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Identification of patients at high cardiovascular risk: a critical appraisal of applicability of statistical risk prediction models.

Assessment of cardiovascular risk is widely proposed as a basis for taking management decisions about patients presenting with hypertension or hypercholesterolemia. Our aim was to critically assess the use of risk equations derived from epidemiological studies for the purpose of identifying high-risk patients. Risk equations were retrieved from the MEDLINE database and then applied to a data set of 118 patients. This data set was an evaluation study of the clinical value of the World Health Organization 1993 hypertension guidelines for the decision to treat mild hypertensive patients. We calculated agreement: 1) between equations and 2) between equations and the decision to treat taken by the physician. Most models were not applicable to our population, mainly because the original population had a narrow age range or comprised only males. Between-model agreement was better for the lower and upper risk quintiles than for the three other risk quintiles (0.58, 0.33, 0.34, 0.45, 0.70, from the lower to the upper risk quintile). When using an arbitrary threshold for defining high-risk patients (i.e. > 2% per year), we observed a huge variation of the proportion of patients classified at high risk (from 0 to 17%). There was a poor agreement between risk models and the decision to treat taken by the physician. These results suggest that risk-based guidelines should be validated before their diffusion.

Antihypertensive Agents↗

Predictive modeling care management program produces dramatic bottom line results.

It's a new-generation effort, but it has already proven its worth in a large population, producing savings of $1 million to $2 million per month. The effort begins with the development of a constantly evolving registry of high-risk patients. Specialized case management efforts are then focused on stabilizing individuals within this group. See how traditional interventions have been combined with new technology to deliver results.

Algorithms↗

Predictive modeling, integrated DM emerge as popular strategies.

The traditional managed care strategies of capitation and selective provider contracting are losing their punch, as members have demanded broader networks and providers have pushed demands for fee-for-service payment. Health plans looking to regain an advantage can capitalize on two emerging disease management strategies--both of which rely on analyzing member data.

Disease Management↗

[Predictive models for the early diagnosis of ectopic pregnancy].

In the past patients with acute abdominal pain and presumably suffering from an ectopic pregnancy often required an immediate surgical intervention. The diagnosis of ectopic pregnancy has become more frequent during the last decades but the incidence of ectopic pregnancy rupture has declined. This decline is due to quantitative hCG measurements, minimally invasive surgery and transvaginal ultrasonography. Gynaecologists presently are increasingly confronted with pregnancies of unknown location (PUL). This is due to the fact that women consult earlier in pregnancy for ultrasonographic assessment. The usual biochemical procedures for diagnosing ectopic pregnancy have been developed for pregnancies associated with abdominal pain and abnormal bleeding and not for asymptomatic pregnant women with a much lower pre-test probability of ectopic pregnancy. For this reason new diagnostic procedures and criteria had to be developed and tested to detect ectopic pregnancies in women with PUL, without exerting any deleterious effect on early intra-uterine pregnancies. In our experience the hCG-ratio and our new mathematical model seem to be the best method to satisfy these criteria.

Abdominal Pain↗