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“Knowledge comes, but wisdom lingers. It may not be difficult to store up in the mind a vast quantity of facts within a comparatively short time, but the ability to form judgments requires the severe discipline of hard work and the tempering heat of experience and maturity.” – Calvin Coolidge
The information we use every day in patient care comes from one of two sources: personal experience (our own or that of another clinician) or a research study. Up until a hundred years ago, medicine was primarily a trade in which more experienced clinicians passed along their wisdom to younger clinicians, teaching them the things that they had learned during their long and difficult careers. Knowledge accrued slowly, influenced by the biased observations of each practicing doctor. People tended to remember their successes or unusual outcomes more than their failures or ordinary outcomes. Eventually, doctors realized that their knowledge base would be more accurate and accumulate more efficiently if they looked at the outcomes of many patients given the same treatment. Thus, the observational trial emerged.
As promising and important as the dawn of observational research was, it quickly became apparent that these trials had important limitations. The most notable limitations were the potential for bias and confounding variables to influence the results. Bias occurs when the opinion of the researcher influences how the result is interpreted. Confounders occur when an outcome is generated by some unexpected aspect of the patient, environment, or medication, rather than the thing that is being studied. An example of this might be a study that finds a higher mortality rate in a city by the sea than a city located inland. From these results, one might initially conclude that the sea is unhealthy. After realizing more retired people lived in the city by the sea; however, an individual would probably change his or her mind. In this example, the older age of this city’s population would be a confounding variable that drove the increased mortality in the city by the sea.
To solve the inherent problems with observational trials, the randomized, controlled trial was developed. Our reliance on information from RCTs runs so deep that it is hard to believe that the first modern clinical trial was not reported until 1948, in a paper on streptomycin in the treatment of pulmonary tuberculosis. It followed that faith in the randomized, controlled trial reached almost religious proportions, and the belief that information that does not come from an RCT should not be relied on was held by many, until recently. Why have things changed and what does this have to do with technology?
The first is an increasing recognition that, for all of their advantages, randomized trials have one nagging but critical limitation – generalizability. Randomized trials have strict inclusion and exclusion criteria. We do not have such inclusion and exclusion criteria when we take care of patients in our offices. For example, a recent trial published in the New England Journal of Medicine (2018 Dec 4. doi: 10.1056/NEJMoa1814468.), entitled “Apixaban to prevent venous thromboembolism in patients with cancer,” concluded that apixaban therapy resulted in a lower rate of venous thromboembolism than did placebo in patients starting chemotherapy for cancer. This was a large trial with more than 500 patients enrolled, and it reached an important conclusion with significant clinical implications. When you look at the details of the article, more than 1,800 patients were assessed to find the 500 patients who were eventually included in the trial. This is fairly typical of clinical trials and raises an important point: We need to be careful about how well the results of these clinical trials can be generalized to apply to the patient in front of us. This leads us to the second development that is something happening behind the scenes that each of us has contributed to.
Real-world research
As we see each patient and type information into the EHR, we add to an enormous database of medical information. That database is increasingly being used to advance our knowledge of how medicines actually work in the real world with real patients, and has already started providing answers that supplement, clarify, and even change our perspectives. The information will provide observations derived from real populations that have not been selected or influenced by the way in which a study is conducted. This new field of research is called “real-world research.”
An example of the difference between randomized controlled trial results and real-world research was published in Diabetes Care. This article examined the effectiveness of dipeptidyl peptidase 4 (DPP-4) inhibitors vs. glucagonlike peptide–1 receptor agonists (GLP-1 RAs) in the treatment of patients with diabetes. The goal of the study was to assess the difference in change in hemoglobin A1c between real-world evidence and randomized-trial evidence after initiation of a GLP-1 RA or a DPP-4 inhibitor. In RCTs, GLP-1 RAs decreased HbA1c by about 1.3% while DPP-4 inhibitors decreased HbA1c by about 0.68% (i.e., DPP-4 inhibitors were about half as effective). However, when the effects of these two diabetes drugs were examined using clinical databases in the real world, the two classes of medications had almost the same effect, each decreasing HbA1c by about 0.5%. This difference between RCT and real-world evidence might have been caused by the differential adherence to the two classes of medications, one being an injectable with significant GI side effects, and the other being a pill with few side effects.
The important take-home point is that we are now all contributing to a massive database that can be queried to give quicker, more accurate, more relevant information. Along with personal experience and randomized trials, this third source of clinical information, when used with wisdom, will provide us with the information we need to take ever better care of patients.
References
Carls GS et al. Understanding the gap between efficacy in randomized controlled trials and effectiveness in real-world use of GLP-1 RA and DPP-4 therapies in patients with type 2 diabetes. Diabetes Care. 2017;40:1469-78.
Blonde L et al. Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther. 2018 Nov;35:1763-74.
“Knowledge comes, but wisdom lingers. It may not be difficult to store up in the mind a vast quantity of facts within a comparatively short time, but the ability to form judgments requires the severe discipline of hard work and the tempering heat of experience and maturity.” – Calvin Coolidge
The information we use every day in patient care comes from one of two sources: personal experience (our own or that of another clinician) or a research study. Up until a hundred years ago, medicine was primarily a trade in which more experienced clinicians passed along their wisdom to younger clinicians, teaching them the things that they had learned during their long and difficult careers. Knowledge accrued slowly, influenced by the biased observations of each practicing doctor. People tended to remember their successes or unusual outcomes more than their failures or ordinary outcomes. Eventually, doctors realized that their knowledge base would be more accurate and accumulate more efficiently if they looked at the outcomes of many patients given the same treatment. Thus, the observational trial emerged.
As promising and important as the dawn of observational research was, it quickly became apparent that these trials had important limitations. The most notable limitations were the potential for bias and confounding variables to influence the results. Bias occurs when the opinion of the researcher influences how the result is interpreted. Confounders occur when an outcome is generated by some unexpected aspect of the patient, environment, or medication, rather than the thing that is being studied. An example of this might be a study that finds a higher mortality rate in a city by the sea than a city located inland. From these results, one might initially conclude that the sea is unhealthy. After realizing more retired people lived in the city by the sea; however, an individual would probably change his or her mind. In this example, the older age of this city’s population would be a confounding variable that drove the increased mortality in the city by the sea.
To solve the inherent problems with observational trials, the randomized, controlled trial was developed. Our reliance on information from RCTs runs so deep that it is hard to believe that the first modern clinical trial was not reported until 1948, in a paper on streptomycin in the treatment of pulmonary tuberculosis. It followed that faith in the randomized, controlled trial reached almost religious proportions, and the belief that information that does not come from an RCT should not be relied on was held by many, until recently. Why have things changed and what does this have to do with technology?
The first is an increasing recognition that, for all of their advantages, randomized trials have one nagging but critical limitation – generalizability. Randomized trials have strict inclusion and exclusion criteria. We do not have such inclusion and exclusion criteria when we take care of patients in our offices. For example, a recent trial published in the New England Journal of Medicine (2018 Dec 4. doi: 10.1056/NEJMoa1814468.), entitled “Apixaban to prevent venous thromboembolism in patients with cancer,” concluded that apixaban therapy resulted in a lower rate of venous thromboembolism than did placebo in patients starting chemotherapy for cancer. This was a large trial with more than 500 patients enrolled, and it reached an important conclusion with significant clinical implications. When you look at the details of the article, more than 1,800 patients were assessed to find the 500 patients who were eventually included in the trial. This is fairly typical of clinical trials and raises an important point: We need to be careful about how well the results of these clinical trials can be generalized to apply to the patient in front of us. This leads us to the second development that is something happening behind the scenes that each of us has contributed to.
Real-world research
As we see each patient and type information into the EHR, we add to an enormous database of medical information. That database is increasingly being used to advance our knowledge of how medicines actually work in the real world with real patients, and has already started providing answers that supplement, clarify, and even change our perspectives. The information will provide observations derived from real populations that have not been selected or influenced by the way in which a study is conducted. This new field of research is called “real-world research.”
An example of the difference between randomized controlled trial results and real-world research was published in Diabetes Care. This article examined the effectiveness of dipeptidyl peptidase 4 (DPP-4) inhibitors vs. glucagonlike peptide–1 receptor agonists (GLP-1 RAs) in the treatment of patients with diabetes. The goal of the study was to assess the difference in change in hemoglobin A1c between real-world evidence and randomized-trial evidence after initiation of a GLP-1 RA or a DPP-4 inhibitor. In RCTs, GLP-1 RAs decreased HbA1c by about 1.3% while DPP-4 inhibitors decreased HbA1c by about 0.68% (i.e., DPP-4 inhibitors were about half as effective). However, when the effects of these two diabetes drugs were examined using clinical databases in the real world, the two classes of medications had almost the same effect, each decreasing HbA1c by about 0.5%. This difference between RCT and real-world evidence might have been caused by the differential adherence to the two classes of medications, one being an injectable with significant GI side effects, and the other being a pill with few side effects.
The important take-home point is that we are now all contributing to a massive database that can be queried to give quicker, more accurate, more relevant information. Along with personal experience and randomized trials, this third source of clinical information, when used with wisdom, will provide us with the information we need to take ever better care of patients.
References
Carls GS et al. Understanding the gap between efficacy in randomized controlled trials and effectiveness in real-world use of GLP-1 RA and DPP-4 therapies in patients with type 2 diabetes. Diabetes Care. 2017;40:1469-78.
Blonde L et al. Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther. 2018 Nov;35:1763-74.
“Knowledge comes, but wisdom lingers. It may not be difficult to store up in the mind a vast quantity of facts within a comparatively short time, but the ability to form judgments requires the severe discipline of hard work and the tempering heat of experience and maturity.” – Calvin Coolidge
The information we use every day in patient care comes from one of two sources: personal experience (our own or that of another clinician) or a research study. Up until a hundred years ago, medicine was primarily a trade in which more experienced clinicians passed along their wisdom to younger clinicians, teaching them the things that they had learned during their long and difficult careers. Knowledge accrued slowly, influenced by the biased observations of each practicing doctor. People tended to remember their successes or unusual outcomes more than their failures or ordinary outcomes. Eventually, doctors realized that their knowledge base would be more accurate and accumulate more efficiently if they looked at the outcomes of many patients given the same treatment. Thus, the observational trial emerged.
As promising and important as the dawn of observational research was, it quickly became apparent that these trials had important limitations. The most notable limitations were the potential for bias and confounding variables to influence the results. Bias occurs when the opinion of the researcher influences how the result is interpreted. Confounders occur when an outcome is generated by some unexpected aspect of the patient, environment, or medication, rather than the thing that is being studied. An example of this might be a study that finds a higher mortality rate in a city by the sea than a city located inland. From these results, one might initially conclude that the sea is unhealthy. After realizing more retired people lived in the city by the sea; however, an individual would probably change his or her mind. In this example, the older age of this city’s population would be a confounding variable that drove the increased mortality in the city by the sea.
To solve the inherent problems with observational trials, the randomized, controlled trial was developed. Our reliance on information from RCTs runs so deep that it is hard to believe that the first modern clinical trial was not reported until 1948, in a paper on streptomycin in the treatment of pulmonary tuberculosis. It followed that faith in the randomized, controlled trial reached almost religious proportions, and the belief that information that does not come from an RCT should not be relied on was held by many, until recently. Why have things changed and what does this have to do with technology?
The first is an increasing recognition that, for all of their advantages, randomized trials have one nagging but critical limitation – generalizability. Randomized trials have strict inclusion and exclusion criteria. We do not have such inclusion and exclusion criteria when we take care of patients in our offices. For example, a recent trial published in the New England Journal of Medicine (2018 Dec 4. doi: 10.1056/NEJMoa1814468.), entitled “Apixaban to prevent venous thromboembolism in patients with cancer,” concluded that apixaban therapy resulted in a lower rate of venous thromboembolism than did placebo in patients starting chemotherapy for cancer. This was a large trial with more than 500 patients enrolled, and it reached an important conclusion with significant clinical implications. When you look at the details of the article, more than 1,800 patients were assessed to find the 500 patients who were eventually included in the trial. This is fairly typical of clinical trials and raises an important point: We need to be careful about how well the results of these clinical trials can be generalized to apply to the patient in front of us. This leads us to the second development that is something happening behind the scenes that each of us has contributed to.
Real-world research
As we see each patient and type information into the EHR, we add to an enormous database of medical information. That database is increasingly being used to advance our knowledge of how medicines actually work in the real world with real patients, and has already started providing answers that supplement, clarify, and even change our perspectives. The information will provide observations derived from real populations that have not been selected or influenced by the way in which a study is conducted. This new field of research is called “real-world research.”
An example of the difference between randomized controlled trial results and real-world research was published in Diabetes Care. This article examined the effectiveness of dipeptidyl peptidase 4 (DPP-4) inhibitors vs. glucagonlike peptide–1 receptor agonists (GLP-1 RAs) in the treatment of patients with diabetes. The goal of the study was to assess the difference in change in hemoglobin A1c between real-world evidence and randomized-trial evidence after initiation of a GLP-1 RA or a DPP-4 inhibitor. In RCTs, GLP-1 RAs decreased HbA1c by about 1.3% while DPP-4 inhibitors decreased HbA1c by about 0.68% (i.e., DPP-4 inhibitors were about half as effective). However, when the effects of these two diabetes drugs were examined using clinical databases in the real world, the two classes of medications had almost the same effect, each decreasing HbA1c by about 0.5%. This difference between RCT and real-world evidence might have been caused by the differential adherence to the two classes of medications, one being an injectable with significant GI side effects, and the other being a pill with few side effects.
The important take-home point is that we are now all contributing to a massive database that can be queried to give quicker, more accurate, more relevant information. Along with personal experience and randomized trials, this third source of clinical information, when used with wisdom, will provide us with the information we need to take ever better care of patients.
References
Carls GS et al. Understanding the gap between efficacy in randomized controlled trials and effectiveness in real-world use of GLP-1 RA and DPP-4 therapies in patients with type 2 diabetes. Diabetes Care. 2017;40:1469-78.
Blonde L et al. Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther. 2018 Nov;35:1763-74.