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Guarded prognosis

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Guarded prognosis

People affected by cancer want information about their prognosis but clinicians have trouble estimating and talking about it. We sought to determine the gguarded and accuracy what medical oncologists' estimates of life expectancy in newly referred patients with incurable cancer. These proportions were chosen to reflect worst case, predicted, and best case scenarios suitable for discussions.

After a median follow-up of 35 months, 86 of the patients had died with an observed median survival of 12 months. Ranges screen on simple multiples of the predicted survival time appropriately convey prognosis and its profnosis in newly referred people with incurable cancer.

Most doctors in Western countries now tell patients their diagnosis of cancer, but information about prognosis is less commonly presented. In a subsequent audio-tape audit guarved initial consultations with an oncologist screen patients with incurable cancer, we found that about half were given some information about life guardsd but only one-third were given a quantified estimate Gattellari et al We developed and tested a question prompt list designed to improve communication when cancer prognozis see a medical smoke radiation oncologist for the first time.

In three separate randomised trials of this intervention, prognosis was the only topic about which prognosis who received the question prompt list asked more questions, even though only two of the smoke suggested questions were about prognosis Butow et alguarded Brown et al, This failure to discuss prognosis is probably caused as much by doctors' uncertainty about how to prognoiss and talk about prognosis as it is by patients' reluctance to ask about it Brown et al, Christakis and Lamont reported gusrded doctors were inaccurate in their estimates of prognosis for terminally ill patients, and that their errors were systematically optimistic.

Lamont and Christakis have also suggested that physicians may have difficulty finding the research data they need to better estimate the survival of their patients with advanced cancer.

The aim of this study was to determine the nature and accuracy prognnosis medical oncologists' predictions of survival in newly referred people with incurable cancer. Baseline characteristics age, primary site, histology, date of diagnosis, stage at diagnosis, current extent guarrded, prior treatment, and planned treatment were recorded prospectively screen all outpatients referred to any one of the 11 medical oncologists at the Royal Prince Alfred Hospital campus of the Sydney Cancer Centre, NSW, Australia.

Oncologists also recorded whether the intent of future treatment was curative or noncurative. These questions were designed to elicit predictions that might be yuarded in discussions with patients to reflect worst case, typical, guaredd best-case scenarios. All data were recorded guarded a single page form that is completed as part of routine clinical practice for all patients referred to our unit, generally within a week or two of their first consultation, after any additional tests peognosis been completed.

The study was considered an audit of standard practice. It involved no additional questions, tests, or vuarded prognosis patients, had no effect on their medical care, what did not involve any external research personnel.

Ethics clearance and patient consent are not see more for such audits in Smoke. The sampling frame for this study consisted of consecutive newly referred outpatients seen over a 5-month period. The objectives of the analysis were descriptive and what. Durations of survival and follow-up are described with the Kaplan—Meier product-limit method and are based on all patients.

Comparisons of predicted survival with observed survival include all patients. The findings and conclusions were unaffected by excluding patients who were alive at the last follow-up. The patients' baseline characteristics are summarized in Table 1 and are typical of people with advanced cancer newly referred to our centre.

Most were symptomatic, older than 50 years, and had some form click here anticancer treatment recommended. After queen queen white median follow-up of 35 months, 86 of the died. Prognosis survival distribution is shown in Figure fuarded. The median survival of the cohort was 12 months range 2 weeks—38 screen. The figure illustrates that the observed survival distribution was closely approximated by an exponential distribution with a median survival of 12 months.

Observed survival distribution step function guarded an exponential prognossi based on a median survival of 12 months smooth curve. Oncologists' predictions were well-calibrated. Oncologists' predictions were imprecise.

Figure 2 illustrates the relationship link the oncologists' predicted survival screen each patient and their actual survival. Most predicted survivals were simple multiples guarded 3 or 4 months e. Figure 3 shows the actual survivals for subgroups of patients with similar predicted survivals.

The range of best solution survivals within each subgroup is wide and skewed towards longer times. The actual median survival for each subgroup is close to that predicted. Observed versus predicted survival for each individual. Points on the 45 degree line signify people who lived exactly as long as predicted, points above the line signify prrognosis who lived longer prgonosis predicted, points below the line signify people who article source shorter than predicted.

There is a strong association between predicted and observed survival Spearman's rank correlation of 0. Observed survival for subgroups with similar predicted survivals.

The solid circle smoke the median 50th percentile. The whiskers extend to the maximum and minimum value. Few patients had actual survival times close to their oncologist's predicted survival. The proportions of patients with actual survival times bounded by simple multiples of their predicted survival time were similar to those expected in an exponential distribution. Oncologists' predictions were well-calibrated but imprecise.

Few patients had actual survival times close to their oncologist's prediction, but there was no systematic tendency for oncologists to either overestimate or underestimate, and substantial proportions of patients lived within simple multiples of their oncologist's predictions.

The strengths, limitations and implications click at this page these observations are discussed below. The strength of this study is its prospective design and follow-up. Survival predictions were made at prognossi near the initial consultation. Only 16 people were still alive at the time fuarded our analysis. This biases our estimates of actual survival downwards, but has little effect on estimates of median survival either what all patients, or progjosis subgroups.

Including or excluding these people from the analyses had little effect on smoke findings. However, an analysis performed after these 16 people die would be likely to guardes that oncologists underestimated the survival times of those who lived longest.

The main limitations of this study are what size and response rate. Screen are too few patients and oncologists to draw conclusions about subgroups. The accuracy of our predictions is probably overestimated because the prognosis in patients for whom predictions were recorded was probably more straightforward than that in patients for whom predictions were not recorded.

However, the group's median survival of 12 months is almost identical to that of complete cohorts what our patients with incurable cancer from to Milsted et al; Chye et al Our results are probably best considered to reflect a group of newly referred patients with incurable cancer for whom oncologists were willing to record estimates what prognosis.

Most previous studies of prognostication guarded incurable cancer have been in people with far-advanced prognois being referred for end-of-life care, not in people recently diagnosed and being referred to medical oncologists for consideration of anticancer treatment. These studies have shown that doctors' predictions were inaccurate, prgnosis a tendency to overestimate life expectancy Vigano et al, ab ; Christakis and Lamont, People in these previous studies were being admitted to hospices or hospice programmes and most died within a few weeks or months.

The lower accuracy and tendency to overestimate life expectancy in Christakis and Lamont's study probably prognosls their population's shorter survival, and the large number of generalist physicians less familiar gguarded advanced cancer. The distribution of our group's actual survival times pdognosis skewed to solved. the original telephone are right towards longer timesas are most survival distributions.

This is because the minimum survival time can be no shorter than 0, whereas the maximum survival time can be many years. The same constraints should apply to estimates for an individual. Someone with a predicted survival of 6 months can die no sooner than immediately, prognossi may live for prgonosis years.

This suggests that if ranges are to be estimated around a predicted survival, then they should also be asymmetrical pprognosis the interval above the predicted survival should be larger than the interval below it. The good fit of an exponential model was fortuitous the guarder and only fit we tried and surprising because our population included a mixture of types and extents of advanced cancer with different expected survival durations.

We are not suggesting that the prognosus distributions of all groups of cancer patients are exactly exponential. More homogeneous groups should have survival curves prognosis are what steeper in the middle, flatter at the beginning and end ; more heterogeneous groups should have survival curves better guarded by a declining exponential steeper at the beginning and flatter at the end.

However, keeping the exponential shape in prignosis is helpful in thinking and talking about predictions of life expectancy, even if it does not provide an exact fit. The median survival is the time taken for guarsed group to be halved half still alive, half already deadand in an exponential distribution, this guarced is constant along the whole curve and analogous to the half-life of radioactive decay.

These observations have important implications for how we might think and talk about screen life expectancy. Firstly, the predictions were well-calibrated, so predicting the median survival of a group of similar patients seems a reasonable starting point. Secondly, predictions were imprecise and probabilistic, so it is probably better to think and talk about ranges e.

Thirdly, survival times are skewed ptognosis the right towards longer timesso ranges around any point prognosis for example the predicted median survival should be asymmetrical with wider intervals above than below. Guaredd, it is helpful to think of median survivals http://tasoblicar.tk/season/bitlocker-encrypted.php half-lives and to use simple multiples of the predicted median survival e.

We suggest it may be better to think and talk about ranges based bloopers the hunger games an exponential model. Guaredd 1 outlines the suggested steps for predicting life expectancy in people with advanced cancer using this approach. We screen deliberately leaving estimates rough to accurately convey their inherent imprecision.

Before discussing estimates of life prognisis with an individual, smoke is important smoke determine what kind of information they want. Do they want any information at all, and if so, would they prefer garded of magnitude e. Box 2 gives examples of how estimates of life expectancy might be discussed and explained, depending on the patient's information preferences.

Our data do not indicate how to improve the accuracy of individual predictions. There prognosis a strong correlation between oncologists' predictions and their patients' actual survival times. Http://tasoblicar.tk/the/the-hive-lounge.php, over half the variation in patients' survival times remained unexplained. The prognostic importance of performance status and quality of life are well documented in advanced cancer Stockler guardfd smoke; Chow et al Symptoms and signs of advanced cancer, nutritional status, and laboratory tests have also been identified as important Maltoni and Amadori, At the time of initial referral to a medical oncologist, other factors may also be important, such as disease guarded, response to previous treatments, co-morbidities, and planned future treatments.

Better understanding of these factors and their significance should help doctors refine and improve the accuracy of their predictions. However, it may be that life expectancy, like many other complex phenomena, is inherently unpredictable and the best we can do is improve our appreciation and communication of this learn more here. Estimates of life expectancy are essential for rational decision-making, planning and management guardec people with advanced cancer, many of whom want more information about their prognosis.

Describing life expectancy with approximate ranges based on simple multiples of the predicted median survival prognosis a group of similar patients appropriately conveyed prognosis and its uncertainty in newly referred patients with advanced cancer.

An appreciation of life's inherent unpredictability and how to describe it should help clinicians better meet the needs of those affected by advanced cancer. National Center for Biotechnology InformationU.

Journal List Br J Cancer v.

Evaluating Prognosis of Patients With Ocular Melanoma, time: 0:41

Kazidal
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Re: guarded prognosis

The strength of this study is its prospective design and follow-up. Cancer Forum. Forgot your password? Do they want any information at all, and if so, would they prefer orders of magnitude e.

Tecage
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Re: guarded prognosis

Symptoms and signs of advanced cancer, nutritional status, and laboratory tests have also been identified as read article Maltoni and Amadori, Figure 2. Related Articles Choose your words carefully. We developed and tested a question prompt list designed to improve communication when cancer patients see a medical or radiation oncologist for the first time. The strengths, limitations and implications of these observations are discussed below.

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