score for predicting 10 year risk of cardiovascular disease in an independent UK cohort of patients from general practice and compare the performance with Framingham equations. Design Prospective open cohort study. Setting 274 practices from England and Wales contributing to the THIN database. Participants 1.07 million patients, registered between 1 January 1995 and 1 April 2006, aged 35-74 years (5.4 million person years) with 43 990 cardiovascular events. Main outcome measures First diagnosis of cardiovascular disease (myocardial infarction, coronary heart disease, stroke, and transient ischaemic attack) recorded in general practice records. Results This independent validation indicated that QRISK offers an improved performance in predicting the 10 year risk of cardiovascular disease in a large cohort of UK patients over the Anderson Framingham equation. Discrimination and calibration statistics were better with QRISK. QRISK explained 32% of the variation in men and 37% in women, compared with 27% and 31% respectively for Anderson Framingham. QRISK underpredicted risk by 13% for men and 10% for women, whereas Anderson Framingham overpredicted risk by 32% for men and 10% for women. In total, 85 010 (8%) of patients would be reclassified from high risk (≥20%) with Anderson Framingham to low risk with QRISK, with an observed 10 year cardiovascular disease risk of 17.5% (95% confidence interval 16.9% to 18.1%) for men and 16.8% (15.7% to 18.0%) for women. The incidence rate of cardiovascular disease events among men was 30.5 per 1000 person years (95% confidence interval 29.9 to 31.2) in high risk patients identified with QRISK and 23.7 per 1000 person years (23.2 to 24.1) in high risk patients identified with Anderson Framingham. Similarly, the incidence rate of cardiovascular disease events among women was 26.7 per 1000 person years (25.8 to 27.7) in high risk patients identified with QRISK compared with 22.2 per 1000 person years (21.4 to 23.0) in high risk patients identified with Anderson Framingham. Conclusions The QRISK cardiovascular disease risk equation offers an improvement over the long established Anderson Framingham equation in terms of identifying a high risk population for cardiovascular disease in the United Kingdom. QRISK underestimates 10 year cardiovascular disease risk, but the magnitude of underprediction is smaller than the overprediction with Anderson Framingham.

Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2

Objective To evaluate the performance of the QRISK2-2011 score for predicting the 10 year risk of cardiovascular disease in an independent UK cohort of patients from general practice and to compare it with earlier versions of the model and a National Institute for Health and Clinical Excellence version of the Framingham equation. Design Prospective cohort study to validate a cardiovascular risk score with routinely collected data between June 1994 and June 2008. Setting 364 practices from the United Kingdom contributing to The Health Improvement Network (THIN) database. Participants Two million patients aged 30 to 84 years (11.8 million person years) with 93 564 cardiovascular events. Main outcome measure First diagnosis of cardiovascular disease (myocardial infarction, angina, coronary heart disease, stroke, and transient ischaemic attack) recorded in general practice records. Results Results from this independent and external validation of QRISK2-2011 indicate good performance data when compared with the NICE version of the Framingham equation. QRISK2-2011 had better ability to identify those at high risk of developing cardiovascular disease than did the NICE Framingham equation. QRISK2-2011 is well calibrated, with reasonable agreement between observed and predicted outcomes, whereas the NICE Framingham equation seems to consistently over-predict risk in men by about 5% and shows poor calibration in women. Conclusions QRISK2-2011 seems to be a useful model, with good discriminative and calibration properties when compared with the NICE version of the Framingham equation. Furthermore, based on current high risk thresholds, concerns exist on the clinical usefulness of the NICE version of the Framingham equation for identifying women at high risk of developing cardiovascular disease. At current thresholds the NICE version of the Framingham equation has no clinical benefit in either men or women.