Sentence examples for most prognostic models from inspiring English sources

Most prognostic models rely on variables recorded within 24 hours of admission to predict the mortality rate of patients in the intensive care unit (ICU).

Furthermore, although most deaths due to trauma occur in low and middle income countries, most prognostic models are based on data from high income countries.

Most prognostic models for melanoma are derived from selected clinical cohorts composed of patients referred to tertiary hospitals or specialist cancer centres [ 11, 12] and/or focussed on specific subgroups of melanomas such as localised [ 13] or nodular melanoma [ 14].

One of the most used prognostic models for traumatic brain injury is the IMPACT-TBI model, which predicts 6-month mortality and unfavorable outcome.

The International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) is currently the most robust prognostic model in patients with traumatic brain injury (TBI).

In this way, a combination of two pretherapeutic markers (CRP and AST) was identified as most powerful prognostic model that would be appropriate for the patient stratification for SIR therapy, with a further improvement of the individual prognostic information by adding the determination of nucleosomes 24 h after therapy.

Table 2 summarizes the most commonly applied prognostic models.

One of the most commonly used prognostic models, the Memorial Sloan Kettering Cancer Center MSKCCC) risk score, uses Karnofsky performance status, time from diagnosis to treatment, and serum haemoglobin, calcium and lactate dehydrogenase to categorise patients as being at favourable, intermediate or poor risk [15].

Prognostic models, most of which are multivariable, have several critical applications.

Most of the ICU prognostic models are based on variables recorded within 24 hours of ICU admission, and there is a paucity of data describing the role of these models in predicting the outcome of patients who survive their initial ICU stay.

Most of these systems are prognostic models, designed to estimate the probability of an adverse event occurring (e.g., patient death), basing quality of care assessment on an outcome indicator.