Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experiences on a monthly basis.

I am a third-year medical student at the University of California, San Diego, as well as a recipient of the SHM Longitudinal Scholar Grant. Ultimately, I intend to pursue a career in academic medicine as a clinician-scientist, where I hope to bridge my interests in neuroscience, research, and clinical medicine.

Since entering medical school, my clinical experiences as a third-year student have truly reinforced my interests in research. During clinical rotations, I witnessed numerous patients desperate for new treatments because conventional treatments continued to fail them, suggesting a real need for research that directly improves patient outcomes and optimizes the patient experience.

Prior to entering medical school, I participated in a wide array of basic science, translational, and clinical research projects, but none in the area of quality improvement (QI). Given the breadth of my previous research experiences, an attractive feature of the SHM Hospitalist grant was the opportunity to complement this breadth of research exposure with increasing depth by exploring a QI project.

This year, I’ll be getting my first exposure to a QI project under the fine mentorship of Ian Jenkins, MD, SFHM, an attending in the division of hospital medicine at UCSD, who is working on an ongoing effort to combat catheter–associated urinary tract infections (CAUTI). Methods for reducing CAUTI include reducing indwelling urinary catheter (IUC) placement, performing proper maintenance of IUCs, and ensuring prompt removal of unnecessary urinary catheters.

Our project aims to combine all three approaches, along with staff education on IUC management and IUC alternatives. We plan to perform a “measure-vention,” or real-time monitoring and correction of defects by examining the rate of CAUTI as well as the percentage IUC utilization rate in participating units. Ultimately, we hope to optimize patient comfort and publicize our experience to help other health care facilities reduce IUC use and CAUTI.

I am excited to see how basic interventions, such as education and measure-vention can drive the development of improved health outcomes and quality patient care.
 

Victor Ekuta is a third-year medical student at the University of California, San Diego.

 

Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experiences on a monthly basis.

I am a third-year medical student at the University of California, San Diego, as well as a recipient of the SHM Longitudinal Scholar Grant. Ultimately, I intend to pursue a career in academic medicine as a clinician-scientist, where I hope to bridge my interests in neuroscience, research, and clinical medicine.

Since entering medical school, my clinical experiences as a third-year student have truly reinforced my interests in research. During clinical rotations, I witnessed numerous patients desperate for new treatments because conventional treatments continued to fail them, suggesting a real need for research that directly improves patient outcomes and optimizes the patient experience.

Prior to entering medical school, I participated in a wide array of basic science, translational, and clinical research projects, but none in the area of quality improvement (QI). Given the breadth of my previous research experiences, an attractive feature of the SHM Hospitalist grant was the opportunity to complement this breadth of research exposure with increasing depth by exploring a QI project.

This year, I’ll be getting my first exposure to a QI project under the fine mentorship of Ian Jenkins, MD, SFHM, an attending in the division of hospital medicine at UCSD, who is working on an ongoing effort to combat catheter–associated urinary tract infections (CAUTI). Methods for reducing CAUTI include reducing indwelling urinary catheter (IUC) placement, performing proper maintenance of IUCs, and ensuring prompt removal of unnecessary urinary catheters.

Our project aims to combine all three approaches, along with staff education on IUC management and IUC alternatives. We plan to perform a “measure-vention,” or real-time monitoring and correction of defects by examining the rate of CAUTI as well as the percentage IUC utilization rate in participating units. Ultimately, we hope to optimize patient comfort and publicize our experience to help other health care facilities reduce IUC use and CAUTI.

I am excited to see how basic interventions, such as education and measure-vention can drive the development of improved health outcomes and quality patient care.
 

Victor Ekuta is a third-year medical student at the University of California, San Diego.

 

Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experiences on a monthly basis.

I am a third-year medical student at the University of California, San Diego, as well as a recipient of the SHM Longitudinal Scholar Grant. Ultimately, I intend to pursue a career in academic medicine as a clinician-scientist, where I hope to bridge my interests in neuroscience, research, and clinical medicine.

Since entering medical school, my clinical experiences as a third-year student have truly reinforced my interests in research. During clinical rotations, I witnessed numerous patients desperate for new treatments because conventional treatments continued to fail them, suggesting a real need for research that directly improves patient outcomes and optimizes the patient experience.

Prior to entering medical school, I participated in a wide array of basic science, translational, and clinical research projects, but none in the area of quality improvement (QI). Given the breadth of my previous research experiences, an attractive feature of the SHM Hospitalist grant was the opportunity to complement this breadth of research exposure with increasing depth by exploring a QI project.

This year, I’ll be getting my first exposure to a QI project under the fine mentorship of Ian Jenkins, MD, SFHM, an attending in the division of hospital medicine at UCSD, who is working on an ongoing effort to combat catheter–associated urinary tract infections (CAUTI). Methods for reducing CAUTI include reducing indwelling urinary catheter (IUC) placement, performing proper maintenance of IUCs, and ensuring prompt removal of unnecessary urinary catheters.

Our project aims to combine all three approaches, along with staff education on IUC management and IUC alternatives. We plan to perform a “measure-vention,” or real-time monitoring and correction of defects by examining the rate of CAUTI as well as the percentage IUC utilization rate in participating units. Ultimately, we hope to optimize patient comfort and publicize our experience to help other health care facilities reduce IUC use and CAUTI.

I am excited to see how basic interventions, such as education and measure-vention can drive the development of improved health outcomes and quality patient care.
 

Victor Ekuta is a third-year medical student at the University of California, San Diego.

 

Clinical Question: Can the HEART score risk stratify emergency department patients with chest pain?

Background: Many patients with chest pain are subjected to unnecessary admission and testing. The HEART (History, Electrocardiogram, Age, Risk factors, and initial Troponin) score can accurately predict outcomes in chest pain patients, though it has undergone limited evaluation in real world settings.

Study Design: A cluster randomized trial.

Setting: Nine emergency departments in the Netherlands.

Synopsis: All sites started by providing usual care, then sequentially switched over to use of the HEART score to guide treatment. HEART care recommended early discharge if low risk (HEART score, 0-3), admission and further testing if intermediate risk (4-6), and early invasive testing if high risk (7-10).

The study included 3,648 adults presenting with chest pain. The HEART score was noninferior to usual care for the safety outcome of major adverse cardiovascular events (MACE) within 6 weeks. Only 2.0% of low risk patients experienced MACE, though 41% of these patients were still admitted or sent for further testing, and reduction in health care cost was minimal.

Bottom Line: The HEART score accurately predicted risk in patients with chest pain, but a significant portion of low risk patients underwent further testing anyway.

Citation: Poldervaart JM, Reitsma JB, Backus BE, et al. Effect of using the HEART score in patients with chest pain in the emergency department. Ann Intern Med. 2017 May 16;166(10):689-97.

Dr. Troy is assistant professor in the University of Kentucky division of hospital medicine.

 

Clinical Question: Can the HEART score risk stratify emergency department patients with chest pain?

Background: Many patients with chest pain are subjected to unnecessary admission and testing. The HEART (History, Electrocardiogram, Age, Risk factors, and initial Troponin) score can accurately predict outcomes in chest pain patients, though it has undergone limited evaluation in real world settings.

Study Design: A cluster randomized trial.

Setting: Nine emergency departments in the Netherlands.

Synopsis: All sites started by providing usual care, then sequentially switched over to use of the HEART score to guide treatment. HEART care recommended early discharge if low risk (HEART score, 0-3), admission and further testing if intermediate risk (4-6), and early invasive testing if high risk (7-10).

The study included 3,648 adults presenting with chest pain. The HEART score was noninferior to usual care for the safety outcome of major adverse cardiovascular events (MACE) within 6 weeks. Only 2.0% of low risk patients experienced MACE, though 41% of these patients were still admitted or sent for further testing, and reduction in health care cost was minimal.

Bottom Line: The HEART score accurately predicted risk in patients with chest pain, but a significant portion of low risk patients underwent further testing anyway.

Citation: Poldervaart JM, Reitsma JB, Backus BE, et al. Effect of using the HEART score in patients with chest pain in the emergency department. Ann Intern Med. 2017 May 16;166(10):689-97.

Dr. Troy is assistant professor in the University of Kentucky division of hospital medicine.

 

Clinical Question: Can the HEART score risk stratify emergency department patients with chest pain?

Background: Many patients with chest pain are subjected to unnecessary admission and testing. The HEART (History, Electrocardiogram, Age, Risk factors, and initial Troponin) score can accurately predict outcomes in chest pain patients, though it has undergone limited evaluation in real world settings.

Study Design: A cluster randomized trial.

Setting: Nine emergency departments in the Netherlands.

Synopsis: All sites started by providing usual care, then sequentially switched over to use of the HEART score to guide treatment. HEART care recommended early discharge if low risk (HEART score, 0-3), admission and further testing if intermediate risk (4-6), and early invasive testing if high risk (7-10).

The study included 3,648 adults presenting with chest pain. The HEART score was noninferior to usual care for the safety outcome of major adverse cardiovascular events (MACE) within 6 weeks. Only 2.0% of low risk patients experienced MACE, though 41% of these patients were still admitted or sent for further testing, and reduction in health care cost was minimal.

Bottom Line: The HEART score accurately predicted risk in patients with chest pain, but a significant portion of low risk patients underwent further testing anyway.

Citation: Poldervaart JM, Reitsma JB, Backus BE, et al. Effect of using the HEART score in patients with chest pain in the emergency department. Ann Intern Med. 2017 May 16;166(10):689-97.

Dr. Troy is assistant professor in the University of Kentucky division of hospital medicine.

Photo by Bill Branson

The US Food and Drug Administration (FDA) has approved use of gemtuzumab ozogamicin (GO, Mylotarg), a treatment that was initially approved by the agency in 2000 but later pulled from the US market.

GO is an antibody-drug conjugate that consists of the cytotoxic agent calicheamicin attached to a monoclonal antibody targeting CD33.

GO is now approved to treat adults with newly diagnosed, CD33-positive acute myeloid leukemia (AML) and patients age 2 and older with CD33-positive, relapsed or refractory AML.

GO can be given alone or in combination with daunorubicin and cytarabine.

The prescribing information for GO includes a boxed warning detailing the risk of hepatotoxicity, including veno-occlusive disease or sinusoidal obstruction syndrome, associated with GO.

GO originates from a collaboration between Pfizer and Celltech, now UCB. Pfizer has sole responsibility for all manufacturing, clinical development, and commercialization activities for this molecule.

Market withdrawal and subsequent trials

GO was originally approved under the FDA’s accelerated approval program in 2000 for use as a single agent in patients with CD33-positive AML who had experienced their first relapse and were 60 years of age or older.

In 2010, Pfizer voluntarily withdrew GO from the US market due to the results of a confirmatory phase 3 trial, SWOG S0106.

This trial showed there was no clinical benefit for patients who received GO plus daunorubicin and cytarabine over patients who received only daunorubicin and cytarabine.

In addition, the rate of fatal, treatment-related toxicity was significantly higher in the GO arm of the study.

Because of the unmet need for effective treatments in AML, investigators expressed an interest in evaluating different doses and schedules of GO.

These independent investigators, with Pfizer’s support, conducted clinical trials that yielded more information on the efficacy and safety of GO.

The trials—ALFA-0701, AML-19, and MyloFrance-1—supported the new approval of GO. Updated data from these trials are included in the prescribing information, which is available for download at www.mylotarg.com.

Photo by Bill Branson

The US Food and Drug Administration (FDA) has approved use of gemtuzumab ozogamicin (GO, Mylotarg), a treatment that was initially approved by the agency in 2000 but later pulled from the US market.

GO is an antibody-drug conjugate that consists of the cytotoxic agent calicheamicin attached to a monoclonal antibody targeting CD33.

GO is now approved to treat adults with newly diagnosed, CD33-positive acute myeloid leukemia (AML) and patients age 2 and older with CD33-positive, relapsed or refractory AML.

GO can be given alone or in combination with daunorubicin and cytarabine.

The prescribing information for GO includes a boxed warning detailing the risk of hepatotoxicity, including veno-occlusive disease or sinusoidal obstruction syndrome, associated with GO.

GO originates from a collaboration between Pfizer and Celltech, now UCB. Pfizer has sole responsibility for all manufacturing, clinical development, and commercialization activities for this molecule.

Market withdrawal and subsequent trials

GO was originally approved under the FDA’s accelerated approval program in 2000 for use as a single agent in patients with CD33-positive AML who had experienced their first relapse and were 60 years of age or older.

In 2010, Pfizer voluntarily withdrew GO from the US market due to the results of a confirmatory phase 3 trial, SWOG S0106.

This trial showed there was no clinical benefit for patients who received GO plus daunorubicin and cytarabine over patients who received only daunorubicin and cytarabine.

In addition, the rate of fatal, treatment-related toxicity was significantly higher in the GO arm of the study.

Because of the unmet need for effective treatments in AML, investigators expressed an interest in evaluating different doses and schedules of GO.

These independent investigators, with Pfizer’s support, conducted clinical trials that yielded more information on the efficacy and safety of GO.

The trials—ALFA-0701, AML-19, and MyloFrance-1—supported the new approval of GO. Updated data from these trials are included in the prescribing information, which is available for download at www.mylotarg.com.

Photo by Bill Branson

The US Food and Drug Administration (FDA) has approved use of gemtuzumab ozogamicin (GO, Mylotarg), a treatment that was initially approved by the agency in 2000 but later pulled from the US market.

GO is an antibody-drug conjugate that consists of the cytotoxic agent calicheamicin attached to a monoclonal antibody targeting CD33.

GO is now approved to treat adults with newly diagnosed, CD33-positive acute myeloid leukemia (AML) and patients age 2 and older with CD33-positive, relapsed or refractory AML.

GO can be given alone or in combination with daunorubicin and cytarabine.

The prescribing information for GO includes a boxed warning detailing the risk of hepatotoxicity, including veno-occlusive disease or sinusoidal obstruction syndrome, associated with GO.

GO originates from a collaboration between Pfizer and Celltech, now UCB. Pfizer has sole responsibility for all manufacturing, clinical development, and commercialization activities for this molecule.

Market withdrawal and subsequent trials

GO was originally approved under the FDA’s accelerated approval program in 2000 for use as a single agent in patients with CD33-positive AML who had experienced their first relapse and were 60 years of age or older.

In 2010, Pfizer voluntarily withdrew GO from the US market due to the results of a confirmatory phase 3 trial, SWOG S0106.

This trial showed there was no clinical benefit for patients who received GO plus daunorubicin and cytarabine over patients who received only daunorubicin and cytarabine.

In addition, the rate of fatal, treatment-related toxicity was significantly higher in the GO arm of the study.

Because of the unmet need for effective treatments in AML, investigators expressed an interest in evaluating different doses and schedules of GO.

These independent investigators, with Pfizer’s support, conducted clinical trials that yielded more information on the efficacy and safety of GO.

The trials—ALFA-0701, AML-19, and MyloFrance-1—supported the new approval of GO. Updated data from these trials are included in the prescribing information, which is available for download at www.mylotarg.com.

Since my high school days, I have used some form of artificial sweetener in lieu of sugar. Long believing that sugar avoidance was the key to weight maintenance, I didn’t give much thought to the published ill effects of sugar substitutes—after all, I wasn’t a mouse, and I wasn’t consuming mass doses. Did the artificial sweeteners assist in controlling my weight? Quite honestly, I doubt it—but I was so used to being “sugar free” that I was habituated to using these products.

Several years ago at a luncheon, I was reaching for a packet of artificial sweetener to pour into my iced tea when an NP friend stopped me. She and her husband (a pharmacist) had sworn off these products after noting that he was having issues with his cognition and experiencing increased irritability. With no obvious cause for these symptoms, they investigated his diet. He had, over the previous year, increased his use of aspartame. They found research supporting an association between aspartame and changes in behavior and cognition. When he stopped using the product, they both noticed a return to his former jovial, intellectual self. I acknowledged their research conclusion as an “n = 1” but gave it no further credence.

More recently, friends who had adopted an “all-natural” diet chastised me for drinking sugar-free seltzer. I had switched years ago from diet sodas to this beverage as my primary source of hydration. What could be wrong? It had zero calories, no sodium, and no sugar. Ah, but it contained aspartame! Since switching to a food plan without aspartame, my friends had observed that they were feeling better and more alert. Hmm, sounded familiar … maybe there was something to these claims after all. I did a little research of my own, and was I surprised!

On the exterior, aspartame is a highly studied food additive with decades of research demonstrating its safety for human consumption.¹ But what exactly happens when this sweetener is ingested? First, aspartame breaks down into amino acids and methanol (ie, wood alcohol). The methanol continues to break down into formaldehyde and formic acid, a substance commonly found in bee and ant venom (see Figure). And if that weren’t enough, a potential brain tumor agent (aspartylphenylalanine diketopiperazine) is also a residual byproduct.^2,3 As you might expect, these components and byproducts come with varying adverse effects and potential health risks.

The majority of artificially sweetened beverages (ASBs) contain aspartame. As early as 1984—a mere six months after aspartame was approved for use in soft drinks—the FDA, with the assistance of the CDC, undertook an investigation of consumer complaints related to its use. The research team interviewed 517 complainants; 346 (67%) reported neurologic/behavioral symptoms, including headache, dizziness, and mood alteration.⁴ Despite that statistic, however, the researchers reported no evidence for the existence of serious, widespread, adverse health consequences resulting from aspartame consumption.⁴

Reading these reports reminded me of my friends’ comments and strongly suggested to me that soft drinks containing aspartame may be hard on the brain. Further to this point, a recent study found that ASB consumption is associated with an increased risk for stroke and dementia.⁵

 

Additional studies—including evaluations of possible associations between aspartame and headaches, seizures, behavior, cognition, and mood, as well as allergic-type reactions and use by potentially sensitive subpopulations—have been conducted. The verdict? Scientists maintain that aspartame is safe and that there are no unresolved questions regarding its safety when used as intended.⁶ Some researchers question the validity of the link between ASB consumption and negative health consequences, suggesting that individuals in worse health consume diet beverages in an effort to slow health deterioration or to lose weight.⁷ Yet, the debate about the effects of aspartame on our organs continues.

The number of epidemiologic studies that document strong associations between frequent ASB consumption and illness suggests that substituting or promoting artificial sweeteners as “healthy alternatives” to sugar may not be advisable.⁸ In fact, the most recent studies indicate that artificial sweeteners—the very compounds marketed to assist with weight control—can lead to weight gain, as they trick our brains into craving high-calorie foods. Moreover, ASB consumption is associated with a 21% increased risk for type 2 diabetes.⁹ Azad and colleagues found that evidence does not clearly support the use of nonnutritive sweeteners for weight management; they recommend using caution with these products until the long-term risks and benefits are fully understood.⁷

Is satisfying your sweet tooth with sugar alternatives worth the potential risk? Most of the studies conducted to support or refute aspartame-related health concerns prove correlation, not causality. A purist might point out that many of the studies have limitations that can lead to faulty conclusions. Be that as it may, it still gives one pause.

Small doses of aspartame each day might not be a tipping point toward the documented health complaints, but the consistent concerns about its effects were enough for me to make the switch to plain water, and sugar for my coffee. I do believe that Mary Poppins was correct—a spoonful of sugar does help—and I, for one, am following her lead.

What do you think? Are these concerns unfounded, or are we sweetening our road to poor health? Share your thoughts with me at [email protected]. 

Since my high school days, I have used some form of artificial sweetener in lieu of sugar. Long believing that sugar avoidance was the key to weight maintenance, I didn’t give much thought to the published ill effects of sugar substitutes—after all, I wasn’t a mouse, and I wasn’t consuming mass doses. Did the artificial sweeteners assist in controlling my weight? Quite honestly, I doubt it—but I was so used to being “sugar free” that I was habituated to using these products.

Several years ago at a luncheon, I was reaching for a packet of artificial sweetener to pour into my iced tea when an NP friend stopped me. She and her husband (a pharmacist) had sworn off these products after noting that he was having issues with his cognition and experiencing increased irritability. With no obvious cause for these symptoms, they investigated his diet. He had, over the previous year, increased his use of aspartame. They found research supporting an association between aspartame and changes in behavior and cognition. When he stopped using the product, they both noticed a return to his former jovial, intellectual self. I acknowledged their research conclusion as an “n = 1” but gave it no further credence.

More recently, friends who had adopted an “all-natural” diet chastised me for drinking sugar-free seltzer. I had switched years ago from diet sodas to this beverage as my primary source of hydration. What could be wrong? It had zero calories, no sodium, and no sugar. Ah, but it contained aspartame! Since switching to a food plan without aspartame, my friends had observed that they were feeling better and more alert. Hmm, sounded familiar … maybe there was something to these claims after all. I did a little research of my own, and was I surprised!

On the exterior, aspartame is a highly studied food additive with decades of research demonstrating its safety for human consumption.¹ But what exactly happens when this sweetener is ingested? First, aspartame breaks down into amino acids and methanol (ie, wood alcohol). The methanol continues to break down into formaldehyde and formic acid, a substance commonly found in bee and ant venom (see Figure). And if that weren’t enough, a potential brain tumor agent (aspartylphenylalanine diketopiperazine) is also a residual byproduct.^2,3 As you might expect, these components and byproducts come with varying adverse effects and potential health risks.

The majority of artificially sweetened beverages (ASBs) contain aspartame. As early as 1984—a mere six months after aspartame was approved for use in soft drinks—the FDA, with the assistance of the CDC, undertook an investigation of consumer complaints related to its use. The research team interviewed 517 complainants; 346 (67%) reported neurologic/behavioral symptoms, including headache, dizziness, and mood alteration.⁴ Despite that statistic, however, the researchers reported no evidence for the existence of serious, widespread, adverse health consequences resulting from aspartame consumption.⁴

Reading these reports reminded me of my friends’ comments and strongly suggested to me that soft drinks containing aspartame may be hard on the brain. Further to this point, a recent study found that ASB consumption is associated with an increased risk for stroke and dementia.⁵

 

Additional studies—including evaluations of possible associations between aspartame and headaches, seizures, behavior, cognition, and mood, as well as allergic-type reactions and use by potentially sensitive subpopulations—have been conducted. The verdict? Scientists maintain that aspartame is safe and that there are no unresolved questions regarding its safety when used as intended.⁶ Some researchers question the validity of the link between ASB consumption and negative health consequences, suggesting that individuals in worse health consume diet beverages in an effort to slow health deterioration or to lose weight.⁷ Yet, the debate about the effects of aspartame on our organs continues.

The number of epidemiologic studies that document strong associations between frequent ASB consumption and illness suggests that substituting or promoting artificial sweeteners as “healthy alternatives” to sugar may not be advisable.⁸ In fact, the most recent studies indicate that artificial sweeteners—the very compounds marketed to assist with weight control—can lead to weight gain, as they trick our brains into craving high-calorie foods. Moreover, ASB consumption is associated with a 21% increased risk for type 2 diabetes.⁹ Azad and colleagues found that evidence does not clearly support the use of nonnutritive sweeteners for weight management; they recommend using caution with these products until the long-term risks and benefits are fully understood.⁷

Is satisfying your sweet tooth with sugar alternatives worth the potential risk? Most of the studies conducted to support or refute aspartame-related health concerns prove correlation, not causality. A purist might point out that many of the studies have limitations that can lead to faulty conclusions. Be that as it may, it still gives one pause.

Small doses of aspartame each day might not be a tipping point toward the documented health complaints, but the consistent concerns about its effects were enough for me to make the switch to plain water, and sugar for my coffee. I do believe that Mary Poppins was correct—a spoonful of sugar does help—and I, for one, am following her lead.

What do you think? Are these concerns unfounded, or are we sweetening our road to poor health? Share your thoughts with me at [email protected]. 

Since my high school days, I have used some form of artificial sweetener in lieu of sugar. Long believing that sugar avoidance was the key to weight maintenance, I didn’t give much thought to the published ill effects of sugar substitutes—after all, I wasn’t a mouse, and I wasn’t consuming mass doses. Did the artificial sweeteners assist in controlling my weight? Quite honestly, I doubt it—but I was so used to being “sugar free” that I was habituated to using these products.

Several years ago at a luncheon, I was reaching for a packet of artificial sweetener to pour into my iced tea when an NP friend stopped me. She and her husband (a pharmacist) had sworn off these products after noting that he was having issues with his cognition and experiencing increased irritability. With no obvious cause for these symptoms, they investigated his diet. He had, over the previous year, increased his use of aspartame. They found research supporting an association between aspartame and changes in behavior and cognition. When he stopped using the product, they both noticed a return to his former jovial, intellectual self. I acknowledged their research conclusion as an “n = 1” but gave it no further credence.

More recently, friends who had adopted an “all-natural” diet chastised me for drinking sugar-free seltzer. I had switched years ago from diet sodas to this beverage as my primary source of hydration. What could be wrong? It had zero calories, no sodium, and no sugar. Ah, but it contained aspartame! Since switching to a food plan without aspartame, my friends had observed that they were feeling better and more alert. Hmm, sounded familiar … maybe there was something to these claims after all. I did a little research of my own, and was I surprised!

On the exterior, aspartame is a highly studied food additive with decades of research demonstrating its safety for human consumption.¹ But what exactly happens when this sweetener is ingested? First, aspartame breaks down into amino acids and methanol (ie, wood alcohol). The methanol continues to break down into formaldehyde and formic acid, a substance commonly found in bee and ant venom (see Figure). And if that weren’t enough, a potential brain tumor agent (aspartylphenylalanine diketopiperazine) is also a residual byproduct.^2,3 As you might expect, these components and byproducts come with varying adverse effects and potential health risks.

The majority of artificially sweetened beverages (ASBs) contain aspartame. As early as 1984—a mere six months after aspartame was approved for use in soft drinks—the FDA, with the assistance of the CDC, undertook an investigation of consumer complaints related to its use. The research team interviewed 517 complainants; 346 (67%) reported neurologic/behavioral symptoms, including headache, dizziness, and mood alteration.⁴ Despite that statistic, however, the researchers reported no evidence for the existence of serious, widespread, adverse health consequences resulting from aspartame consumption.⁴

Reading these reports reminded me of my friends’ comments and strongly suggested to me that soft drinks containing aspartame may be hard on the brain. Further to this point, a recent study found that ASB consumption is associated with an increased risk for stroke and dementia.⁵

 

Additional studies—including evaluations of possible associations between aspartame and headaches, seizures, behavior, cognition, and mood, as well as allergic-type reactions and use by potentially sensitive subpopulations—have been conducted. The verdict? Scientists maintain that aspartame is safe and that there are no unresolved questions regarding its safety when used as intended.⁶ Some researchers question the validity of the link between ASB consumption and negative health consequences, suggesting that individuals in worse health consume diet beverages in an effort to slow health deterioration or to lose weight.⁷ Yet, the debate about the effects of aspartame on our organs continues.

The number of epidemiologic studies that document strong associations between frequent ASB consumption and illness suggests that substituting or promoting artificial sweeteners as “healthy alternatives” to sugar may not be advisable.⁸ In fact, the most recent studies indicate that artificial sweeteners—the very compounds marketed to assist with weight control—can lead to weight gain, as they trick our brains into craving high-calorie foods. Moreover, ASB consumption is associated with a 21% increased risk for type 2 diabetes.⁹ Azad and colleagues found that evidence does not clearly support the use of nonnutritive sweeteners for weight management; they recommend using caution with these products until the long-term risks and benefits are fully understood.⁷

Is satisfying your sweet tooth with sugar alternatives worth the potential risk? Most of the studies conducted to support or refute aspartame-related health concerns prove correlation, not causality. A purist might point out that many of the studies have limitations that can lead to faulty conclusions. Be that as it may, it still gives one pause.

Small doses of aspartame each day might not be a tipping point toward the documented health complaints, but the consistent concerns about its effects were enough for me to make the switch to plain water, and sugar for my coffee. I do believe that Mary Poppins was correct—a spoonful of sugar does help—and I, for one, am following her lead.

What do you think? Are these concerns unfounded, or are we sweetening our road to poor health? Share your thoughts with me at [email protected]. 

 

Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.

When patients are admitted to the hospital, the focus for the first 24 hours is on the work-up: What do the data point values tell you about how sick this patient is, and what will they need to get better? While the goal for this information is to develop the appropriate treatment and management for the patient’s acute problem, it could be leveraged to help with other parts of the patient’s hospital stay as well. In particular, it could help avoid unnecessarily long stays in the hospital caused by patients’ waiting for a bed at a lower level of care.

Post-acute care placement is a major issue in discharge planning because it involves extensive coordination of resources not just from within the hospital but from other institutions as well, such as skilled nursing facilities and long-term acute care hospitals. About one in four Medicare patient hospitalizations result in a post-acute care placement. Discharge planning is a time-consuming process that can result in an unnecessarily increased length of stay, which can pose risks to the patient and tie up resources in the hospital. Discharge planning does not necessarily have to start late in the hospital stay. What if it could start within a day of admission?

My research mentor, Eduard Vasilevskis, MD, created a rough scoring system for predicting post-acute care placement using admission data, just based on his clinical gestalt. Even at this preliminary stage, the model has already functioned well without much refinement; however a validated, statistically robust model could potentially transform the way that we initiate the discharge planning process. Jesse Ehrenfeld, MD has helped us develop it further by giving us access to a curated database of deidentified EHR data, which contains all of the variables we would like to assess.

The strengths of this potential model are manifold. First, it relies on data collected early in the patient’s hospital course. Second, it relies on routinely collected information (both at our home institution and elsewhere, making it potentially generalizable). And third, it relies on objective patient data rather than requiring providers use their impressions of the patients’ functional status to guess whether they will require discharge planning services. Although such prediction models have been generated before, this model would be among the first to incorporate information routinely collected by nursing staff, such as the Braden Scale, instead of relying on additional instruments or surveys. In addition to predicting placement destination, the model may also be predictive of in-hospital mortality.

With this information, we hope to give hospital teams an additional tool to help mobilize resources toward patients who need the most attention – not just while they’re in the hospital, but also on their way out.

Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.

 

Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.

When patients are admitted to the hospital, the focus for the first 24 hours is on the work-up: What do the data point values tell you about how sick this patient is, and what will they need to get better? While the goal for this information is to develop the appropriate treatment and management for the patient’s acute problem, it could be leveraged to help with other parts of the patient’s hospital stay as well. In particular, it could help avoid unnecessarily long stays in the hospital caused by patients’ waiting for a bed at a lower level of care.

Post-acute care placement is a major issue in discharge planning because it involves extensive coordination of resources not just from within the hospital but from other institutions as well, such as skilled nursing facilities and long-term acute care hospitals. About one in four Medicare patient hospitalizations result in a post-acute care placement. Discharge planning is a time-consuming process that can result in an unnecessarily increased length of stay, which can pose risks to the patient and tie up resources in the hospital. Discharge planning does not necessarily have to start late in the hospital stay. What if it could start within a day of admission?

My research mentor, Eduard Vasilevskis, MD, created a rough scoring system for predicting post-acute care placement using admission data, just based on his clinical gestalt. Even at this preliminary stage, the model has already functioned well without much refinement; however a validated, statistically robust model could potentially transform the way that we initiate the discharge planning process. Jesse Ehrenfeld, MD has helped us develop it further by giving us access to a curated database of deidentified EHR data, which contains all of the variables we would like to assess.

The strengths of this potential model are manifold. First, it relies on data collected early in the patient’s hospital course. Second, it relies on routinely collected information (both at our home institution and elsewhere, making it potentially generalizable). And third, it relies on objective patient data rather than requiring providers use their impressions of the patients’ functional status to guess whether they will require discharge planning services. Although such prediction models have been generated before, this model would be among the first to incorporate information routinely collected by nursing staff, such as the Braden Scale, instead of relying on additional instruments or surveys. In addition to predicting placement destination, the model may also be predictive of in-hospital mortality.

With this information, we hope to give hospital teams an additional tool to help mobilize resources toward patients who need the most attention – not just while they’re in the hospital, but also on their way out.

Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.

 

Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.

When patients are admitted to the hospital, the focus for the first 24 hours is on the work-up: What do the data point values tell you about how sick this patient is, and what will they need to get better? While the goal for this information is to develop the appropriate treatment and management for the patient’s acute problem, it could be leveraged to help with other parts of the patient’s hospital stay as well. In particular, it could help avoid unnecessarily long stays in the hospital caused by patients’ waiting for a bed at a lower level of care.

Post-acute care placement is a major issue in discharge planning because it involves extensive coordination of resources not just from within the hospital but from other institutions as well, such as skilled nursing facilities and long-term acute care hospitals. About one in four Medicare patient hospitalizations result in a post-acute care placement. Discharge planning is a time-consuming process that can result in an unnecessarily increased length of stay, which can pose risks to the patient and tie up resources in the hospital. Discharge planning does not necessarily have to start late in the hospital stay. What if it could start within a day of admission?

My research mentor, Eduard Vasilevskis, MD, created a rough scoring system for predicting post-acute care placement using admission data, just based on his clinical gestalt. Even at this preliminary stage, the model has already functioned well without much refinement; however a validated, statistically robust model could potentially transform the way that we initiate the discharge planning process. Jesse Ehrenfeld, MD has helped us develop it further by giving us access to a curated database of deidentified EHR data, which contains all of the variables we would like to assess.

The strengths of this potential model are manifold. First, it relies on data collected early in the patient’s hospital course. Second, it relies on routinely collected information (both at our home institution and elsewhere, making it potentially generalizable). And third, it relies on objective patient data rather than requiring providers use their impressions of the patients’ functional status to guess whether they will require discharge planning services. Although such prediction models have been generated before, this model would be among the first to incorporate information routinely collected by nursing staff, such as the Braden Scale, instead of relying on additional instruments or surveys. In addition to predicting placement destination, the model may also be predictive of in-hospital mortality.

With this information, we hope to give hospital teams an additional tool to help mobilize resources toward patients who need the most attention – not just while they’re in the hospital, but also on their way out.

Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.

 

The Food and Drug Administration has approved gemtuzumab ozogamicin (Mylotarg) for the treatment of newly diagnosed CD33-positive acute myeloid leukemia in adults, according to a press release.

Approval was based on results from three clinical trials. In the first, newly diagnosed AML patients who received gemtuzumab ozogamicin plus chemotherapy had significantly longer event-free survival than did patients who received chemotherapy alone. In a second trial, patients who received gemtuzumab ozogamicin alone had better overall survival compared to those who received only best supportive care. In the third clinical trial, 26% of patients who had experienced a relapse and received gemtuzumab ozogamicin experienced a remission.

Initial approval for gemtuzumab ozogamicin was granted in May 2000 as a stand-alone treatment for CD33-positive AML in older patients, but was withdrawn from the market after clinical trials failed to find any benefit and health concerns were noted. The current approval is based on a lower dosage, a more expansive patient population, and a schedule in combination with chemotherapy.

Common side effects of gemtuzumab ozogamicin include fever, nausea, infection, vomiting, bleeding, thrombocytopenia, stomatitis, constipation, rash, headache, elevated liver function tests, and neutropenia; it is not recommended for women who are pregnant or breastfeeding.

Gemtuzumab ozogamicin was also approved to treat patients older than 2 years old who have experienced a relapse or have not responded to initial treatment.

“Mylotarg’s history underscores the importance of examining alternative dosing, scheduling, and administration of therapies for patients with cancer, especially in those who may be most vulnerable to the side effects of treatment,” Richard Pazdur, MD, director of the FDA’s Oncology Center of Excellence, said in the press release.

[email protected]

 

The Food and Drug Administration has approved gemtuzumab ozogamicin (Mylotarg) for the treatment of newly diagnosed CD33-positive acute myeloid leukemia in adults, according to a press release.

Approval was based on results from three clinical trials. In the first, newly diagnosed AML patients who received gemtuzumab ozogamicin plus chemotherapy had significantly longer event-free survival than did patients who received chemotherapy alone. In a second trial, patients who received gemtuzumab ozogamicin alone had better overall survival compared to those who received only best supportive care. In the third clinical trial, 26% of patients who had experienced a relapse and received gemtuzumab ozogamicin experienced a remission.

Initial approval for gemtuzumab ozogamicin was granted in May 2000 as a stand-alone treatment for CD33-positive AML in older patients, but was withdrawn from the market after clinical trials failed to find any benefit and health concerns were noted. The current approval is based on a lower dosage, a more expansive patient population, and a schedule in combination with chemotherapy.

Common side effects of gemtuzumab ozogamicin include fever, nausea, infection, vomiting, bleeding, thrombocytopenia, stomatitis, constipation, rash, headache, elevated liver function tests, and neutropenia; it is not recommended for women who are pregnant or breastfeeding.

Gemtuzumab ozogamicin was also approved to treat patients older than 2 years old who have experienced a relapse or have not responded to initial treatment.

“Mylotarg’s history underscores the importance of examining alternative dosing, scheduling, and administration of therapies for patients with cancer, especially in those who may be most vulnerable to the side effects of treatment,” Richard Pazdur, MD, director of the FDA’s Oncology Center of Excellence, said in the press release.

[email protected]

 

The Food and Drug Administration has approved gemtuzumab ozogamicin (Mylotarg) for the treatment of newly diagnosed CD33-positive acute myeloid leukemia in adults, according to a press release.

Approval was based on results from three clinical trials. In the first, newly diagnosed AML patients who received gemtuzumab ozogamicin plus chemotherapy had significantly longer event-free survival than did patients who received chemotherapy alone. In a second trial, patients who received gemtuzumab ozogamicin alone had better overall survival compared to those who received only best supportive care. In the third clinical trial, 26% of patients who had experienced a relapse and received gemtuzumab ozogamicin experienced a remission.

Initial approval for gemtuzumab ozogamicin was granted in May 2000 as a stand-alone treatment for CD33-positive AML in older patients, but was withdrawn from the market after clinical trials failed to find any benefit and health concerns were noted. The current approval is based on a lower dosage, a more expansive patient population, and a schedule in combination with chemotherapy.

Common side effects of gemtuzumab ozogamicin include fever, nausea, infection, vomiting, bleeding, thrombocytopenia, stomatitis, constipation, rash, headache, elevated liver function tests, and neutropenia; it is not recommended for women who are pregnant or breastfeeding.

Gemtuzumab ozogamicin was also approved to treat patients older than 2 years old who have experienced a relapse or have not responded to initial treatment.

“Mylotarg’s history underscores the importance of examining alternative dosing, scheduling, and administration of therapies for patients with cancer, especially in those who may be most vulnerable to the side effects of treatment,” Richard Pazdur, MD, director of the FDA’s Oncology Center of Excellence, said in the press release.

[email protected]

 

Physicians will likely see a 0.31% uptick in their Medicare payments in 2018 and not the 0.5% promised in the Medicare Access and CHIP Reauthorization Act.

Officials at the Centers for Medicare and Medicaid Services were not able to find adequate funding in so-called misvalued codes to back the larger increase, as required by law, according to the proposed Medicare physician fee schedule for 2018.

TheaDesign/Thinkstock

CMS also failed to hit its misvalued code target in 2016, resulting in a 0.18% across-the-board reduction to the physician fee schedule in 2017 instead of the statutorily promised 0.5% increase.

Other provisions in the proposed Medicare physician fee schedule may be more palatable than the petite pay raise.

The proposal would roll back data reporting requirements of the Physician Quality Reporting System (PQRS), to better align them with the new Quality Payment Program (QPP), and will waive half of penalties assessed for not meeting PQRS requirements in 2016.

“We are proposing these changes based on stakeholder feedback and to better align with the MIPS [Merit-based Incentive Payment System track of the QPP] data submission requirements for the quality performance category,” according to a CMS fact sheet on the proposed fee schedule.

“This will allow some physicians who attempted to report for the 2016 performance period to avoid penalties and better align PQRS with MIPS as physicians transition to QPP,” officials from the American College of Physicians said in a statement.

Other physician organizations said they believed the proposal did not go far enough.

“While the reductions in penalties represent a move in the right direction, the [American College of Rheumatology] believes CMS should establish a value modifier adjustment of zero for 2018,” ACR officials said in a statement. “This would align with the agency’s policy to ‘zero out’ the impact of the resource use component of the Merit-based Incentive Payment System in 2019, the successor to the value modifier program. This provides additional time to continue refining the cost measures and gives physicians more time to understand the program.”

The proposed fee schedule also would delay implementation of the appropriate use criteria (AUC) for imaging services, a program that would deny payments for imaging services unless the ordering physician consulted the appropriate use criteria.

The American Medical Association “appreciates CMS’ decision to postpone the implementation of this requirement until 2019 and to make the first year an opportunity for testing and education where consultation would not be required as a condition of payment for imaging services,” according to a statement.

“We also applaud the proposed delay in implementing AUC for diagnostic imaging studies,” ACR said in its statement. “We will be gauging the readiness of our members to use clinical support systems. ... We support simplifying and phasing-in the program requirements. The ACR also strongly supports larger exemptions to the program,” such as physicians in small groups and rural and underserved areas.

The proposed fee schedule also seeks feedback from physicians and organizations on how Medicare Part B pays for biosimilars. Under the 2016 fee schedule, the average sales prices (ASPs) for all biosimilar products assigned to the same reference product are included in the same CPT code, meaning the ASPs for all biosimilars of a common reference product are used to determine a single reimbursement rate.

That CMS is looking deeper at this is being seen as a plus.

Biosimilars “tied to the same reference product may not share all indications with one another or the reference product [and] a blended payment model may cause significant confusion in a multitiered biosimilars market that may include both interchangeable and noninterchangeable products,” the Biosimilars Forum said in a statement. The current situation “may lead to decreased physician confidence in how they are reimbursed and also dramatically reduce the investment in the development of biosimilars and thereby limit treatment options available to patients.”

Both the Biosimilars Forum and the ACR support unique codes for each biosimilar.

“Physicians can better track and monitor their effectiveness and ensure adequate pharmacovigilance in the area of biosimilars” by employing unique codes, according to ACR officials.

The fee schedule proposal also would expand the Medicare Diabetes Prevention Program (DPP), currently a demonstration project, taking it nationwide in 2018. The proposal outlines the payment structure and supplier enrollment requirements and compliance standards, as well as beneficiary engagement incentives.

Physicians would be paid based on performance goals being met by patients, including meeting certain numbers of service and maintenance sessions with the program as well as achieving specific weight loss goals. For beneficiaries who are able to lose at least 5% of body weight, physicians could receive up to $810. If that weight loss goal is not achieved, the most a physician could receive is $125, according to a CMS fact sheet. Currently, DPP can only be employed via office visit; however, the proposal would allow virtual make-up sessions.

“The new proposal provides more flexibility to DPP providers in supporting patient engagement and attendance and by making performance-based payments available if patients meet weight-loss targets over longer periods of time,” according to the AMA.

The fee schedule also proposes more telemedicine coverage, specifically for counseling to discuss the need for lung cancer screening, including eligibility determination and shared decision making, as well psychotherapy for crisis, with codes for the first 60 minutes of intervention and a separate code for each additional 30 minutes. Four add-on codes have been proposed to supplement existing codes that cover interactive complexity, chronic care management services, and health risk assessment.

For clinicians providing behavioral health services, CMS is proposing an increased payment for providing face-to-face office-based services that better reflects overhead expenses.

Comments on the fee schedule update are due Sept. 11 and can be made here. The final rule is expected in early November.

 

[email protected]

 

Physicians will likely see a 0.31% uptick in their Medicare payments in 2018 and not the 0.5% promised in the Medicare Access and CHIP Reauthorization Act.

Officials at the Centers for Medicare and Medicaid Services were not able to find adequate funding in so-called misvalued codes to back the larger increase, as required by law, according to the proposed Medicare physician fee schedule for 2018.

TheaDesign/Thinkstock

CMS also failed to hit its misvalued code target in 2016, resulting in a 0.18% across-the-board reduction to the physician fee schedule in 2017 instead of the statutorily promised 0.5% increase.

Other provisions in the proposed Medicare physician fee schedule may be more palatable than the petite pay raise.

The proposal would roll back data reporting requirements of the Physician Quality Reporting System (PQRS), to better align them with the new Quality Payment Program (QPP), and will waive half of penalties assessed for not meeting PQRS requirements in 2016.

“We are proposing these changes based on stakeholder feedback and to better align with the MIPS [Merit-based Incentive Payment System track of the QPP] data submission requirements for the quality performance category,” according to a CMS fact sheet on the proposed fee schedule.

“This will allow some physicians who attempted to report for the 2016 performance period to avoid penalties and better align PQRS with MIPS as physicians transition to QPP,” officials from the American College of Physicians said in a statement.

Other physician organizations said they believed the proposal did not go far enough.

“While the reductions in penalties represent a move in the right direction, the [American College of Rheumatology] believes CMS should establish a value modifier adjustment of zero for 2018,” ACR officials said in a statement. “This would align with the agency’s policy to ‘zero out’ the impact of the resource use component of the Merit-based Incentive Payment System in 2019, the successor to the value modifier program. This provides additional time to continue refining the cost measures and gives physicians more time to understand the program.”

The proposed fee schedule also would delay implementation of the appropriate use criteria (AUC) for imaging services, a program that would deny payments for imaging services unless the ordering physician consulted the appropriate use criteria.

The American Medical Association “appreciates CMS’ decision to postpone the implementation of this requirement until 2019 and to make the first year an opportunity for testing and education where consultation would not be required as a condition of payment for imaging services,” according to a statement.

“We also applaud the proposed delay in implementing AUC for diagnostic imaging studies,” ACR said in its statement. “We will be gauging the readiness of our members to use clinical support systems. ... We support simplifying and phasing-in the program requirements. The ACR also strongly supports larger exemptions to the program,” such as physicians in small groups and rural and underserved areas.

The proposed fee schedule also seeks feedback from physicians and organizations on how Medicare Part B pays for biosimilars. Under the 2016 fee schedule, the average sales prices (ASPs) for all biosimilar products assigned to the same reference product are included in the same CPT code, meaning the ASPs for all biosimilars of a common reference product are used to determine a single reimbursement rate.

That CMS is looking deeper at this is being seen as a plus.

Biosimilars “tied to the same reference product may not share all indications with one another or the reference product [and] a blended payment model may cause significant confusion in a multitiered biosimilars market that may include both interchangeable and noninterchangeable products,” the Biosimilars Forum said in a statement. The current situation “may lead to decreased physician confidence in how they are reimbursed and also dramatically reduce the investment in the development of biosimilars and thereby limit treatment options available to patients.”

Both the Biosimilars Forum and the ACR support unique codes for each biosimilar.

“Physicians can better track and monitor their effectiveness and ensure adequate pharmacovigilance in the area of biosimilars” by employing unique codes, according to ACR officials.

The fee schedule proposal also would expand the Medicare Diabetes Prevention Program (DPP), currently a demonstration project, taking it nationwide in 2018. The proposal outlines the payment structure and supplier enrollment requirements and compliance standards, as well as beneficiary engagement incentives.

Physicians would be paid based on performance goals being met by patients, including meeting certain numbers of service and maintenance sessions with the program as well as achieving specific weight loss goals. For beneficiaries who are able to lose at least 5% of body weight, physicians could receive up to $810. If that weight loss goal is not achieved, the most a physician could receive is $125, according to a CMS fact sheet. Currently, DPP can only be employed via office visit; however, the proposal would allow virtual make-up sessions.

“The new proposal provides more flexibility to DPP providers in supporting patient engagement and attendance and by making performance-based payments available if patients meet weight-loss targets over longer periods of time,” according to the AMA.

The fee schedule also proposes more telemedicine coverage, specifically for counseling to discuss the need for lung cancer screening, including eligibility determination and shared decision making, as well psychotherapy for crisis, with codes for the first 60 minutes of intervention and a separate code for each additional 30 minutes. Four add-on codes have been proposed to supplement existing codes that cover interactive complexity, chronic care management services, and health risk assessment.

For clinicians providing behavioral health services, CMS is proposing an increased payment for providing face-to-face office-based services that better reflects overhead expenses.

Comments on the fee schedule update are due Sept. 11 and can be made here. The final rule is expected in early November.

 

[email protected]

 

Physicians will likely see a 0.31% uptick in their Medicare payments in 2018 and not the 0.5% promised in the Medicare Access and CHIP Reauthorization Act.

Officials at the Centers for Medicare and Medicaid Services were not able to find adequate funding in so-called misvalued codes to back the larger increase, as required by law, according to the proposed Medicare physician fee schedule for 2018.

TheaDesign/Thinkstock

CMS also failed to hit its misvalued code target in 2016, resulting in a 0.18% across-the-board reduction to the physician fee schedule in 2017 instead of the statutorily promised 0.5% increase.

Other provisions in the proposed Medicare physician fee schedule may be more palatable than the petite pay raise.

The proposal would roll back data reporting requirements of the Physician Quality Reporting System (PQRS), to better align them with the new Quality Payment Program (QPP), and will waive half of penalties assessed for not meeting PQRS requirements in 2016.

“We are proposing these changes based on stakeholder feedback and to better align with the MIPS [Merit-based Incentive Payment System track of the QPP] data submission requirements for the quality performance category,” according to a CMS fact sheet on the proposed fee schedule.

“This will allow some physicians who attempted to report for the 2016 performance period to avoid penalties and better align PQRS with MIPS as physicians transition to QPP,” officials from the American College of Physicians said in a statement.

Other physician organizations said they believed the proposal did not go far enough.

“While the reductions in penalties represent a move in the right direction, the [American College of Rheumatology] believes CMS should establish a value modifier adjustment of zero for 2018,” ACR officials said in a statement. “This would align with the agency’s policy to ‘zero out’ the impact of the resource use component of the Merit-based Incentive Payment System in 2019, the successor to the value modifier program. This provides additional time to continue refining the cost measures and gives physicians more time to understand the program.”

The proposed fee schedule also would delay implementation of the appropriate use criteria (AUC) for imaging services, a program that would deny payments for imaging services unless the ordering physician consulted the appropriate use criteria.

The American Medical Association “appreciates CMS’ decision to postpone the implementation of this requirement until 2019 and to make the first year an opportunity for testing and education where consultation would not be required as a condition of payment for imaging services,” according to a statement.

“We also applaud the proposed delay in implementing AUC for diagnostic imaging studies,” ACR said in its statement. “We will be gauging the readiness of our members to use clinical support systems. ... We support simplifying and phasing-in the program requirements. The ACR also strongly supports larger exemptions to the program,” such as physicians in small groups and rural and underserved areas.

The proposed fee schedule also seeks feedback from physicians and organizations on how Medicare Part B pays for biosimilars. Under the 2016 fee schedule, the average sales prices (ASPs) for all biosimilar products assigned to the same reference product are included in the same CPT code, meaning the ASPs for all biosimilars of a common reference product are used to determine a single reimbursement rate.

That CMS is looking deeper at this is being seen as a plus.

Biosimilars “tied to the same reference product may not share all indications with one another or the reference product [and] a blended payment model may cause significant confusion in a multitiered biosimilars market that may include both interchangeable and noninterchangeable products,” the Biosimilars Forum said in a statement. The current situation “may lead to decreased physician confidence in how they are reimbursed and also dramatically reduce the investment in the development of biosimilars and thereby limit treatment options available to patients.”

Both the Biosimilars Forum and the ACR support unique codes for each biosimilar.

“Physicians can better track and monitor their effectiveness and ensure adequate pharmacovigilance in the area of biosimilars” by employing unique codes, according to ACR officials.

The fee schedule proposal also would expand the Medicare Diabetes Prevention Program (DPP), currently a demonstration project, taking it nationwide in 2018. The proposal outlines the payment structure and supplier enrollment requirements and compliance standards, as well as beneficiary engagement incentives.

Physicians would be paid based on performance goals being met by patients, including meeting certain numbers of service and maintenance sessions with the program as well as achieving specific weight loss goals. For beneficiaries who are able to lose at least 5% of body weight, physicians could receive up to $810. If that weight loss goal is not achieved, the most a physician could receive is $125, according to a CMS fact sheet. Currently, DPP can only be employed via office visit; however, the proposal would allow virtual make-up sessions.

“The new proposal provides more flexibility to DPP providers in supporting patient engagement and attendance and by making performance-based payments available if patients meet weight-loss targets over longer periods of time,” according to the AMA.

The fee schedule also proposes more telemedicine coverage, specifically for counseling to discuss the need for lung cancer screening, including eligibility determination and shared decision making, as well psychotherapy for crisis, with codes for the first 60 minutes of intervention and a separate code for each additional 30 minutes. Four add-on codes have been proposed to supplement existing codes that cover interactive complexity, chronic care management services, and health risk assessment.

For clinicians providing behavioral health services, CMS is proposing an increased payment for providing face-to-face office-based services that better reflects overhead expenses.

Comments on the fee schedule update are due Sept. 11 and can be made here. The final rule is expected in early November.

 

[email protected]

In the early 1970s, clindamycin had only been on the market for a few years when patients taking the antibiotic began to present with diarrhea and associated colitis. Initial attempts to culture a pathologic organism were unsuccessful, so other possible pathophysiologic mechanisms, including medication toxicity, altered bacterial flora, or the emergence of a new bacterial or viral pathogen were considered. Patients were initially given treatments similar to those for ulcerative colitis, with systemic and topical steroids and colectomy. Several years later, Clostridium difficile infection (CDI) was identified as the culprit, and these presentations became increasingly common in U.S. hospitals, and later in community settings.

Incidentally, the organism had been discovered years earlier, in 1935, by a group of scientists studying normal bacterial flora in neonates, but it was not known to be pathogenic in adults. By 2007, CDI had become the most common cause of health care–associated infection in U.S. hospitals. This prompted the Centers for Disease Control and Prevention to begin active population- and laboratory-based surveillance for C. difficile through its Emerging Infections Program (EIP) with the goal of more accurately assessing disease burden, incidence, recurrence, and mortality by capturing data across the spectrum of health care delivery settings. The April 2015 issue of GI & Hepatology News highlighted a report of 2011 CDC data from 10 EIP sites (N Engl J Med. 372;9:825-34), demonstrating that CDI was responsible for nearly half a million infections and 29,000 deaths in that year across sites, with the hypervirulent NAP1 strain found to be more prevalent among health care–associated than community-associated infections.

Treatment of CDI continues to evolve. With increased use of fecal microbiota transplantation, emerging evidence regarding the efficacy of other novel therapies such as the monoclonal antibodies actoxumab and bezlotoxumab (providing passive immunity to toxins A and B, respectively), and development of preventive vaccines (currently in phase 2 trials), there is hope on the horizon of being able to improve patient outcomes and reduce the burden of CDI on the health care system.

Megan A. Adams, MD, JD, MSc, is a clinical lecturer in the division of gastroenterology at the University of Michigan, a gastroenterologist at the Ann Arbor, Mich., VA, and an investigator in the VA Ann Arbor Center for Clinical Management Research. She is an associate editor of GI & Hepatology News.

In the early 1970s, clindamycin had only been on the market for a few years when patients taking the antibiotic began to present with diarrhea and associated colitis. Initial attempts to culture a pathologic organism were unsuccessful, so other possible pathophysiologic mechanisms, including medication toxicity, altered bacterial flora, or the emergence of a new bacterial or viral pathogen were considered. Patients were initially given treatments similar to those for ulcerative colitis, with systemic and topical steroids and colectomy. Several years later, Clostridium difficile infection (CDI) was identified as the culprit, and these presentations became increasingly common in U.S. hospitals, and later in community settings.

Incidentally, the organism had been discovered years earlier, in 1935, by a group of scientists studying normal bacterial flora in neonates, but it was not known to be pathogenic in adults. By 2007, CDI had become the most common cause of health care–associated infection in U.S. hospitals. This prompted the Centers for Disease Control and Prevention to begin active population- and laboratory-based surveillance for C. difficile through its Emerging Infections Program (EIP) with the goal of more accurately assessing disease burden, incidence, recurrence, and mortality by capturing data across the spectrum of health care delivery settings. The April 2015 issue of GI & Hepatology News highlighted a report of 2011 CDC data from 10 EIP sites (N Engl J Med. 372;9:825-34), demonstrating that CDI was responsible for nearly half a million infections and 29,000 deaths in that year across sites, with the hypervirulent NAP1 strain found to be more prevalent among health care–associated than community-associated infections.

Treatment of CDI continues to evolve. With increased use of fecal microbiota transplantation, emerging evidence regarding the efficacy of other novel therapies such as the monoclonal antibodies actoxumab and bezlotoxumab (providing passive immunity to toxins A and B, respectively), and development of preventive vaccines (currently in phase 2 trials), there is hope on the horizon of being able to improve patient outcomes and reduce the burden of CDI on the health care system.

Megan A. Adams, MD, JD, MSc, is a clinical lecturer in the division of gastroenterology at the University of Michigan, a gastroenterologist at the Ann Arbor, Mich., VA, and an investigator in the VA Ann Arbor Center for Clinical Management Research. She is an associate editor of GI & Hepatology News.

In the early 1970s, clindamycin had only been on the market for a few years when patients taking the antibiotic began to present with diarrhea and associated colitis. Initial attempts to culture a pathologic organism were unsuccessful, so other possible pathophysiologic mechanisms, including medication toxicity, altered bacterial flora, or the emergence of a new bacterial or viral pathogen were considered. Patients were initially given treatments similar to those for ulcerative colitis, with systemic and topical steroids and colectomy. Several years later, Clostridium difficile infection (CDI) was identified as the culprit, and these presentations became increasingly common in U.S. hospitals, and later in community settings.

Incidentally, the organism had been discovered years earlier, in 1935, by a group of scientists studying normal bacterial flora in neonates, but it was not known to be pathogenic in adults. By 2007, CDI had become the most common cause of health care–associated infection in U.S. hospitals. This prompted the Centers for Disease Control and Prevention to begin active population- and laboratory-based surveillance for C. difficile through its Emerging Infections Program (EIP) with the goal of more accurately assessing disease burden, incidence, recurrence, and mortality by capturing data across the spectrum of health care delivery settings. The April 2015 issue of GI & Hepatology News highlighted a report of 2011 CDC data from 10 EIP sites (N Engl J Med. 372;9:825-34), demonstrating that CDI was responsible for nearly half a million infections and 29,000 deaths in that year across sites, with the hypervirulent NAP1 strain found to be more prevalent among health care–associated than community-associated infections.

Treatment of CDI continues to evolve. With increased use of fecal microbiota transplantation, emerging evidence regarding the efficacy of other novel therapies such as the monoclonal antibodies actoxumab and bezlotoxumab (providing passive immunity to toxins A and B, respectively), and development of preventive vaccines (currently in phase 2 trials), there is hope on the horizon of being able to improve patient outcomes and reduce the burden of CDI on the health care system.

Megan A. Adams, MD, JD, MSc, is a clinical lecturer in the division of gastroenterology at the University of Michigan, a gastroenterologist at the Ann Arbor, Mich., VA, and an investigator in the VA Ann Arbor Center for Clinical Management Research. She is an associate editor of GI & Hepatology News.

Fatigue is the most common clinical symptom of anemia and is a significant concern to patients.^1,2 In ambulatory patients, lower hemoglobin (Hb) concentration is associated with increased fatigue.^2,3 Accordingly, therapies that treat anemia by increasing Hb concentration, such as erythropoiesis stimulating agents,^4-7 often use fatigue as an outcome measure.

In hospitalized patients, transfusion of red blood cell increases Hb concentration and is the primary treatment for anemia. However, the extent to which transfusion and changes in Hb concentration affect hospitalized patients’ fatigue levels is not well established. Guidelines support transfusing patients with symptoms of anemia, such as fatigue, on the assumption that the increased oxygen delivery will improve the symptoms of anemia. While transfusion studies in hospitalized patients have consistently reported that transfusion at lower or “restrictive” Hb concentrations is safe compared with transfusion at higher Hb concentrations,^8-10 these studies have mainly used cardiac events and mortality as outcomes rather than patient symptoms, such as fatigue. Nevertheless, they have resulted in hospitals increasingly adopting restrictive transfusion policies that discourage transfusion at higher Hb levels.^11,12 Consequently, the rate of transfusion in hospitalized patients has decreased,¹³ raising questions of whether some patients with lower Hb concentrations may experience increased fatigue as a result of restrictive transfusion policies. Fatigue among hospitalized patients is important not only because it is an adverse symptom but because it may result in decreased activity levels, deconditioning, and losses in functional status.^14,15While the effect of alternative transfusion policies on fatigue in hospitalized patients could be answered by a randomized clinical trial using fatigue and functional status as outcomes, an important first step is to assess whether the Hb concentration of hospitalized patients is associated with their fatigue level during hospitalization. Because hospitalized patients often have acute illnesses that can cause fatigue in and of themselves, it is possible that anemia is not associated with fatigue in hospitalized patients despite anemia’s association with fatigue in ambulatory patients. Additionally, Hb concentration varies during hospitalization,¹⁶ raising the question of what measures of Hb during hospitalization might be most associated with anemia-related fatigue.

The objective of this study is to explore multiple Hb measures in hospitalized medical patients with anemia and test whether any of these Hb measures are associated with patients’ fatigue level.

METHODS

Study Design

We performed a prospective, observational study of hospitalized patients with anemia on the general medicine services at The University of Chicago Medical Center (UCMC). The institutional review board approved the study procedures, and all study subjects provided informed consent.

Study Eligibility

Between April 2014 and June 2015, all general medicine inpatients were approached for written consent for The University of Chicago Hospitalist Project,¹⁷ a research infrastructure at UCMC. Among patients consenting to participate in the Hospitalist Project, patients were eligible if they had Hb <9 g/dL at any point during their hospitalization and were age ≥50 years. Hb concentration of <9 g/dL was chosen to include the range of Hb values covered by most restrictive transfusion policies.^8-10,18 Age ≥50 years was an inclusion criteria because anemia is more strongly associated with poor outcomes, including functional impairment, among older patients compared with younger patients.^14,19-21 If patients were not eligible for inclusion at the time of consent for the Hospitalist Project, their Hb values were reviewed twice daily until hospital discharge to assess if their Hb was <9 g/dL. Proxies were sought to answer questions for patients who failed the Short Portable Mental Status Questionnaire.²²

Patient Demographic Data Collection

Research assistants abstracted patient age and sex from the electronic health record (EHR), and asked patients to self-identify their race. The individual comorbidities included as part of the Charlson Comorbidity Index were identified using International Classification of Diseases, 9th Revision codes from hospital administrative data for each encounter and specifically included the following: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, hemiplegia and/or paraplegia, renal disease, cancer, and human immunodeficiency virus/acquired immunodeficiency syndrome.²³ We also used Healthcare Cost and Utilization Project (www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp) diagnosis categories to identify whether patients had sickle cell (SC) anemia, gastrointestinal bleeding (GIB), or a depressive disorder (DD) because these conditions are associated with anemia (SC and GIB) and fatigue (DD).²⁴

 

Measuring Anemia

Hb measures were available only when hospital providers ordered them as part of routine practice. The first Hb concentration <9 g/dL during a patient’s hospitalization, which made them eligible for study participation, was obtained through manual review of the EHR. All additional Hb values during the patient’s hospitalization were obtained from the hospital’s administrative data mart. All Hb values collected for each patient during the hospitalization were used to calculate summary measures of Hb during the hospitalization, including the mean Hb, median Hb, minimum Hb, maximum Hb, admission (first recorded) Hb, and discharge (last recorded) Hb. Hb measures were analyzed both as a continuous variable and as a categorical variable created by dividing the continuous Hb measures into integer ranges of 3 groups of approximately the same size.

Measuring Fatigue

Our primary outcome was patients’ level of fatigue reported during hospitalization, measured using the Functional Assessment of Chronic Illness Therapy (FACIT)-Anemia questionnaire. Fatigue was measured using a 13-question fatigue subscale,^1,2,25 which measures fatigue within the past 7 days. Scores on the fatigue subscale range from 0 to 52, with lower scores reflecting greater levels of fatigue. As soon as patients met the eligibility criteria for study participation during their hospitalization (age ≥50 years and Hb <9 g/dL), they were approached to answer the FACIT questions. Values for missing data in the fatigue subscale for individual subjects were filled in using a prorated score from their answered questions as long as >50% of the items in the fatigue subscale were answered, in accordance with recommendations for addressing missing data in the FACIT.²⁶ Fatigue was analyzed as a continuous variable and as a dichotomous variable created by dividing the sample into high (FACIT <27) and low (FACIT ≥27) levels of fatigue based on the median FACIT score of the population. Previous literature has shown a FACIT fatigue subscale score between 23 and 26 to be associated with an Eastern Cooperative Oncology Group (ECOG)²⁷ C Performance Status rating of 2 to 3³ compared to scores ≥27.

Statistical Analysis

Statistical analysis was performed using Stata statistical software (StataCorp, College Station, TX). Descriptive statistics were used to characterize patient demographics. Analysis of variance was used to test for differences in the mean fatigue levels across Hb measures. χ² tests were performed to test for associations between high fatigue levels and the Hb measures. Multivariable analysis, including both linear and logistic regression models, were used to test the association of Hb concentration and fatigue. P values <0.05 using a 2-tailed test were deemed statistically significant.

RESULTS

Patient Characteristics

During the study period, 8559 patients were admitted to the general medicine service. Of those, 5073 (59%) consented for participation in the Hospitalist Project, and 3670 (72%) completed the Hospitalist Project inpatient interview. Of these patients, 1292 (35%) had Hb <9 g/dL, and 784 (61%) were 50 years or older and completed the FACIT questionnaire.

Table 1 reports the demographic characteristics and comorbidities for the sample, the mean (standard deviation [SD]) for the 6 Hb measures, and mean (SD) and median FACIT scores.

Bivariate Association of Fatigue and Hb

Categorizing patients into low, middle, or high Hb for each of the 6 Hb measures, minimum Hb was strongly associated with fatigue, with a weaker association for mean Hb and no statistically significant association for the other measures.

Minimum Hb. Patients with a minimum Hb <7 g/dL and patients with Hb 7-8 g/dL had higher fatigue levels (FACIT = 25 for each) than patients with a minimum Hb ≥8 g/dL (FACIT = 29; P < 0.001; Table 2). When excluding patients with SC and/or GIB because their average minimum Hb differed from the average minimum Hb of the full population (P < 0.001), patients with a minimum Hb <7 g/dL or 7-8 g/dL had even higher fatigue levels (FACIT = 23 and FACIT = 24, respectively), with no change in the fatigue level of patients with a minimum Hb ≥8 g/dL (FACIT = 29; P < 0.001; Table 2). Lower minimum Hb continued to be associated with higher fatigue levels when analyzed in 0.5 g/dL increments (Figure).

Lower values of minimum Hb were also associated with patients reporting high fatigue levels (FACIT <27). Fatigue levels were high for 50% of patients with a minimum Hb <7 g/dL and 56% of patients with a minimum Hb 7–8 g/dL compared with only 41% of patients with a minimum Hb ≥8 g/dL (P < 0.002). Excluding patients with SC and/or GIB, fatigue levels were high for 54% of patients with a minimum Hb <7 g/dL and 57% of patients with a minimum Hb 7-8 g/dL compared with 41% of patients with a minimum Hb ≥8 g/dL (P < 0.001; Table 2).

Mean Hb and Other Measures. Fatigue levels were high for 47% of patients with a mean Hb <8g /dL and 53% of patients with a mean Hb 8-9 g/dL compared with 43% of patients with a mean Hb ≥9 g/dL (P = 0.05). However, the association between high fatigue and mean Hb was not statistically significant when patients with SC and/or GIB were excluded (Table 2). None of the other 4 Hb measures was significantly associated with fatigue.

 

Linear Regression of Fatigue on Hb

In linear regression models, minimum Hb consistently predicted patient fatigue, mean Hb had a less robust association with fatigue, and the other Hb measures were not associated with patient fatigue. Increases in minimum Hb (analyzed as a continuous variable) were associated with reduced fatigue (higher FACIT score; β = 1.4; P = 0.005). In models in which minimum Hb was a categorical variable, patients with a minimum Hb of <7 g/dL or 7-8 g/dL had greater fatigue (lower FACIT score) than patients whose minimum Hb was ≥8 g/dL (Hb <7 g/dL: β = −4.2; P ≤ 0.001; Hb 7-8 g/dL: β = −4.1; P < 0.001). These results control for patients’ age, sex, individual comorbidities, and whether their minimum Hb occurred before or after the measurement of fatigue during hospitalization (Model 1), and the results are unchanged when also controlling for the number of Hb laboratory draws patients had during their hospitalization (Model 2; Table 3). In a stratified analysis excluding patients with either SC and/or GIB, changes in minimum Hb were associated with larger changes in patient fatigue levels (Supplemental Table 1). We also stratified our analysis to include only patients whose minimum Hb occurred before the measurement of their fatigue level during hospitalization to avoid a spurious association of fatigue with minimum Hb occurring after fatigue was measured. In both Models 1 and 2, minimum Hb remained a predictor of patients’ fatigue levels with similar effect sizes, although in Model 2, the results did not quite reach a statistically significant level, in part due to larger confidence intervals from the smaller sample size of this stratified analysis (Supplemental Table 2a). We further stratified this analysis to include only patients whose transfusion, if they received one, occurred after their minimum Hb and the measurement of their fatigue level to account for the possibility that a transfusion could affect the fatigue level patients report. In this analysis, most of the estimates of the effect of minimum Hb on fatigue were larger than those seen when only analyzing patients whose minimum Hb occurred before the measurement of their fatigue level, although again, the smaller sample size of this additional stratified analysis does produce larger confidence intervals for these estimates (Supplemental Table 2b).

Analyzed as a categorical variable, a mean Hb <8 g/dL or 8-9 g/dL was also associated with higher levels of fatigue compared with patients whose mean Hb is ≥9 g/dL in both Models 1 and 2, although the results were only statistically significant for patients with a mean Hb 8-9 g/dL (β=−2.5; P < 0.04; Table 3). There were no statistically significant associations between mean Hb and fatigue when excluding SC and/or GIB patients (Supplemental Table 3).

No Hb measure other than minimum or mean had significant association with patient fatigue levels in linear regression models.

Logistic Regression of High Fatigue Level on Hb

Using logistic regression, minimum Hb analyzed as a categorical variable predicted increased odds of a high fatigue level. Patients with a minimum Hb <7 g/dL were 50% (odds ratio [OR] = 1.5; P = 0.03) more likely to have high fatigue and patients with a minimum Hb 7-8 g/dL were 90% (OR = 1.9; P < 0.001) more likely to have high fatigue compared with patients with a minimum Hb ≥8 g/dL in Model 1. These results were similar in Model 2, although the effect was only statistically significant in the 7-8 g/dL Hb group (Table 3). When excluding SC and/or GIB patients, the odds of having high fatigue as minimum Hb decreased were the same or higher for both models compared to the full population of patients. However, again, in Model 2, the effect was only statistically significant in the 7-8 g/dL Hb group (Supplemental Table 1).

Patients with a mean Hb <8 g/dL were 20% to 30% more likely to have high fatigue and patients with mean Hb 8-9 g/dL were 50% more likely to have high fatigue compared with patients with a mean Hb ≥9 g/dL, but the effects were only statistically significant for patients with a mean Hb 8-9 g/dL in both Models 1 and 2 (Table 3). These results were similar when excluding patients with SC and/or GIB, but they were only significant for patients with a mean Hb 8-9 g/dL in Model 1 and patients with a mean Hb <8 g/dL in the Model 2 (Supplemental Table 3).

DISCUSSION

These results demonstrate that minimum Hb during hospitalization is associated with fatigue in hospitalized patients age ≥50 years, and the association is stronger among patients without SC and/or GIB as comorbidities. The analysis of Hb as a continuous and categorical variable and the use of both linear and logistic regression models support the robustness of these associations and illuminate their clinical significance. For example, in linear regression with minimum Hb a continuous variable, the coefficient of 1.4 suggests that an increase of 2 g/dL in Hb, as might be expected from transfusion of 2 units of red blood cells, would be associated with about a 3-point improvement in fatigue. Additionally, as a categorical variable, a minimum Hb ≥8 g/dL compared with a minimum Hb <7 g/dL or 7-8 g/dL is associated with a 3- to 4-point improvement in fatigue. Previous literature suggests that a difference of 3 in the FACIT score is the minimum clinically important difference in fatigue,³ and changes in minimum Hb in either model predict changes in fatigue that are in the range of potential clinical significance.

 

The clinical significance of the findings is also reflected in the results of the logistic regressions, which may be mapped to potential effects on functional status. Specifically, the odds of having a high fatigue level (FACIT <27) increase 90% for persons with a minimum Hb 7–8 g/dL compared with persons with a minimum Hb ≥8 g/dL. For persons with a minimum Hb <7 g/dL, point estimates suggest a smaller (50%) increase in the odds of high fatigue, but the 95% confidence interval overlaps heavily with the estimate of patients whose minimum Hb is 7-8 g/dL. While it might be expected that patients with a minimum Hb <7 g/dL have greater levels of fatigue compared with patients with a minimum Hb 7-8 g/dL, we did not observe such a pattern. One reason may be that the confidence intervals of our estimated effects are wide enough that we cannot exclude such a pattern. Another possible explanation is that in both groups, the fatigue levels are sufficiently severe, such that the difference in their fatigue levels may not be clinically meaningful. For example, a FACIT score of 23 to 26 has been shown to be associated with an ECOG performance status of 2 to 3, requiring bed rest for at least part of the day.³ Therefore, patients with a minimum Hb 7-8 g/dL (mean FACIT score = 24; Table 2) or a minimum Hb of <7 g/dL (mean FACIT score = 23; Table 2) are already functionally limited to the point of being partially bed bound, such that further decreases in their Hb may not produce additional fatigue in part because they reduce their activity sufficiently to prevent an increase in fatigue. In such cases, the potential benefits of increased Hb may be better assessed by measuring fatigue in response to a specific and provoked activity level, a concept known as fatigability.²⁰

That minimum Hb is more strongly associated with fatigue than any other measure of Hb during hospitalization may not be surprising. Mean, median, maximum, and discharge Hb may all be affected by transfusion during hospitalization that could affect fatigue. Admission Hb may not reflect true oxygen-carrying capacity because of hemoconcentration.

The association between Hb and fatigue in hospitalized patients is important because increased fatigue could contribute to slower clinical recovery in hospitalized patients. Additionally, increased fatigue during hospitalization and at hospital discharge could exacerbate the known deleterious consequences of fatigue on patients and their health outcomes^14,15 after hospital discharge. Although one previous study, the Functional Outcomes in Cardiovascular Patients Undergoing Surgical Hip Fracture Repair (FOCUS)⁸ trial, did not report differences in patients’ fatigue levels at 30 and 60 days postdischarge when transfused at restrictive (8 g/dL) compared with liberal (10 g/dL) Hb thresholds, confidence in the validity of this finding is reduced by the fact that more than half of the patients were lost to follow-up at the 30- and 60-day time points. Further, patients in the restrictive transfusion arm of FOCUS were transfused to maintain Hb levels at or above 8 g/dL. This transfusion threshold of 8 g/dL may have mitigated the high levels of fatigue that are seen in our study when patients’ Hb drops below 8 g/dL, and maintaining a Hb level of 7 g/dL is now the standard of care in stable hospitalized patients. Lastly, FOCUS was limited to postoperative hip fracture patients, and the generalizability of FOCUS to hospitalized medicine patients with anemia is limited.

Therefore, our results support guideline suggestions that practitioners incorporate the presence of patient symptoms such as fatigue into transfusion decisions, particularly if patients’ Hb is <8 g/dL.¹⁸ Though reasonable, the suggestion to incorporate symptoms such as fatigue into transfusion decisions has not been strongly supported by evidence so far, and it may often be neglected in practice. Definitive evidence to support such recommendations would benefit from study through an optimal trial¹⁸ that incorporates symptoms into decision making. Our findings add support for a study of transfusion strategies that incorporates patients’ fatigue level in addition to Hb concentration.

This study has several limitations. Although our sample size is large and includes patients with a range of comorbidities that we believe are representative of hospitalized general medicine patients, as a single-center, observational study, our results may not be generalizable to other centers. Additionally, although these data support a reliable association between hospitalized patients’ minimum Hb and fatigue level, the observational design of this study cannot prove that this relationship is causal. Also, patients’ Hb values were measured at the discretion of their clinician, and therefore, the measures of Hb were not uniformly measured for participating patients. In addition, fatigue was only measured at one time point during a patient’s hospitalization, and it is possible that patients’ fatigue levels change during hospitalization in relation to variables we did not consider. Finally, our study was not designed to assess the association of Hb with longer-term functional outcomes, which may be of greater concern than fatigue.

 

CONCLUSION

In hospitalized patients ≥50 years old, minimum Hb is reliably associated with patients’ fatigue level. Patients whose minimum Hb is <8 g/dL experience higher fatigue levels compared to patients whose minimum Hb is ≥8 g/dL. Additional studies are warranted to understand if patients may benefit from improved fatigue levels by correcting their anemia through transfusion.

Fatigue is the most common clinical symptom of anemia and is a significant concern to patients.^1,2 In ambulatory patients, lower hemoglobin (Hb) concentration is associated with increased fatigue.^2,3 Accordingly, therapies that treat anemia by increasing Hb concentration, such as erythropoiesis stimulating agents,^4-7 often use fatigue as an outcome measure.

In hospitalized patients, transfusion of red blood cell increases Hb concentration and is the primary treatment for anemia. However, the extent to which transfusion and changes in Hb concentration affect hospitalized patients’ fatigue levels is not well established. Guidelines support transfusing patients with symptoms of anemia, such as fatigue, on the assumption that the increased oxygen delivery will improve the symptoms of anemia. While transfusion studies in hospitalized patients have consistently reported that transfusion at lower or “restrictive” Hb concentrations is safe compared with transfusion at higher Hb concentrations,^8-10 these studies have mainly used cardiac events and mortality as outcomes rather than patient symptoms, such as fatigue. Nevertheless, they have resulted in hospitals increasingly adopting restrictive transfusion policies that discourage transfusion at higher Hb levels.^11,12 Consequently, the rate of transfusion in hospitalized patients has decreased,¹³ raising questions of whether some patients with lower Hb concentrations may experience increased fatigue as a result of restrictive transfusion policies. Fatigue among hospitalized patients is important not only because it is an adverse symptom but because it may result in decreased activity levels, deconditioning, and losses in functional status.^14,15While the effect of alternative transfusion policies on fatigue in hospitalized patients could be answered by a randomized clinical trial using fatigue and functional status as outcomes, an important first step is to assess whether the Hb concentration of hospitalized patients is associated with their fatigue level during hospitalization. Because hospitalized patients often have acute illnesses that can cause fatigue in and of themselves, it is possible that anemia is not associated with fatigue in hospitalized patients despite anemia’s association with fatigue in ambulatory patients. Additionally, Hb concentration varies during hospitalization,¹⁶ raising the question of what measures of Hb during hospitalization might be most associated with anemia-related fatigue.

The objective of this study is to explore multiple Hb measures in hospitalized medical patients with anemia and test whether any of these Hb measures are associated with patients’ fatigue level.

METHODS

Study Design

We performed a prospective, observational study of hospitalized patients with anemia on the general medicine services at The University of Chicago Medical Center (UCMC). The institutional review board approved the study procedures, and all study subjects provided informed consent.

Study Eligibility

Between April 2014 and June 2015, all general medicine inpatients were approached for written consent for The University of Chicago Hospitalist Project,¹⁷ a research infrastructure at UCMC. Among patients consenting to participate in the Hospitalist Project, patients were eligible if they had Hb <9 g/dL at any point during their hospitalization and were age ≥50 years. Hb concentration of <9 g/dL was chosen to include the range of Hb values covered by most restrictive transfusion policies.^8-10,18 Age ≥50 years was an inclusion criteria because anemia is more strongly associated with poor outcomes, including functional impairment, among older patients compared with younger patients.^14,19-21 If patients were not eligible for inclusion at the time of consent for the Hospitalist Project, their Hb values were reviewed twice daily until hospital discharge to assess if their Hb was <9 g/dL. Proxies were sought to answer questions for patients who failed the Short Portable Mental Status Questionnaire.²²

Patient Demographic Data Collection

Research assistants abstracted patient age and sex from the electronic health record (EHR), and asked patients to self-identify their race. The individual comorbidities included as part of the Charlson Comorbidity Index were identified using International Classification of Diseases, 9th Revision codes from hospital administrative data for each encounter and specifically included the following: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, hemiplegia and/or paraplegia, renal disease, cancer, and human immunodeficiency virus/acquired immunodeficiency syndrome.²³ We also used Healthcare Cost and Utilization Project (www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp) diagnosis categories to identify whether patients had sickle cell (SC) anemia, gastrointestinal bleeding (GIB), or a depressive disorder (DD) because these conditions are associated with anemia (SC and GIB) and fatigue (DD).²⁴

 

Measuring Anemia

Hb measures were available only when hospital providers ordered them as part of routine practice. The first Hb concentration <9 g/dL during a patient’s hospitalization, which made them eligible for study participation, was obtained through manual review of the EHR. All additional Hb values during the patient’s hospitalization were obtained from the hospital’s administrative data mart. All Hb values collected for each patient during the hospitalization were used to calculate summary measures of Hb during the hospitalization, including the mean Hb, median Hb, minimum Hb, maximum Hb, admission (first recorded) Hb, and discharge (last recorded) Hb. Hb measures were analyzed both as a continuous variable and as a categorical variable created by dividing the continuous Hb measures into integer ranges of 3 groups of approximately the same size.

Measuring Fatigue

Our primary outcome was patients’ level of fatigue reported during hospitalization, measured using the Functional Assessment of Chronic Illness Therapy (FACIT)-Anemia questionnaire. Fatigue was measured using a 13-question fatigue subscale,^1,2,25 which measures fatigue within the past 7 days. Scores on the fatigue subscale range from 0 to 52, with lower scores reflecting greater levels of fatigue. As soon as patients met the eligibility criteria for study participation during their hospitalization (age ≥50 years and Hb <9 g/dL), they were approached to answer the FACIT questions. Values for missing data in the fatigue subscale for individual subjects were filled in using a prorated score from their answered questions as long as >50% of the items in the fatigue subscale were answered, in accordance with recommendations for addressing missing data in the FACIT.²⁶ Fatigue was analyzed as a continuous variable and as a dichotomous variable created by dividing the sample into high (FACIT <27) and low (FACIT ≥27) levels of fatigue based on the median FACIT score of the population. Previous literature has shown a FACIT fatigue subscale score between 23 and 26 to be associated with an Eastern Cooperative Oncology Group (ECOG)²⁷ C Performance Status rating of 2 to 3³ compared to scores ≥27.

Statistical Analysis

Statistical analysis was performed using Stata statistical software (StataCorp, College Station, TX). Descriptive statistics were used to characterize patient demographics. Analysis of variance was used to test for differences in the mean fatigue levels across Hb measures. χ² tests were performed to test for associations between high fatigue levels and the Hb measures. Multivariable analysis, including both linear and logistic regression models, were used to test the association of Hb concentration and fatigue. P values <0.05 using a 2-tailed test were deemed statistically significant.

RESULTS

Patient Characteristics

During the study period, 8559 patients were admitted to the general medicine service. Of those, 5073 (59%) consented for participation in the Hospitalist Project, and 3670 (72%) completed the Hospitalist Project inpatient interview. Of these patients, 1292 (35%) had Hb <9 g/dL, and 784 (61%) were 50 years or older and completed the FACIT questionnaire.

Table 1 reports the demographic characteristics and comorbidities for the sample, the mean (standard deviation [SD]) for the 6 Hb measures, and mean (SD) and median FACIT scores.

Bivariate Association of Fatigue and Hb

Categorizing patients into low, middle, or high Hb for each of the 6 Hb measures, minimum Hb was strongly associated with fatigue, with a weaker association for mean Hb and no statistically significant association for the other measures.

Minimum Hb. Patients with a minimum Hb <7 g/dL and patients with Hb 7-8 g/dL had higher fatigue levels (FACIT = 25 for each) than patients with a minimum Hb ≥8 g/dL (FACIT = 29; P < 0.001; Table 2). When excluding patients with SC and/or GIB because their average minimum Hb differed from the average minimum Hb of the full population (P < 0.001), patients with a minimum Hb <7 g/dL or 7-8 g/dL had even higher fatigue levels (FACIT = 23 and FACIT = 24, respectively), with no change in the fatigue level of patients with a minimum Hb ≥8 g/dL (FACIT = 29; P < 0.001; Table 2). Lower minimum Hb continued to be associated with higher fatigue levels when analyzed in 0.5 g/dL increments (Figure).

Lower values of minimum Hb were also associated with patients reporting high fatigue levels (FACIT <27). Fatigue levels were high for 50% of patients with a minimum Hb <7 g/dL and 56% of patients with a minimum Hb 7–8 g/dL compared with only 41% of patients with a minimum Hb ≥8 g/dL (P < 0.002). Excluding patients with SC and/or GIB, fatigue levels were high for 54% of patients with a minimum Hb <7 g/dL and 57% of patients with a minimum Hb 7-8 g/dL compared with 41% of patients with a minimum Hb ≥8 g/dL (P < 0.001; Table 2).

Mean Hb and Other Measures. Fatigue levels were high for 47% of patients with a mean Hb <8g /dL and 53% of patients with a mean Hb 8-9 g/dL compared with 43% of patients with a mean Hb ≥9 g/dL (P = 0.05). However, the association between high fatigue and mean Hb was not statistically significant when patients with SC and/or GIB were excluded (Table 2). None of the other 4 Hb measures was significantly associated with fatigue.

 

Linear Regression of Fatigue on Hb

In linear regression models, minimum Hb consistently predicted patient fatigue, mean Hb had a less robust association with fatigue, and the other Hb measures were not associated with patient fatigue. Increases in minimum Hb (analyzed as a continuous variable) were associated with reduced fatigue (higher FACIT score; β = 1.4; P = 0.005). In models in which minimum Hb was a categorical variable, patients with a minimum Hb of <7 g/dL or 7-8 g/dL had greater fatigue (lower FACIT score) than patients whose minimum Hb was ≥8 g/dL (Hb <7 g/dL: β = −4.2; P ≤ 0.001; Hb 7-8 g/dL: β = −4.1; P < 0.001). These results control for patients’ age, sex, individual comorbidities, and whether their minimum Hb occurred before or after the measurement of fatigue during hospitalization (Model 1), and the results are unchanged when also controlling for the number of Hb laboratory draws patients had during their hospitalization (Model 2; Table 3). In a stratified analysis excluding patients with either SC and/or GIB, changes in minimum Hb were associated with larger changes in patient fatigue levels (Supplemental Table 1). We also stratified our analysis to include only patients whose minimum Hb occurred before the measurement of their fatigue level during hospitalization to avoid a spurious association of fatigue with minimum Hb occurring after fatigue was measured. In both Models 1 and 2, minimum Hb remained a predictor of patients’ fatigue levels with similar effect sizes, although in Model 2, the results did not quite reach a statistically significant level, in part due to larger confidence intervals from the smaller sample size of this stratified analysis (Supplemental Table 2a). We further stratified this analysis to include only patients whose transfusion, if they received one, occurred after their minimum Hb and the measurement of their fatigue level to account for the possibility that a transfusion could affect the fatigue level patients report. In this analysis, most of the estimates of the effect of minimum Hb on fatigue were larger than those seen when only analyzing patients whose minimum Hb occurred before the measurement of their fatigue level, although again, the smaller sample size of this additional stratified analysis does produce larger confidence intervals for these estimates (Supplemental Table 2b).

Analyzed as a categorical variable, a mean Hb <8 g/dL or 8-9 g/dL was also associated with higher levels of fatigue compared with patients whose mean Hb is ≥9 g/dL in both Models 1 and 2, although the results were only statistically significant for patients with a mean Hb 8-9 g/dL (β=−2.5; P < 0.04; Table 3). There were no statistically significant associations between mean Hb and fatigue when excluding SC and/or GIB patients (Supplemental Table 3).

No Hb measure other than minimum or mean had significant association with patient fatigue levels in linear regression models.

Logistic Regression of High Fatigue Level on Hb

Using logistic regression, minimum Hb analyzed as a categorical variable predicted increased odds of a high fatigue level. Patients with a minimum Hb <7 g/dL were 50% (odds ratio [OR] = 1.5; P = 0.03) more likely to have high fatigue and patients with a minimum Hb 7-8 g/dL were 90% (OR = 1.9; P < 0.001) more likely to have high fatigue compared with patients with a minimum Hb ≥8 g/dL in Model 1. These results were similar in Model 2, although the effect was only statistically significant in the 7-8 g/dL Hb group (Table 3). When excluding SC and/or GIB patients, the odds of having high fatigue as minimum Hb decreased were the same or higher for both models compared to the full population of patients. However, again, in Model 2, the effect was only statistically significant in the 7-8 g/dL Hb group (Supplemental Table 1).

Patients with a mean Hb <8 g/dL were 20% to 30% more likely to have high fatigue and patients with mean Hb 8-9 g/dL were 50% more likely to have high fatigue compared with patients with a mean Hb ≥9 g/dL, but the effects were only statistically significant for patients with a mean Hb 8-9 g/dL in both Models 1 and 2 (Table 3). These results were similar when excluding patients with SC and/or GIB, but they were only significant for patients with a mean Hb 8-9 g/dL in Model 1 and patients with a mean Hb <8 g/dL in the Model 2 (Supplemental Table 3).

DISCUSSION

These results demonstrate that minimum Hb during hospitalization is associated with fatigue in hospitalized patients age ≥50 years, and the association is stronger among patients without SC and/or GIB as comorbidities. The analysis of Hb as a continuous and categorical variable and the use of both linear and logistic regression models support the robustness of these associations and illuminate their clinical significance. For example, in linear regression with minimum Hb a continuous variable, the coefficient of 1.4 suggests that an increase of 2 g/dL in Hb, as might be expected from transfusion of 2 units of red blood cells, would be associated with about a 3-point improvement in fatigue. Additionally, as a categorical variable, a minimum Hb ≥8 g/dL compared with a minimum Hb <7 g/dL or 7-8 g/dL is associated with a 3- to 4-point improvement in fatigue. Previous literature suggests that a difference of 3 in the FACIT score is the minimum clinically important difference in fatigue,³ and changes in minimum Hb in either model predict changes in fatigue that are in the range of potential clinical significance.

 

The clinical significance of the findings is also reflected in the results of the logistic regressions, which may be mapped to potential effects on functional status. Specifically, the odds of having a high fatigue level (FACIT <27) increase 90% for persons with a minimum Hb 7–8 g/dL compared with persons with a minimum Hb ≥8 g/dL. For persons with a minimum Hb <7 g/dL, point estimates suggest a smaller (50%) increase in the odds of high fatigue, but the 95% confidence interval overlaps heavily with the estimate of patients whose minimum Hb is 7-8 g/dL. While it might be expected that patients with a minimum Hb <7 g/dL have greater levels of fatigue compared with patients with a minimum Hb 7-8 g/dL, we did not observe such a pattern. One reason may be that the confidence intervals of our estimated effects are wide enough that we cannot exclude such a pattern. Another possible explanation is that in both groups, the fatigue levels are sufficiently severe, such that the difference in their fatigue levels may not be clinically meaningful. For example, a FACIT score of 23 to 26 has been shown to be associated with an ECOG performance status of 2 to 3, requiring bed rest for at least part of the day.³ Therefore, patients with a minimum Hb 7-8 g/dL (mean FACIT score = 24; Table 2) or a minimum Hb of <7 g/dL (mean FACIT score = 23; Table 2) are already functionally limited to the point of being partially bed bound, such that further decreases in their Hb may not produce additional fatigue in part because they reduce their activity sufficiently to prevent an increase in fatigue. In such cases, the potential benefits of increased Hb may be better assessed by measuring fatigue in response to a specific and provoked activity level, a concept known as fatigability.²⁰

That minimum Hb is more strongly associated with fatigue than any other measure of Hb during hospitalization may not be surprising. Mean, median, maximum, and discharge Hb may all be affected by transfusion during hospitalization that could affect fatigue. Admission Hb may not reflect true oxygen-carrying capacity because of hemoconcentration.

The association between Hb and fatigue in hospitalized patients is important because increased fatigue could contribute to slower clinical recovery in hospitalized patients. Additionally, increased fatigue during hospitalization and at hospital discharge could exacerbate the known deleterious consequences of fatigue on patients and their health outcomes^14,15 after hospital discharge. Although one previous study, the Functional Outcomes in Cardiovascular Patients Undergoing Surgical Hip Fracture Repair (FOCUS)⁸ trial, did not report differences in patients’ fatigue levels at 30 and 60 days postdischarge when transfused at restrictive (8 g/dL) compared with liberal (10 g/dL) Hb thresholds, confidence in the validity of this finding is reduced by the fact that more than half of the patients were lost to follow-up at the 30- and 60-day time points. Further, patients in the restrictive transfusion arm of FOCUS were transfused to maintain Hb levels at or above 8 g/dL. This transfusion threshold of 8 g/dL may have mitigated the high levels of fatigue that are seen in our study when patients’ Hb drops below 8 g/dL, and maintaining a Hb level of 7 g/dL is now the standard of care in stable hospitalized patients. Lastly, FOCUS was limited to postoperative hip fracture patients, and the generalizability of FOCUS to hospitalized medicine patients with anemia is limited.

Therefore, our results support guideline suggestions that practitioners incorporate the presence of patient symptoms such as fatigue into transfusion decisions, particularly if patients’ Hb is <8 g/dL.¹⁸ Though reasonable, the suggestion to incorporate symptoms such as fatigue into transfusion decisions has not been strongly supported by evidence so far, and it may often be neglected in practice. Definitive evidence to support such recommendations would benefit from study through an optimal trial¹⁸ that incorporates symptoms into decision making. Our findings add support for a study of transfusion strategies that incorporates patients’ fatigue level in addition to Hb concentration.

This study has several limitations. Although our sample size is large and includes patients with a range of comorbidities that we believe are representative of hospitalized general medicine patients, as a single-center, observational study, our results may not be generalizable to other centers. Additionally, although these data support a reliable association between hospitalized patients’ minimum Hb and fatigue level, the observational design of this study cannot prove that this relationship is causal. Also, patients’ Hb values were measured at the discretion of their clinician, and therefore, the measures of Hb were not uniformly measured for participating patients. In addition, fatigue was only measured at one time point during a patient’s hospitalization, and it is possible that patients’ fatigue levels change during hospitalization in relation to variables we did not consider. Finally, our study was not designed to assess the association of Hb with longer-term functional outcomes, which may be of greater concern than fatigue.

 

CONCLUSION

In hospitalized patients ≥50 years old, minimum Hb is reliably associated with patients’ fatigue level. Patients whose minimum Hb is <8 g/dL experience higher fatigue levels compared to patients whose minimum Hb is ≥8 g/dL. Additional studies are warranted to understand if patients may benefit from improved fatigue levels by correcting their anemia through transfusion.

Fatigue is the most common clinical symptom of anemia and is a significant concern to patients.^1,2 In ambulatory patients, lower hemoglobin (Hb) concentration is associated with increased fatigue.^2,3 Accordingly, therapies that treat anemia by increasing Hb concentration, such as erythropoiesis stimulating agents,^4-7 often use fatigue as an outcome measure.

In hospitalized patients, transfusion of red blood cell increases Hb concentration and is the primary treatment for anemia. However, the extent to which transfusion and changes in Hb concentration affect hospitalized patients’ fatigue levels is not well established. Guidelines support transfusing patients with symptoms of anemia, such as fatigue, on the assumption that the increased oxygen delivery will improve the symptoms of anemia. While transfusion studies in hospitalized patients have consistently reported that transfusion at lower or “restrictive” Hb concentrations is safe compared with transfusion at higher Hb concentrations,^8-10 these studies have mainly used cardiac events and mortality as outcomes rather than patient symptoms, such as fatigue. Nevertheless, they have resulted in hospitals increasingly adopting restrictive transfusion policies that discourage transfusion at higher Hb levels.^11,12 Consequently, the rate of transfusion in hospitalized patients has decreased,¹³ raising questions of whether some patients with lower Hb concentrations may experience increased fatigue as a result of restrictive transfusion policies. Fatigue among hospitalized patients is important not only because it is an adverse symptom but because it may result in decreased activity levels, deconditioning, and losses in functional status.^14,15While the effect of alternative transfusion policies on fatigue in hospitalized patients could be answered by a randomized clinical trial using fatigue and functional status as outcomes, an important first step is to assess whether the Hb concentration of hospitalized patients is associated with their fatigue level during hospitalization. Because hospitalized patients often have acute illnesses that can cause fatigue in and of themselves, it is possible that anemia is not associated with fatigue in hospitalized patients despite anemia’s association with fatigue in ambulatory patients. Additionally, Hb concentration varies during hospitalization,¹⁶ raising the question of what measures of Hb during hospitalization might be most associated with anemia-related fatigue.

The objective of this study is to explore multiple Hb measures in hospitalized medical patients with anemia and test whether any of these Hb measures are associated with patients’ fatigue level.

METHODS

Study Design

We performed a prospective, observational study of hospitalized patients with anemia on the general medicine services at The University of Chicago Medical Center (UCMC). The institutional review board approved the study procedures, and all study subjects provided informed consent.

Study Eligibility

Between April 2014 and June 2015, all general medicine inpatients were approached for written consent for The University of Chicago Hospitalist Project,¹⁷ a research infrastructure at UCMC. Among patients consenting to participate in the Hospitalist Project, patients were eligible if they had Hb <9 g/dL at any point during their hospitalization and were age ≥50 years. Hb concentration of <9 g/dL was chosen to include the range of Hb values covered by most restrictive transfusion policies.^8-10,18 Age ≥50 years was an inclusion criteria because anemia is more strongly associated with poor outcomes, including functional impairment, among older patients compared with younger patients.^14,19-21 If patients were not eligible for inclusion at the time of consent for the Hospitalist Project, their Hb values were reviewed twice daily until hospital discharge to assess if their Hb was <9 g/dL. Proxies were sought to answer questions for patients who failed the Short Portable Mental Status Questionnaire.²²

Patient Demographic Data Collection

Research assistants abstracted patient age and sex from the electronic health record (EHR), and asked patients to self-identify their race. The individual comorbidities included as part of the Charlson Comorbidity Index were identified using International Classification of Diseases, 9th Revision codes from hospital administrative data for each encounter and specifically included the following: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, hemiplegia and/or paraplegia, renal disease, cancer, and human immunodeficiency virus/acquired immunodeficiency syndrome.²³ We also used Healthcare Cost and Utilization Project (www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp) diagnosis categories to identify whether patients had sickle cell (SC) anemia, gastrointestinal bleeding (GIB), or a depressive disorder (DD) because these conditions are associated with anemia (SC and GIB) and fatigue (DD).²⁴

 

Measuring Anemia

Hb measures were available only when hospital providers ordered them as part of routine practice. The first Hb concentration <9 g/dL during a patient’s hospitalization, which made them eligible for study participation, was obtained through manual review of the EHR. All additional Hb values during the patient’s hospitalization were obtained from the hospital’s administrative data mart. All Hb values collected for each patient during the hospitalization were used to calculate summary measures of Hb during the hospitalization, including the mean Hb, median Hb, minimum Hb, maximum Hb, admission (first recorded) Hb, and discharge (last recorded) Hb. Hb measures were analyzed both as a continuous variable and as a categorical variable created by dividing the continuous Hb measures into integer ranges of 3 groups of approximately the same size.

Measuring Fatigue

Our primary outcome was patients’ level of fatigue reported during hospitalization, measured using the Functional Assessment of Chronic Illness Therapy (FACIT)-Anemia questionnaire. Fatigue was measured using a 13-question fatigue subscale,^1,2,25 which measures fatigue within the past 7 days. Scores on the fatigue subscale range from 0 to 52, with lower scores reflecting greater levels of fatigue. As soon as patients met the eligibility criteria for study participation during their hospitalization (age ≥50 years and Hb <9 g/dL), they were approached to answer the FACIT questions. Values for missing data in the fatigue subscale for individual subjects were filled in using a prorated score from their answered questions as long as >50% of the items in the fatigue subscale were answered, in accordance with recommendations for addressing missing data in the FACIT.²⁶ Fatigue was analyzed as a continuous variable and as a dichotomous variable created by dividing the sample into high (FACIT <27) and low (FACIT ≥27) levels of fatigue based on the median FACIT score of the population. Previous literature has shown a FACIT fatigue subscale score between 23 and 26 to be associated with an Eastern Cooperative Oncology Group (ECOG)²⁷ C Performance Status rating of 2 to 3³ compared to scores ≥27.

Statistical Analysis

Statistical analysis was performed using Stata statistical software (StataCorp, College Station, TX). Descriptive statistics were used to characterize patient demographics. Analysis of variance was used to test for differences in the mean fatigue levels across Hb measures. χ² tests were performed to test for associations between high fatigue levels and the Hb measures. Multivariable analysis, including both linear and logistic regression models, were used to test the association of Hb concentration and fatigue. P values <0.05 using a 2-tailed test were deemed statistically significant.

RESULTS

Patient Characteristics

During the study period, 8559 patients were admitted to the general medicine service. Of those, 5073 (59%) consented for participation in the Hospitalist Project, and 3670 (72%) completed the Hospitalist Project inpatient interview. Of these patients, 1292 (35%) had Hb <9 g/dL, and 784 (61%) were 50 years or older and completed the FACIT questionnaire.

Table 1 reports the demographic characteristics and comorbidities for the sample, the mean (standard deviation [SD]) for the 6 Hb measures, and mean (SD) and median FACIT scores.

Bivariate Association of Fatigue and Hb

Categorizing patients into low, middle, or high Hb for each of the 6 Hb measures, minimum Hb was strongly associated with fatigue, with a weaker association for mean Hb and no statistically significant association for the other measures.

Minimum Hb. Patients with a minimum Hb <7 g/dL and patients with Hb 7-8 g/dL had higher fatigue levels (FACIT = 25 for each) than patients with a minimum Hb ≥8 g/dL (FACIT = 29; P < 0.001; Table 2). When excluding patients with SC and/or GIB because their average minimum Hb differed from the average minimum Hb of the full population (P < 0.001), patients with a minimum Hb <7 g/dL or 7-8 g/dL had even higher fatigue levels (FACIT = 23 and FACIT = 24, respectively), with no change in the fatigue level of patients with a minimum Hb ≥8 g/dL (FACIT = 29; P < 0.001; Table 2). Lower minimum Hb continued to be associated with higher fatigue levels when analyzed in 0.5 g/dL increments (Figure).

Lower values of minimum Hb were also associated with patients reporting high fatigue levels (FACIT <27). Fatigue levels were high for 50% of patients with a minimum Hb <7 g/dL and 56% of patients with a minimum Hb 7–8 g/dL compared with only 41% of patients with a minimum Hb ≥8 g/dL (P < 0.002). Excluding patients with SC and/or GIB, fatigue levels were high for 54% of patients with a minimum Hb <7 g/dL and 57% of patients with a minimum Hb 7-8 g/dL compared with 41% of patients with a minimum Hb ≥8 g/dL (P < 0.001; Table 2).

Mean Hb and Other Measures. Fatigue levels were high for 47% of patients with a mean Hb <8g /dL and 53% of patients with a mean Hb 8-9 g/dL compared with 43% of patients with a mean Hb ≥9 g/dL (P = 0.05). However, the association between high fatigue and mean Hb was not statistically significant when patients with SC and/or GIB were excluded (Table 2). None of the other 4 Hb measures was significantly associated with fatigue.

 

Linear Regression of Fatigue on Hb

In linear regression models, minimum Hb consistently predicted patient fatigue, mean Hb had a less robust association with fatigue, and the other Hb measures were not associated with patient fatigue. Increases in minimum Hb (analyzed as a continuous variable) were associated with reduced fatigue (higher FACIT score; β = 1.4; P = 0.005). In models in which minimum Hb was a categorical variable, patients with a minimum Hb of <7 g/dL or 7-8 g/dL had greater fatigue (lower FACIT score) than patients whose minimum Hb was ≥8 g/dL (Hb <7 g/dL: β = −4.2; P ≤ 0.001; Hb 7-8 g/dL: β = −4.1; P < 0.001). These results control for patients’ age, sex, individual comorbidities, and whether their minimum Hb occurred before or after the measurement of fatigue during hospitalization (Model 1), and the results are unchanged when also controlling for the number of Hb laboratory draws patients had during their hospitalization (Model 2; Table 3). In a stratified analysis excluding patients with either SC and/or GIB, changes in minimum Hb were associated with larger changes in patient fatigue levels (Supplemental Table 1). We also stratified our analysis to include only patients whose minimum Hb occurred before the measurement of their fatigue level during hospitalization to avoid a spurious association of fatigue with minimum Hb occurring after fatigue was measured. In both Models 1 and 2, minimum Hb remained a predictor of patients’ fatigue levels with similar effect sizes, although in Model 2, the results did not quite reach a statistically significant level, in part due to larger confidence intervals from the smaller sample size of this stratified analysis (Supplemental Table 2a). We further stratified this analysis to include only patients whose transfusion, if they received one, occurred after their minimum Hb and the measurement of their fatigue level to account for the possibility that a transfusion could affect the fatigue level patients report. In this analysis, most of the estimates of the effect of minimum Hb on fatigue were larger than those seen when only analyzing patients whose minimum Hb occurred before the measurement of their fatigue level, although again, the smaller sample size of this additional stratified analysis does produce larger confidence intervals for these estimates (Supplemental Table 2b).

Analyzed as a categorical variable, a mean Hb <8 g/dL or 8-9 g/dL was also associated with higher levels of fatigue compared with patients whose mean Hb is ≥9 g/dL in both Models 1 and 2, although the results were only statistically significant for patients with a mean Hb 8-9 g/dL (β=−2.5; P < 0.04; Table 3). There were no statistically significant associations between mean Hb and fatigue when excluding SC and/or GIB patients (Supplemental Table 3).

No Hb measure other than minimum or mean had significant association with patient fatigue levels in linear regression models.

Logistic Regression of High Fatigue Level on Hb

Using logistic regression, minimum Hb analyzed as a categorical variable predicted increased odds of a high fatigue level. Patients with a minimum Hb <7 g/dL were 50% (odds ratio [OR] = 1.5; P = 0.03) more likely to have high fatigue and patients with a minimum Hb 7-8 g/dL were 90% (OR = 1.9; P < 0.001) more likely to have high fatigue compared with patients with a minimum Hb ≥8 g/dL in Model 1. These results were similar in Model 2, although the effect was only statistically significant in the 7-8 g/dL Hb group (Table 3). When excluding SC and/or GIB patients, the odds of having high fatigue as minimum Hb decreased were the same or higher for both models compared to the full population of patients. However, again, in Model 2, the effect was only statistically significant in the 7-8 g/dL Hb group (Supplemental Table 1).

Patients with a mean Hb <8 g/dL were 20% to 30% more likely to have high fatigue and patients with mean Hb 8-9 g/dL were 50% more likely to have high fatigue compared with patients with a mean Hb ≥9 g/dL, but the effects were only statistically significant for patients with a mean Hb 8-9 g/dL in both Models 1 and 2 (Table 3). These results were similar when excluding patients with SC and/or GIB, but they were only significant for patients with a mean Hb 8-9 g/dL in Model 1 and patients with a mean Hb <8 g/dL in the Model 2 (Supplemental Table 3).

DISCUSSION

These results demonstrate that minimum Hb during hospitalization is associated with fatigue in hospitalized patients age ≥50 years, and the association is stronger among patients without SC and/or GIB as comorbidities. The analysis of Hb as a continuous and categorical variable and the use of both linear and logistic regression models support the robustness of these associations and illuminate their clinical significance. For example, in linear regression with minimum Hb a continuous variable, the coefficient of 1.4 suggests that an increase of 2 g/dL in Hb, as might be expected from transfusion of 2 units of red blood cells, would be associated with about a 3-point improvement in fatigue. Additionally, as a categorical variable, a minimum Hb ≥8 g/dL compared with a minimum Hb <7 g/dL or 7-8 g/dL is associated with a 3- to 4-point improvement in fatigue. Previous literature suggests that a difference of 3 in the FACIT score is the minimum clinically important difference in fatigue,³ and changes in minimum Hb in either model predict changes in fatigue that are in the range of potential clinical significance.

 

The clinical significance of the findings is also reflected in the results of the logistic regressions, which may be mapped to potential effects on functional status. Specifically, the odds of having a high fatigue level (FACIT <27) increase 90% for persons with a minimum Hb 7–8 g/dL compared with persons with a minimum Hb ≥8 g/dL. For persons with a minimum Hb <7 g/dL, point estimates suggest a smaller (50%) increase in the odds of high fatigue, but the 95% confidence interval overlaps heavily with the estimate of patients whose minimum Hb is 7-8 g/dL. While it might be expected that patients with a minimum Hb <7 g/dL have greater levels of fatigue compared with patients with a minimum Hb 7-8 g/dL, we did not observe such a pattern. One reason may be that the confidence intervals of our estimated effects are wide enough that we cannot exclude such a pattern. Another possible explanation is that in both groups, the fatigue levels are sufficiently severe, such that the difference in their fatigue levels may not be clinically meaningful. For example, a FACIT score of 23 to 26 has been shown to be associated with an ECOG performance status of 2 to 3, requiring bed rest for at least part of the day.³ Therefore, patients with a minimum Hb 7-8 g/dL (mean FACIT score = 24; Table 2) or a minimum Hb of <7 g/dL (mean FACIT score = 23; Table 2) are already functionally limited to the point of being partially bed bound, such that further decreases in their Hb may not produce additional fatigue in part because they reduce their activity sufficiently to prevent an increase in fatigue. In such cases, the potential benefits of increased Hb may be better assessed by measuring fatigue in response to a specific and provoked activity level, a concept known as fatigability.²⁰

That minimum Hb is more strongly associated with fatigue than any other measure of Hb during hospitalization may not be surprising. Mean, median, maximum, and discharge Hb may all be affected by transfusion during hospitalization that could affect fatigue. Admission Hb may not reflect true oxygen-carrying capacity because of hemoconcentration.

The association between Hb and fatigue in hospitalized patients is important because increased fatigue could contribute to slower clinical recovery in hospitalized patients. Additionally, increased fatigue during hospitalization and at hospital discharge could exacerbate the known deleterious consequences of fatigue on patients and their health outcomes^14,15 after hospital discharge. Although one previous study, the Functional Outcomes in Cardiovascular Patients Undergoing Surgical Hip Fracture Repair (FOCUS)⁸ trial, did not report differences in patients’ fatigue levels at 30 and 60 days postdischarge when transfused at restrictive (8 g/dL) compared with liberal (10 g/dL) Hb thresholds, confidence in the validity of this finding is reduced by the fact that more than half of the patients were lost to follow-up at the 30- and 60-day time points. Further, patients in the restrictive transfusion arm of FOCUS were transfused to maintain Hb levels at or above 8 g/dL. This transfusion threshold of 8 g/dL may have mitigated the high levels of fatigue that are seen in our study when patients’ Hb drops below 8 g/dL, and maintaining a Hb level of 7 g/dL is now the standard of care in stable hospitalized patients. Lastly, FOCUS was limited to postoperative hip fracture patients, and the generalizability of FOCUS to hospitalized medicine patients with anemia is limited.

Therefore, our results support guideline suggestions that practitioners incorporate the presence of patient symptoms such as fatigue into transfusion decisions, particularly if patients’ Hb is <8 g/dL.¹⁸ Though reasonable, the suggestion to incorporate symptoms such as fatigue into transfusion decisions has not been strongly supported by evidence so far, and it may often be neglected in practice. Definitive evidence to support such recommendations would benefit from study through an optimal trial¹⁸ that incorporates symptoms into decision making. Our findings add support for a study of transfusion strategies that incorporates patients’ fatigue level in addition to Hb concentration.

This study has several limitations. Although our sample size is large and includes patients with a range of comorbidities that we believe are representative of hospitalized general medicine patients, as a single-center, observational study, our results may not be generalizable to other centers. Additionally, although these data support a reliable association between hospitalized patients’ minimum Hb and fatigue level, the observational design of this study cannot prove that this relationship is causal. Also, patients’ Hb values were measured at the discretion of their clinician, and therefore, the measures of Hb were not uniformly measured for participating patients. In addition, fatigue was only measured at one time point during a patient’s hospitalization, and it is possible that patients’ fatigue levels change during hospitalization in relation to variables we did not consider. Finally, our study was not designed to assess the association of Hb with longer-term functional outcomes, which may be of greater concern than fatigue.

 

CONCLUSION

In hospitalized patients ≥50 years old, minimum Hb is reliably associated with patients’ fatigue level. Patients whose minimum Hb is <8 g/dL experience higher fatigue levels compared to patients whose minimum Hb is ≥8 g/dL. Additional studies are warranted to understand if patients may benefit from improved fatigue levels by correcting their anemia through transfusion.