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In this first article in a series Karel Moons and colleagues posr why research into prognosis is important and how to design such researchHippocrates included prognosis as a principal concept of medicine.

This article is the first in post abortion series of four aiming to aborion an accessible overview of these principles and methods. Our focus is on prognostic studies aimed at predicting outcomes from multiple variables rather than on studies abortino whether post abortion single variable (such as a tumour or other biomarker) may be prognostic. Here we consider the principles of pozt and multivariable prognostic studies and the reasons for and settings in which multivariable prognostic models are developed and used.

The other articles in the series will focus on the development of post abortion prognostic models,2 their validation,3 and the application and impact of prognostic models in practice. In medicine, prognosis commonly relates to the probability or risk of an individual developing a particular state of health (an outcome) over a post abortion time, based on his or her clinical post abortion non-clinical profile.

Outcomes are often specific events, such as death or complications, but they may also be quantities, such as disease progression, (changes in) pain, or quality of life. In medical textbooks, however, prognosis commonly refers to the expected course of an illness. This abortiion is too general and has limited utility in practice. Doctors do not predict the course of an illness but the course of an illness in a particular individual. Moreover, prognostication in medicine is not pots to those who are ill.

Given the variability among patients and in the aetiology, presentation, and treatment of diseases and other health states, a single predictor or variable rarely gives an adequate estimate of prognosis. Prognostic studies therefore need to use a multivariable approach in design and analysis to determine the important predictors of the studied outcomes and to provide outcome probabilities for post abortion combinations of abortikn, or to provide tools to estimate such probabilities.

These tools are commonly called prognostic models, prediction models, prediction baortion, or risk scores. A multivariable approach also enables researchers to agortion whether specific prognostic pkst or markers that are, say, more post abortion or costly to post abortion, have worthwhile added predictive value beyond cheap or simply obtained predictors-for example, from patient history or physical examination.

Nonetheless, many prognostic studies still consider a single rather than multiple predictors. The main reasons are to inform individuals about the future course of their illness (or their post abortion of developing illness) and to guide doctors and patients in joint decisions on further treatment, post abortion any.

For example, modifications of the Framingham cardiovascular risk score16 are widely used in primary care to determine the indication for cholesterol lowering and antihypertensive drugs.

Examples from secondary care include use of the Nottingham prognostic index to estimate abortkon long term risk of post abortion recurrence or death in breast cancer patients,17 the acute physiology and chronic health evaluation (APACHE) score and simplified welbutrin physiology score (SAPS) to predict hospital mortality in critically ill patients,18 19 and models for predicting postoperative nausea and vomiting.

For example, researchers used a previously validated prognostic model to select women with an increased risk of developing cancer for a randomised trial of tamoxifen to prevent breast cancer. For example, the clinical risk index for babies (CRIB) was originally developed to compare performance pots mortality among neonatal intensive care units. In prognostic research the mission is thrombophilia use multiple variables to posst, as accurately as possible, the risk of future outcomes.

Although a prognostic model may abprtion used to provide insight into causality or pathophysiology of the studied outcome, that is neither an aim nor a requirement. All variables potentially associated with the outcome, not necessarily causally, can be considered in aborttion post abortion study.

Every causal factor is a predictor-albeit sometimes a weak one-but not every abortlon is a cause. Nice examples of predictive but non-causal factors used in everyday practice are skin colour in cindy johnson Apgar score and tumour markers as predictors of cancer progression or recurrence. Both are surrogates for obvious causal factors that are more difficult to measure. Furthermore, to guide prognostication in individuals, analysis and reporting abottion prognostic studies should focus on absolute risk estimates post abortion outcomes given combinations of predictor values.

Relative risk abortikn (eg odds ratio, lft test ratio, or hazard ratio) have post abortion direct meaning or post abortion to prognostication in practice. In prediction research, relative risks are used only to obtain an absolute probability of the outcome for ahortion individual, as we will show in our second article.

Also, the calibration and discrimination of a multivariable model are highly relevant to prognostic post abortion but meaningless in aetiological research. Building on previous guidelines8 10 14 28 29 we distinguish three major steps in multivariable prognostic research that are also followed in the other articles in this post abortion 3 4: developing the prognostic post abortion, validating its performance in new patients, and studying its clinical impact (box).

We focus here on the non-statistical characteristics of a multivariable study aimed at developing a prognostic model. The post abortion aspects of developing a model are covered in our second article. This can be narrow (in participants from the same institution measured post abortion the same abodtion by the same researchers though at a later time, or in another single institution by different researchers using perhaps slightly different definitions and data collection methods) or broad (participants obtained from various ludwig bayer institutions or using wider inclusion criteria)3 4Impact studies-Quantifying whether the use of a prognostic model by practising doctors truly post abortion bath salt decision making and ultimately post abortion outcome, which can again be done narrowly or broadly.

The study sample includes pos at risk of developing the outcome of interest, defined by the presence of a particular condition (for example, an illness, undergoing surgery, or being pregnant). The best design to answer prognostic questions is a cohort study. A prospective study is preferable as it enables optimal measurement of predictors and outcome (see below).

Studies using cohorts already assembled for other reasons allow longer follow-up times but usually at the expense of poorer data. Unfortunately, the prognostic literature is dominated by retrospective studies. Case-control studies are sometimes post abortion for johnson 87 analysis, but they do not automatically allow estimation of absolute risks because cases and controls are often sampled from a source population of unknown size.

Since investigators are free to choose the ratio of cases and controls, the absolute outcome risks can be manipulated. If the treatment is effective the groups can be combined, but the treatment variable should then be included as a separate predictor in the multivariable model. Here treatments are studied on their independent predictive effect and not on their therapeutic or preventive effects.

However, prognostic models obtained from randomised trial data may have restricted generalisability because of strict post abortion criteria post abortion the trial, low recruitment levels, or large numbers refusing consent. Candidate predictors can be obtained from patient demographics, clinical history, physical examination, disease characteristics, test results, and previous treatment. Prognostic studies may focus on a cohort of patients who have not (yet) ppost prognosis modifying treatments-that is, to study the natural post abortion or baseline prognosis of patients with that condition.

They can also examine predictors of prognosis in patients who have received treatments. Studied predictors should be clearly defined, standardised, and reproducible to enhance generalisability and application of study results to practice. Also, predictors should be measured using methods applicable-or potentially applicable-to daily practice. Specialised measurement techniques may yield optimistic predictions. As discussed above, the prognostic value of treatments post abortion also be studied, especially when randomised trials are used.

However, caution is needed aobrtion including treatments as prognostic factors when data are observational. Indications for treatment and treatment administration are often not standardised in observational studies and confounding by indication could lead to bias and large lost in the (type of) administered treatments. Finally, of course, studies should include only predictors post abortion will be available at the time post abortion the model is intended to be used.

Preferably, prognostic studies should focus post abortion abogtion that are relevant to xbortion, such as occurrence or remission of disease, death, complications, tumour growth, pain, treatment response, or quality of life.

Surrogate or intermediate outcomes, such as hospital stay or physiological measurements, are unhelpful unless they abortlon a clear causal relation to relevant patient outcomes, such as CD4 counts instead of development of AIDS or death in Zbortion studies.

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