Exemplos de uso de Prognostic models em Inglês e suas traduções para o Português
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Prognostic models in acute heart failure* Author.
Limitations to the use of prognostic models for clinical decision support.
Several works have used different factors andtechniques ofmachine learning in search of other factors and prognostic models for several cancer types.
How to explain that the prognostic models are not good predictive models of the anatomy?
Both scores remain highly reproducible today and accepted as prognostic models for CP patients Chart 2.
It should help to determine risk factors and prognostic models of various events and might be used for resource allocations between regions or hospitals.
Although the analysis of discrimination seems to be less affected by sample sizes than calibration,external validation of prognostic models for critically ill patients requires substantial sample sizes.
However, the prognostic models in traumatic brain injury, such as severe which is the focus of this research, can not have a high accuracy for prediction of death by logistic regression.
A new biomarker usefulness depends on the demonstration of its incremental value in prognostic models traditionally used in clinical practice.
In theory, many limitations of these traditional prognostic models could be overcome by the early collection of samples for laboratory tests, such as the free nucleic acid test.
Analysis concerning the GCS discriminatory capacity is also performed to verify its performance at different times andto compare it in relation to other prognostic models, such as the probability of survival offered by the Trauma Injury Severity Score TRISS.
Disease severity scores or prognostic models evaluate the 30-day mortality risk and are used to identify low-risk patients, who are therefore candidates for outpatient treatment.
However, despite its strong pathophysiological bases andthe initial results about cardiovascular risk prediction, its incremental predictive value to clinical prognostic models has not been established. In addition, solid evidence supporting its use in routine clinical practice to predict cardiovascular risk still lacks.
There are several prognostic models for patients with ACS; however, these can present with limitations in terms of calibration or discrimination, either because they were designed several years ago or because they were developed using samples selected from clinical trials.
Further, this study is noteworthy for the simultaneous evaluation of six models in this population,among which are the overall prognostic models SAPSII and APACHE II,models for evaluation of organic dysfunction LOD and ODIN and specific models for patients with AKI Mehta and Liaño.
Prognostic models may help answer ICU outcomes research questions and may aid with risk stratification of patients for entry into clinical trials, although this latter approach is controversial because of calculation complexity, timing, and inter-observer variability.
Left ventricular ejection fraction was not considered in other prognostic models, probably because this variable was not available within the initial few hours for most patients.
In general, the prognostic models developed as from an overall population of ICU patients have an unsatisfactory performance, when tested in a more specific population, including patients with AKI. Nevertheless, information on the applicability of such models in patients with AKI requiring RRT is still scarce.
Although there are numerous examples of the use of prognostic models to make decisions for individual patients e.g., use of the Model for End-Stage Liver Disease[MELD] score for organ allocation for liver transplantation, such use is not without problems.
Barriers to widespread acceptance of prognostic models include the cost of the information technology infrastructure required to acquire data for complex models, clinician resistance because of perceived superiority of their own estimates of patient survival or their disregard for the model's relevance for their patients, and the focus on prediction of mortality rather than functional outcome, such as quality of life years.
The study did not aim to develop a prognostic model of ARDS.
The other major category is the severity-of-illness prognostic model, a discussion of which will occupy the majority of this commentary.
Lee et al. built and validated a prognostic model for cardiovascular complications after noncardiac surgery.
Validation of a prognostic model can be done using the same cohort internal: data-splitting, bootstrap and cross validation or using patients from another cohort external.
Nonetheless, the technique is an attractive alternative to the onerous process of developing andvalidating a new prognostic model.
When a variable was missing,a zero value or normal values were attributed, according to instructions for the estimate of each prognostic model.
Reviewing 126 patients with thick melanoma,Ferrone et al. developed a prognostic model based on age 60 years, depth of melanoma> 5.5 mm, ulceration and histological status of the sentinel node.
The following clinical variables were recorded at hospital discharge and included in the prognostic model: age, sex, body mass index, mean arterial pressures, heart rate, etiology of HF, NYHA functional class, N-terminal pro-brain natriuretic peptide NTproBNP levels determined within 30 minutes before or after echocardiography.
The following clinical variables were recorded and included in the prognostic model: age, sex, body mass index, mean arterial pressure, heart rate, etiology of HF, New York Heart Association NYHA functional class, N-terminal pro-brain natriuretic peptide NTproBNP levels determined simultaneously with echocardiography.
However, the SMR is not a perfect measure; in addition to differences in the quality of care,it may be influenced by the accuracy of the prognostic model, artifacts of data collection or analysis, case mix, lead-time bias, and inter-rater reliability.