MATRIZ FODA
REQUISITOS ISO 9001:
12. REVISIÓN HISTÓRICA
Calibration plots were used to determine the degree of over/under-
prediction among participants grouped into six risk strata. The size of each strata was determined by dual consideration of having at least 10 events per strata, as well as clinically meaningful thresholds.
Discrimination was assessed by Harrell’s C-statistic, using the R package rms (specifically, the commands cph & survest). 95% confidence intervals
were calculated manually, by extracting standard errors from the package
Hmisc (specifically, the command rcorr.cens).
Classification ability was assessed by dichotomizing participants’ risk at
the 5% risk threshold for CVD mortality, a commonly suggested threshold above which to consider clinical interventions like statins.(253) This dichotomized approach allowed the calculation of sensitivity, specificity, PPV and NPV as per usual conventions.
Given how I defined the clinical threshold of interest as 5%, this was used to infer an appropriate weighting for the Net Benefit calculation. This was made with the initial assumption that benefit gained from correctly
identifying one additional True Positives is approximately 20x as important, when compared to the cost of incorrectly labelling one additional person as a False Positive. This is based on the rationale as proposed by Vickers.(255) This assumption was relaxed by the visual use of Decision Curve Analysis, where the clinical intervention threshold was varied from 0% to 20% (denoting a Net Benefit weighting that ranges from infinity to 5x, respectively).
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2.3.6. Evaluating change across two models
Change in calibration was assessed visually. Change in discrimination was assessed by ΔC. Change in classification was assessed by ΔNet
Benefit and Decision Curve Analysis.
As sensitivity analyses, Reclassification Plots were used to inspect
reclassification performance across the entire risk spectrum, and to gain a better insight about changes near and far away from clinical thresholds. Despite my reservations around using the Net Reclassification
Improvement (NRI, detailed in Annex 2), given the popularity of this metric in the literature I will nonetheless present continuous NRI and binary NRI (also known as categorical NRI) stratified for cases and controls, using the R package PredictABEL.
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2.3.7. Modelled clinical effect from statins
It was beyond the scope of this thesis to produce a formal cost-
effectiveness analysis of these models, when scaled to real-life settings. Nonetheless, I wanted to briefly explore the viability of CVD screening for a country such as Estonia. The purpose of this was not to provide reliable estimates of anticipated consequences. Rather, this was done as a
sensitivity analyses, which sought to identify how the risks and benefits of an intervention such as statins, when used across a range different clinical risk prediction models. It was beyond the scope of the thesis to formally test a range of parameters and assumptions built into the model. Instead, I calculated changes to costs, clinical outcomes and cost-effectiveness for just one hypothetical scenario with the following assumptions:
• It costs around €50 of clinical care time to initiate statin treatment among one person of high risk. This is followed with €2/month for the price of the statin itself. Altogether, this amounts to €170 per person over 5 years. I assume 100% adherence to treatment in my models. • Statins lower LDL cholesterol by 2 mmol/L, which would lower CVD
mortality by 23% (95% CI = 17 to 29%) (256) among the true positives over five years. One year of CVD mortality averted is equivalent to living with 100% quality of life.
• Statins additionally cause benefits by preventing the development of non-fatal disease. However, as some of these later transition into fatal events within a 5-year window, then this requires more sophisticated modelling. Consequently, I did not model nonfatal benefits.
• Statins cause serious adverse events (predominantly diabetes) in 90/10 000 people treated over 5-years. I assume that these
consequences are immediate and lifelong (i.e. lasting throughout the 5-year window of my model), whereby one year of such illness is equivalent to 75% of full health.
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Another way of illustrating this calculation is to consider a hypothetical dataset where 10 000 people are screened, following which 3000 are treated with statins. Of these 3000, 300 people are correctly treated since they would otherwise have developed the event, while the remaining 2700 are potentially over treated, since they do not develop the event in the follow-up period. Of the 300 who are correctly treated, this may lead to 69 events averted (since 300 * an intervention effect of -23% = 69 events averted). From this:
176 2.3.7.1. Number Needed to Treat
Following the example above, the Number Needed to Treat (NNT) denotes the number of people required to treat, to prevent one CVD fatality.
NNT = (N treated * CT:TT) ÷ (N correctly treated)
NNT = (3000 * 4.34) ÷ 300 = 43
Optionally, this equation can also be rearranged for simplicity as: NNT = N treated ÷ (N of cases among those of predicted high risk × 0.23)
2.3.7.1. Number Needed to Screen
Following the example above, the Number Needed to Screen (NNS) denotes the number of people required to screen, to prevent one CVD fatality.
NNS = (N screened * NNT) ÷ (N identified as high risk) NNS = (10000 * 43) ÷ 3000 = 145
Optionally, this equation can also be rearranged for simplicity as: NNS = N screened ÷ (N of cases among those of predicted high risk × 0.23)
A summary of the relationships is illustrated in table 6.
Table 6. Hypothetical illustration of the calculation of the ratio of Correct Treatment to Total Treatment (CT:TT), the Number Needed to Treat (NNT), and the Number needed to Screen (NNS).
Hypothetical cohort
Nr of events averted 69 1
Nr treated, correctly 300 4.3 = CT:TT
Nr treated, correctly + incorrectly 3000 43 = NNT
Nr screened 10000 145 = NNS
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