This document discusses clinical prediction models. It defines a clinical prediction model as using multiple clinical predictors to calculate the probability of a patient outcome in order to help with diagnostic and treatment decisions. An example given is the Framingham risk score, which predicts cardiovascular disease risk within 10 years. The document notes that developing accurate prediction models requires adequate sample sizes and appropriate statistical methods. It describes calibration and discrimination as important measures of a model's accuracy, with discrimination referring to a model's ability to differentiate outcome classes using tools like ROC curves. Clinical prediction models can be used for targeting preventive interventions and informing surgical decision-making.

1. Clinical prediction models
Dr Vinodh Kumar O.R
Senior Scientist
Division of Epidemiology

2. Introduction
• In medicine, improving prognosis is the ultimate goal of all diagnostic
and therapeutic decisions.
• Prediction models have been developed to predict the outcome of
patients in many clinical fields.
• Clinical prediction models are also referred to as clinical prediction
rules, prognostic models, or nomograms or prognostic model, or risk
score.

3. Introduction
• A clinical prediction model is developed to calculate estimates of the
probability of the presence/occurrence or future course of a
particular patient outcome from multiple clinical or non-clinical
predictors in order to help individualize diagnostic and therapeutic
decision-making in healthcare practice.
• Framingham risk score: Prediction model used to estimate the
probability of the occurrence of a cardiovascular disease in an
individual within 10 years.

4. Development of Prediction Model
• Adequate sample size
• Statistical models for prediction :
• regression, classification, and neural networks.

5. Calibration
• Calibration is related to goodness-of-fit, which reflects the agreement
between observed outcomes and predictions.

6. Discrimination
• Discrimination refers to the ability of a prediction model to
differentiate between two outcome classes.
• Receiver operating characteristic (ROC) curves.
0 5 0 1 0 0 1 5 0
0
5 0
1 0 0
1 5 0
R O C c u rv e : R O C o f R O C c u rv e d a ta
1 0 0 % - S p e c ific ity %
S
e
n
s
itiv
it
y
%

8.  Prediction Models for Public Health
Targeting of Preventive Interventions : Framingham risk
functions
 Prediction models for Clinical Practice
 Surgical Decision-Making

9. Schematic representation of surgical decision-making on short-term vs. long-term risk
in replacement of a risky BScc heart valve. Square indicates a decision, circle a chance node.
Predictions (‘p’) are needed for four probabilities: surgical mortality, long-term survival, fracture,
and fatality of fracture