Why translational research is important

Translational research acts as a bridge between different areas of research, connecting their findings to each other, and ultimately, to the community at large. It does not necessarily have to have any effort connected with it to take the research to a practical level.

Translational research seeks to produce more meaningful, applicable results that directly benefit human health. The goal of translational research is to translate move basic science discoveries more quickly and efficiently into practice. The steps in translational research are designed to ensure that the discoveries that advance into human trials have the highest possible chance of success in terms of both safety and efficacy.

Weeding out failures earlier in the process can significantly decrease the overall cost of developing new products. Moss and colleagues have reported that a prior history of alcohol abuse, an acquired risk factor, is associated with an increased incidence and severity of ARDS [ 8 ].

Work by Nuckton and colleagues highlights an example of a component of acute illness correlating with outcome [ 9 ]. They identified risk groups at risk for high ARDS mortality based on the dead space fraction, a component of this acute illness.

In this study ARDS mortality increased in a "dose-dependent" fashion with the dead-space fraction. Quality of care is another component associated with acute illness. For example, whether or not a consultant intensivist supervises patient care in the ICU has an impact on both ICU and overall hospital mortality [ 10 ]. Unfortunately, not all risk factors for clinically relevant outcomes fit into a nice package, as no factor is purely genetic, acquired, or related to the acute illness.

For example, Martin and colleagues reported clinically relevant associations that do not neatly fall inside any one of these categorizations [ 11 ] utilizing the National Hospital Discharge Survey that accounts for million hospitalizations over a year period. When the data were examined and patients where stratified by gender, men consistently had a higher incidence of sepsis figure 4. Moreover, when the data were stratified by race, African-Americans and individuals of other races had a consistently higher incidence of sepsis figure 5.

The etiology of these racial and gender disparities is probably related to genetic factors, acquired factors, and components of the acute illness.

Future studies are necessary to further explore these associations. It is important to realize that factors associated with disease development may not be associated with disease outcomes. In Nuckton's study [ 9 ], the size of the dead space ventilation, although associated with mortality, is unlikely to be a risk factor for the development of ARDS. When studying an exposure-disease relationship, it is imperative to determine if the association is causal in nature.

Observed associations may actually be either completely or in part due to one or more other factors that had been unrecognized. These are known as confounders. Therefore, once an association has been identified in a univariate analysis, it is important to confirm its effects in a stratified or multivariable analysis to account for possible confounders or effect modification.

As illustrated in figure 6 , an exposure is identified that is believed to be associated with a specific outcome. A confounding variable is a third factor that is associated with both the exposure and the outcome. Using a classic epidemiological example, it is believed that alcohol abuse the exposure is related to the development of lung cancer the outcome.

Our confounding variable of concern is smoking. To be a true confounder, smoking must be associated with alcohol abuse, and must also be associated with lung cancer. Additionally, in a stratified analysis accounting for the effects of the confounder smoking , the association between alcohol and lung cancer should be eliminated.

In a hypothetical model, a cohort of subjects with a positive history of alcohol abuse is more likely to develop lung cancer than non- drinkers, with an odds ratio of 2. However, analyzing the entire cohort after first stratifying subjects into smokers and non-smokers, and then examining the effects of alcohol on the incidence of lung cancer in each of these two groups separately, one finds that the effects of alcohol abuse on the development of lung cancer go away odds ratio for both analyses is 0.

Obviously, many more cases of lung cancer are present in the smoking individuals compared to the non-smokers. The initial concept that alcohol abuse is associated with lung cancer is not substantiated, and the association was completely due to the effects of the confounding variable in this case, smoking. Potential confounding variables can be identified by careful examination of the literature. When something has been reported previously to be an important risk factor for the outcome of interest such as smoking for lung cancer , it needs to be examined as a confounding variable.

With large samples databases , some factors such as race, gender, and age should be examined as potential confounding variables. Finally, avoid including variables in the causal pathway as potential confounders as they will mask risk factors that may be causal in a given disease state. For example, if regular physical activity is being examined in relationship to cardiovascular disease, one might not include HDL levels in the multivariable analysis because the effect of exercise on HDL levels may be the mechanism by which physical activity limits the cardiovascular disease, thus masking the effects of a sedentary life style on cardiovascular disease.

Effect modification describes the variation in association between exposure and disease according to the magnitude of another factor. An example from the recent literature is the relationship between body position the primary exposure and the incidence of ventilator-associated pneumonia the outcome variable [ 12 ].

What is Translational Research? Why is translational research important? Where does multidisciplinary collaboration come into translational research?