Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_2806_Библиотеки_им_академика_М_И_Перельмана

In recent years there has been an extended imple­mentation of MCID in various medical special­ties (Fig.1.1).

The concept behind the employment and implementation of the MCID is to provide an appropriate level of clinical data interpretation regarding patient-reported changes, using a numerical scale on the basis of whether the observed change is meaningful to patients, rather than plain statistical importance. In this context, similar changes on a numerical scale may repre­sent different levels of clinical importance in dif-

Distribution-based estimates are associated not only with the outcomes of interest, but also with the context in which they are implemented. For instance, they might differ in response to differ­ent interventions or populations, where the vari­ance is homogenous [8]. These estimates rely on the statistical properties of distribution of the out­come scores, along with the variability among patients. They also identify and quantify the magnitude of change that is required to show that the change in an outcome measure is more than would be expected from chance alone [6]. Because distribution-based methods are not derived from individual patient’s assessments, they probably should not be used to determine the MCID.Its logic is based on statistical reason­ing, where it can only identify a minimum detect­able effect, that is, an effect which is unlikely to be attributable to random measurement error. The lack of an “anchor” linking these numerical scores to assessing what is important to patients means that these methods fail to identify impor­tant and clinically meaningful outcomes for patients, as they do not include their perspective. In fact, the term MCID is sometimes replaced by “minimal detectable change” when distribution­based methods calculate the difference. For this reason, these methods are not recommended as the rst line for the determination of an MCID.

worse”, “somewhat worse”, “almost the same”, “somewhat better”, and “much better”) could be used in this case to understand the patient’s impression of change. The anchor question needs to be easily understandable and relevant to patients. Typical anchors may be ratings around a change in health status, presence of symptoms, disease severity, response to treatment, or prog­nosis of future events such as death.

On the other hand, consensus methods (delphi­approaches) are based on experts’ opinions, rather than patients, to dene the MCID.Nonetheless, expert estimates might not represent a reliable method to determine clini­cally important outcomes for patients.

A not uncommon phenomenon that has been reported in certain studies [10, 20] is the smaller observed effect compared with the predened MCID. This phenomenon is present when the study population is appropriately selected to achieve a high probability of detecting a benet equal to the MCID, thus identifying statistically important differences even in cases where the effect of an intervention is smaller than the MCID [19]. Another important aspect of MCIDs is the need to consider potential improvements derived from an intervention in relation to morbidity, mortality and costs. In this context, when den­ing a meaningful improvement from the patients’ perspective it is crucial to consider all aspects of clinical care, both favorable and unfavorable.

Taking everything into consideration, there are certain alternative approaches to derive a MCID and it is crucial for the clinician/scientist/reader to know the way it was measured. As previously was commented not every MCID applies to a particu­lar situation. In addition, the terms MID and MCID are often challenging to distinguish. To face this issue, Houchen-Wolloff etal. [21] have suggested that all MIDs should be described as such, but adding a sufx: MID-S (MID— Statistical), MID-C (MID—Clinical outcome), MID-P (MID—Patient determined). Finally, spe­cial caution should be taken when combining dif­ferent MCID methods. Nonetheless, whichever methodology is chosen and employed, the MCID represents an aiding tool for the interpretation of outcomes and effects measures of interventions.

Following a thorough literature search, we have identied only three studies implementing the MCID concept to evaluate quality of life in car­diac surgery [11, 17, 18]. This study employed anchor-based methods to estimate MCID [11]. According to that study, a statistically signicant difference was reported regarding preoperative and post-operative quality of life scores [11]. Nonetheless, this difference was below the threshold dened as a MCID [11]. A certain limi­tation posed in this study was that the MCID was employed in patients undergoing different car­diac surgical operations in different clinical set­tings [11].

In the present study, we tried to present the basic principles of MCID.Given that cardiac surgery is associated with signicant morbidity, it repre­sents a surgical eld where MCID might be a valuable tool to interpret clinical outcomes. However, it is necessary to validate different MCID methods in the cardiac surgery context.

• The MCID is dened as the smallest dif­ference in score in any domain or out­come of interest that patients can perceive as benecial or harmful.

Conicts of Interest The participating authors declare no conicts of interest.