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Harrison DA, Ferrando-Vivas P, Shahin J, et al. Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients. Southampton (UK): NIHR Journals Library; 2015 Oct. (Health Services and Delivery Research, No. 3.41.)
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Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients.
Show detailsThe raw data fields required for calculation of the new ICNARC model for predicting acute hospital mortality, ICNARCH-2014, are shown in Table 42 and the final model coefficients are provided in Table 43. For continuous predictors, coefficients are provided for the cubic spline base variables, calculated for each predictor as follows.
TABLE 42
Raw data fields for the ICNARCH-2014 risk prediction model for acute hospital mortality
TABLE 43
Final coefficients for the ICNARCH-2014 risk prediction model for acute hospital mortality
Restricted cubic splines with j knots (positioned at k1,. . .,kj) require (j − 1) base variables x1 to xj − 1 calculated from the continuous predictor x as:
Right-restricted cubic splines with j knots (positioned at k1,. . ., kj) require (j + 1) base variables x1 to xj+ 1 calculated from the continuous predictor x as:
The predicted log-odds of acute hospital mortality for patient i, li, are calculated by multiplying each coefficient from the preceding table by the value of the corresponding predictor variable and summing. The predicted risk of acute hospital mortality, pi, is calculated from the predicted log-odds by the inverse logit function:
where exp() denotes the exponential function.
- The new Intensive Care National Audit & Research Centre model: ICNARCH-2014 - En...The new Intensive Care National Audit & Research Centre model: ICNARCH-2014 - Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients
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