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Bariatric surgery and alcohol use disorder
As obesity continues to ravage the health of the United States, bariatric surgery offers an effective strategy for individual patients suffering from medical complications.
When performed in adults with a body mass index of at least 30 kg/m2, bariatric surgery is associated with a mean weight loss of 20%-35% of baseline weight at 2-3 years. Bariatric surgery is associated with greater reductions in obesity comorbidities, compared with lifestyle intervention and supervised weight loss. Contemporary bariatric surgeries include Roux-en-Y gastric bypass, laparoscopic adjustable gastric banding, biliopancreatic diversion with duodenal switch, sleeve gastrectomy, and mini–gastric bypass.
Bariatric surgical procedures affect weight loss through two mechanisms: malabsorption and restriction. Such alterations in human physiology can change the absorption of common drugs of addiction, such as alcohol. This can increase the risk for problem drinking behaviors.
Wendy C. King, Ph.D., of the department of epidemiology at the University of Pittsburgh and her colleagues conducted an analysis of data from 1,945 patients in a cohort who underwent bariatric surgery in 10 U.S. hospitals. Symptoms of alcohol use disorder (AUD) were assessed pre- and postoperatively (JAMA 2012;307:2516-25).
The prevalence of AUD was significantly higher at 2 years postoperatively (9.6%), compared with the preoperative period (7.6%; P less than .01). Factors associated with a higher risk of postoperative AUD included male gender, younger age, smoking, regular alcohol consumption, a history of AUD, recreational drug use, low social support, and receiving Roux-en-Y gastric bypass.
AUD can disqualify patients from bariatric surgery – but 7.6% of patients in this survey (taken independently of clinical care) reported it. The authors noted that a 2% increase in AUD associated with bariatric surgery translates into 2,000 additional people with AUD each year.
This is particularly problematic for this population, because a large number of calories are associated with alcohol intake, and alcohol intake can lower inhibitions for other types of eating behaviors – all of which can lead to weight regain.
So, what do we do?
I think it may be helpful to take alcohol use histories in the patients we are seeing in bariatric surgery follow-up, especially those who appear to be regaining weight. Some patients may not be aware of this connection. For the patients who I have told about this relationship, they recognize it, which may be the first step toward dealing with it.
Dr. Ebbert is a professor of medicine and a general internist at the Mayo Clinic in Rochester, Minn., and a diplomate of the American Board of Addiction Medicine. The opinions expressed are those of the author. He reports no disclosures.
As obesity continues to ravage the health of the United States, bariatric surgery offers an effective strategy for individual patients suffering from medical complications.
When performed in adults with a body mass index of at least 30 kg/m2, bariatric surgery is associated with a mean weight loss of 20%-35% of baseline weight at 2-3 years. Bariatric surgery is associated with greater reductions in obesity comorbidities, compared with lifestyle intervention and supervised weight loss. Contemporary bariatric surgeries include Roux-en-Y gastric bypass, laparoscopic adjustable gastric banding, biliopancreatic diversion with duodenal switch, sleeve gastrectomy, and mini–gastric bypass.
Bariatric surgical procedures affect weight loss through two mechanisms: malabsorption and restriction. Such alterations in human physiology can change the absorption of common drugs of addiction, such as alcohol. This can increase the risk for problem drinking behaviors.
Wendy C. King, Ph.D., of the department of epidemiology at the University of Pittsburgh and her colleagues conducted an analysis of data from 1,945 patients in a cohort who underwent bariatric surgery in 10 U.S. hospitals. Symptoms of alcohol use disorder (AUD) were assessed pre- and postoperatively (JAMA 2012;307:2516-25).
The prevalence of AUD was significantly higher at 2 years postoperatively (9.6%), compared with the preoperative period (7.6%; P less than .01). Factors associated with a higher risk of postoperative AUD included male gender, younger age, smoking, regular alcohol consumption, a history of AUD, recreational drug use, low social support, and receiving Roux-en-Y gastric bypass.
AUD can disqualify patients from bariatric surgery – but 7.6% of patients in this survey (taken independently of clinical care) reported it. The authors noted that a 2% increase in AUD associated with bariatric surgery translates into 2,000 additional people with AUD each year.
This is particularly problematic for this population, because a large number of calories are associated with alcohol intake, and alcohol intake can lower inhibitions for other types of eating behaviors – all of which can lead to weight regain.
So, what do we do?
I think it may be helpful to take alcohol use histories in the patients we are seeing in bariatric surgery follow-up, especially those who appear to be regaining weight. Some patients may not be aware of this connection. For the patients who I have told about this relationship, they recognize it, which may be the first step toward dealing with it.
Dr. Ebbert is a professor of medicine and a general internist at the Mayo Clinic in Rochester, Minn., and a diplomate of the American Board of Addiction Medicine. The opinions expressed are those of the author. He reports no disclosures.
As obesity continues to ravage the health of the United States, bariatric surgery offers an effective strategy for individual patients suffering from medical complications.
When performed in adults with a body mass index of at least 30 kg/m2, bariatric surgery is associated with a mean weight loss of 20%-35% of baseline weight at 2-3 years. Bariatric surgery is associated with greater reductions in obesity comorbidities, compared with lifestyle intervention and supervised weight loss. Contemporary bariatric surgeries include Roux-en-Y gastric bypass, laparoscopic adjustable gastric banding, biliopancreatic diversion with duodenal switch, sleeve gastrectomy, and mini–gastric bypass.
Bariatric surgical procedures affect weight loss through two mechanisms: malabsorption and restriction. Such alterations in human physiology can change the absorption of common drugs of addiction, such as alcohol. This can increase the risk for problem drinking behaviors.
Wendy C. King, Ph.D., of the department of epidemiology at the University of Pittsburgh and her colleagues conducted an analysis of data from 1,945 patients in a cohort who underwent bariatric surgery in 10 U.S. hospitals. Symptoms of alcohol use disorder (AUD) were assessed pre- and postoperatively (JAMA 2012;307:2516-25).
The prevalence of AUD was significantly higher at 2 years postoperatively (9.6%), compared with the preoperative period (7.6%; P less than .01). Factors associated with a higher risk of postoperative AUD included male gender, younger age, smoking, regular alcohol consumption, a history of AUD, recreational drug use, low social support, and receiving Roux-en-Y gastric bypass.
AUD can disqualify patients from bariatric surgery – but 7.6% of patients in this survey (taken independently of clinical care) reported it. The authors noted that a 2% increase in AUD associated with bariatric surgery translates into 2,000 additional people with AUD each year.
This is particularly problematic for this population, because a large number of calories are associated with alcohol intake, and alcohol intake can lower inhibitions for other types of eating behaviors – all of which can lead to weight regain.
So, what do we do?
I think it may be helpful to take alcohol use histories in the patients we are seeing in bariatric surgery follow-up, especially those who appear to be regaining weight. Some patients may not be aware of this connection. For the patients who I have told about this relationship, they recognize it, which may be the first step toward dealing with it.
Dr. Ebbert is a professor of medicine and a general internist at the Mayo Clinic in Rochester, Minn., and a diplomate of the American Board of Addiction Medicine. The opinions expressed are those of the author. He reports no disclosures.
Long-term complications after ALL minimal, study shows
Credit: Bill Branson
Children recently diagnosed with standard-risk acute lymphoblastic leukemia (ALL) are likely to have few long-term complications into adulthood, investigators have reported in The Lancet Oncology.
The team said this is because current therapies are less harsh than their predecessors.
Newer protocols have eliminated radiation and restricted the use of chemotherapeutics that may cause subsequent malignancies and other chronic health conditions.
Previous research had only assessed the very long-term outcomes of children treated with older protocols, leaving physicians to piece together information from this outdated data and anecdotal evidence.
“This is one of the first studies to show that, in addition to their excellent probability of survival, long-term survivors of standard-risk childhood ALL are at low risk for complications of their therapy once they enter adulthood,” said study author Paul Nathan, MD, of The Hospital for Sick Children in Toronto, Canada.
He and his colleagues used longitudinal data from the Childhood Cancer Survivor Study, a North American study of 5-year survivors of childhood cancer diagnosed between 1970 and 1986.
The team analyzed 556 patients from this study who were older than 1 year of age and younger than 10 at the time of diagnosis and who had received treatment consistent with current therapies for standard-risk ALL.
Patients were followed from 5 years following diagnosis to a median of 18 years. The survivor group was compared with a group of their siblings who had not had cancer, as well as the general population.
Twenty-eight patients in the survivor group died. Sixteen of these deaths were from causes other than relapse.
Survivors were at a slightly increased risk of death due to non-relapse causes, when compared to controls. However, no individual cause was increased.
Six survivors developed another malignancy. The risk for most chronic health disorders did not differ between survivors and siblings. But survivors appeared to have a moderately increased risk for osteoporosis or osteopenia, short stature, and cataracts.
Survivors and their siblings had similar socioeconomic outcomes, including education, rates of marriage, independent living, and household income.
While these results suggest the prognosis is favorable, Dr Nathan noted that ALL survivors should receive ongoing care from a knowledgeable primary-care practitioner.
“Among kids with standard-risk ALL, we expect most to survive,” Dr Nathan said. “Now we can say with more certainty that they will probably do well in the long-term as well. This information will be very useful for oncologists counselling newly diagnosed patients and their families and will be quite reassuring to parents.” 
Credit: Bill Branson
Children recently diagnosed with standard-risk acute lymphoblastic leukemia (ALL) are likely to have few long-term complications into adulthood, investigators have reported in The Lancet Oncology.
The team said this is because current therapies are less harsh than their predecessors.
Newer protocols have eliminated radiation and restricted the use of chemotherapeutics that may cause subsequent malignancies and other chronic health conditions.
Previous research had only assessed the very long-term outcomes of children treated with older protocols, leaving physicians to piece together information from this outdated data and anecdotal evidence.
“This is one of the first studies to show that, in addition to their excellent probability of survival, long-term survivors of standard-risk childhood ALL are at low risk for complications of their therapy once they enter adulthood,” said study author Paul Nathan, MD, of The Hospital for Sick Children in Toronto, Canada.
He and his colleagues used longitudinal data from the Childhood Cancer Survivor Study, a North American study of 5-year survivors of childhood cancer diagnosed between 1970 and 1986.
The team analyzed 556 patients from this study who were older than 1 year of age and younger than 10 at the time of diagnosis and who had received treatment consistent with current therapies for standard-risk ALL.
Patients were followed from 5 years following diagnosis to a median of 18 years. The survivor group was compared with a group of their siblings who had not had cancer, as well as the general population.
Twenty-eight patients in the survivor group died. Sixteen of these deaths were from causes other than relapse.
Survivors were at a slightly increased risk of death due to non-relapse causes, when compared to controls. However, no individual cause was increased.
Six survivors developed another malignancy. The risk for most chronic health disorders did not differ between survivors and siblings. But survivors appeared to have a moderately increased risk for osteoporosis or osteopenia, short stature, and cataracts.
Survivors and their siblings had similar socioeconomic outcomes, including education, rates of marriage, independent living, and household income.
While these results suggest the prognosis is favorable, Dr Nathan noted that ALL survivors should receive ongoing care from a knowledgeable primary-care practitioner.
“Among kids with standard-risk ALL, we expect most to survive,” Dr Nathan said. “Now we can say with more certainty that they will probably do well in the long-term as well. This information will be very useful for oncologists counselling newly diagnosed patients and their families and will be quite reassuring to parents.”
Credit: Bill Branson
Children recently diagnosed with standard-risk acute lymphoblastic leukemia (ALL) are likely to have few long-term complications into adulthood, investigators have reported in The Lancet Oncology.
The team said this is because current therapies are less harsh than their predecessors.
Newer protocols have eliminated radiation and restricted the use of chemotherapeutics that may cause subsequent malignancies and other chronic health conditions.
Previous research had only assessed the very long-term outcomes of children treated with older protocols, leaving physicians to piece together information from this outdated data and anecdotal evidence.
“This is one of the first studies to show that, in addition to their excellent probability of survival, long-term survivors of standard-risk childhood ALL are at low risk for complications of their therapy once they enter adulthood,” said study author Paul Nathan, MD, of The Hospital for Sick Children in Toronto, Canada.
He and his colleagues used longitudinal data from the Childhood Cancer Survivor Study, a North American study of 5-year survivors of childhood cancer diagnosed between 1970 and 1986.
The team analyzed 556 patients from this study who were older than 1 year of age and younger than 10 at the time of diagnosis and who had received treatment consistent with current therapies for standard-risk ALL.
Patients were followed from 5 years following diagnosis to a median of 18 years. The survivor group was compared with a group of their siblings who had not had cancer, as well as the general population.
Twenty-eight patients in the survivor group died. Sixteen of these deaths were from causes other than relapse.
Survivors were at a slightly increased risk of death due to non-relapse causes, when compared to controls. However, no individual cause was increased.
Six survivors developed another malignancy. The risk for most chronic health disorders did not differ between survivors and siblings. But survivors appeared to have a moderately increased risk for osteoporosis or osteopenia, short stature, and cataracts.
Survivors and their siblings had similar socioeconomic outcomes, including education, rates of marriage, independent living, and household income.
While these results suggest the prognosis is favorable, Dr Nathan noted that ALL survivors should receive ongoing care from a knowledgeable primary-care practitioner.
“Among kids with standard-risk ALL, we expect most to survive,” Dr Nathan said. “Now we can say with more certainty that they will probably do well in the long-term as well. This information will be very useful for oncologists counselling newly diagnosed patients and their families and will be quite reassuring to parents.”
Team reports new method of chemo delivery
Credit: Kathy Atkinson
Researchers have devised a novel way to deliver chemotherapy drugs “on demand,” according to a paper published in Proceedings of the National Academy of Sciences.
The team loaded a biocompatible hydrogel with a chemotherapy drug and used ultrasound to trigger the gel to release the drug.
Like many other injectable gels, this one gradually releases a low level of the drug by diffusion over time. But the new hydrogel differs from others in a key way.
Researchers previously applied ultrasound to gels to temporarily increase doses of drug, but that approach was a one-shot deal, as the ultrasound was used to destroy those gels.
In the current study, the researchers used ultrasound to temporarily disrupt the gel so that it released short, high-dose bursts of the drug. But when they stopped the ultrasound, the hydrogels self-healed.
By closing back up, they were ready to go for the next “on demand” drug burst, providing a way to administer drugs with a greater level of control than was possible before.
The researchers also demonstrated in lab cultures and in mouse models of breast cancer that the pulsed, ultrasound-triggered hydrogel approach to drug delivery was more effective at stopping the growth of tumor cells than traditional, sustained-release drug therapy.
“Our approach counters the whole idea of sustained drug release and offers a double whammy,” said study author David J. Mooney, PhD, of the Harvard School of Engineering and Applied Sciences in Boston.
“We have shown that we can use the hydrogels repeatedly and turn the drug pulses on and off at will, and that the drug bursts in concert with the baseline low-level drug delivery seems to be particularly effective in killing cancer cells.”
Self-healing hydrogel
Key to the researchers’ success in designing a hydrogel that self-heals was choosing the right kind of hydrogel with the right kind of drug and applying the right intensity of ultrasound.
“We were able to trigger our system with a level of ultrasound that was much lower than high-intensity focused ultrasound that is used clinically to heat and destroy tumors,” said study author Cathal Kearney, PhD, of the Royal College of Surgeons in Ireland. “The careful selection of materials and properties make it a reversible process.”
The team carried out the majority of their work for this study with a gel made out of alginate, a natural polysaccharide from algae that is held together with calcium ions.
In a series of tests, they found that, with the right level of ultrasound, the bonds break up and enable the gel to release its drug cargo. But as long as the gel is in the presence of more calcium, the bonds reform and the gel self-heals.
Drug testing
Once the researchers knew the gel would self-heal, they tested out a drug they suspected it would hold well: the chemotherapy drug mitoxantrone.
Sure enough, the ultrasound triggered the gel to release the blue-colored drug, as indicated by the newly blue color of the surrounding medium. Just a single ultrasound dose was effective, and the gel reformed after it was disrupted, making multiple cycles possible.
Next, the team tested the treatment in mouse models of breast cancer. They injected the drug-laden gel close to the tumors.
Over the course of 6 months, the mice that received a low-level, sustained release of the drug with a daily concentrated pulse of ultrasound (2.5 minutes) fared significantly better than mice treated the same but without ultrasound.
In contrast to controls, the tumors in the ultrasound-treated mice did not grow substantially. And the mice survived for an additional 80 days.
Potential applications
The researchers believe their technique could help improve cancer treatment and other therapies requiring drugs to be delivered at the right place and the right time—from post-surgery pain medications to protein-based drugs that require daily injections.
It requires an initial injection of the hydrogel, but the approach could be a much less traumatic, minimally invasive, and more effective method of drug delivery than current methods, Dr Mooney said.
The researchers also found their hydrogel can release cargo other than drugs, including proteins and condensed plasmid DNA. This lays the groundwork for using these hydrogels for tissue regeneration and gene therapy.
Dr Mooney said he and his colleagues plan to explore these potential applications, as well as the possibility of unleashing 2 different drugs independently from the same hydrogel.
Credit: Kathy Atkinson
Researchers have devised a novel way to deliver chemotherapy drugs “on demand,” according to a paper published in Proceedings of the National Academy of Sciences.
The team loaded a biocompatible hydrogel with a chemotherapy drug and used ultrasound to trigger the gel to release the drug.
Like many other injectable gels, this one gradually releases a low level of the drug by diffusion over time. But the new hydrogel differs from others in a key way.
Researchers previously applied ultrasound to gels to temporarily increase doses of drug, but that approach was a one-shot deal, as the ultrasound was used to destroy those gels.
In the current study, the researchers used ultrasound to temporarily disrupt the gel so that it released short, high-dose bursts of the drug. But when they stopped the ultrasound, the hydrogels self-healed.
By closing back up, they were ready to go for the next “on demand” drug burst, providing a way to administer drugs with a greater level of control than was possible before.
The researchers also demonstrated in lab cultures and in mouse models of breast cancer that the pulsed, ultrasound-triggered hydrogel approach to drug delivery was more effective at stopping the growth of tumor cells than traditional, sustained-release drug therapy.
“Our approach counters the whole idea of sustained drug release and offers a double whammy,” said study author David J. Mooney, PhD, of the Harvard School of Engineering and Applied Sciences in Boston.
“We have shown that we can use the hydrogels repeatedly and turn the drug pulses on and off at will, and that the drug bursts in concert with the baseline low-level drug delivery seems to be particularly effective in killing cancer cells.”
Self-healing hydrogel
Key to the researchers’ success in designing a hydrogel that self-heals was choosing the right kind of hydrogel with the right kind of drug and applying the right intensity of ultrasound.
“We were able to trigger our system with a level of ultrasound that was much lower than high-intensity focused ultrasound that is used clinically to heat and destroy tumors,” said study author Cathal Kearney, PhD, of the Royal College of Surgeons in Ireland. “The careful selection of materials and properties make it a reversible process.”
The team carried out the majority of their work for this study with a gel made out of alginate, a natural polysaccharide from algae that is held together with calcium ions.
In a series of tests, they found that, with the right level of ultrasound, the bonds break up and enable the gel to release its drug cargo. But as long as the gel is in the presence of more calcium, the bonds reform and the gel self-heals.
Drug testing
Once the researchers knew the gel would self-heal, they tested out a drug they suspected it would hold well: the chemotherapy drug mitoxantrone.
Sure enough, the ultrasound triggered the gel to release the blue-colored drug, as indicated by the newly blue color of the surrounding medium. Just a single ultrasound dose was effective, and the gel reformed after it was disrupted, making multiple cycles possible.
Next, the team tested the treatment in mouse models of breast cancer. They injected the drug-laden gel close to the tumors.
Over the course of 6 months, the mice that received a low-level, sustained release of the drug with a daily concentrated pulse of ultrasound (2.5 minutes) fared significantly better than mice treated the same but without ultrasound.
In contrast to controls, the tumors in the ultrasound-treated mice did not grow substantially. And the mice survived for an additional 80 days.
Potential applications
The researchers believe their technique could help improve cancer treatment and other therapies requiring drugs to be delivered at the right place and the right time—from post-surgery pain medications to protein-based drugs that require daily injections.
It requires an initial injection of the hydrogel, but the approach could be a much less traumatic, minimally invasive, and more effective method of drug delivery than current methods, Dr Mooney said.
The researchers also found their hydrogel can release cargo other than drugs, including proteins and condensed plasmid DNA. This lays the groundwork for using these hydrogels for tissue regeneration and gene therapy.
Dr Mooney said he and his colleagues plan to explore these potential applications, as well as the possibility of unleashing 2 different drugs independently from the same hydrogel.
Credit: Kathy Atkinson
Researchers have devised a novel way to deliver chemotherapy drugs “on demand,” according to a paper published in Proceedings of the National Academy of Sciences.
The team loaded a biocompatible hydrogel with a chemotherapy drug and used ultrasound to trigger the gel to release the drug.
Like many other injectable gels, this one gradually releases a low level of the drug by diffusion over time. But the new hydrogel differs from others in a key way.
Researchers previously applied ultrasound to gels to temporarily increase doses of drug, but that approach was a one-shot deal, as the ultrasound was used to destroy those gels.
In the current study, the researchers used ultrasound to temporarily disrupt the gel so that it released short, high-dose bursts of the drug. But when they stopped the ultrasound, the hydrogels self-healed.
By closing back up, they were ready to go for the next “on demand” drug burst, providing a way to administer drugs with a greater level of control than was possible before.
The researchers also demonstrated in lab cultures and in mouse models of breast cancer that the pulsed, ultrasound-triggered hydrogel approach to drug delivery was more effective at stopping the growth of tumor cells than traditional, sustained-release drug therapy.
“Our approach counters the whole idea of sustained drug release and offers a double whammy,” said study author David J. Mooney, PhD, of the Harvard School of Engineering and Applied Sciences in Boston.
“We have shown that we can use the hydrogels repeatedly and turn the drug pulses on and off at will, and that the drug bursts in concert with the baseline low-level drug delivery seems to be particularly effective in killing cancer cells.”
Self-healing hydrogel
Key to the researchers’ success in designing a hydrogel that self-heals was choosing the right kind of hydrogel with the right kind of drug and applying the right intensity of ultrasound.
“We were able to trigger our system with a level of ultrasound that was much lower than high-intensity focused ultrasound that is used clinically to heat and destroy tumors,” said study author Cathal Kearney, PhD, of the Royal College of Surgeons in Ireland. “The careful selection of materials and properties make it a reversible process.”
The team carried out the majority of their work for this study with a gel made out of alginate, a natural polysaccharide from algae that is held together with calcium ions.
In a series of tests, they found that, with the right level of ultrasound, the bonds break up and enable the gel to release its drug cargo. But as long as the gel is in the presence of more calcium, the bonds reform and the gel self-heals.
Drug testing
Once the researchers knew the gel would self-heal, they tested out a drug they suspected it would hold well: the chemotherapy drug mitoxantrone.
Sure enough, the ultrasound triggered the gel to release the blue-colored drug, as indicated by the newly blue color of the surrounding medium. Just a single ultrasound dose was effective, and the gel reformed after it was disrupted, making multiple cycles possible.
Next, the team tested the treatment in mouse models of breast cancer. They injected the drug-laden gel close to the tumors.
Over the course of 6 months, the mice that received a low-level, sustained release of the drug with a daily concentrated pulse of ultrasound (2.5 minutes) fared significantly better than mice treated the same but without ultrasound.
In contrast to controls, the tumors in the ultrasound-treated mice did not grow substantially. And the mice survived for an additional 80 days.
Potential applications
The researchers believe their technique could help improve cancer treatment and other therapies requiring drugs to be delivered at the right place and the right time—from post-surgery pain medications to protein-based drugs that require daily injections.
It requires an initial injection of the hydrogel, but the approach could be a much less traumatic, minimally invasive, and more effective method of drug delivery than current methods, Dr Mooney said.
The researchers also found their hydrogel can release cargo other than drugs, including proteins and condensed plasmid DNA. This lays the groundwork for using these hydrogels for tissue regeneration and gene therapy.
Dr Mooney said he and his colleagues plan to explore these potential applications, as well as the possibility of unleashing 2 different drugs independently from the same hydrogel.
Team reports new method of chemo delivery
Credit: Kathy Atkinson
Researchers have devised a novel way to deliver chemotherapy drugs “on demand,” according to a paper published in Proceedings of the National Academy of Sciences.
The team loaded a biocompatible hydrogel with a chemotherapy drug and used ultrasound to trigger the gel to release the drug.
Like many other injectable gels, this one gradually releases a low level of the drug by diffusion over time. But the new hydrogel differs from others in a key way.
Researchers previously applied ultrasound to gels to temporarily increase doses of drug, but that approach was a one-shot deal, as the ultrasound was used to destroy those gels.
In the current study, the researchers used ultrasound to temporarily disrupt the gel so that it released short, high-dose bursts of the drug. But when they stopped the ultrasound, the hydrogels self-healed.
By closing back up, they were ready to go for the next “on demand” drug burst, providing a way to administer drugs with a greater level of control than was possible before.
The researchers also demonstrated in lab cultures and in mouse models of breast cancer that the pulsed, ultrasound-triggered hydrogel approach to drug delivery was more effective at stopping the growth of tumor cells than traditional, sustained-release drug therapy.
“Our approach counters the whole idea of sustained drug release and offers a double whammy,” said study author David J. Mooney, PhD, of the Harvard School of Engineering and Applied Sciences in Boston.
“We have shown that we can use the hydrogels repeatedly and turn the drug pulses on and off at will, and that the drug bursts in concert with the baseline low-level drug delivery seems to be particularly effective in killing cancer cells.”
Self-healing hydrogel
Key to the researchers’ success in designing a hydrogel that self-heals was choosing the right kind of hydrogel with the right kind of drug and applying the right intensity of ultrasound.
“We were able to trigger our system with a level of ultrasound that was much lower than high-intensity focused ultrasound that is used clinically to heat and destroy tumors,” said study author Cathal Kearney, PhD, of the Royal College of Surgeons in Ireland. “The careful selection of materials and properties make it a reversible process.”
The team carried out the majority of their work for this study with a gel made out of alginate, a natural polysaccharide from algae that is held together with calcium ions.
In a series of tests, they found that, with the right level of ultrasound, the bonds break up and enable the gel to release its drug cargo. But as long as the gel is in the presence of more calcium, the bonds reform and the gel self-heals.
Drug testing
Once the researchers knew the gel would self-heal, they tested out a drug they suspected it would hold well: the chemotherapy drug mitoxantrone.
Sure enough, the ultrasound triggered the gel to release the blue-colored drug, as indicated by the newly blue color of the surrounding medium. Just a single ultrasound dose was effective, and the gel reformed after it was disrupted, making multiple cycles possible.
Next, the team tested the treatment in mouse models of breast cancer. They injected the drug-laden gel close to the tumors.
Over the course of 6 months, the mice that received a low-level, sustained release of the drug with a daily concentrated pulse of ultrasound (2.5 minutes) fared significantly better than mice treated the same but without ultrasound.
In contrast to controls, the tumors in the ultrasound-treated mice did not grow substantially. And the mice survived for an additional 80 days.
Potential applications
The researchers believe their technique could help improve cancer treatment and other therapies requiring drugs to be delivered at the right place and the right time—from post-surgery pain medications to protein-based drugs that require daily injections.
It requires an initial injection of the hydrogel, but the approach could be a much less traumatic, minimally invasive, and more effective method of drug delivery than current methods, Dr Mooney said.
The researchers also found their hydrogel can release cargo other than drugs, including proteins and condensed plasmid DNA. This lays the groundwork for using these hydrogels for tissue regeneration and gene therapy.
Dr Mooney said he and his colleagues plan to explore these potential applications, as well as the possibility of unleashing 2 different drugs independently from the same hydrogel.
Credit: Kathy Atkinson
Researchers have devised a novel way to deliver chemotherapy drugs “on demand,” according to a paper published in Proceedings of the National Academy of Sciences.
The team loaded a biocompatible hydrogel with a chemotherapy drug and used ultrasound to trigger the gel to release the drug.
Like many other injectable gels, this one gradually releases a low level of the drug by diffusion over time. But the new hydrogel differs from others in a key way.
Researchers previously applied ultrasound to gels to temporarily increase doses of drug, but that approach was a one-shot deal, as the ultrasound was used to destroy those gels.
In the current study, the researchers used ultrasound to temporarily disrupt the gel so that it released short, high-dose bursts of the drug. But when they stopped the ultrasound, the hydrogels self-healed.
By closing back up, they were ready to go for the next “on demand” drug burst, providing a way to administer drugs with a greater level of control than was possible before.
The researchers also demonstrated in lab cultures and in mouse models of breast cancer that the pulsed, ultrasound-triggered hydrogel approach to drug delivery was more effective at stopping the growth of tumor cells than traditional, sustained-release drug therapy.
“Our approach counters the whole idea of sustained drug release and offers a double whammy,” said study author David J. Mooney, PhD, of the Harvard School of Engineering and Applied Sciences in Boston.
“We have shown that we can use the hydrogels repeatedly and turn the drug pulses on and off at will, and that the drug bursts in concert with the baseline low-level drug delivery seems to be particularly effective in killing cancer cells.”
Self-healing hydrogel
Key to the researchers’ success in designing a hydrogel that self-heals was choosing the right kind of hydrogel with the right kind of drug and applying the right intensity of ultrasound.
“We were able to trigger our system with a level of ultrasound that was much lower than high-intensity focused ultrasound that is used clinically to heat and destroy tumors,” said study author Cathal Kearney, PhD, of the Royal College of Surgeons in Ireland. “The careful selection of materials and properties make it a reversible process.”
The team carried out the majority of their work for this study with a gel made out of alginate, a natural polysaccharide from algae that is held together with calcium ions.
In a series of tests, they found that, with the right level of ultrasound, the bonds break up and enable the gel to release its drug cargo. But as long as the gel is in the presence of more calcium, the bonds reform and the gel self-heals.
Drug testing
Once the researchers knew the gel would self-heal, they tested out a drug they suspected it would hold well: the chemotherapy drug mitoxantrone.
Sure enough, the ultrasound triggered the gel to release the blue-colored drug, as indicated by the newly blue color of the surrounding medium. Just a single ultrasound dose was effective, and the gel reformed after it was disrupted, making multiple cycles possible.
Next, the team tested the treatment in mouse models of breast cancer. They injected the drug-laden gel close to the tumors.
Over the course of 6 months, the mice that received a low-level, sustained release of the drug with a daily concentrated pulse of ultrasound (2.5 minutes) fared significantly better than mice treated the same but without ultrasound.
In contrast to controls, the tumors in the ultrasound-treated mice did not grow substantially. And the mice survived for an additional 80 days.
Potential applications
The researchers believe their technique could help improve cancer treatment and other therapies requiring drugs to be delivered at the right place and the right time—from post-surgery pain medications to protein-based drugs that require daily injections.
It requires an initial injection of the hydrogel, but the approach could be a much less traumatic, minimally invasive, and more effective method of drug delivery than current methods, Dr Mooney said.
The researchers also found their hydrogel can release cargo other than drugs, including proteins and condensed plasmid DNA. This lays the groundwork for using these hydrogels for tissue regeneration and gene therapy.
Dr Mooney said he and his colleagues plan to explore these potential applications, as well as the possibility of unleashing 2 different drugs independently from the same hydrogel.
Credit: Kathy Atkinson
Researchers have devised a novel way to deliver chemotherapy drugs “on demand,” according to a paper published in Proceedings of the National Academy of Sciences.
The team loaded a biocompatible hydrogel with a chemotherapy drug and used ultrasound to trigger the gel to release the drug.
Like many other injectable gels, this one gradually releases a low level of the drug by diffusion over time. But the new hydrogel differs from others in a key way.
Researchers previously applied ultrasound to gels to temporarily increase doses of drug, but that approach was a one-shot deal, as the ultrasound was used to destroy those gels.
In the current study, the researchers used ultrasound to temporarily disrupt the gel so that it released short, high-dose bursts of the drug. But when they stopped the ultrasound, the hydrogels self-healed.
By closing back up, they were ready to go for the next “on demand” drug burst, providing a way to administer drugs with a greater level of control than was possible before.
The researchers also demonstrated in lab cultures and in mouse models of breast cancer that the pulsed, ultrasound-triggered hydrogel approach to drug delivery was more effective at stopping the growth of tumor cells than traditional, sustained-release drug therapy.
“Our approach counters the whole idea of sustained drug release and offers a double whammy,” said study author David J. Mooney, PhD, of the Harvard School of Engineering and Applied Sciences in Boston.
“We have shown that we can use the hydrogels repeatedly and turn the drug pulses on and off at will, and that the drug bursts in concert with the baseline low-level drug delivery seems to be particularly effective in killing cancer cells.”
Self-healing hydrogel
Key to the researchers’ success in designing a hydrogel that self-heals was choosing the right kind of hydrogel with the right kind of drug and applying the right intensity of ultrasound.
“We were able to trigger our system with a level of ultrasound that was much lower than high-intensity focused ultrasound that is used clinically to heat and destroy tumors,” said study author Cathal Kearney, PhD, of the Royal College of Surgeons in Ireland. “The careful selection of materials and properties make it a reversible process.”
The team carried out the majority of their work for this study with a gel made out of alginate, a natural polysaccharide from algae that is held together with calcium ions.
In a series of tests, they found that, with the right level of ultrasound, the bonds break up and enable the gel to release its drug cargo. But as long as the gel is in the presence of more calcium, the bonds reform and the gel self-heals.
Drug testing
Once the researchers knew the gel would self-heal, they tested out a drug they suspected it would hold well: the chemotherapy drug mitoxantrone.
Sure enough, the ultrasound triggered the gel to release the blue-colored drug, as indicated by the newly blue color of the surrounding medium. Just a single ultrasound dose was effective, and the gel reformed after it was disrupted, making multiple cycles possible.
Next, the team tested the treatment in mouse models of breast cancer. They injected the drug-laden gel close to the tumors.
Over the course of 6 months, the mice that received a low-level, sustained release of the drug with a daily concentrated pulse of ultrasound (2.5 minutes) fared significantly better than mice treated the same but without ultrasound.
In contrast to controls, the tumors in the ultrasound-treated mice did not grow substantially. And the mice survived for an additional 80 days.
Potential applications
The researchers believe their technique could help improve cancer treatment and other therapies requiring drugs to be delivered at the right place and the right time—from post-surgery pain medications to protein-based drugs that require daily injections.
It requires an initial injection of the hydrogel, but the approach could be a much less traumatic, minimally invasive, and more effective method of drug delivery than current methods, Dr Mooney said.
The researchers also found their hydrogel can release cargo other than drugs, including proteins and condensed plasmid DNA. This lays the groundwork for using these hydrogels for tissue regeneration and gene therapy.
Dr Mooney said he and his colleagues plan to explore these potential applications, as well as the possibility of unleashing 2 different drugs independently from the same hydrogel.
Risk of Vehicle Accidents for Returning Military
Motor vehicle crashes (MVCs) account for almost one-third of deaths among service members every year. One study showed that within 6 months after deployment, military personnel had a 13% increase in at-fault accidents.
Using data from the Millennium Cohort Study and the Military Health System Data Repository, researchers from the Naval Health Research Center in San Diego, California, investigated possible risk factors.The researchers looked at both demographic and military-specific data, including service branch, health status (such as hearing loss and hours of sleep), and whether the individual had been diagnosed with depression, pain, or anxiety.
Of the 13,620 service members included in the study, 6,800 reported combat experiences; 107 had a MVC within 6 months following deployment.
Service members who had an MVC within 6 months postdeployment were more likely to report combat experiences, have more than one deployment, and be deployed for more than 365 cumulative days. Women, service members born after 1980, enlisted rank, African Americans, and those with baseline physical health symptoms and problem drinking were among those at greater risk. After adjusting for all variables, combat experiences nearly doubled the risk, and 3 or more deployments nearly tripled the risk of a MVC in the 6 months after deployment.
The researchers did not find an association between physical health symptoms, such as hearing loss, headache, or confusion, and MVCs, nor did they find a link between mental health symptoms and the severity of scores. That suggests that physical and mental health may not be the primary contributors to the association. In fact, the researchers say, the link between deployment and crashes may be multifactorial. For instance, service members may engage in risky driving behavior to recreate the emotions felt during combat. Although this study did not include questions about the use of seat belts, alcohol while driving, speeding, or other risk-taking behaviors, the researchers point to other studies that say that military personnel may not wear seat belts and may speed because that is what they got used to during deployment. The researchers also say that those who have participated in military combat may be inherent risk takers.
Multiple deployments, the researchers suggest, may instill “greater levels of risky driving behaviors that accumulate over time.” Length of deployment was not as crucial, which may mean that returning home more frequently puts service members in the driver’s seat more often.
The data suggest that a critical “window of time” may exist for preventing MVCs among the recently deployed. The researchers advocate intervention strategies early in the transition home.
Source
Woodall KA, Jacobson IG, Crum-Cianflone NF. Am J Prev Med. 2014;46(4):350–358.
doi: 10.1016/j.amepre.2013.11.015.
Motor vehicle crashes (MVCs) account for almost one-third of deaths among service members every year. One study showed that within 6 months after deployment, military personnel had a 13% increase in at-fault accidents.
Using data from the Millennium Cohort Study and the Military Health System Data Repository, researchers from the Naval Health Research Center in San Diego, California, investigated possible risk factors.The researchers looked at both demographic and military-specific data, including service branch, health status (such as hearing loss and hours of sleep), and whether the individual had been diagnosed with depression, pain, or anxiety.
Of the 13,620 service members included in the study, 6,800 reported combat experiences; 107 had a MVC within 6 months following deployment.
Service members who had an MVC within 6 months postdeployment were more likely to report combat experiences, have more than one deployment, and be deployed for more than 365 cumulative days. Women, service members born after 1980, enlisted rank, African Americans, and those with baseline physical health symptoms and problem drinking were among those at greater risk. After adjusting for all variables, combat experiences nearly doubled the risk, and 3 or more deployments nearly tripled the risk of a MVC in the 6 months after deployment.
The researchers did not find an association between physical health symptoms, such as hearing loss, headache, or confusion, and MVCs, nor did they find a link between mental health symptoms and the severity of scores. That suggests that physical and mental health may not be the primary contributors to the association. In fact, the researchers say, the link between deployment and crashes may be multifactorial. For instance, service members may engage in risky driving behavior to recreate the emotions felt during combat. Although this study did not include questions about the use of seat belts, alcohol while driving, speeding, or other risk-taking behaviors, the researchers point to other studies that say that military personnel may not wear seat belts and may speed because that is what they got used to during deployment. The researchers also say that those who have participated in military combat may be inherent risk takers.
Multiple deployments, the researchers suggest, may instill “greater levels of risky driving behaviors that accumulate over time.” Length of deployment was not as crucial, which may mean that returning home more frequently puts service members in the driver’s seat more often.
The data suggest that a critical “window of time” may exist for preventing MVCs among the recently deployed. The researchers advocate intervention strategies early in the transition home.
Source
Woodall KA, Jacobson IG, Crum-Cianflone NF. Am J Prev Med. 2014;46(4):350–358.
doi: 10.1016/j.amepre.2013.11.015.
Motor vehicle crashes (MVCs) account for almost one-third of deaths among service members every year. One study showed that within 6 months after deployment, military personnel had a 13% increase in at-fault accidents.
Using data from the Millennium Cohort Study and the Military Health System Data Repository, researchers from the Naval Health Research Center in San Diego, California, investigated possible risk factors.The researchers looked at both demographic and military-specific data, including service branch, health status (such as hearing loss and hours of sleep), and whether the individual had been diagnosed with depression, pain, or anxiety.
Of the 13,620 service members included in the study, 6,800 reported combat experiences; 107 had a MVC within 6 months following deployment.
Service members who had an MVC within 6 months postdeployment were more likely to report combat experiences, have more than one deployment, and be deployed for more than 365 cumulative days. Women, service members born after 1980, enlisted rank, African Americans, and those with baseline physical health symptoms and problem drinking were among those at greater risk. After adjusting for all variables, combat experiences nearly doubled the risk, and 3 or more deployments nearly tripled the risk of a MVC in the 6 months after deployment.
The researchers did not find an association between physical health symptoms, such as hearing loss, headache, or confusion, and MVCs, nor did they find a link between mental health symptoms and the severity of scores. That suggests that physical and mental health may not be the primary contributors to the association. In fact, the researchers say, the link between deployment and crashes may be multifactorial. For instance, service members may engage in risky driving behavior to recreate the emotions felt during combat. Although this study did not include questions about the use of seat belts, alcohol while driving, speeding, or other risk-taking behaviors, the researchers point to other studies that say that military personnel may not wear seat belts and may speed because that is what they got used to during deployment. The researchers also say that those who have participated in military combat may be inherent risk takers.
Multiple deployments, the researchers suggest, may instill “greater levels of risky driving behaviors that accumulate over time.” Length of deployment was not as crucial, which may mean that returning home more frequently puts service members in the driver’s seat more often.
The data suggest that a critical “window of time” may exist for preventing MVCs among the recently deployed. The researchers advocate intervention strategies early in the transition home.
Source
Woodall KA, Jacobson IG, Crum-Cianflone NF. Am J Prev Med. 2014;46(4):350–358.
doi: 10.1016/j.amepre.2013.11.015.
Predicting problems in families of cancer patients
Credit: Rhoda Baer
A new analysis suggests family dysfunction is the greatest predictor of emotional and behavioral problems among children who have a parent with cancer.
Other variables, such as the child’s age, did not predict the risk as accurately.
And illness-related factors, such as the parent’s prognosis, did not appear to have an impact at all.
Birgit Möller, PhD, of the University Medical Center Hamburg-Eppendorf in Germany, and her colleagues reported these findings in Cancer.
The researchers evaluated 235 families in which at least 1 parent was diagnosed with cancer. This included 402 parents and 324 children aged 11 to 21 years. Parents and children completed questionnaires that assessed emotional and behavioral health.
Responses suggested that children of cancer patients have higher-than-average levels of emotional and behavioral symptoms.
The overall mean values for emotional and behavioral problems—from both the parents’ and children’s perspectives—were significantly higher in the study population than the average values from a representative non-cancer population.
General family functioning was the strongest predictor of children’s symptom status from both the parents’ and child’s perspectives.
The effects of the child’s age and gender on behavioral and emotional symptoms varied according to the subject asked. But none of the respondents reported an association between child adjustment and illness-related factors such as poor prognoses or recurrent illness.
Dr Möller noted that screening for child mental health problems, family dysfunction, and parental depression can be easily adopted into cancer care so that families in need of support can be identified.
“Additional training of oncologists, interdisciplinary approaches, and family-based mental health liaison services are recommended to meet the needs of minor
children and their families and to minimize negative long-term effects in children,” she said.
Dr Möller and her team have developed a preventive counseling program—called the Children of Somatically Ill Parents (COSIP) program—that focuses on family communication, involvement of family members, flexible problem solving, mutual support, and parenting issues.
Credit: Rhoda Baer
A new analysis suggests family dysfunction is the greatest predictor of emotional and behavioral problems among children who have a parent with cancer.
Other variables, such as the child’s age, did not predict the risk as accurately.
And illness-related factors, such as the parent’s prognosis, did not appear to have an impact at all.
Birgit Möller, PhD, of the University Medical Center Hamburg-Eppendorf in Germany, and her colleagues reported these findings in Cancer.
The researchers evaluated 235 families in which at least 1 parent was diagnosed with cancer. This included 402 parents and 324 children aged 11 to 21 years. Parents and children completed questionnaires that assessed emotional and behavioral health.
Responses suggested that children of cancer patients have higher-than-average levels of emotional and behavioral symptoms.
The overall mean values for emotional and behavioral problems—from both the parents’ and children’s perspectives—were significantly higher in the study population than the average values from a representative non-cancer population.
General family functioning was the strongest predictor of children’s symptom status from both the parents’ and child’s perspectives.
The effects of the child’s age and gender on behavioral and emotional symptoms varied according to the subject asked. But none of the respondents reported an association between child adjustment and illness-related factors such as poor prognoses or recurrent illness.
Dr Möller noted that screening for child mental health problems, family dysfunction, and parental depression can be easily adopted into cancer care so that families in need of support can be identified.
“Additional training of oncologists, interdisciplinary approaches, and family-based mental health liaison services are recommended to meet the needs of minor
children and their families and to minimize negative long-term effects in children,” she said.
Dr Möller and her team have developed a preventive counseling program—called the Children of Somatically Ill Parents (COSIP) program—that focuses on family communication, involvement of family members, flexible problem solving, mutual support, and parenting issues.
Credit: Rhoda Baer
A new analysis suggests family dysfunction is the greatest predictor of emotional and behavioral problems among children who have a parent with cancer.
Other variables, such as the child’s age, did not predict the risk as accurately.
And illness-related factors, such as the parent’s prognosis, did not appear to have an impact at all.
Birgit Möller, PhD, of the University Medical Center Hamburg-Eppendorf in Germany, and her colleagues reported these findings in Cancer.
The researchers evaluated 235 families in which at least 1 parent was diagnosed with cancer. This included 402 parents and 324 children aged 11 to 21 years. Parents and children completed questionnaires that assessed emotional and behavioral health.
Responses suggested that children of cancer patients have higher-than-average levels of emotional and behavioral symptoms.
The overall mean values for emotional and behavioral problems—from both the parents’ and children’s perspectives—were significantly higher in the study population than the average values from a representative non-cancer population.
General family functioning was the strongest predictor of children’s symptom status from both the parents’ and child’s perspectives.
The effects of the child’s age and gender on behavioral and emotional symptoms varied according to the subject asked. But none of the respondents reported an association between child adjustment and illness-related factors such as poor prognoses or recurrent illness.
Dr Möller noted that screening for child mental health problems, family dysfunction, and parental depression can be easily adopted into cancer care so that families in need of support can be identified.
“Additional training of oncologists, interdisciplinary approaches, and family-based mental health liaison services are recommended to meet the needs of minor
children and their families and to minimize negative long-term effects in children,” she said.
Dr Möller and her team have developed a preventive counseling program—called the Children of Somatically Ill Parents (COSIP) program—that focuses on family communication, involvement of family members, flexible problem solving, mutual support, and parenting issues.
Hospital Readmissions and Preventability
Hospital readmissions cost Medicare $15 to $17 billion per year.[1, 2] In 2010, the Hospital Readmission Reduction Program (HRRP), created by the Patient Protection and Affordable Care Act, authorized the Centers for Medicare and Medicaid Services (CMS) to penalize hospitals with higher‐than‐expected readmission rates for certain index conditions.[3] Other payers may follow suit, so hospitals and health systems nationwide are devoting significant resources to reducing readmissions.[4, 5, 6]
Implicit in these efforts are the assumptions that a significant proportion of readmissions are preventable, and that preventable readmissions can be identified. Unfortunately, estimates of preventability vary widely.[7, 8] In this article, we examine how preventable readmissions have been defined, measured, and calculated, and explore the associated implications for readmission reduction efforts.
THE MEDICARE READMISSION METRIC
The medical literature reveals substantial heterogeneity in how readmissions are assessed. Time periods range from 14 days to 4 years, and readmissions may be counted differently depending on whether they are to the same hospital or to any hospital, whether they are for the same (or a related) condition or for any condition, whether a patient is allowed to count only once during the follow‐up period, how mortality is treated, and whether observation stays are considered.[9]
Despite a lack of consensus in the literature, the approach adopted by CMS is endorsed by the National Quality Forum (NQF)[10] and has become the de facto standard for calculating readmission rates. CMS derives risk‐standardized readmission rates for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN), using administrative claims data for each Medicare fee‐for‐service beneficiary 65 years or older.[11, 12, 13, 14] CMS counts the first readmission (but not subsequent ones) for any cause within 30 days of the index discharge, including readmissions to other facilities. Certain planned readmissions for revascularization are excluded, as are patients who left against medical advice, transferred to another acute‐care hospital, or died during the index admission. Admissions to psychiatric, rehabilitation, cancer specialty, and children's hospitals[12] are also excluded, as well as patients classified as observation status for either hospital stay.[15] Only administrative data are used in readmission calculations (ie, there are no chart reviews or interviews with healthcare personnel or patients). Details are published online and updated at least annually.[15]
EFFECTS AND LIMITATIONS OF THE HRRP AND THE CMS READMISSION METRIC
Penalizing hospitals for higher‐than‐expected readmission rates based on the CMS metric has been successful in the sense that hospitals now feel more accountable for patient outcomes after discharge; they are implementing transitional care programs, improving communication, and building relationships with community programs.[4, 5, 16] Early data suggest a small decline in readmission rates of Medicare beneficiaries nationally.[17] Previously, such readmission rates were constant.[18]
Nevertheless, significant concerns with the current approach have surfaced.[19, 20, 21] First, why choose 30 days? This time horizon was believed to be long enough to identify readmissions attributable to an index admission and short enough to reflect hospital‐delivered care and transitions to the outpatient setting, and it allows for collaboration between hospitals and their communities to reduce readmissions.[3] However, some have argued that this time horizon has little scientific basis,[22] and that hospitals are unfairly held accountable for a timeframe when outcomes may largely be influenced by the quality of outpatient care or the development of new problems.[23, 24] Approximately one‐third of 30‐day readmissions occur within the first 7 days, and more than half (55.7%) occur within the first 14 days[22, 25]; such time frames may be more appropriate for hospital accountability.[26]
Second, spurred by the focus of CMS penalties, efforts to reduce readmissions have largely concerned patients admitted for HF, AMI, or PN, although these 3 medical conditions account for only 10% of Medicare hospitalizations.[18] Programs focused on a narrow patient population may not benefit other patients with high readmission rates, such as those with gastrointestinal or psychiatric problems,[2] or lead to improvements in the underlying processes of care that could benefit patients in additional ways. Indeed, research suggests that low readmission rates may not be related to other measures of hospital quality.[27, 28]
Third, public reporting and hospital penalties are based on 3‐year historical performance, in part to accumulate a large enough sample size for each diagnosis. Hospitals that seek real‐time performance monitoring are limited to tracking surrogate outcomes, such as readmissions back to their own facility.[29, 30] Moreover, because of the long performance time frame, hospitals that achieve rapid improvement may endure penalties precisely when they are attempting to sustain their achievements.
Fourth, the CMS approach utilizes a complex risk‐standardization methodology, which has only modest ability to predict readmissions and allow hospital comparisons.[9] There is no adjustment for community characteristics, even though practice patterns are significantly associated with readmission rates,[9, 31] and more than half of the variation in readmission rates across hospitals can be explained by characteristics of the community such as access to care.[32] Moreover, patient factors, such as race and socioeconomic status, are currently not included in an attempt to hold hospitals to similar standards regardless of their patient population. This is hotly contested, however, and critics note this policy penalizes hospitals for factors outside of their control, such as patients' ability to afford medications.[33] Indeed, the June 2013 Medicare Payment Advisory Committee (MedPAC) report to Congress recommended evaluating hospital performance against facilities with a like percentage of low‐income patients as a way to take into account socioeconomic status.[34]
Fifth, observation stays are excluded, so patients who remain in observation status during their index or subsequent hospitalization cannot be counted as a readmission. Prevalence of observation care has increased, raising concerns that inpatient admissions are being shifted to observation status, producing an artificial decline in readmissions.[35] Fortunately, recent population‐level data provide some reassuring evidence to the contrary.[36]
Finally, and perhaps most significantly, the current readmission metric does not consider preventability. Recent reviews have demonstrated that estimates of preventability vary widely in individual studies, ranging from 5% to 79%, depending on study methodology and setting.[7, 8] Across these studies, on average, only 23% of 30‐day readmissions appear to be avoidable.[8] Another way to consider the preventability of hospital readmissions is by noting that the most effective multimodal care‐transition interventions reduce readmission rates by only about 30%, and most interventions are much less effective.[26] The likely fact that only 23% to 30% of readmissions are preventable has profound implications for the anticipated results of hospital readmission reduction efforts. Interventions that are 75% effective in reducing preventable readmissions should be expected to produce only an 18% to 22% reduction in overall readmission rates.[37]
FOCUSING ON PREVENTABLE READMISSIONS
A greater focus on identifying and targeting preventable readmissions would offer a number of advantages over the present approach. First, it is more meaningful to compare hospitals based on their percentage of discharges resulting in a preventable readmission, than on the basis of highly complex risk standardization procedures for selected conditions. Second, a focus on preventable readmissions more clearly identifies and permits hospitals to target opportunities for improvement. Third, if the focus were on preventable readmissions for a large number of conditions, the necessary sample size could be obtained over a shorter period of time. Overall, such a preventable readmissions metric could serve as a more agile and undiluted performance indicator, as opposed to the present 3‐year rolling average rate of all‐cause readmissions for certain conditions, the majority of which are probably not preventable.
DEFINING PREVENTABILITY
Defining a preventable readmission is critically important. However, neither a consensus definition nor a validated standard for assessing preventable hospital readmissions exists. Different conceptual frameworks and terms (eg, avoidable, potentially preventable, or urgent readmission) complicate the issue.[38, 39, 40]
Although the CMS measure does not address preventability, it is helpful to consider whether other readmission metrics incorporate this concept. The United Health Group's (UHG, formerly Pacificare) All‐Cause Readmission Index, University HealthSystem Consortium's 30‐Day Readmission Rate (all cause), and 3M Health Information Systems' (3M) Potentially Preventable Readmissions (PPR) are 3 commonly used measures.
Of these, only the 3M PPR metric includes the concept of preventability. 3M created a proprietary matrix of 98,000 readmission‐index admission All Patient Refined Diagnosis Related Group pairs based on the review of several physicians and the logical assumption that a readmission for a clinically related diagnosis is potentially preventable.[24, 41] Readmission and index admissions are considered clinically related if any of the following occur: (1) medical readmission for continuation or recurrence of an initial, or closely related, condition; (2) medical readmission for acute decompensation of a chronic condition that was not the reason for the index admission but was plausibly related to care during or immediately afterward (eg, readmission for diabetes in a patient whose index admission was AMI); (3) medical readmission for acute complication plausibly related to care during index admission; (4) readmission for surgical procedure for continuation or recurrence of initial problem (eg, readmission for appendectomy following admission for abdominal pain and fever); or (5) readmission for surgical procedure to address complication resulting from care during index admission.[24, 41] The readmission time frame is not standardized and may be set by the user. Though conceptually appealing in some ways, CMS and the NQF have expressed concern about this specific approach because of the uncertain reliability of the relatedness of the admission‐readmission diagnosis dyads.[3]
In the research literature, only a few studies have examined the 3M PPR or other preventability assessments that rely on the relatedness of diagnostic codes.[8] Using the 3M PPR, a study showed that 78% of readmissions were classified as potentially preventable,[42] which explains why the 3M PPR and all‐cause readmission metric may correlate highly.[43] Others have demonstrated that ratings of hospital performance on readmission rates vary by a moderate to large amount, depending on whether the 3M PPR, CMS, or UHG methodology is used.[43, 44] An algorithm called SQLape[45, 46] is used in Switzerland to benchmark hospitals and defines potentially avoidable readmissions as being related to index diagnoses or complications of those conditions. It has recently been tested in the United States in a single‐center study,[47] and a multihospital study is underway.
Aside from these algorithms using related diagnosis codes, most ratings of preventability have relied on subjective assessments made primarily through a review of hospital records, and approximately one‐third also included data from clinic visits or interviews with the treating medical team or patients/families.[8] Unfortunately, these reports provide insufficient detail on how to apply their preventability criteria to subsequent readmission reviews. Studies did, however, provide categories of preventability into which readmissions could be organized (see Supporting Information, Appendix Table 1, in the online version of this article for details from a subset of studies cited in van Walraven's reviews that illustrate this point).
Assessment of preventability by clinician review can be challenging. In general, such assessments have considered readmissions resulting from factors within the hospital's control to be avoidable (eg, providing appropriate discharge instructions, reconciling medications, arranging timely postdischarge follow‐up appointments), whereas readmissions resulting from factors not within the hospital's control are unavoidable (eg, patient socioeconomic status, social support, disease progression). However, readmissions resulting from patient behaviors or social reasons could potentially be classified as avoidable or unavoidable depending on the circumstances. For example, if a patient decides not to take a prescribed antibiotic and is readmitted with worsening infection, this could be classified as an unavoidable readmission from the hospital's perspective. Alternatively, if the physician prescribing the antibiotic was inattentive to the cost of the medication and the patient would have taken a less expensive medication had it been prescribed, this could be classified as an avoidable readmission. Differing interpretations of contextual factors may partially account for the variability in clinical assessments of preventability.
Indeed, despite the lack of consensus around a standard method of defining preventability, hospitals and health systems are moving forward to address the issue and reduce readmissions. A recent survey by America's Essential Hospitals (previously the National Association of Public Hospitals and Health Systems), indicated that: (1) reducing readmissions was a high priority for the majority (86%) of members, (2) most had established interdisciplinary teams to address the issue, and (3) over half had a formal process for determining which readmissions were potentially preventable. Of the survey respondents, just over one‐third rely on staff review of individual patient charts or patient and family interviews, and slightly less than one‐third rely on other mechanisms such as external consultants, criteria developed by other entities, or the Institute for Clinical Systems Improvement methodology.[48] Approximately one‐fifth make use of 3M's PPR product, and slightly fewer use the list of the Agency for Healthcare Research and Quality's ambulatory care sensitive conditions (ACSCs). These are medical conditions for which it is believed that good outpatient care could prevent the need for hospitalization (eg, asthma, congestive heart failure, diabetes) or for which early intervention minimizes complications.[49] Hospitalization rates for ACSCs may represent a good measure of excess hospitalization, with a focus on the quality of outpatient care.
RECOMMENDATIONS
We recommend that reporting of hospital readmission rates be based on preventable or potentially preventable readmissions. Although we acknowledge the challenges in doing so, the advantages are notable. At minimum, a preventable readmission rate would more accurately reflect the true gap in care and therefore hospitals' real opportunity for improvement, without being obscured by readmissions that are not preventable.
Because readmission rates are used for public reporting and financial penalties for hospitals, we favor a measure of preventability that reflects the readmissions that the hospital or hospital system has the ability to prevent. This would not penalize hospitals for factors that are under the control of others, namely patients and caregivers, community supports, or society at large. We further recommend that this measure apply to a broader composite of unplanned care, inclusive of both inpatient and observation stays, which have little distinction in patients' eyes, and both represent potentially unnecessary utilization of acute‐care resources.[50] Such a measure would require development, validation, and appropriate vetting before it is implemented.
The first step is for researchers and policy makers to agree on how a measure of preventable or potentially preventable readmissions could be defined. A common element of preventability assessment is to identify the degree to which the reasons for readmission are related to the diagnoses of the index hospitalization. To be reliable and scalable, this measure will need to be based on algorithms that relate the index and readmission diagnoses, most likely using claims data. Choosing common medical and surgical conditions and developing a consensus‐based list of related readmission diagnoses is an important first step. It would also be important to include some less common conditions, because they may reflect very different aspects of hospital care.
An approach based on a list of related diagnoses would represent potentially preventable rehospitalizations. Generally, clinical review is required to determine actual preventability, taking into account patient factors such as a high level of illness or functional impairment that leads to clinical decompensation in spite of excellent management.[51, 52] Clinical review, like a root cause analysis, also provides greater insight into hospital processes that may warrant improvement. Therefore, even if an administrative measure of potentially preventable readmissions is implemented, hospitals may wish to continue performing detailed clinical review of some readmissions for quality improvement purposes. When clinical review becomes more standardized,[53] a combined approach that uses administrative data plus clinical verification and arbitration may be feasible, as with hospital‐acquired infections.
Similar work to develop related sets of admission and readmission diagnoses has already been undertaken in development of the 3M PPR and SQLape measures.[41, 46] However, the 3M PPR is a proprietary system that has low specificity and a high false‐positive rate for identifying preventable readmissions when compared to clinical review.[42] Moreover, neither measure has yet achieved the consensus required for widespread adoption in the United States. What is needed is a nonproprietary listing of related admission and readmission diagnoses, developed with the engagement of relevant stakeholders, that goes through a period of public comment and vetting by a body such as the NQF.
Until a validated measure of potentially preventable readmission can be developed, how could the current approach evolve toward preventability? The most feasible, rapidly implementable change would be to alter the readmission time horizon from 30 days to 7 or 15 days. A 30‐day period holds hospitals accountable for complications of outpatient care or new problems that may develop weeks after discharge. Even though this may foster shared accountability and collaboration among hospitals and outpatient or community settings, research has demonstrated that early readmissions (eg, within 715 days of discharge) are more likely preventable.[54] Second, consideration of the socioeconomic status of hospital patients, as recommended by MedPAC,[34] would improve on the current model by comparing hospitals to like facilities when determining penalties for excess readmission rates. Finally, adjustment for community factors, such as practice patterns and access to care, would enable readmission metrics to better reflect factors under the hospital's control.[32]
CONCLUSION
Holding hospitals accountable for the quality of acute and transitional care is an important policy initiative that has accelerated many improvements in discharge planning and care coordination. Optimally, the policies, public reporting, and penalties should target preventable readmissions, which may represent as little as one‐quarter of all readmissions. By summarizing some of the issues in defining preventability, we hope to foster continued refinement of quality metrics used in this arena.
Acknowledgements
We thank Eduard Vasilevskis, MD, MPH, for feedback on an earlier draft of this article. This manuscript was informed by a special report titled Preventable Readmissions, written by Julia Lavenberg, Joel Betesh, David Goldmann, Craig Kean, and Kendal Williams of the Penn Medicine Center for Evidence‐based Practice. The review was performed at the request of the Penn Medicine Chief Medical Officer Patrick J. Brennan to inform the development of local readmission prevention metrics, and is available at
Disclosures
Dr. Umscheid's contribution to this project was supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1TR000003. Dr. Kripalani receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R01HL109388, and from the Centers for Medicare and Medicaid Services under awards 1C1CMS331006‐01 and 1C1CMS330979‐01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Centers for Medicare and Medicaid Services.
- , . Physician Visits After Hospital Discharge: Implications for Reducing Readmissions. Washington, DC: National Institute for Health Care Reform; 2011. Report no. 6.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- Centers for Medicare and Medicaid Services, US Department of Health and Human Services. Medicare program: hospital inpatient prospective payment systems for acute care hospitals and the long‐term care hospital prospective payment system and FY 2012 rates. Fed Regist. 2011;76(160):51476–51846.
- , , , , , . Quality collaboratives and campaigns to reduce readmissions: what strategies are hospitals using? J Hosp Med. 2013;8:601–608.
- , , , , . Contemporary data about hospital strategies to reduce unplanned readmissions: what has changed [research letter]? JAMA Intern Med. 2014;174(1):154–156.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528.
- , , , . Comparing methods to calculate hospital‐specific rates of early death or urgent readmission. CMAJ. 2012;184(15):E810–E817.
- , , , , . Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–E402.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- National Quality Forum. Patient outcomes: all‐cause readmissions expedited review 2011. Available at: http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id60(7):607–614.
- , , , , , . Data shows reduction in Medicare hospital readmission rates during 2012. Medicare Medicaid Res Rev. 2013;3(2):E1–E11.
- , . Thirty‐day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366–1369.
- , , , . Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102–109.
- , . A path forward on Medicare readmissions. N Engl J Med. 2013;368(13):1175–1177.
- American Hospital Association. TrendWatch: examining the drivers of readmissions and reducing unnecessary readmissions for better patient care. Washington, DC: American Hospital Association; 2011.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363.
- , . Characteristics of hospitals receiving penalties under the hospital readmissions reduction program. JAMA. 2013;309(4):342–343.
- , , , , , , et al. Identifying potentially preventable readmissions. Health Care Financ Rev. 2008;30(1):75–91.
- , , , et al. Use of hospital‐based acute care among patients recently discharged from the hospital. JAMA. 2013;309(4):364–371.
- , , , . Reducing hospital readmission rates: current strategies and future directions. Annu Rev Med. 2014;65:471–485.
- , , , et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587–593.
- , , , et al. Hospital performance measures and 30‐day readmission rates. J Gen Intern Med. 2013;28(3):377–385.
- , , , . Limitations of using same‐hospital readmission metrics. Int J Qual Health Care. 2013;25(6):633–639.
- , , , et al. Is same‐hospital readmission rate a good surrogate for all‐hospital readmission rate? Med Care. 2010;48(5):477–481.
- , , . The relationship between hospital admission rates and rehospitalizations. N Engl J Med. 2011;365(24):2287–2295.
- . Community factors and hospital readmission rates [published online April 9, 2014]. Health Serv Res. doi: 10.1111/1475–6773.12177.
- American Hospital Association. Hospital readmissions reduction program: factsheet. American Hospital Association. Available at: http://www.aha.org/content/13/fs‐readmissions.pdf. Published April 14, 2014. Accessed May 5, 2014.
- Medicare Payment Advisory Commission. Report to the congress: Medicare and the health care delivery system. Available at: http://www.medpac.gov/documents/Jun13_EntireReport.pdf. Published June 14, 2013. Accessed May 5, 2014.
- , , . Sharp rise in Medicare enrollees being held in hospitals for observation raises concerns about causes and consequences. Health Aff (Millwood). 2012;31(6):1251–1259.
- , , . Quality improvement of care transitions and the trend of composite hospital care. JAMA. 2014;311(10):1013–1014.
- , . When projecting required effectiveness of interventions for hospital readmission reduction, the percentage that is potentially avoidable must be considered. J Clin Epidemiol. 2013;66(6):688–690.
- , , . Urgent readmission rates can be used to infer differences in avoidable readmission rates between hospitals. J Clin Epidemiol. 2012;65(10):1124–1130.
- , , , , . Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–E402.
- , , , , , . Measuring and preventing potentially avoidable hospital readmissions: a review of the literature. Hong Kong Med J. 2010;16(5):383–389.
- 3M Health Information Systems. Potentially preventable readmissions classification system methodology: overview. 3M Health Information Systems; May 2008. Report No.: GRP‐139. Available at: http://multimedia.3m.com/mws/mediawebserver?66666UuZjcFSLXTtNXMtmxMEEVuQEcuZgVs6EVs6E666666‐‐. Accessed June 8, 2014.
- , , , , , . Manual and automated methods for identifying potentially preventable readmissions: a comparison in a large healthcare system. BMC Med Inform Decis Mak. 2014;14:28.
- , , , , , , et al. Comparing 2 methods of assessing 30‐day readmissions: what is the impact on hospital profiling in the Veterans Health Administration? Med Care. 2013;51(7):589–596.
- , . It's not six of one, half‐dozen the other: a comparative analysis of 3 rehospitalization measurement systems for Massachusetts. Academy Health Annual Research Meeting. Seattle, WA. 2011. Available at: http://www.academyhealth.org/files/2011/tuesday/boutwell.pdf. Accessed May 9, 2014.
- , , , , , . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11):972–981.
- , , , , , . Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587.
- , , , . Potentially avoidable 30‐day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–638.
- National Association of Public Hospitals and Health Systems. NAPH members focus on reducing readmissions. Available at: www.naph.org. Published June 2011. Accessed October 19, 2011.
- Agency for Healthcare Research and Quality. AHRQ quality indicators: prevention quality indicators. Available at: http://www.qualityindicators.ahrq.gov/Modules/pqi_resources.aspx. Accessed February 11, 2014.
- , , , , , . Shifting the dialogue from hospital readmissions to unplanned care. Am J Manag Care. 2013;19(6):450–453.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102.
- , . The hospital‐dependent patient. N Engl J Med. 2014;370(8):694–697.
- , , , et al. The hospital medicine reengineering network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415–420.
- , , , et al. Incidence of potentially avoidable urgent readmissions and their relation to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
Hospital readmissions cost Medicare $15 to $17 billion per year.[1, 2] In 2010, the Hospital Readmission Reduction Program (HRRP), created by the Patient Protection and Affordable Care Act, authorized the Centers for Medicare and Medicaid Services (CMS) to penalize hospitals with higher‐than‐expected readmission rates for certain index conditions.[3] Other payers may follow suit, so hospitals and health systems nationwide are devoting significant resources to reducing readmissions.[4, 5, 6]
Implicit in these efforts are the assumptions that a significant proportion of readmissions are preventable, and that preventable readmissions can be identified. Unfortunately, estimates of preventability vary widely.[7, 8] In this article, we examine how preventable readmissions have been defined, measured, and calculated, and explore the associated implications for readmission reduction efforts.
THE MEDICARE READMISSION METRIC
The medical literature reveals substantial heterogeneity in how readmissions are assessed. Time periods range from 14 days to 4 years, and readmissions may be counted differently depending on whether they are to the same hospital or to any hospital, whether they are for the same (or a related) condition or for any condition, whether a patient is allowed to count only once during the follow‐up period, how mortality is treated, and whether observation stays are considered.[9]
Despite a lack of consensus in the literature, the approach adopted by CMS is endorsed by the National Quality Forum (NQF)[10] and has become the de facto standard for calculating readmission rates. CMS derives risk‐standardized readmission rates for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN), using administrative claims data for each Medicare fee‐for‐service beneficiary 65 years or older.[11, 12, 13, 14] CMS counts the first readmission (but not subsequent ones) for any cause within 30 days of the index discharge, including readmissions to other facilities. Certain planned readmissions for revascularization are excluded, as are patients who left against medical advice, transferred to another acute‐care hospital, or died during the index admission. Admissions to psychiatric, rehabilitation, cancer specialty, and children's hospitals[12] are also excluded, as well as patients classified as observation status for either hospital stay.[15] Only administrative data are used in readmission calculations (ie, there are no chart reviews or interviews with healthcare personnel or patients). Details are published online and updated at least annually.[15]
EFFECTS AND LIMITATIONS OF THE HRRP AND THE CMS READMISSION METRIC
Penalizing hospitals for higher‐than‐expected readmission rates based on the CMS metric has been successful in the sense that hospitals now feel more accountable for patient outcomes after discharge; they are implementing transitional care programs, improving communication, and building relationships with community programs.[4, 5, 16] Early data suggest a small decline in readmission rates of Medicare beneficiaries nationally.[17] Previously, such readmission rates were constant.[18]
Nevertheless, significant concerns with the current approach have surfaced.[19, 20, 21] First, why choose 30 days? This time horizon was believed to be long enough to identify readmissions attributable to an index admission and short enough to reflect hospital‐delivered care and transitions to the outpatient setting, and it allows for collaboration between hospitals and their communities to reduce readmissions.[3] However, some have argued that this time horizon has little scientific basis,[22] and that hospitals are unfairly held accountable for a timeframe when outcomes may largely be influenced by the quality of outpatient care or the development of new problems.[23, 24] Approximately one‐third of 30‐day readmissions occur within the first 7 days, and more than half (55.7%) occur within the first 14 days[22, 25]; such time frames may be more appropriate for hospital accountability.[26]
Second, spurred by the focus of CMS penalties, efforts to reduce readmissions have largely concerned patients admitted for HF, AMI, or PN, although these 3 medical conditions account for only 10% of Medicare hospitalizations.[18] Programs focused on a narrow patient population may not benefit other patients with high readmission rates, such as those with gastrointestinal or psychiatric problems,[2] or lead to improvements in the underlying processes of care that could benefit patients in additional ways. Indeed, research suggests that low readmission rates may not be related to other measures of hospital quality.[27, 28]
Third, public reporting and hospital penalties are based on 3‐year historical performance, in part to accumulate a large enough sample size for each diagnosis. Hospitals that seek real‐time performance monitoring are limited to tracking surrogate outcomes, such as readmissions back to their own facility.[29, 30] Moreover, because of the long performance time frame, hospitals that achieve rapid improvement may endure penalties precisely when they are attempting to sustain their achievements.
Fourth, the CMS approach utilizes a complex risk‐standardization methodology, which has only modest ability to predict readmissions and allow hospital comparisons.[9] There is no adjustment for community characteristics, even though practice patterns are significantly associated with readmission rates,[9, 31] and more than half of the variation in readmission rates across hospitals can be explained by characteristics of the community such as access to care.[32] Moreover, patient factors, such as race and socioeconomic status, are currently not included in an attempt to hold hospitals to similar standards regardless of their patient population. This is hotly contested, however, and critics note this policy penalizes hospitals for factors outside of their control, such as patients' ability to afford medications.[33] Indeed, the June 2013 Medicare Payment Advisory Committee (MedPAC) report to Congress recommended evaluating hospital performance against facilities with a like percentage of low‐income patients as a way to take into account socioeconomic status.[34]
Fifth, observation stays are excluded, so patients who remain in observation status during their index or subsequent hospitalization cannot be counted as a readmission. Prevalence of observation care has increased, raising concerns that inpatient admissions are being shifted to observation status, producing an artificial decline in readmissions.[35] Fortunately, recent population‐level data provide some reassuring evidence to the contrary.[36]
Finally, and perhaps most significantly, the current readmission metric does not consider preventability. Recent reviews have demonstrated that estimates of preventability vary widely in individual studies, ranging from 5% to 79%, depending on study methodology and setting.[7, 8] Across these studies, on average, only 23% of 30‐day readmissions appear to be avoidable.[8] Another way to consider the preventability of hospital readmissions is by noting that the most effective multimodal care‐transition interventions reduce readmission rates by only about 30%, and most interventions are much less effective.[26] The likely fact that only 23% to 30% of readmissions are preventable has profound implications for the anticipated results of hospital readmission reduction efforts. Interventions that are 75% effective in reducing preventable readmissions should be expected to produce only an 18% to 22% reduction in overall readmission rates.[37]
FOCUSING ON PREVENTABLE READMISSIONS
A greater focus on identifying and targeting preventable readmissions would offer a number of advantages over the present approach. First, it is more meaningful to compare hospitals based on their percentage of discharges resulting in a preventable readmission, than on the basis of highly complex risk standardization procedures for selected conditions. Second, a focus on preventable readmissions more clearly identifies and permits hospitals to target opportunities for improvement. Third, if the focus were on preventable readmissions for a large number of conditions, the necessary sample size could be obtained over a shorter period of time. Overall, such a preventable readmissions metric could serve as a more agile and undiluted performance indicator, as opposed to the present 3‐year rolling average rate of all‐cause readmissions for certain conditions, the majority of which are probably not preventable.
DEFINING PREVENTABILITY
Defining a preventable readmission is critically important. However, neither a consensus definition nor a validated standard for assessing preventable hospital readmissions exists. Different conceptual frameworks and terms (eg, avoidable, potentially preventable, or urgent readmission) complicate the issue.[38, 39, 40]
Although the CMS measure does not address preventability, it is helpful to consider whether other readmission metrics incorporate this concept. The United Health Group's (UHG, formerly Pacificare) All‐Cause Readmission Index, University HealthSystem Consortium's 30‐Day Readmission Rate (all cause), and 3M Health Information Systems' (3M) Potentially Preventable Readmissions (PPR) are 3 commonly used measures.
Of these, only the 3M PPR metric includes the concept of preventability. 3M created a proprietary matrix of 98,000 readmission‐index admission All Patient Refined Diagnosis Related Group pairs based on the review of several physicians and the logical assumption that a readmission for a clinically related diagnosis is potentially preventable.[24, 41] Readmission and index admissions are considered clinically related if any of the following occur: (1) medical readmission for continuation or recurrence of an initial, or closely related, condition; (2) medical readmission for acute decompensation of a chronic condition that was not the reason for the index admission but was plausibly related to care during or immediately afterward (eg, readmission for diabetes in a patient whose index admission was AMI); (3) medical readmission for acute complication plausibly related to care during index admission; (4) readmission for surgical procedure for continuation or recurrence of initial problem (eg, readmission for appendectomy following admission for abdominal pain and fever); or (5) readmission for surgical procedure to address complication resulting from care during index admission.[24, 41] The readmission time frame is not standardized and may be set by the user. Though conceptually appealing in some ways, CMS and the NQF have expressed concern about this specific approach because of the uncertain reliability of the relatedness of the admission‐readmission diagnosis dyads.[3]
In the research literature, only a few studies have examined the 3M PPR or other preventability assessments that rely on the relatedness of diagnostic codes.[8] Using the 3M PPR, a study showed that 78% of readmissions were classified as potentially preventable,[42] which explains why the 3M PPR and all‐cause readmission metric may correlate highly.[43] Others have demonstrated that ratings of hospital performance on readmission rates vary by a moderate to large amount, depending on whether the 3M PPR, CMS, or UHG methodology is used.[43, 44] An algorithm called SQLape[45, 46] is used in Switzerland to benchmark hospitals and defines potentially avoidable readmissions as being related to index diagnoses or complications of those conditions. It has recently been tested in the United States in a single‐center study,[47] and a multihospital study is underway.
Aside from these algorithms using related diagnosis codes, most ratings of preventability have relied on subjective assessments made primarily through a review of hospital records, and approximately one‐third also included data from clinic visits or interviews with the treating medical team or patients/families.[8] Unfortunately, these reports provide insufficient detail on how to apply their preventability criteria to subsequent readmission reviews. Studies did, however, provide categories of preventability into which readmissions could be organized (see Supporting Information, Appendix Table 1, in the online version of this article for details from a subset of studies cited in van Walraven's reviews that illustrate this point).
Assessment of preventability by clinician review can be challenging. In general, such assessments have considered readmissions resulting from factors within the hospital's control to be avoidable (eg, providing appropriate discharge instructions, reconciling medications, arranging timely postdischarge follow‐up appointments), whereas readmissions resulting from factors not within the hospital's control are unavoidable (eg, patient socioeconomic status, social support, disease progression). However, readmissions resulting from patient behaviors or social reasons could potentially be classified as avoidable or unavoidable depending on the circumstances. For example, if a patient decides not to take a prescribed antibiotic and is readmitted with worsening infection, this could be classified as an unavoidable readmission from the hospital's perspective. Alternatively, if the physician prescribing the antibiotic was inattentive to the cost of the medication and the patient would have taken a less expensive medication had it been prescribed, this could be classified as an avoidable readmission. Differing interpretations of contextual factors may partially account for the variability in clinical assessments of preventability.
Indeed, despite the lack of consensus around a standard method of defining preventability, hospitals and health systems are moving forward to address the issue and reduce readmissions. A recent survey by America's Essential Hospitals (previously the National Association of Public Hospitals and Health Systems), indicated that: (1) reducing readmissions was a high priority for the majority (86%) of members, (2) most had established interdisciplinary teams to address the issue, and (3) over half had a formal process for determining which readmissions were potentially preventable. Of the survey respondents, just over one‐third rely on staff review of individual patient charts or patient and family interviews, and slightly less than one‐third rely on other mechanisms such as external consultants, criteria developed by other entities, or the Institute for Clinical Systems Improvement methodology.[48] Approximately one‐fifth make use of 3M's PPR product, and slightly fewer use the list of the Agency for Healthcare Research and Quality's ambulatory care sensitive conditions (ACSCs). These are medical conditions for which it is believed that good outpatient care could prevent the need for hospitalization (eg, asthma, congestive heart failure, diabetes) or for which early intervention minimizes complications.[49] Hospitalization rates for ACSCs may represent a good measure of excess hospitalization, with a focus on the quality of outpatient care.
RECOMMENDATIONS
We recommend that reporting of hospital readmission rates be based on preventable or potentially preventable readmissions. Although we acknowledge the challenges in doing so, the advantages are notable. At minimum, a preventable readmission rate would more accurately reflect the true gap in care and therefore hospitals' real opportunity for improvement, without being obscured by readmissions that are not preventable.
Because readmission rates are used for public reporting and financial penalties for hospitals, we favor a measure of preventability that reflects the readmissions that the hospital or hospital system has the ability to prevent. This would not penalize hospitals for factors that are under the control of others, namely patients and caregivers, community supports, or society at large. We further recommend that this measure apply to a broader composite of unplanned care, inclusive of both inpatient and observation stays, which have little distinction in patients' eyes, and both represent potentially unnecessary utilization of acute‐care resources.[50] Such a measure would require development, validation, and appropriate vetting before it is implemented.
The first step is for researchers and policy makers to agree on how a measure of preventable or potentially preventable readmissions could be defined. A common element of preventability assessment is to identify the degree to which the reasons for readmission are related to the diagnoses of the index hospitalization. To be reliable and scalable, this measure will need to be based on algorithms that relate the index and readmission diagnoses, most likely using claims data. Choosing common medical and surgical conditions and developing a consensus‐based list of related readmission diagnoses is an important first step. It would also be important to include some less common conditions, because they may reflect very different aspects of hospital care.
An approach based on a list of related diagnoses would represent potentially preventable rehospitalizations. Generally, clinical review is required to determine actual preventability, taking into account patient factors such as a high level of illness or functional impairment that leads to clinical decompensation in spite of excellent management.[51, 52] Clinical review, like a root cause analysis, also provides greater insight into hospital processes that may warrant improvement. Therefore, even if an administrative measure of potentially preventable readmissions is implemented, hospitals may wish to continue performing detailed clinical review of some readmissions for quality improvement purposes. When clinical review becomes more standardized,[53] a combined approach that uses administrative data plus clinical verification and arbitration may be feasible, as with hospital‐acquired infections.
Similar work to develop related sets of admission and readmission diagnoses has already been undertaken in development of the 3M PPR and SQLape measures.[41, 46] However, the 3M PPR is a proprietary system that has low specificity and a high false‐positive rate for identifying preventable readmissions when compared to clinical review.[42] Moreover, neither measure has yet achieved the consensus required for widespread adoption in the United States. What is needed is a nonproprietary listing of related admission and readmission diagnoses, developed with the engagement of relevant stakeholders, that goes through a period of public comment and vetting by a body such as the NQF.
Until a validated measure of potentially preventable readmission can be developed, how could the current approach evolve toward preventability? The most feasible, rapidly implementable change would be to alter the readmission time horizon from 30 days to 7 or 15 days. A 30‐day period holds hospitals accountable for complications of outpatient care or new problems that may develop weeks after discharge. Even though this may foster shared accountability and collaboration among hospitals and outpatient or community settings, research has demonstrated that early readmissions (eg, within 715 days of discharge) are more likely preventable.[54] Second, consideration of the socioeconomic status of hospital patients, as recommended by MedPAC,[34] would improve on the current model by comparing hospitals to like facilities when determining penalties for excess readmission rates. Finally, adjustment for community factors, such as practice patterns and access to care, would enable readmission metrics to better reflect factors under the hospital's control.[32]
CONCLUSION
Holding hospitals accountable for the quality of acute and transitional care is an important policy initiative that has accelerated many improvements in discharge planning and care coordination. Optimally, the policies, public reporting, and penalties should target preventable readmissions, which may represent as little as one‐quarter of all readmissions. By summarizing some of the issues in defining preventability, we hope to foster continued refinement of quality metrics used in this arena.
Acknowledgements
We thank Eduard Vasilevskis, MD, MPH, for feedback on an earlier draft of this article. This manuscript was informed by a special report titled Preventable Readmissions, written by Julia Lavenberg, Joel Betesh, David Goldmann, Craig Kean, and Kendal Williams of the Penn Medicine Center for Evidence‐based Practice. The review was performed at the request of the Penn Medicine Chief Medical Officer Patrick J. Brennan to inform the development of local readmission prevention metrics, and is available at
Disclosures
Dr. Umscheid's contribution to this project was supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1TR000003. Dr. Kripalani receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R01HL109388, and from the Centers for Medicare and Medicaid Services under awards 1C1CMS331006‐01 and 1C1CMS330979‐01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Centers for Medicare and Medicaid Services.
Hospital readmissions cost Medicare $15 to $17 billion per year.[1, 2] In 2010, the Hospital Readmission Reduction Program (HRRP), created by the Patient Protection and Affordable Care Act, authorized the Centers for Medicare and Medicaid Services (CMS) to penalize hospitals with higher‐than‐expected readmission rates for certain index conditions.[3] Other payers may follow suit, so hospitals and health systems nationwide are devoting significant resources to reducing readmissions.[4, 5, 6]
Implicit in these efforts are the assumptions that a significant proportion of readmissions are preventable, and that preventable readmissions can be identified. Unfortunately, estimates of preventability vary widely.[7, 8] In this article, we examine how preventable readmissions have been defined, measured, and calculated, and explore the associated implications for readmission reduction efforts.
THE MEDICARE READMISSION METRIC
The medical literature reveals substantial heterogeneity in how readmissions are assessed. Time periods range from 14 days to 4 years, and readmissions may be counted differently depending on whether they are to the same hospital or to any hospital, whether they are for the same (or a related) condition or for any condition, whether a patient is allowed to count only once during the follow‐up period, how mortality is treated, and whether observation stays are considered.[9]
Despite a lack of consensus in the literature, the approach adopted by CMS is endorsed by the National Quality Forum (NQF)[10] and has become the de facto standard for calculating readmission rates. CMS derives risk‐standardized readmission rates for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN), using administrative claims data for each Medicare fee‐for‐service beneficiary 65 years or older.[11, 12, 13, 14] CMS counts the first readmission (but not subsequent ones) for any cause within 30 days of the index discharge, including readmissions to other facilities. Certain planned readmissions for revascularization are excluded, as are patients who left against medical advice, transferred to another acute‐care hospital, or died during the index admission. Admissions to psychiatric, rehabilitation, cancer specialty, and children's hospitals[12] are also excluded, as well as patients classified as observation status for either hospital stay.[15] Only administrative data are used in readmission calculations (ie, there are no chart reviews or interviews with healthcare personnel or patients). Details are published online and updated at least annually.[15]
EFFECTS AND LIMITATIONS OF THE HRRP AND THE CMS READMISSION METRIC
Penalizing hospitals for higher‐than‐expected readmission rates based on the CMS metric has been successful in the sense that hospitals now feel more accountable for patient outcomes after discharge; they are implementing transitional care programs, improving communication, and building relationships with community programs.[4, 5, 16] Early data suggest a small decline in readmission rates of Medicare beneficiaries nationally.[17] Previously, such readmission rates were constant.[18]
Nevertheless, significant concerns with the current approach have surfaced.[19, 20, 21] First, why choose 30 days? This time horizon was believed to be long enough to identify readmissions attributable to an index admission and short enough to reflect hospital‐delivered care and transitions to the outpatient setting, and it allows for collaboration between hospitals and their communities to reduce readmissions.[3] However, some have argued that this time horizon has little scientific basis,[22] and that hospitals are unfairly held accountable for a timeframe when outcomes may largely be influenced by the quality of outpatient care or the development of new problems.[23, 24] Approximately one‐third of 30‐day readmissions occur within the first 7 days, and more than half (55.7%) occur within the first 14 days[22, 25]; such time frames may be more appropriate for hospital accountability.[26]
Second, spurred by the focus of CMS penalties, efforts to reduce readmissions have largely concerned patients admitted for HF, AMI, or PN, although these 3 medical conditions account for only 10% of Medicare hospitalizations.[18] Programs focused on a narrow patient population may not benefit other patients with high readmission rates, such as those with gastrointestinal or psychiatric problems,[2] or lead to improvements in the underlying processes of care that could benefit patients in additional ways. Indeed, research suggests that low readmission rates may not be related to other measures of hospital quality.[27, 28]
Third, public reporting and hospital penalties are based on 3‐year historical performance, in part to accumulate a large enough sample size for each diagnosis. Hospitals that seek real‐time performance monitoring are limited to tracking surrogate outcomes, such as readmissions back to their own facility.[29, 30] Moreover, because of the long performance time frame, hospitals that achieve rapid improvement may endure penalties precisely when they are attempting to sustain their achievements.
Fourth, the CMS approach utilizes a complex risk‐standardization methodology, which has only modest ability to predict readmissions and allow hospital comparisons.[9] There is no adjustment for community characteristics, even though practice patterns are significantly associated with readmission rates,[9, 31] and more than half of the variation in readmission rates across hospitals can be explained by characteristics of the community such as access to care.[32] Moreover, patient factors, such as race and socioeconomic status, are currently not included in an attempt to hold hospitals to similar standards regardless of their patient population. This is hotly contested, however, and critics note this policy penalizes hospitals for factors outside of their control, such as patients' ability to afford medications.[33] Indeed, the June 2013 Medicare Payment Advisory Committee (MedPAC) report to Congress recommended evaluating hospital performance against facilities with a like percentage of low‐income patients as a way to take into account socioeconomic status.[34]
Fifth, observation stays are excluded, so patients who remain in observation status during their index or subsequent hospitalization cannot be counted as a readmission. Prevalence of observation care has increased, raising concerns that inpatient admissions are being shifted to observation status, producing an artificial decline in readmissions.[35] Fortunately, recent population‐level data provide some reassuring evidence to the contrary.[36]
Finally, and perhaps most significantly, the current readmission metric does not consider preventability. Recent reviews have demonstrated that estimates of preventability vary widely in individual studies, ranging from 5% to 79%, depending on study methodology and setting.[7, 8] Across these studies, on average, only 23% of 30‐day readmissions appear to be avoidable.[8] Another way to consider the preventability of hospital readmissions is by noting that the most effective multimodal care‐transition interventions reduce readmission rates by only about 30%, and most interventions are much less effective.[26] The likely fact that only 23% to 30% of readmissions are preventable has profound implications for the anticipated results of hospital readmission reduction efforts. Interventions that are 75% effective in reducing preventable readmissions should be expected to produce only an 18% to 22% reduction in overall readmission rates.[37]
FOCUSING ON PREVENTABLE READMISSIONS
A greater focus on identifying and targeting preventable readmissions would offer a number of advantages over the present approach. First, it is more meaningful to compare hospitals based on their percentage of discharges resulting in a preventable readmission, than on the basis of highly complex risk standardization procedures for selected conditions. Second, a focus on preventable readmissions more clearly identifies and permits hospitals to target opportunities for improvement. Third, if the focus were on preventable readmissions for a large number of conditions, the necessary sample size could be obtained over a shorter period of time. Overall, such a preventable readmissions metric could serve as a more agile and undiluted performance indicator, as opposed to the present 3‐year rolling average rate of all‐cause readmissions for certain conditions, the majority of which are probably not preventable.
DEFINING PREVENTABILITY
Defining a preventable readmission is critically important. However, neither a consensus definition nor a validated standard for assessing preventable hospital readmissions exists. Different conceptual frameworks and terms (eg, avoidable, potentially preventable, or urgent readmission) complicate the issue.[38, 39, 40]
Although the CMS measure does not address preventability, it is helpful to consider whether other readmission metrics incorporate this concept. The United Health Group's (UHG, formerly Pacificare) All‐Cause Readmission Index, University HealthSystem Consortium's 30‐Day Readmission Rate (all cause), and 3M Health Information Systems' (3M) Potentially Preventable Readmissions (PPR) are 3 commonly used measures.
Of these, only the 3M PPR metric includes the concept of preventability. 3M created a proprietary matrix of 98,000 readmission‐index admission All Patient Refined Diagnosis Related Group pairs based on the review of several physicians and the logical assumption that a readmission for a clinically related diagnosis is potentially preventable.[24, 41] Readmission and index admissions are considered clinically related if any of the following occur: (1) medical readmission for continuation or recurrence of an initial, or closely related, condition; (2) medical readmission for acute decompensation of a chronic condition that was not the reason for the index admission but was plausibly related to care during or immediately afterward (eg, readmission for diabetes in a patient whose index admission was AMI); (3) medical readmission for acute complication plausibly related to care during index admission; (4) readmission for surgical procedure for continuation or recurrence of initial problem (eg, readmission for appendectomy following admission for abdominal pain and fever); or (5) readmission for surgical procedure to address complication resulting from care during index admission.[24, 41] The readmission time frame is not standardized and may be set by the user. Though conceptually appealing in some ways, CMS and the NQF have expressed concern about this specific approach because of the uncertain reliability of the relatedness of the admission‐readmission diagnosis dyads.[3]
In the research literature, only a few studies have examined the 3M PPR or other preventability assessments that rely on the relatedness of diagnostic codes.[8] Using the 3M PPR, a study showed that 78% of readmissions were classified as potentially preventable,[42] which explains why the 3M PPR and all‐cause readmission metric may correlate highly.[43] Others have demonstrated that ratings of hospital performance on readmission rates vary by a moderate to large amount, depending on whether the 3M PPR, CMS, or UHG methodology is used.[43, 44] An algorithm called SQLape[45, 46] is used in Switzerland to benchmark hospitals and defines potentially avoidable readmissions as being related to index diagnoses or complications of those conditions. It has recently been tested in the United States in a single‐center study,[47] and a multihospital study is underway.
Aside from these algorithms using related diagnosis codes, most ratings of preventability have relied on subjective assessments made primarily through a review of hospital records, and approximately one‐third also included data from clinic visits or interviews with the treating medical team or patients/families.[8] Unfortunately, these reports provide insufficient detail on how to apply their preventability criteria to subsequent readmission reviews. Studies did, however, provide categories of preventability into which readmissions could be organized (see Supporting Information, Appendix Table 1, in the online version of this article for details from a subset of studies cited in van Walraven's reviews that illustrate this point).
Assessment of preventability by clinician review can be challenging. In general, such assessments have considered readmissions resulting from factors within the hospital's control to be avoidable (eg, providing appropriate discharge instructions, reconciling medications, arranging timely postdischarge follow‐up appointments), whereas readmissions resulting from factors not within the hospital's control are unavoidable (eg, patient socioeconomic status, social support, disease progression). However, readmissions resulting from patient behaviors or social reasons could potentially be classified as avoidable or unavoidable depending on the circumstances. For example, if a patient decides not to take a prescribed antibiotic and is readmitted with worsening infection, this could be classified as an unavoidable readmission from the hospital's perspective. Alternatively, if the physician prescribing the antibiotic was inattentive to the cost of the medication and the patient would have taken a less expensive medication had it been prescribed, this could be classified as an avoidable readmission. Differing interpretations of contextual factors may partially account for the variability in clinical assessments of preventability.
Indeed, despite the lack of consensus around a standard method of defining preventability, hospitals and health systems are moving forward to address the issue and reduce readmissions. A recent survey by America's Essential Hospitals (previously the National Association of Public Hospitals and Health Systems), indicated that: (1) reducing readmissions was a high priority for the majority (86%) of members, (2) most had established interdisciplinary teams to address the issue, and (3) over half had a formal process for determining which readmissions were potentially preventable. Of the survey respondents, just over one‐third rely on staff review of individual patient charts or patient and family interviews, and slightly less than one‐third rely on other mechanisms such as external consultants, criteria developed by other entities, or the Institute for Clinical Systems Improvement methodology.[48] Approximately one‐fifth make use of 3M's PPR product, and slightly fewer use the list of the Agency for Healthcare Research and Quality's ambulatory care sensitive conditions (ACSCs). These are medical conditions for which it is believed that good outpatient care could prevent the need for hospitalization (eg, asthma, congestive heart failure, diabetes) or for which early intervention minimizes complications.[49] Hospitalization rates for ACSCs may represent a good measure of excess hospitalization, with a focus on the quality of outpatient care.
RECOMMENDATIONS
We recommend that reporting of hospital readmission rates be based on preventable or potentially preventable readmissions. Although we acknowledge the challenges in doing so, the advantages are notable. At minimum, a preventable readmission rate would more accurately reflect the true gap in care and therefore hospitals' real opportunity for improvement, without being obscured by readmissions that are not preventable.
Because readmission rates are used for public reporting and financial penalties for hospitals, we favor a measure of preventability that reflects the readmissions that the hospital or hospital system has the ability to prevent. This would not penalize hospitals for factors that are under the control of others, namely patients and caregivers, community supports, or society at large. We further recommend that this measure apply to a broader composite of unplanned care, inclusive of both inpatient and observation stays, which have little distinction in patients' eyes, and both represent potentially unnecessary utilization of acute‐care resources.[50] Such a measure would require development, validation, and appropriate vetting before it is implemented.
The first step is for researchers and policy makers to agree on how a measure of preventable or potentially preventable readmissions could be defined. A common element of preventability assessment is to identify the degree to which the reasons for readmission are related to the diagnoses of the index hospitalization. To be reliable and scalable, this measure will need to be based on algorithms that relate the index and readmission diagnoses, most likely using claims data. Choosing common medical and surgical conditions and developing a consensus‐based list of related readmission diagnoses is an important first step. It would also be important to include some less common conditions, because they may reflect very different aspects of hospital care.
An approach based on a list of related diagnoses would represent potentially preventable rehospitalizations. Generally, clinical review is required to determine actual preventability, taking into account patient factors such as a high level of illness or functional impairment that leads to clinical decompensation in spite of excellent management.[51, 52] Clinical review, like a root cause analysis, also provides greater insight into hospital processes that may warrant improvement. Therefore, even if an administrative measure of potentially preventable readmissions is implemented, hospitals may wish to continue performing detailed clinical review of some readmissions for quality improvement purposes. When clinical review becomes more standardized,[53] a combined approach that uses administrative data plus clinical verification and arbitration may be feasible, as with hospital‐acquired infections.
Similar work to develop related sets of admission and readmission diagnoses has already been undertaken in development of the 3M PPR and SQLape measures.[41, 46] However, the 3M PPR is a proprietary system that has low specificity and a high false‐positive rate for identifying preventable readmissions when compared to clinical review.[42] Moreover, neither measure has yet achieved the consensus required for widespread adoption in the United States. What is needed is a nonproprietary listing of related admission and readmission diagnoses, developed with the engagement of relevant stakeholders, that goes through a period of public comment and vetting by a body such as the NQF.
Until a validated measure of potentially preventable readmission can be developed, how could the current approach evolve toward preventability? The most feasible, rapidly implementable change would be to alter the readmission time horizon from 30 days to 7 or 15 days. A 30‐day period holds hospitals accountable for complications of outpatient care or new problems that may develop weeks after discharge. Even though this may foster shared accountability and collaboration among hospitals and outpatient or community settings, research has demonstrated that early readmissions (eg, within 715 days of discharge) are more likely preventable.[54] Second, consideration of the socioeconomic status of hospital patients, as recommended by MedPAC,[34] would improve on the current model by comparing hospitals to like facilities when determining penalties for excess readmission rates. Finally, adjustment for community factors, such as practice patterns and access to care, would enable readmission metrics to better reflect factors under the hospital's control.[32]
CONCLUSION
Holding hospitals accountable for the quality of acute and transitional care is an important policy initiative that has accelerated many improvements in discharge planning and care coordination. Optimally, the policies, public reporting, and penalties should target preventable readmissions, which may represent as little as one‐quarter of all readmissions. By summarizing some of the issues in defining preventability, we hope to foster continued refinement of quality metrics used in this arena.
Acknowledgements
We thank Eduard Vasilevskis, MD, MPH, for feedback on an earlier draft of this article. This manuscript was informed by a special report titled Preventable Readmissions, written by Julia Lavenberg, Joel Betesh, David Goldmann, Craig Kean, and Kendal Williams of the Penn Medicine Center for Evidence‐based Practice. The review was performed at the request of the Penn Medicine Chief Medical Officer Patrick J. Brennan to inform the development of local readmission prevention metrics, and is available at
Disclosures
Dr. Umscheid's contribution to this project was supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1TR000003. Dr. Kripalani receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R01HL109388, and from the Centers for Medicare and Medicaid Services under awards 1C1CMS331006‐01 and 1C1CMS330979‐01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Centers for Medicare and Medicaid Services.
- , . Physician Visits After Hospital Discharge: Implications for Reducing Readmissions. Washington, DC: National Institute for Health Care Reform; 2011. Report no. 6.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- Centers for Medicare and Medicaid Services, US Department of Health and Human Services. Medicare program: hospital inpatient prospective payment systems for acute care hospitals and the long‐term care hospital prospective payment system and FY 2012 rates. Fed Regist. 2011;76(160):51476–51846.
- , , , , , . Quality collaboratives and campaigns to reduce readmissions: what strategies are hospitals using? J Hosp Med. 2013;8:601–608.
- , , , , . Contemporary data about hospital strategies to reduce unplanned readmissions: what has changed [research letter]? JAMA Intern Med. 2014;174(1):154–156.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528.
- , , , . Comparing methods to calculate hospital‐specific rates of early death or urgent readmission. CMAJ. 2012;184(15):E810–E817.
- , , , , . Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–E402.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- National Quality Forum. Patient outcomes: all‐cause readmissions expedited review 2011. Available at: http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id60(7):607–614.
- , , , , , . Data shows reduction in Medicare hospital readmission rates during 2012. Medicare Medicaid Res Rev. 2013;3(2):E1–E11.
- , . Thirty‐day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366–1369.
- , , , . Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102–109.
- , . A path forward on Medicare readmissions. N Engl J Med. 2013;368(13):1175–1177.
- American Hospital Association. TrendWatch: examining the drivers of readmissions and reducing unnecessary readmissions for better patient care. Washington, DC: American Hospital Association; 2011.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363.
- , . Characteristics of hospitals receiving penalties under the hospital readmissions reduction program. JAMA. 2013;309(4):342–343.
- , , , , , , et al. Identifying potentially preventable readmissions. Health Care Financ Rev. 2008;30(1):75–91.
- , , , et al. Use of hospital‐based acute care among patients recently discharged from the hospital. JAMA. 2013;309(4):364–371.
- , , , . Reducing hospital readmission rates: current strategies and future directions. Annu Rev Med. 2014;65:471–485.
- , , , et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587–593.
- , , , et al. Hospital performance measures and 30‐day readmission rates. J Gen Intern Med. 2013;28(3):377–385.
- , , , . Limitations of using same‐hospital readmission metrics. Int J Qual Health Care. 2013;25(6):633–639.
- , , , et al. Is same‐hospital readmission rate a good surrogate for all‐hospital readmission rate? Med Care. 2010;48(5):477–481.
- , , . The relationship between hospital admission rates and rehospitalizations. N Engl J Med. 2011;365(24):2287–2295.
- . Community factors and hospital readmission rates [published online April 9, 2014]. Health Serv Res. doi: 10.1111/1475–6773.12177.
- American Hospital Association. Hospital readmissions reduction program: factsheet. American Hospital Association. Available at: http://www.aha.org/content/13/fs‐readmissions.pdf. Published April 14, 2014. Accessed May 5, 2014.
- Medicare Payment Advisory Commission. Report to the congress: Medicare and the health care delivery system. Available at: http://www.medpac.gov/documents/Jun13_EntireReport.pdf. Published June 14, 2013. Accessed May 5, 2014.
- , , . Sharp rise in Medicare enrollees being held in hospitals for observation raises concerns about causes and consequences. Health Aff (Millwood). 2012;31(6):1251–1259.
- , , . Quality improvement of care transitions and the trend of composite hospital care. JAMA. 2014;311(10):1013–1014.
- , . When projecting required effectiveness of interventions for hospital readmission reduction, the percentage that is potentially avoidable must be considered. J Clin Epidemiol. 2013;66(6):688–690.
- , , . Urgent readmission rates can be used to infer differences in avoidable readmission rates between hospitals. J Clin Epidemiol. 2012;65(10):1124–1130.
- , , , , . Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–E402.
- , , , , , . Measuring and preventing potentially avoidable hospital readmissions: a review of the literature. Hong Kong Med J. 2010;16(5):383–389.
- 3M Health Information Systems. Potentially preventable readmissions classification system methodology: overview. 3M Health Information Systems; May 2008. Report No.: GRP‐139. Available at: http://multimedia.3m.com/mws/mediawebserver?66666UuZjcFSLXTtNXMtmxMEEVuQEcuZgVs6EVs6E666666‐‐. Accessed June 8, 2014.
- , , , , , . Manual and automated methods for identifying potentially preventable readmissions: a comparison in a large healthcare system. BMC Med Inform Decis Mak. 2014;14:28.
- , , , , , , et al. Comparing 2 methods of assessing 30‐day readmissions: what is the impact on hospital profiling in the Veterans Health Administration? Med Care. 2013;51(7):589–596.
- , . It's not six of one, half‐dozen the other: a comparative analysis of 3 rehospitalization measurement systems for Massachusetts. Academy Health Annual Research Meeting. Seattle, WA. 2011. Available at: http://www.academyhealth.org/files/2011/tuesday/boutwell.pdf. Accessed May 9, 2014.
- , , , , , . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11):972–981.
- , , , , , . Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587.
- , , , . Potentially avoidable 30‐day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–638.
- National Association of Public Hospitals and Health Systems. NAPH members focus on reducing readmissions. Available at: www.naph.org. Published June 2011. Accessed October 19, 2011.
- Agency for Healthcare Research and Quality. AHRQ quality indicators: prevention quality indicators. Available at: http://www.qualityindicators.ahrq.gov/Modules/pqi_resources.aspx. Accessed February 11, 2014.
- , , , , , . Shifting the dialogue from hospital readmissions to unplanned care. Am J Manag Care. 2013;19(6):450–453.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102.
- , . The hospital‐dependent patient. N Engl J Med. 2014;370(8):694–697.
- , , , et al. The hospital medicine reengineering network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415–420.
- , , , et al. Incidence of potentially avoidable urgent readmissions and their relation to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , . Physician Visits After Hospital Discharge: Implications for Reducing Readmissions. Washington, DC: National Institute for Health Care Reform; 2011. Report no. 6.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- Centers for Medicare and Medicaid Services, US Department of Health and Human Services. Medicare program: hospital inpatient prospective payment systems for acute care hospitals and the long‐term care hospital prospective payment system and FY 2012 rates. Fed Regist. 2011;76(160):51476–51846.
- , , , , , . Quality collaboratives and campaigns to reduce readmissions: what strategies are hospitals using? J Hosp Med. 2013;8:601–608.
- , , , , . Contemporary data about hospital strategies to reduce unplanned readmissions: what has changed [research letter]? JAMA Intern Med. 2014;174(1):154–156.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528.
- , , , . Comparing methods to calculate hospital‐specific rates of early death or urgent readmission. CMAJ. 2012;184(15):E810–E817.
- , , , , . Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–E402.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- National Quality Forum. Patient outcomes: all‐cause readmissions expedited review 2011. Available at: http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id60(7):607–614.
- , , , , , . Data shows reduction in Medicare hospital readmission rates during 2012. Medicare Medicaid Res Rev. 2013;3(2):E1–E11.
- , . Thirty‐day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366–1369.
- , , , . Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102–109.
- , . A path forward on Medicare readmissions. N Engl J Med. 2013;368(13):1175–1177.
- American Hospital Association. TrendWatch: examining the drivers of readmissions and reducing unnecessary readmissions for better patient care. Washington, DC: American Hospital Association; 2011.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363.
- , . Characteristics of hospitals receiving penalties under the hospital readmissions reduction program. JAMA. 2013;309(4):342–343.
- , , , , , , et al. Identifying potentially preventable readmissions. Health Care Financ Rev. 2008;30(1):75–91.
- , , , et al. Use of hospital‐based acute care among patients recently discharged from the hospital. JAMA. 2013;309(4):364–371.
- , , , . Reducing hospital readmission rates: current strategies and future directions. Annu Rev Med. 2014;65:471–485.
- , , , et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587–593.
- , , , et al. Hospital performance measures and 30‐day readmission rates. J Gen Intern Med. 2013;28(3):377–385.
- , , , . Limitations of using same‐hospital readmission metrics. Int J Qual Health Care. 2013;25(6):633–639.
- , , , et al. Is same‐hospital readmission rate a good surrogate for all‐hospital readmission rate? Med Care. 2010;48(5):477–481.
- , , . The relationship between hospital admission rates and rehospitalizations. N Engl J Med. 2011;365(24):2287–2295.
- . Community factors and hospital readmission rates [published online April 9, 2014]. Health Serv Res. doi: 10.1111/1475–6773.12177.
- American Hospital Association. Hospital readmissions reduction program: factsheet. American Hospital Association. Available at: http://www.aha.org/content/13/fs‐readmissions.pdf. Published April 14, 2014. Accessed May 5, 2014.
- Medicare Payment Advisory Commission. Report to the congress: Medicare and the health care delivery system. Available at: http://www.medpac.gov/documents/Jun13_EntireReport.pdf. Published June 14, 2013. Accessed May 5, 2014.
- , , . Sharp rise in Medicare enrollees being held in hospitals for observation raises concerns about causes and consequences. Health Aff (Millwood). 2012;31(6):1251–1259.
- , , . Quality improvement of care transitions and the trend of composite hospital care. JAMA. 2014;311(10):1013–1014.
- , . When projecting required effectiveness of interventions for hospital readmission reduction, the percentage that is potentially avoidable must be considered. J Clin Epidemiol. 2013;66(6):688–690.
- , , . Urgent readmission rates can be used to infer differences in avoidable readmission rates between hospitals. J Clin Epidemiol. 2012;65(10):1124–1130.
- , , , , . Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–E402.
- , , , , , . Measuring and preventing potentially avoidable hospital readmissions: a review of the literature. Hong Kong Med J. 2010;16(5):383–389.
- 3M Health Information Systems. Potentially preventable readmissions classification system methodology: overview. 3M Health Information Systems; May 2008. Report No.: GRP‐139. Available at: http://multimedia.3m.com/mws/mediawebserver?66666UuZjcFSLXTtNXMtmxMEEVuQEcuZgVs6EVs6E666666‐‐. Accessed June 8, 2014.
- , , , , , . Manual and automated methods for identifying potentially preventable readmissions: a comparison in a large healthcare system. BMC Med Inform Decis Mak. 2014;14:28.
- , , , , , , et al. Comparing 2 methods of assessing 30‐day readmissions: what is the impact on hospital profiling in the Veterans Health Administration? Med Care. 2013;51(7):589–596.
- , . It's not six of one, half‐dozen the other: a comparative analysis of 3 rehospitalization measurement systems for Massachusetts. Academy Health Annual Research Meeting. Seattle, WA. 2011. Available at: http://www.academyhealth.org/files/2011/tuesday/boutwell.pdf. Accessed May 9, 2014.
- , , , , , . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11):972–981.
- , , , , , . Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587.
- , , , . Potentially avoidable 30‐day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–638.
- National Association of Public Hospitals and Health Systems. NAPH members focus on reducing readmissions. Available at: www.naph.org. Published June 2011. Accessed October 19, 2011.
- Agency for Healthcare Research and Quality. AHRQ quality indicators: prevention quality indicators. Available at: http://www.qualityindicators.ahrq.gov/Modules/pqi_resources.aspx. Accessed February 11, 2014.
- , , , , , . Shifting the dialogue from hospital readmissions to unplanned care. Am J Manag Care. 2013;19(6):450–453.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102.
- , . The hospital‐dependent patient. N Engl J Med. 2014;370(8):694–697.
- , , , et al. The hospital medicine reengineering network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415–420.
- , , , et al. Incidence of potentially avoidable urgent readmissions and their relation to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
Ramelteon Reduces Risk of Delirium Among Hospitalized Patients
Clinical question
Does ramelteon, a melatonin agonist, prevent delirium in hospitalized patients?
Bottom line
In this small, single-blinded study, ramelteon was shown to be effective in preventing delirium in elderly patients who required hospitalization for acute illness. You would have to treat 3 patients with ramelteon to prevent one episode of delirium. (LOE = 1b)
Reference
Hatta K, Kishi Y, Wada K, et al, for the DELIRIA-J Group. Preventive effects of ramelteon on delirium. JAMA Psychiatry 2014;71(4):397-403.
Study design
Randomized controlled trial (single-blinded)
Funding source
Government
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
Using concealed allocation, these investigators randomized 67 hospitalized patients (24 admitted to intensive care units, 43 admitted to general wards) to receive either ramelteon 8 mg or placebo nightly up to 7 days or until the onset of delirium. Eligible patients were 65 years to 89 years old, were admitted to the hospital via the emergency department, were able to take oral medications, and had an expected length of stay of greater than 48 hours. Patients with psychiatric disorders, severe liver disease, Lewy body disease, or alcohol dependency were excluded. Nurses provided similar delirium prevention care to all patients, including frequent reorientation, adequate lighting, and noise reduction. If patients required treatment for insomnia, hydroxyzine was used with a dose limit of 25 mg per night. Baseline characteristics were similar in the 2 groups, with a mean age of 78 years. For the primary outcome of onset of delirium, experienced psychiatrists, masked to study group, assessed patients in the mornings and afternoons for up to 7 days using a delirium rating scale. Only 1 patient in the ramelteon group was diagnosed with delirium as compared with 11 patients in the placebo group (3% vs 32%; number needed to treat = 3; P = .003). Interestingly, there were no significant differences between the groups in sleep metrics such as difficulty falling asleep and poor sleep quality, although the sample was likely too small to detect such differences. Note that the patients were not masked in this study, which could have potentially affected the overall outcomes. No adverse effects attributed to the study drug were reported.
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Clinical question
Does ramelteon, a melatonin agonist, prevent delirium in hospitalized patients?
Bottom line
In this small, single-blinded study, ramelteon was shown to be effective in preventing delirium in elderly patients who required hospitalization for acute illness. You would have to treat 3 patients with ramelteon to prevent one episode of delirium. (LOE = 1b)
Reference
Hatta K, Kishi Y, Wada K, et al, for the DELIRIA-J Group. Preventive effects of ramelteon on delirium. JAMA Psychiatry 2014;71(4):397-403.
Study design
Randomized controlled trial (single-blinded)
Funding source
Government
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
Using concealed allocation, these investigators randomized 67 hospitalized patients (24 admitted to intensive care units, 43 admitted to general wards) to receive either ramelteon 8 mg or placebo nightly up to 7 days or until the onset of delirium. Eligible patients were 65 years to 89 years old, were admitted to the hospital via the emergency department, were able to take oral medications, and had an expected length of stay of greater than 48 hours. Patients with psychiatric disorders, severe liver disease, Lewy body disease, or alcohol dependency were excluded. Nurses provided similar delirium prevention care to all patients, including frequent reorientation, adequate lighting, and noise reduction. If patients required treatment for insomnia, hydroxyzine was used with a dose limit of 25 mg per night. Baseline characteristics were similar in the 2 groups, with a mean age of 78 years. For the primary outcome of onset of delirium, experienced psychiatrists, masked to study group, assessed patients in the mornings and afternoons for up to 7 days using a delirium rating scale. Only 1 patient in the ramelteon group was diagnosed with delirium as compared with 11 patients in the placebo group (3% vs 32%; number needed to treat = 3; P = .003). Interestingly, there were no significant differences between the groups in sleep metrics such as difficulty falling asleep and poor sleep quality, although the sample was likely too small to detect such differences. Note that the patients were not masked in this study, which could have potentially affected the overall outcomes. No adverse effects attributed to the study drug were reported.
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Clinical question
Does ramelteon, a melatonin agonist, prevent delirium in hospitalized patients?
Bottom line
In this small, single-blinded study, ramelteon was shown to be effective in preventing delirium in elderly patients who required hospitalization for acute illness. You would have to treat 3 patients with ramelteon to prevent one episode of delirium. (LOE = 1b)
Reference
Hatta K, Kishi Y, Wada K, et al, for the DELIRIA-J Group. Preventive effects of ramelteon on delirium. JAMA Psychiatry 2014;71(4):397-403.
Study design
Randomized controlled trial (single-blinded)
Funding source
Government
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
Using concealed allocation, these investigators randomized 67 hospitalized patients (24 admitted to intensive care units, 43 admitted to general wards) to receive either ramelteon 8 mg or placebo nightly up to 7 days or until the onset of delirium. Eligible patients were 65 years to 89 years old, were admitted to the hospital via the emergency department, were able to take oral medications, and had an expected length of stay of greater than 48 hours. Patients with psychiatric disorders, severe liver disease, Lewy body disease, or alcohol dependency were excluded. Nurses provided similar delirium prevention care to all patients, including frequent reorientation, adequate lighting, and noise reduction. If patients required treatment for insomnia, hydroxyzine was used with a dose limit of 25 mg per night. Baseline characteristics were similar in the 2 groups, with a mean age of 78 years. For the primary outcome of onset of delirium, experienced psychiatrists, masked to study group, assessed patients in the mornings and afternoons for up to 7 days using a delirium rating scale. Only 1 patient in the ramelteon group was diagnosed with delirium as compared with 11 patients in the placebo group (3% vs 32%; number needed to treat = 3; P = .003). Interestingly, there were no significant differences between the groups in sleep metrics such as difficulty falling asleep and poor sleep quality, although the sample was likely too small to detect such differences. Note that the patients were not masked in this study, which could have potentially affected the overall outcomes. No adverse effects attributed to the study drug were reported.
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Fibrinolysis for Intermediate-Risk PE: Increased Bleeding, No Mortality Effect
Clinical question
Does the use of fibrinolytic therapy improve mortality and morbidity in normotensive patients with acute pulmonary embolism who are at intermediate risk for adverse outcomes?
Bottom line
For patients with acute pulmonary embolism (PE) at intermediate risk for adverse outcomes, fibrinolytic therapy (tenecteplase plus standard anticoagulation) decreases the incidence of hemodynamic decompensation but does not decrease mortality as compared with anticoagulation alone. Not surprisingly, fibrinolysis also increases bleeding and strokes. You would need to treat 20 people with tenecteplase to cause one episode of bleeding, and 45 people to cause one additional stroke. Given the significant bleeding risk with this therapy, fibrinolysis in patients who are at higher risk of adverse outcomes but are hemodynamically stable cannot be recommended until further research shows a greater clinical benefit. (LOE = 1b)
Reference
Study design
Randomized controlled trial (nonblinded)
Funding source
Industry + govt
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
These authors enrolled adult patients with acute PE who were hemodynamically stable and were considered to have an intermediate risk for adverse outcomes as indicated by right ventricular dysfunction or myocardial injury. Right ventricular dysfunction was confirmed by either echocardiography or chest computed tomography, and myocardial injury was confirmed by a positive troponin test result. Using concealed allocation, investigators randomized these patients (N = 1006) to receive either fibrinolysis with tenecteplase at a weight-based dose or matching placebo. Both groups also received full anticoagulation with unfractionated heparin. Baseline characteristics were similar in the 2 groups and analysis was by intention to treat. Fewer patients in the tenecteplase group experienced the primary outcome of death or hemodynamic decompensation at 7 days (2.6% vs 5.6%; odds ratio = 0.44; 95% CI, 0.23 - 0.87). Looking at the individual components of the composite outcome, there was no significant difference in mortality between the 2 groups (1.2% vs 1.8%), but the tenecteplase group had a decreased incidence of hemodynamic decompensation (1.6% vs 5%; P = .002). The significance of this is unclear, as one of the definitions of hemodynamic decompensation was an isolated drop in systolic blood pressure to below 90 mmHg for at least 15 minutes. Major bleeding and hemorrhagic strokes were more common in the tenecteplase group (bleeding: 11.5% vs 2.4%; strokes: 2% vs 0.2%).
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Clinical question
Does the use of fibrinolytic therapy improve mortality and morbidity in normotensive patients with acute pulmonary embolism who are at intermediate risk for adverse outcomes?
Bottom line
For patients with acute pulmonary embolism (PE) at intermediate risk for adverse outcomes, fibrinolytic therapy (tenecteplase plus standard anticoagulation) decreases the incidence of hemodynamic decompensation but does not decrease mortality as compared with anticoagulation alone. Not surprisingly, fibrinolysis also increases bleeding and strokes. You would need to treat 20 people with tenecteplase to cause one episode of bleeding, and 45 people to cause one additional stroke. Given the significant bleeding risk with this therapy, fibrinolysis in patients who are at higher risk of adverse outcomes but are hemodynamically stable cannot be recommended until further research shows a greater clinical benefit. (LOE = 1b)
Reference
Study design
Randomized controlled trial (nonblinded)
Funding source
Industry + govt
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
These authors enrolled adult patients with acute PE who were hemodynamically stable and were considered to have an intermediate risk for adverse outcomes as indicated by right ventricular dysfunction or myocardial injury. Right ventricular dysfunction was confirmed by either echocardiography or chest computed tomography, and myocardial injury was confirmed by a positive troponin test result. Using concealed allocation, investigators randomized these patients (N = 1006) to receive either fibrinolysis with tenecteplase at a weight-based dose or matching placebo. Both groups also received full anticoagulation with unfractionated heparin. Baseline characteristics were similar in the 2 groups and analysis was by intention to treat. Fewer patients in the tenecteplase group experienced the primary outcome of death or hemodynamic decompensation at 7 days (2.6% vs 5.6%; odds ratio = 0.44; 95% CI, 0.23 - 0.87). Looking at the individual components of the composite outcome, there was no significant difference in mortality between the 2 groups (1.2% vs 1.8%), but the tenecteplase group had a decreased incidence of hemodynamic decompensation (1.6% vs 5%; P = .002). The significance of this is unclear, as one of the definitions of hemodynamic decompensation was an isolated drop in systolic blood pressure to below 90 mmHg for at least 15 minutes. Major bleeding and hemorrhagic strokes were more common in the tenecteplase group (bleeding: 11.5% vs 2.4%; strokes: 2% vs 0.2%).
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Clinical question
Does the use of fibrinolytic therapy improve mortality and morbidity in normotensive patients with acute pulmonary embolism who are at intermediate risk for adverse outcomes?
Bottom line
For patients with acute pulmonary embolism (PE) at intermediate risk for adverse outcomes, fibrinolytic therapy (tenecteplase plus standard anticoagulation) decreases the incidence of hemodynamic decompensation but does not decrease mortality as compared with anticoagulation alone. Not surprisingly, fibrinolysis also increases bleeding and strokes. You would need to treat 20 people with tenecteplase to cause one episode of bleeding, and 45 people to cause one additional stroke. Given the significant bleeding risk with this therapy, fibrinolysis in patients who are at higher risk of adverse outcomes but are hemodynamically stable cannot be recommended until further research shows a greater clinical benefit. (LOE = 1b)
Reference
Study design
Randomized controlled trial (nonblinded)
Funding source
Industry + govt
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
These authors enrolled adult patients with acute PE who were hemodynamically stable and were considered to have an intermediate risk for adverse outcomes as indicated by right ventricular dysfunction or myocardial injury. Right ventricular dysfunction was confirmed by either echocardiography or chest computed tomography, and myocardial injury was confirmed by a positive troponin test result. Using concealed allocation, investigators randomized these patients (N = 1006) to receive either fibrinolysis with tenecteplase at a weight-based dose or matching placebo. Both groups also received full anticoagulation with unfractionated heparin. Baseline characteristics were similar in the 2 groups and analysis was by intention to treat. Fewer patients in the tenecteplase group experienced the primary outcome of death or hemodynamic decompensation at 7 days (2.6% vs 5.6%; odds ratio = 0.44; 95% CI, 0.23 - 0.87). Looking at the individual components of the composite outcome, there was no significant difference in mortality between the 2 groups (1.2% vs 1.8%), but the tenecteplase group had a decreased incidence of hemodynamic decompensation (1.6% vs 5%; P = .002). The significance of this is unclear, as one of the definitions of hemodynamic decompensation was an isolated drop in systolic blood pressure to below 90 mmHg for at least 15 minutes. Major bleeding and hemorrhagic strokes were more common in the tenecteplase group (bleeding: 11.5% vs 2.4%; strokes: 2% vs 0.2%).
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Ramelteon Reduces Risk of Delirium in Hospitalized Patients
Clinical question
Does ramelteon, a melatonin agonist, prevent delirium in hospitalized patients?
Bottom line
In this small, single-blinded study, ramelteon was shown to be effective in preventing delirium in elderly patients who required hospitalization for acute illness. You would have to treat 3 patients with ramelteon to prevent one episode of delirium. (LOE = 1b)
Reference
Study design
Randomized controlled trial (single-blinded)
Funding source
Government
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
Using concealed allocation, these investigators randomized 67 hospitalized patients (24 admitted to intensive care units, 43 admitted to general wards) to receive either ramelteon 8 mg or placebo nightly up to 7 days or until the onset of delirium. Eligible patients were 65 years to 89 years old, were admitted to the hospital via the emergency department, were able to take oral medications, and had an expected length of stay of greater than 48 hours. Patients with psychiatric disorders, severe liver disease, Lewy body disease, or alcohol dependency were excluded. Nurses provided similar delirium prevention care to all patients, including frequent reorientation, adequate lighting, and noise reduction. If patients required treatment for insomnia, hydroxyzine was used with a dose limit of 25 mg per night. Baseline characteristics were similar in the 2 groups, with a mean age of 78 years. For the primary outcome of onset of delirium, experienced psychiatrists, masked to study group, assessed patients in the mornings and afternoons for up to 7 days using a delirium rating scale. Only 1 patient in the ramelteon group was diagnosed with delirium as compared with 11 patients in the placebo group (3% vs 32%; number needed to treat = 3; P = .003). Interestingly, there were no significant differences between the groups in sleep metrics such as difficulty falling asleep and poor sleep quality, although the sample was likely too small to detect such differences. Note that the patients were not masked in this study, which could have potentially affected the overall outcomes. No adverse effects attributed to the study drug were reported.
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Clinical question
Does ramelteon, a melatonin agonist, prevent delirium in hospitalized patients?
Bottom line
In this small, single-blinded study, ramelteon was shown to be effective in preventing delirium in elderly patients who required hospitalization for acute illness. You would have to treat 3 patients with ramelteon to prevent one episode of delirium. (LOE = 1b)
Reference
Study design
Randomized controlled trial (single-blinded)
Funding source
Government
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
Using concealed allocation, these investigators randomized 67 hospitalized patients (24 admitted to intensive care units, 43 admitted to general wards) to receive either ramelteon 8 mg or placebo nightly up to 7 days or until the onset of delirium. Eligible patients were 65 years to 89 years old, were admitted to the hospital via the emergency department, were able to take oral medications, and had an expected length of stay of greater than 48 hours. Patients with psychiatric disorders, severe liver disease, Lewy body disease, or alcohol dependency were excluded. Nurses provided similar delirium prevention care to all patients, including frequent reorientation, adequate lighting, and noise reduction. If patients required treatment for insomnia, hydroxyzine was used with a dose limit of 25 mg per night. Baseline characteristics were similar in the 2 groups, with a mean age of 78 years. For the primary outcome of onset of delirium, experienced psychiatrists, masked to study group, assessed patients in the mornings and afternoons for up to 7 days using a delirium rating scale. Only 1 patient in the ramelteon group was diagnosed with delirium as compared with 11 patients in the placebo group (3% vs 32%; number needed to treat = 3; P = .003). Interestingly, there were no significant differences between the groups in sleep metrics such as difficulty falling asleep and poor sleep quality, although the sample was likely too small to detect such differences. Note that the patients were not masked in this study, which could have potentially affected the overall outcomes. No adverse effects attributed to the study drug were reported.
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.
Clinical question
Does ramelteon, a melatonin agonist, prevent delirium in hospitalized patients?
Bottom line
In this small, single-blinded study, ramelteon was shown to be effective in preventing delirium in elderly patients who required hospitalization for acute illness. You would have to treat 3 patients with ramelteon to prevent one episode of delirium. (LOE = 1b)
Reference
Study design
Randomized controlled trial (single-blinded)
Funding source
Government
Allocation
Concealed
Setting
Inpatient (any location)
Synopsis
Using concealed allocation, these investigators randomized 67 hospitalized patients (24 admitted to intensive care units, 43 admitted to general wards) to receive either ramelteon 8 mg or placebo nightly up to 7 days or until the onset of delirium. Eligible patients were 65 years to 89 years old, were admitted to the hospital via the emergency department, were able to take oral medications, and had an expected length of stay of greater than 48 hours. Patients with psychiatric disorders, severe liver disease, Lewy body disease, or alcohol dependency were excluded. Nurses provided similar delirium prevention care to all patients, including frequent reorientation, adequate lighting, and noise reduction. If patients required treatment for insomnia, hydroxyzine was used with a dose limit of 25 mg per night. Baseline characteristics were similar in the 2 groups, with a mean age of 78 years. For the primary outcome of onset of delirium, experienced psychiatrists, masked to study group, assessed patients in the mornings and afternoons for up to 7 days using a delirium rating scale. Only 1 patient in the ramelteon group was diagnosed with delirium as compared with 11 patients in the placebo group (3% vs 32%; number needed to treat = 3; P = .003). Interestingly, there were no significant differences between the groups in sleep metrics such as difficulty falling asleep and poor sleep quality, although the sample was likely too small to detect such differences. Note that the patients were not masked in this study, which could have potentially affected the overall outcomes. No adverse effects attributed to the study drug were reported.
Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.