Med Sci Monit 2005; 11(12): LE25-25
I read with interest the study by Vanagas and Kinduris on assessing the validity of cardiac surgery risk stratification systems for CABG patients, using patient-relatedfactors to predict mortality and postoperative morbidity. Their findings deserve consideration, as manyjudgments and decisions are made in clinical work, where the assessment of risk is necessary. There isrisk involved in the choice of tests to be used to reach a diagnosis. There is also an uncertainty andrisk in how to interpret results from tests used. Taken this uncertainty into consideration how shouldinformation from clinical and biomedical knowledge be combined to reach a diagnosis ? With a diagnosisobtained with some certainty or uncertainty what treatment should be chosen? In all these proceduresthere is risk involved. An issue is how clinical inferences generally are arrived at when making judgmentsand decisions. Theories have been provided about how doctors could include relevant information to improvedecision-making . However, a reasoning error could be made in clinical inference, as it is characterizedby backward reasoning, where diagnosticians attempt to link observed effects to prior causes . Incontrast to this post hoc explanation, statistical prediction entails forward reasoning, because it isconcerned with forecasting future outcomes given observed information. Clinical inference utilizes informationfrom prior periods to make a statement about today, and tends to consider error as a nuisance variable.The statistical approach, on the other hand, accepts error as inevitable, and in so doing probably makesfewer errors in prediction for periods extending over a relatively long time . Moreover, the statisticalapproach is based upon group data to arrive at a conclusion. The situation is different in clinical inferenceand decision-making, where group data concerning risk constitute the basis for diagnostic and treatmentchoices concerning the individual patient. It has also been found that doctors exhibit an interindividual,as well as an intraindividual variation in judgments . One example in practical work is the outcomeof clinical examinations that could vary between doctors. Another example is the interpretations of radiologicalpictures that could exhibit a variation between doctors. Many people tend to overestimate how much theyknow, even about the easiest knowledge tasks . Overconfidence (i.e., greater certainty than circumstanceswarrant) leads to overestimation of the importance of occurrences that confirm their hypothesis. It impedeslearning from environmental feedback, and hence results in deleterious effects on future predictions.In many decision settings, inexperienced practitioners and even naive laboratory subjects perform aswell (or as poorly) as performers with more experience . The performance of the patient could be asgood or bad as these subjects. The daily work with patients implies considering risks at many stagesof the decision process. How to convey this information about risk and error to the patients, being anunavoidable condition in clinical work, in order to reach a mutual agreement on treatment judgments anddecisions? By being aware of errors that can be made some of the errors can be counteracted. Therefore,it is a challenge in clinical practice to include different features of risk, and engage providers andpatients in present and future health.
Monica Ortendahl MD, PhD, Malma Backe 3 H, 756 47Uppsala, Sweden, e-mail: email@example.com
1.Vanagas G, Kinduris S: Assessing thevalidity of cardiac surgery risk stratification systems for CABG patients in a single center. Med SciMonit, 2005;11: CR215-CR218
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Keywords: Coronary Artery Bypass - mortality, Diagnostic Errors, Humans, Medical Errors - mortality, Prognosis, Risk Factors