Risk scores models

The William T. Fine System A Three Dimension Numerical Risk Scoring Model... 

As the work proceeded in crafting the numerical risk-scoring model presented here, the following guidelines emerged ... 

Adaptations can be made of the risk assessment matrices shown in Chapter 8, A Primer on Hazard Analysis and Risk Assessment, to develop a single risk-scoring model that addresses injury to people (employees and the public) facilities, product, or equipment loss operations downtime and chemical releases and environmental damage. 

Antman developed a thrombosis in myocardial infarction (TIMI) risk score based on a database of 15,078 patients with STEMI or new onset of complete left bundle branch block (8), The score was validated in the TIMI 9 data set. Ten characteristics of these patients accounted for 97% of the predictive capacity of their multivariate model. These are included in the risk score (Table I). Points were given for difference parameters as listed in Table I. The risk score had a strong association with 30-day mortality. There was a greater >40-fold increase in mortality from TIMI risk score 0 to >8 at 30 days (Table I) (8), The TIMI risk score is easy to apply and can be done at the bedside. 

The system then adjusts these scores to account for the way different compounds react in the different media — air, ground water, and surface water. On the basis primarily of a compound s half-life in a medium and on dispersion patterns, we assign each compound a separate inherent risk score for each medium. The scale we use is very coarse each level is 10 times greater than the previous level. The data now in the model is, therefore, insensitive to risks that are only two or thre times as great as others. We found it convenient to express the ten-fold differences on a logarithmic unit scale. 

A prognostic model is the mathematical combination of two or more patient or disease characteristics to predict outcome. Confusingly, prognostic models are also termed prognostic indexes, risk scores, probability models, risk stratification schemes or clinical prediction rules (Reilly and Evans 2006). To be useful, they must be shown to predict clinically relevant outcomes reliably. They must, therefore, be derived from a representative cohort in which outcome has been measured accurately. Next, they must be validated, not just in the data from which they were derived (internal validation) but also on data from independent cohorts (external validation) (Wyatt and Altman 1995 Justice et al. 1999 Altman and Royston 2000). Lastly, a model must be simple to use and have clinical credibility, otherwise it is unlikely to be taken up in routine clinical practice (Table 14.1). 

On the other hand, a potential problem with simple risk scores is that they may not use the full information from the prognostic variables (Christensen 1987 Royston et al. 2006). If continuous predictors such as age are dichotomized (e.g. old versus young), power is usually reduced (Altman and Royston 2000). Furthermore, if the dichotomy is data derived at the point where it looks best, it may also compromise the generalizability of the score. However, although some loss of prognostic power is almost inevitable, simple scores often perform almost as well as more complex models. One reason for this is that a simple score based on a small number of highly predictive variables is much less likely to be overfitted than a complex score with additional weakly predictive variables and interaction terms. 

Prediction of risk using models requires a computer, a pocket calculator with an exponential function or internet-access (the ECST model can be found at www.stroke. ox.ac.uk). As an alternative, a simplified risk score based on the hazard ratios derived from the relevant risk model can be derived. Table 27.3 shows a score for the five-year risk of stroke on medical treatment in patients with recently symptomatic carotid stenosis derived from the ECST model. As is shown in the example, the total risk score is the product of the scores for each risk factor. Fig. 27.7 shows a plot of the total risk score against the five-year predicted risk of ipsilateral carotid territory ischemic stroke derived from the full model and is used as a nomogram for the conversion of the score into a risk prediction. 

Christensen E (2004) Prognostic models including the Child-Pugh, MELD and Mayo risk scores - where are we and where should we go. Hepatol 41 334-350. 

In calculating the hazards, EPA relies upon animal experiments and human studies for information to establish the probability of illness as a function of different exposure levels. The exposures used in the equation are derived from smokestack monitors strategically placed or from mathematical models that take meteorological factors into consideration. The paths taken by the pollution and numbers of males and females of different ages who reside in each area are taken into consideration in calculating health risk scores. 

Das, A., and Wong, R. C. (2004). Prediction of outcome of acute GI hemorrhage A review of risk scores and predictive models. Gastrointest. Endosc., 60(1) 85-93. 

A relative risk index model is an analytical model or tool that is used to calculate a numerical score, representing the relative risk of a pipeline segment. This score is calculated based on variables that represent characteristics of the pipeline segment and the perceived importance of these characteristics to the risk of the segment. Relief Valve... 

The probability estimate obtained from a model of the form of Eq. 5.1 may also be thought of as a risk score . This probability provides an estimate of the proportion of occurrence and non-occurrence [39]. The odds is the probability of occurrence relative to probability of non-occurrence [39]. The odds are defined as [39] ... 

In practice, risk scoring methods are easier to use to get a "quick and dirty" ranking of the risk events. In Sections 7.8-7.13, we discuss mathematical models to quantify the risk factors. 

A MODEL THREE-DIMENSIONAL NUMERICAL RISK-SCORING SYSTEM... 

One of the aims in this author s study of multidimensional numerical risk-scoring systems was to determine whether a model could be proposed that ... 

The most important objective of the risk analysis is to derive a risk score based on certain criteria that objectively can identify the greatest risks to help prioritize action. The risk score analysis (Model 7.6) takes into consideration the likelihood, the exposure, and the possible consequences and equates them to a risk score under the following categories ... 

On the top-right corner, the various risk assessment model elements, i.e. the various subjective probabilities in the risk model, are identified. These are subsequently mapped according to their sensitivity values and their aggregate score in terms of epistemic and non-epistemic justification in a set of matrices. 

In this chapter, we present a disruption risk assessment procedure for determining the disruption risk scores of facilities and transportation links in a supply chain network. Disruption risk score is calculated from three factors hazard, vulnerability, and risk management practice. We also present the use of disruption risk scores as a risk parameter in a mathematical model. 

In Tables 14.9 and 14.10, the last column reports the environmental impact points (EIPs) for typical applications of organic and conventional pesticides derived from the Pesticide Environmental Assessment System, or PEAS. This model produces relative rankings of risks based on defined use rates and use patterns (the formulation used to apply a pesticide, timing, target of the application, spray equipment used, etc). PEAS scores reflect an equal balancing of acute pesticide risks to farm workers, chronic risks via dietary exposure and exposures to birds, Daphnia and bees. 

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