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