7 Hours of Sleep Can Reduce Heart Disease

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7 Hours of Sleep Can Reduce Heart Disease

Too little sleep, or poor-quality sleep, may be linked to early markers of heart disease in asymptomatic healthy adults, a new study from South Korea suggests.

More than 47,000 men and women completed a sleep questionnaire and underwent assessments of coronary artery calcium and plaque as well as brachial-ankle pulse wave velocity (PWV).

Participants' average sleep duration was 6.4 hours per night, and about 84 percent said their sleep quality was "good," according to Dr. Chan-Won Kim of Kangbuk Samsung Hospital of Sungkyunkwan University School of Medicine in Seoul, South Korea and colleagues.

The researchers considered those who got five hours or less per night to be "short" sleepers, and those who got nine or more hours to be "long" sleepers.

Short sleepers had 50% more coronary artery calcium than those who slept for seven hours per night, according to the results in Arteriosclerosis, Thrombosis and Vascular Biology. Long sleepers had 70% more calcium than those who slept seven hours.

Those who reported poor sleep quality also tended to have more coronary calcium and more arterial stiffness.

In a 2013 study, people who tended to get less than six hours of sleep nightly were more likely to have high blood pressure, high cholesterol, diabetes and to be obese.

"Adults with poor sleep quality have stiffer arteries than those who sleep seven hours a day or had good sleep quality," co-lead author Dr. Yoosoo Chang of the Center for Cohort Studies at Kangbuk Samsung Hospital said in a statement accompanying the study. "Overall, we saw the lowest levels of vascular disease in adults sleeping seven hours a day and reporting good sleep quality."

Short sleepers were more likely than others to be older, have depression, type 2 diabetes or to be smokers.

"The associations of too short or too long sleep duration and of poor sleep quality with early indicators of heart disease, such as coronary calcium and arterial stiffness, provides strong support to the increasing body of evidence that links inadequate sleep with an increased risk of heart attacks," Kim said by email.

"It is still not clear if inadequate sleep is the cause or the consequence of ill health," but good sleep hygiene, including avoiding electronic media at bedtime, should be part of a healthy lifestyle, Kim said.

"For doctors, it can be helpful to evaluate sleep duration and sleep quality when assessing the health status of their patients," Kim said.

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Too little sleep, or poor-quality sleep, may be linked to early markers of heart disease in asymptomatic healthy adults, a new study from South Korea suggests.

More than 47,000 men and women completed a sleep questionnaire and underwent assessments of coronary artery calcium and plaque as well as brachial-ankle pulse wave velocity (PWV).

Participants' average sleep duration was 6.4 hours per night, and about 84 percent said their sleep quality was "good," according to Dr. Chan-Won Kim of Kangbuk Samsung Hospital of Sungkyunkwan University School of Medicine in Seoul, South Korea and colleagues.

The researchers considered those who got five hours or less per night to be "short" sleepers, and those who got nine or more hours to be "long" sleepers.

Short sleepers had 50% more coronary artery calcium than those who slept for seven hours per night, according to the results in Arteriosclerosis, Thrombosis and Vascular Biology. Long sleepers had 70% more calcium than those who slept seven hours.

Those who reported poor sleep quality also tended to have more coronary calcium and more arterial stiffness.

In a 2013 study, people who tended to get less than six hours of sleep nightly were more likely to have high blood pressure, high cholesterol, diabetes and to be obese.

"Adults with poor sleep quality have stiffer arteries than those who sleep seven hours a day or had good sleep quality," co-lead author Dr. Yoosoo Chang of the Center for Cohort Studies at Kangbuk Samsung Hospital said in a statement accompanying the study. "Overall, we saw the lowest levels of vascular disease in adults sleeping seven hours a day and reporting good sleep quality."

Short sleepers were more likely than others to be older, have depression, type 2 diabetes or to be smokers.

"The associations of too short or too long sleep duration and of poor sleep quality with early indicators of heart disease, such as coronary calcium and arterial stiffness, provides strong support to the increasing body of evidence that links inadequate sleep with an increased risk of heart attacks," Kim said by email.

"It is still not clear if inadequate sleep is the cause or the consequence of ill health," but good sleep hygiene, including avoiding electronic media at bedtime, should be part of a healthy lifestyle, Kim said.

"For doctors, it can be helpful to evaluate sleep duration and sleep quality when assessing the health status of their patients," Kim said.

Too little sleep, or poor-quality sleep, may be linked to early markers of heart disease in asymptomatic healthy adults, a new study from South Korea suggests.

More than 47,000 men and women completed a sleep questionnaire and underwent assessments of coronary artery calcium and plaque as well as brachial-ankle pulse wave velocity (PWV).

Participants' average sleep duration was 6.4 hours per night, and about 84 percent said their sleep quality was "good," according to Dr. Chan-Won Kim of Kangbuk Samsung Hospital of Sungkyunkwan University School of Medicine in Seoul, South Korea and colleagues.

The researchers considered those who got five hours or less per night to be "short" sleepers, and those who got nine or more hours to be "long" sleepers.

Short sleepers had 50% more coronary artery calcium than those who slept for seven hours per night, according to the results in Arteriosclerosis, Thrombosis and Vascular Biology. Long sleepers had 70% more calcium than those who slept seven hours.

Those who reported poor sleep quality also tended to have more coronary calcium and more arterial stiffness.

In a 2013 study, people who tended to get less than six hours of sleep nightly were more likely to have high blood pressure, high cholesterol, diabetes and to be obese.

"Adults with poor sleep quality have stiffer arteries than those who sleep seven hours a day or had good sleep quality," co-lead author Dr. Yoosoo Chang of the Center for Cohort Studies at Kangbuk Samsung Hospital said in a statement accompanying the study. "Overall, we saw the lowest levels of vascular disease in adults sleeping seven hours a day and reporting good sleep quality."

Short sleepers were more likely than others to be older, have depression, type 2 diabetes or to be smokers.

"The associations of too short or too long sleep duration and of poor sleep quality with early indicators of heart disease, such as coronary calcium and arterial stiffness, provides strong support to the increasing body of evidence that links inadequate sleep with an increased risk of heart attacks," Kim said by email.

"It is still not clear if inadequate sleep is the cause or the consequence of ill health," but good sleep hygiene, including avoiding electronic media at bedtime, should be part of a healthy lifestyle, Kim said.

"For doctors, it can be helpful to evaluate sleep duration and sleep quality when assessing the health status of their patients," Kim said.

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Gene linked to aggressive AML

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Gene linked to aggressive AML

Researcher in the lab

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The gene FOXC1 is associated with aggressive acute myeloid leukemia (AML), according to research published in Cancer Cell.

Researchers said tissue-inappropriate derepression of FOXC1 has functional consequences and prognostic significance in AML.

They found evidence suggesting that FOXC1 enhances clonogenic potential, helps block monocyte/macrophage differentiation, accelerates leukemia onset in mice, and leads to inferior survival in AML patients.

“This is an important finding which helps us understand how acute myeloid leukemia develops and why some cases of AML are more aggressive than others,” said study author Tim Somervaille, MBBS, PhD, of The University of Manchester in the UK.

“Here, instead of being faulty or mutated, this normal gene is turned on in the wrong place at the wrong time, which makes the cancer grow more rapidly. There are certain situations where this gene is necessary, as in the development of the eye and skeleton before birth, but when it’s switched on in the wrong tissue, it causes more aggressive forms of leukemia.”

Dr Somervaille and his colleagues said FOXC1 is expressed in at least 20% of human AML cases but not in normal hematopoietic populations.

The researchers analyzed levels of transcription factor genes in data from published studies to identify transcription regulators expressed in human AML hematopoietic stem and progenitor cells (HSPCs) but not normal HSPCs. In these studies, FOXC1 was among the genes that were most highly upregulated in AML HSPCs.

Further investigation revealed that FOXC1 expression is associated with mutations in NPM1 and t(6;9) but no other recurring mutations in AML.

When Dr Somervaille and his colleagues conducted experiments with human AML cells, they found that FOXC1 “contributes to oncogenic potential by maintaining differentiation block and clonogenic activity.”

In vitro experiments with normal HSPCs showed that FOXC1 expression temporarily impairs myeloid differentiation. In mice, expression of FOXC1 in normal HSPCs reduced donor:recipient chimerism in the blood and skewed differentiation toward the myeloid lineage and away from the B-cell lineage.

By comparing samples from AML patients, the researchers found that FOXC1 expression is associated with high HOX gene expression.

Subsequent experiments showed that FOXC1 collaborates with HOXA9 to enhance clonogenic potential and cell-cycle progression, help block monocyte/macrophage and B-lineage differentiation, and accelerate the onset of symptomatic leukemia in mice.

To determine if the same effects occur in humans, the researchers again analyzed data from AML patients. The results indicated that FOXC1 expression helps block monocyte/macrophage differentiation and leads to inferior survival.

Dr Somervaille and his colleagues said these findings may have therapeutic implications, as previous research has shown that, in basal-like breast cancer, high FOXC1 expression renders cells more susceptible to pharmacological inhibition of NF-kB. But additional research is needed to investigate therapeutic implications for AML.

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Researcher in the lab

Photo courtesy of NIH

The gene FOXC1 is associated with aggressive acute myeloid leukemia (AML), according to research published in Cancer Cell.

Researchers said tissue-inappropriate derepression of FOXC1 has functional consequences and prognostic significance in AML.

They found evidence suggesting that FOXC1 enhances clonogenic potential, helps block monocyte/macrophage differentiation, accelerates leukemia onset in mice, and leads to inferior survival in AML patients.

“This is an important finding which helps us understand how acute myeloid leukemia develops and why some cases of AML are more aggressive than others,” said study author Tim Somervaille, MBBS, PhD, of The University of Manchester in the UK.

“Here, instead of being faulty or mutated, this normal gene is turned on in the wrong place at the wrong time, which makes the cancer grow more rapidly. There are certain situations where this gene is necessary, as in the development of the eye and skeleton before birth, but when it’s switched on in the wrong tissue, it causes more aggressive forms of leukemia.”

Dr Somervaille and his colleagues said FOXC1 is expressed in at least 20% of human AML cases but not in normal hematopoietic populations.

The researchers analyzed levels of transcription factor genes in data from published studies to identify transcription regulators expressed in human AML hematopoietic stem and progenitor cells (HSPCs) but not normal HSPCs. In these studies, FOXC1 was among the genes that were most highly upregulated in AML HSPCs.

Further investigation revealed that FOXC1 expression is associated with mutations in NPM1 and t(6;9) but no other recurring mutations in AML.

When Dr Somervaille and his colleagues conducted experiments with human AML cells, they found that FOXC1 “contributes to oncogenic potential by maintaining differentiation block and clonogenic activity.”

In vitro experiments with normal HSPCs showed that FOXC1 expression temporarily impairs myeloid differentiation. In mice, expression of FOXC1 in normal HSPCs reduced donor:recipient chimerism in the blood and skewed differentiation toward the myeloid lineage and away from the B-cell lineage.

By comparing samples from AML patients, the researchers found that FOXC1 expression is associated with high HOX gene expression.

Subsequent experiments showed that FOXC1 collaborates with HOXA9 to enhance clonogenic potential and cell-cycle progression, help block monocyte/macrophage and B-lineage differentiation, and accelerate the onset of symptomatic leukemia in mice.

To determine if the same effects occur in humans, the researchers again analyzed data from AML patients. The results indicated that FOXC1 expression helps block monocyte/macrophage differentiation and leads to inferior survival.

Dr Somervaille and his colleagues said these findings may have therapeutic implications, as previous research has shown that, in basal-like breast cancer, high FOXC1 expression renders cells more susceptible to pharmacological inhibition of NF-kB. But additional research is needed to investigate therapeutic implications for AML.

Researcher in the lab

Photo courtesy of NIH

The gene FOXC1 is associated with aggressive acute myeloid leukemia (AML), according to research published in Cancer Cell.

Researchers said tissue-inappropriate derepression of FOXC1 has functional consequences and prognostic significance in AML.

They found evidence suggesting that FOXC1 enhances clonogenic potential, helps block monocyte/macrophage differentiation, accelerates leukemia onset in mice, and leads to inferior survival in AML patients.

“This is an important finding which helps us understand how acute myeloid leukemia develops and why some cases of AML are more aggressive than others,” said study author Tim Somervaille, MBBS, PhD, of The University of Manchester in the UK.

“Here, instead of being faulty or mutated, this normal gene is turned on in the wrong place at the wrong time, which makes the cancer grow more rapidly. There are certain situations where this gene is necessary, as in the development of the eye and skeleton before birth, but when it’s switched on in the wrong tissue, it causes more aggressive forms of leukemia.”

Dr Somervaille and his colleagues said FOXC1 is expressed in at least 20% of human AML cases but not in normal hematopoietic populations.

The researchers analyzed levels of transcription factor genes in data from published studies to identify transcription regulators expressed in human AML hematopoietic stem and progenitor cells (HSPCs) but not normal HSPCs. In these studies, FOXC1 was among the genes that were most highly upregulated in AML HSPCs.

Further investigation revealed that FOXC1 expression is associated with mutations in NPM1 and t(6;9) but no other recurring mutations in AML.

When Dr Somervaille and his colleagues conducted experiments with human AML cells, they found that FOXC1 “contributes to oncogenic potential by maintaining differentiation block and clonogenic activity.”

In vitro experiments with normal HSPCs showed that FOXC1 expression temporarily impairs myeloid differentiation. In mice, expression of FOXC1 in normal HSPCs reduced donor:recipient chimerism in the blood and skewed differentiation toward the myeloid lineage and away from the B-cell lineage.

By comparing samples from AML patients, the researchers found that FOXC1 expression is associated with high HOX gene expression.

Subsequent experiments showed that FOXC1 collaborates with HOXA9 to enhance clonogenic potential and cell-cycle progression, help block monocyte/macrophage and B-lineage differentiation, and accelerate the onset of symptomatic leukemia in mice.

To determine if the same effects occur in humans, the researchers again analyzed data from AML patients. The results indicated that FOXC1 expression helps block monocyte/macrophage differentiation and leads to inferior survival.

Dr Somervaille and his colleagues said these findings may have therapeutic implications, as previous research has shown that, in basal-like breast cancer, high FOXC1 expression renders cells more susceptible to pharmacological inhibition of NF-kB. But additional research is needed to investigate therapeutic implications for AML.

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Living near dams increases malaria risk, study shows

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Living near dams increases malaria risk, study shows

A dam in Africa

Photo courtesy of the

International Water

Management Institute

More than 1 million people in sub-Saharan Africa will contract malaria this year because they live near a large dam, according to a study published in Malaria Journal.

For the first time, researchers correlated the location of large dams in sub-Saharan Africa with the incidence of malaria.

And they found evidence to suggest that construction of an expected 78 major new dams over the next few years will lead to an additional 56,000 malaria cases annually.

The researchers said these findings have major implications for new dam projects and how health impacts should be assessed prior to construction.

“Dams are at the center of much development planning in Africa,” said study author Solomon Kibret, a graduate student at the University of New England in Armidale, New South Wales, Australia.

“While dams clearly bring many benefits—contributing to economic growth, poverty alleviation, and food security—adverse malaria impacts need to be addressed or they will undermine the sustainability of Africa’s drive for development.”

As part of the CGIAR Research Program on Water, Land, and Ecosystems, Kibret and colleagues looked at 1268 dams in sub-Saharan Africa. Of these, just under two-thirds (n=723) are in malarious areas.

The researchers compared detailed maps of malaria incidence with the dam sites. The number of annual malaria cases associated with the dams was estimated by comparing the number of cases for communities less than 5 kilometers from the dam reservoir with the number of cases for communities further away.

The team found that 15 million people live within 5 kilometers of dam reservoirs and are therefore at risk of contracting malaria. And at least 1.1 million malaria cases annually are linked to the presence of the dams.

“Our study showed that the population at risk of malaria around dams is at least 4 times greater than previously estimated,” Kibret said, noting that the authors were conservative in all their analyses.

The risk is particularly high in areas of sub-Saharan Africa with “unstable” malaria transmission, where malaria is seasonal. The study indicated that the impact of dams on malaria in unstable areas could either lead to intensified malaria transmission or change the nature of transmission from seasonal to perennial.

Explaining the risk

Previous research revealed increases in malaria incidence near major sub-Saharan dams such as the Akosombo Dam in Ghana, the Koka Dam in Ethiopia, and the Kamburu Dam in Kenya. But until now, no attempt has been made to assess the cumulative effect of large dam-building on malaria.

Malaria is transmitted by the Anopheles mosquito, which needs slow-moving or stagnant water in which to breed. Dam reservoirs, particularly shallow puddles that often form along shorelines, provide a perfect environment for the insects to multiply. Thus, dam construction can intensify transmission and shift patterns of malaria infection.

Many African countries are planning new dams to help drive economic growth and increase water security. Improved water storage for growing populations, irrigation, and hydropower generation are needed for a fast-developing continent, but the researchers warn that building new dams has potential costs as well as benefits.

“Dams are an important option for governments anxious to develop,” said study author Matthew McCartney, PhD, of the International Water Management Institute in Vientiane, Laos.

“But it is unethical that people living close to them pay the price of that development through increased suffering and, possibly in extreme cases, loss of life due to disease.”

Lowering the risk

The researchers noted that, despite growing evidence of the impact of dams on malaria, there is scant evidence of their negative impacts being fully offset.

 

 

The team therefore made recommendations for managing the increased malaria risk. They said dam reservoirs could be more effectively designed and managed to reduce mosquito breeding. For instance, one option is to adopt operating schedules that, at critical times, dry out shoreline areas where mosquitoes tend to breed.

The researchers said dam developers should also consider increasing investment in integrated malaria intervention programs that include measures such as bed net distribution. Other environmental controls, such as introducing fish that eat mosquito larva in dam reservoirs, could also help reduce malaria cases in some instances.

“The bottom line is that adverse malaria impacts of dams routinely receive recognition in Environmental Impact Assessments, and areas around dams are frequently earmarked for intensive control efforts,” said study author Jonathan Lautze, PhD, of the International Water Management Institute in Pretoria, South Africa.

“The findings of our work hammer home the reality that this recognition and effort—well-intentioned though it may be—is simply not sufficient. Given the need for water resources development in Africa, malaria control around dams requires interdisciplinary cooperation, particularly between water and health communities. Malaria must be addressed while planning, designing, and operating African dams.”

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Topics

A dam in Africa

Photo courtesy of the

International Water

Management Institute

More than 1 million people in sub-Saharan Africa will contract malaria this year because they live near a large dam, according to a study published in Malaria Journal.

For the first time, researchers correlated the location of large dams in sub-Saharan Africa with the incidence of malaria.

And they found evidence to suggest that construction of an expected 78 major new dams over the next few years will lead to an additional 56,000 malaria cases annually.

The researchers said these findings have major implications for new dam projects and how health impacts should be assessed prior to construction.

“Dams are at the center of much development planning in Africa,” said study author Solomon Kibret, a graduate student at the University of New England in Armidale, New South Wales, Australia.

“While dams clearly bring many benefits—contributing to economic growth, poverty alleviation, and food security—adverse malaria impacts need to be addressed or they will undermine the sustainability of Africa’s drive for development.”

As part of the CGIAR Research Program on Water, Land, and Ecosystems, Kibret and colleagues looked at 1268 dams in sub-Saharan Africa. Of these, just under two-thirds (n=723) are in malarious areas.

The researchers compared detailed maps of malaria incidence with the dam sites. The number of annual malaria cases associated with the dams was estimated by comparing the number of cases for communities less than 5 kilometers from the dam reservoir with the number of cases for communities further away.

The team found that 15 million people live within 5 kilometers of dam reservoirs and are therefore at risk of contracting malaria. And at least 1.1 million malaria cases annually are linked to the presence of the dams.

“Our study showed that the population at risk of malaria around dams is at least 4 times greater than previously estimated,” Kibret said, noting that the authors were conservative in all their analyses.

The risk is particularly high in areas of sub-Saharan Africa with “unstable” malaria transmission, where malaria is seasonal. The study indicated that the impact of dams on malaria in unstable areas could either lead to intensified malaria transmission or change the nature of transmission from seasonal to perennial.

Explaining the risk

Previous research revealed increases in malaria incidence near major sub-Saharan dams such as the Akosombo Dam in Ghana, the Koka Dam in Ethiopia, and the Kamburu Dam in Kenya. But until now, no attempt has been made to assess the cumulative effect of large dam-building on malaria.

Malaria is transmitted by the Anopheles mosquito, which needs slow-moving or stagnant water in which to breed. Dam reservoirs, particularly shallow puddles that often form along shorelines, provide a perfect environment for the insects to multiply. Thus, dam construction can intensify transmission and shift patterns of malaria infection.

Many African countries are planning new dams to help drive economic growth and increase water security. Improved water storage for growing populations, irrigation, and hydropower generation are needed for a fast-developing continent, but the researchers warn that building new dams has potential costs as well as benefits.

“Dams are an important option for governments anxious to develop,” said study author Matthew McCartney, PhD, of the International Water Management Institute in Vientiane, Laos.

“But it is unethical that people living close to them pay the price of that development through increased suffering and, possibly in extreme cases, loss of life due to disease.”

Lowering the risk

The researchers noted that, despite growing evidence of the impact of dams on malaria, there is scant evidence of their negative impacts being fully offset.

 

 

The team therefore made recommendations for managing the increased malaria risk. They said dam reservoirs could be more effectively designed and managed to reduce mosquito breeding. For instance, one option is to adopt operating schedules that, at critical times, dry out shoreline areas where mosquitoes tend to breed.

The researchers said dam developers should also consider increasing investment in integrated malaria intervention programs that include measures such as bed net distribution. Other environmental controls, such as introducing fish that eat mosquito larva in dam reservoirs, could also help reduce malaria cases in some instances.

“The bottom line is that adverse malaria impacts of dams routinely receive recognition in Environmental Impact Assessments, and areas around dams are frequently earmarked for intensive control efforts,” said study author Jonathan Lautze, PhD, of the International Water Management Institute in Pretoria, South Africa.

“The findings of our work hammer home the reality that this recognition and effort—well-intentioned though it may be—is simply not sufficient. Given the need for water resources development in Africa, malaria control around dams requires interdisciplinary cooperation, particularly between water and health communities. Malaria must be addressed while planning, designing, and operating African dams.”

A dam in Africa

Photo courtesy of the

International Water

Management Institute

More than 1 million people in sub-Saharan Africa will contract malaria this year because they live near a large dam, according to a study published in Malaria Journal.

For the first time, researchers correlated the location of large dams in sub-Saharan Africa with the incidence of malaria.

And they found evidence to suggest that construction of an expected 78 major new dams over the next few years will lead to an additional 56,000 malaria cases annually.

The researchers said these findings have major implications for new dam projects and how health impacts should be assessed prior to construction.

“Dams are at the center of much development planning in Africa,” said study author Solomon Kibret, a graduate student at the University of New England in Armidale, New South Wales, Australia.

“While dams clearly bring many benefits—contributing to economic growth, poverty alleviation, and food security—adverse malaria impacts need to be addressed or they will undermine the sustainability of Africa’s drive for development.”

As part of the CGIAR Research Program on Water, Land, and Ecosystems, Kibret and colleagues looked at 1268 dams in sub-Saharan Africa. Of these, just under two-thirds (n=723) are in malarious areas.

The researchers compared detailed maps of malaria incidence with the dam sites. The number of annual malaria cases associated with the dams was estimated by comparing the number of cases for communities less than 5 kilometers from the dam reservoir with the number of cases for communities further away.

The team found that 15 million people live within 5 kilometers of dam reservoirs and are therefore at risk of contracting malaria. And at least 1.1 million malaria cases annually are linked to the presence of the dams.

“Our study showed that the population at risk of malaria around dams is at least 4 times greater than previously estimated,” Kibret said, noting that the authors were conservative in all their analyses.

The risk is particularly high in areas of sub-Saharan Africa with “unstable” malaria transmission, where malaria is seasonal. The study indicated that the impact of dams on malaria in unstable areas could either lead to intensified malaria transmission or change the nature of transmission from seasonal to perennial.

Explaining the risk

Previous research revealed increases in malaria incidence near major sub-Saharan dams such as the Akosombo Dam in Ghana, the Koka Dam in Ethiopia, and the Kamburu Dam in Kenya. But until now, no attempt has been made to assess the cumulative effect of large dam-building on malaria.

Malaria is transmitted by the Anopheles mosquito, which needs slow-moving or stagnant water in which to breed. Dam reservoirs, particularly shallow puddles that often form along shorelines, provide a perfect environment for the insects to multiply. Thus, dam construction can intensify transmission and shift patterns of malaria infection.

Many African countries are planning new dams to help drive economic growth and increase water security. Improved water storage for growing populations, irrigation, and hydropower generation are needed for a fast-developing continent, but the researchers warn that building new dams has potential costs as well as benefits.

“Dams are an important option for governments anxious to develop,” said study author Matthew McCartney, PhD, of the International Water Management Institute in Vientiane, Laos.

“But it is unethical that people living close to them pay the price of that development through increased suffering and, possibly in extreme cases, loss of life due to disease.”

Lowering the risk

The researchers noted that, despite growing evidence of the impact of dams on malaria, there is scant evidence of their negative impacts being fully offset.

 

 

The team therefore made recommendations for managing the increased malaria risk. They said dam reservoirs could be more effectively designed and managed to reduce mosquito breeding. For instance, one option is to adopt operating schedules that, at critical times, dry out shoreline areas where mosquitoes tend to breed.

The researchers said dam developers should also consider increasing investment in integrated malaria intervention programs that include measures such as bed net distribution. Other environmental controls, such as introducing fish that eat mosquito larva in dam reservoirs, could also help reduce malaria cases in some instances.

“The bottom line is that adverse malaria impacts of dams routinely receive recognition in Environmental Impact Assessments, and areas around dams are frequently earmarked for intensive control efforts,” said study author Jonathan Lautze, PhD, of the International Water Management Institute in Pretoria, South Africa.

“The findings of our work hammer home the reality that this recognition and effort—well-intentioned though it may be—is simply not sufficient. Given the need for water resources development in Africa, malaria control around dams requires interdisciplinary cooperation, particularly between water and health communities. Malaria must be addressed while planning, designing, and operating African dams.”

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Doc stresses importance of vitamin K shots

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Doc stresses importance of vitamin K shots

Sleeping newborn

Photo by Vera Kratochvil

Cases of vitamin K-deficiency bleeding (VKDB) reported in infants have healthcare professionals concerned about parents refusing vitamin K shots for their newborns.

Some parents have been declining the shots in what is believed to be an extension of the anti-vaccination movement.

But avoiding vitamin K shots can result in dire consequences for newborns, said DeeAnne Jackson, MD, of the University of Alabama at Birmingham.

“Newborns have been receiving vitamin K booster injections since 1961 to prevent internal bleeding,” Dr Jackson noted. “These injections are necessary because babies have very low levels of vitamin K at birth, which can lead to serious bleeding problems if not supplemented. It is an essential nutrient babies need to assist the body in blood clot formation.”

In a recent issue of the Journal of Emergency Medicine, doctors in Ohio documented a case where a 10-week-old child had profound anemia and intracranial bleeding after the child’s parents refused both the vitamin K shot and the hepatitis B vaccine.

The parents brought the child to the emergency room when the mother noticed flecks of blood in the baby’s stool. Emergency physicians were able to stop the intracranial bleeding before it became severe with an infusion of vitamin K.

A previous report published in 2013 revealed 4 cases of VKDB at a hospital in Nashville, Tennessee. These incidents were directly related to newborns not receiving their vitamin K shot.

When the US Centers for Disease Control and Prevention investigated this issue, the agency found that 28% of parents with babies born at private birthing centers in Nashville had refused the shot.

An update published in 2014 detailed 5 cases of late VKDB treated at the aforementioned hospital between February and September 2013 and 2 additional infants who had severe vitamin K deficiency but no bleeding.

Dr Jackson believes incidents like these might be avoided by better communication between parents and healthcare professionals.

“I would encourage parents who may be nervous about vitamin K shots or vaccines to start these conversations prior to their baby’s delivery so they can learn more about why these treatments are recommended ahead of time,” she said.

“You really shouldn’t wait to see if your baby needs a vitamin K shot after birth, because delaying medical care can lead to serious and life-threatening consequences.”

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Sleeping newborn

Photo by Vera Kratochvil

Cases of vitamin K-deficiency bleeding (VKDB) reported in infants have healthcare professionals concerned about parents refusing vitamin K shots for their newborns.

Some parents have been declining the shots in what is believed to be an extension of the anti-vaccination movement.

But avoiding vitamin K shots can result in dire consequences for newborns, said DeeAnne Jackson, MD, of the University of Alabama at Birmingham.

“Newborns have been receiving vitamin K booster injections since 1961 to prevent internal bleeding,” Dr Jackson noted. “These injections are necessary because babies have very low levels of vitamin K at birth, which can lead to serious bleeding problems if not supplemented. It is an essential nutrient babies need to assist the body in blood clot formation.”

In a recent issue of the Journal of Emergency Medicine, doctors in Ohio documented a case where a 10-week-old child had profound anemia and intracranial bleeding after the child’s parents refused both the vitamin K shot and the hepatitis B vaccine.

The parents brought the child to the emergency room when the mother noticed flecks of blood in the baby’s stool. Emergency physicians were able to stop the intracranial bleeding before it became severe with an infusion of vitamin K.

A previous report published in 2013 revealed 4 cases of VKDB at a hospital in Nashville, Tennessee. These incidents were directly related to newborns not receiving their vitamin K shot.

When the US Centers for Disease Control and Prevention investigated this issue, the agency found that 28% of parents with babies born at private birthing centers in Nashville had refused the shot.

An update published in 2014 detailed 5 cases of late VKDB treated at the aforementioned hospital between February and September 2013 and 2 additional infants who had severe vitamin K deficiency but no bleeding.

Dr Jackson believes incidents like these might be avoided by better communication between parents and healthcare professionals.

“I would encourage parents who may be nervous about vitamin K shots or vaccines to start these conversations prior to their baby’s delivery so they can learn more about why these treatments are recommended ahead of time,” she said.

“You really shouldn’t wait to see if your baby needs a vitamin K shot after birth, because delaying medical care can lead to serious and life-threatening consequences.”

Sleeping newborn

Photo by Vera Kratochvil

Cases of vitamin K-deficiency bleeding (VKDB) reported in infants have healthcare professionals concerned about parents refusing vitamin K shots for their newborns.

Some parents have been declining the shots in what is believed to be an extension of the anti-vaccination movement.

But avoiding vitamin K shots can result in dire consequences for newborns, said DeeAnne Jackson, MD, of the University of Alabama at Birmingham.

“Newborns have been receiving vitamin K booster injections since 1961 to prevent internal bleeding,” Dr Jackson noted. “These injections are necessary because babies have very low levels of vitamin K at birth, which can lead to serious bleeding problems if not supplemented. It is an essential nutrient babies need to assist the body in blood clot formation.”

In a recent issue of the Journal of Emergency Medicine, doctors in Ohio documented a case where a 10-week-old child had profound anemia and intracranial bleeding after the child’s parents refused both the vitamin K shot and the hepatitis B vaccine.

The parents brought the child to the emergency room when the mother noticed flecks of blood in the baby’s stool. Emergency physicians were able to stop the intracranial bleeding before it became severe with an infusion of vitamin K.

A previous report published in 2013 revealed 4 cases of VKDB at a hospital in Nashville, Tennessee. These incidents were directly related to newborns not receiving their vitamin K shot.

When the US Centers for Disease Control and Prevention investigated this issue, the agency found that 28% of parents with babies born at private birthing centers in Nashville had refused the shot.

An update published in 2014 detailed 5 cases of late VKDB treated at the aforementioned hospital between February and September 2013 and 2 additional infants who had severe vitamin K deficiency but no bleeding.

Dr Jackson believes incidents like these might be avoided by better communication between parents and healthcare professionals.

“I would encourage parents who may be nervous about vitamin K shots or vaccines to start these conversations prior to their baby’s delivery so they can learn more about why these treatments are recommended ahead of time,” she said.

“You really shouldn’t wait to see if your baby needs a vitamin K shot after birth, because delaying medical care can lead to serious and life-threatening consequences.”

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Group creates guide for PICC use

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PICC

A group of international experts has created a guide to promote the appropriate use of peripherally inserted central catheters (PICCs) in adults.

The guide, called Michigan Appropriateness Guide for Intravenous Catheters (MAGIC), was published in Annals of Internal Medicine.

MAGIC is based on a review of evidence and was designed to give clinicians an easy-to-use framework to pick the right venous access device for each patient.

“PICCs, or peripherally inserted central catheters, have become especially convenient to place, and their use has gone up dramatically, as have the complications from them,” said guideline author Vineet Chopra, MD, of the University of Michigan in Ann Arbor.

“The easiest way to prevent these complications is not to place a PICC in the first place. So we set out to determine when the use of a PICC is appropriate and when other choices are the best.”

The experts reviewed 665 scenarios in which PICCs were used. Their use was deemed appropriate in 38% (n=253) of cases and inappropriate in 43% (n=288). In 19% (n=124) of cases, the experts could not agree or were unsure about whether PICC use was appropriate.

The experts said that, in patients with cancer, PICCs are appropriate for irritant or vesicant infusion, regardless of the duration of use.

On the other hand, they said PICC use is inappropriate for peripherally compatible infusions when the proposed duration of use is 5 days or fewer. And when the duration is between 6 days and 14 days, midline and ultrasonography-guided peripheral intravenous catheters should be used over PICCs.

The experts also said that nontunneled central venous catheters should be used over PICCs in critically ill patients when the duration of use is likely to be 14 days or fewer.

How MAGIC happened

The panel of 15 experts included doctors and nurses from a range of fields where PICCs and other such devices are commonly used, such as vascular nursing, critical care, infectious disease, and oncology. Also participating was a patient who had suffered complications from various intravenous devices and still lives with the consequences.

The panel evaluated the scenarios and supporting medical literature, and made its recommendations, using the RAND/UCLA Appropriateness Method.

The panel did not consider pediatric use of PICCs and other vascular access devices, but they hope their work could provide a framework for a similar effort in pediatrics.

Putting MAGIC to the test

MAGIC is getting its first test in 47 Michigan hospitals taking part in a patient safety project known as the Michigan Hospital Medicine Safety Consortium.

Researchers also plan to test ways to deploy MAGIC across the Veterans Affairs health system, working with the VA National Center for Patient Safety and the No Preventable Harms Campaign.

Even as they evaluate MAGIC’s ability to improve appropriate use of different devices and reduce complications, the team behind the new guide hopes other clinicians will begin using it.

“IV devices of all kinds are being put into patients without much thought about risks, benefits, or alternatives,” Dr Chopra said. “At the end of the day, we hope MAGIC will give providers the information they need to make a good decision for their patient, one that will render these devices appropriate and safe.”

Dr Chopra and his colleagues have also launched a website, improvepicc.com, that provides links to research on PICCs and other resources for clinicians.

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PICC

A group of international experts has created a guide to promote the appropriate use of peripherally inserted central catheters (PICCs) in adults.

The guide, called Michigan Appropriateness Guide for Intravenous Catheters (MAGIC), was published in Annals of Internal Medicine.

MAGIC is based on a review of evidence and was designed to give clinicians an easy-to-use framework to pick the right venous access device for each patient.

“PICCs, or peripherally inserted central catheters, have become especially convenient to place, and their use has gone up dramatically, as have the complications from them,” said guideline author Vineet Chopra, MD, of the University of Michigan in Ann Arbor.

“The easiest way to prevent these complications is not to place a PICC in the first place. So we set out to determine when the use of a PICC is appropriate and when other choices are the best.”

The experts reviewed 665 scenarios in which PICCs were used. Their use was deemed appropriate in 38% (n=253) of cases and inappropriate in 43% (n=288). In 19% (n=124) of cases, the experts could not agree or were unsure about whether PICC use was appropriate.

The experts said that, in patients with cancer, PICCs are appropriate for irritant or vesicant infusion, regardless of the duration of use.

On the other hand, they said PICC use is inappropriate for peripherally compatible infusions when the proposed duration of use is 5 days or fewer. And when the duration is between 6 days and 14 days, midline and ultrasonography-guided peripheral intravenous catheters should be used over PICCs.

The experts also said that nontunneled central venous catheters should be used over PICCs in critically ill patients when the duration of use is likely to be 14 days or fewer.

How MAGIC happened

The panel of 15 experts included doctors and nurses from a range of fields where PICCs and other such devices are commonly used, such as vascular nursing, critical care, infectious disease, and oncology. Also participating was a patient who had suffered complications from various intravenous devices and still lives with the consequences.

The panel evaluated the scenarios and supporting medical literature, and made its recommendations, using the RAND/UCLA Appropriateness Method.

The panel did not consider pediatric use of PICCs and other vascular access devices, but they hope their work could provide a framework for a similar effort in pediatrics.

Putting MAGIC to the test

MAGIC is getting its first test in 47 Michigan hospitals taking part in a patient safety project known as the Michigan Hospital Medicine Safety Consortium.

Researchers also plan to test ways to deploy MAGIC across the Veterans Affairs health system, working with the VA National Center for Patient Safety and the No Preventable Harms Campaign.

Even as they evaluate MAGIC’s ability to improve appropriate use of different devices and reduce complications, the team behind the new guide hopes other clinicians will begin using it.

“IV devices of all kinds are being put into patients without much thought about risks, benefits, or alternatives,” Dr Chopra said. “At the end of the day, we hope MAGIC will give providers the information they need to make a good decision for their patient, one that will render these devices appropriate and safe.”

Dr Chopra and his colleagues have also launched a website, improvepicc.com, that provides links to research on PICCs and other resources for clinicians.

PICC

A group of international experts has created a guide to promote the appropriate use of peripherally inserted central catheters (PICCs) in adults.

The guide, called Michigan Appropriateness Guide for Intravenous Catheters (MAGIC), was published in Annals of Internal Medicine.

MAGIC is based on a review of evidence and was designed to give clinicians an easy-to-use framework to pick the right venous access device for each patient.

“PICCs, or peripherally inserted central catheters, have become especially convenient to place, and their use has gone up dramatically, as have the complications from them,” said guideline author Vineet Chopra, MD, of the University of Michigan in Ann Arbor.

“The easiest way to prevent these complications is not to place a PICC in the first place. So we set out to determine when the use of a PICC is appropriate and when other choices are the best.”

The experts reviewed 665 scenarios in which PICCs were used. Their use was deemed appropriate in 38% (n=253) of cases and inappropriate in 43% (n=288). In 19% (n=124) of cases, the experts could not agree or were unsure about whether PICC use was appropriate.

The experts said that, in patients with cancer, PICCs are appropriate for irritant or vesicant infusion, regardless of the duration of use.

On the other hand, they said PICC use is inappropriate for peripherally compatible infusions when the proposed duration of use is 5 days or fewer. And when the duration is between 6 days and 14 days, midline and ultrasonography-guided peripheral intravenous catheters should be used over PICCs.

The experts also said that nontunneled central venous catheters should be used over PICCs in critically ill patients when the duration of use is likely to be 14 days or fewer.

How MAGIC happened

The panel of 15 experts included doctors and nurses from a range of fields where PICCs and other such devices are commonly used, such as vascular nursing, critical care, infectious disease, and oncology. Also participating was a patient who had suffered complications from various intravenous devices and still lives with the consequences.

The panel evaluated the scenarios and supporting medical literature, and made its recommendations, using the RAND/UCLA Appropriateness Method.

The panel did not consider pediatric use of PICCs and other vascular access devices, but they hope their work could provide a framework for a similar effort in pediatrics.

Putting MAGIC to the test

MAGIC is getting its first test in 47 Michigan hospitals taking part in a patient safety project known as the Michigan Hospital Medicine Safety Consortium.

Researchers also plan to test ways to deploy MAGIC across the Veterans Affairs health system, working with the VA National Center for Patient Safety and the No Preventable Harms Campaign.

Even as they evaluate MAGIC’s ability to improve appropriate use of different devices and reduce complications, the team behind the new guide hopes other clinicians will begin using it.

“IV devices of all kinds are being put into patients without much thought about risks, benefits, or alternatives,” Dr Chopra said. “At the end of the day, we hope MAGIC will give providers the information they need to make a good decision for their patient, one that will render these devices appropriate and safe.”

Dr Chopra and his colleagues have also launched a website, improvepicc.com, that provides links to research on PICCs and other resources for clinicians.

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Two‐Item Bedside Test for Delirium

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Preliminary development of an ultrabrief two‐item bedside test for delirium

Delirium (acute confusion) is common in older adults and leads to poor outcomes, such as death, clinician and caregiver burden, and prolonged cognitive and functional decline.[1, 2, 3, 4] Delirium is extremely costly, with estimates ranging from $143 to $152 billion annually (2005 US$).[5, 6] Early detection and management may improve the poor outcomes and reduce costs attributable to delirium,[3, 7] yet delirium identification in clinical practice has been challenging, particularly when translating research tools to the bedside.[8, 9, 10]As a result, only 12% to 35% of delirium cases are detected in routine care, with hypoactive delirium and delirium superimposed on dementia most likely to be missed.[11, 12, 13, 14, 15]

To address these issues, we recently developed and published the three‐dimensional Confusion Assessment Method (3D‐CAM), the 3‐minute diagnostic assessment for CAM‐defined delirium.[16] The 3D‐CAM is a structured assessment tool that includes mental status testing, patient symptom probes, and guided interviewer observations for signs of delirium. 3D‐CAM items were selected through a rigorous process to determine the most informative items for the 4 CAM diagnostic features.[17] The 3D‐CAM can be completed in 3 minutes, and has 95% sensitivity and 94% specificity relative to a reference standard.[16]

Despite the capabilities of the 3D‐CAM, there are situations when even 3 minutes is too long to devote to delirium identification. Moreover, a 2‐step approach in which a sensitive ultrabrief screen is administered, followed by the 3D‐CAM in positives, may be the most efficient approach for large‐scale delirium case identification. The aim of the current study was to use the 3D‐CAM database to identify the most sensitive single item and pair of items in the diagnosis of delirium, using the reference standard in the diagnostic accuracy analysis. We hypothesized that we could identify a single item with greater than 80% sensitivity and a pair of items with greater than 90% sensitivity for detection of delirium.

METHODS

Study Sample and Design

We analyzed data from the 3D‐CAM validation study,[16] which prospectively enrolled participants from a large urban teaching hospital in Boston, Massachusetts, using a consecutive enrollment sampling strategy. Inclusion criteria were: (1) 75 years old, (2) admitted to general or geriatric medicine services, (3) able to communicate in English, (4) without terminal conditions, (5) expected hospital stay of 2 days, (6) not a previous study participant. Experienced clinicians screened patients for eligibility. If the patient lacked capacity to provide consent, the designated surrogate decision maker was contacted. The study was approved by the institutional review board.

Reference Standard Delirium Diagnosis

The reference standard delirium diagnosis was based on an extensive (45 minutes) face‐to‐face patient interview by experienced clinician assessors (neuropsychologists or advanced practice nurses), medical record review, and input from the nurse and family members. This comprehensive assessment included: (1) reason for hospital admission, hospital course, and presence of cognitive concerns, (2) family, social, and functional history, (3) Montreal Cognitive Assessment,[18] (4) Geriatric Depression Scale,[19] (5) medical record review including scoring of comorbidities using the Charlson index,[20] determination of functional status using the basic and Instrumental Activities of Daily Living,[21, 22] psychoactive medications administered, and (6) a family member interview to assess the patient's baseline cognitive status that included the Eight‐Item Interview to Differentiate Aging and Dementia,[23] to assess the presence of dementia. Using all of these data, an expert panel, including the clinical assessor, the study principal investigator (E.R.M.), a geriatrician, and an experienced neuropsychologist, adjudicated the final delirium diagnoses using Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM‐IV) criteria. The panel also adjudicated for the presence or absence of dementia and mild cognitive impairment based on National Institute on Aging‐Alzheimer's Association (NIA‐AA) criteria.[24] This approach has been used in other delirium studies.[25]

3D‐CAM Assessments

After the reference standard assessment, the 3D‐CAM was administered by trained research assistants (RAs) who were blinded to the results of the reference standard. To reduce the likelihood of fluctuations or temporal changes, all assessments were completed between 11:00 am and 2:00 pm and for each participant, within a 2‐hour time period (for example, 11:23 am to 1:23 pm).

Statistical Analyses to Determine the Best Single‐ and Two‐Item Screeners

To determine the best single 3D‐CAM item to identify delirium, the responses of the 20 individual items in the 3D‐CAM (see Supporting Table 1 in the online version of this article) were compared to the reference standard to determine their sensitivity and specificity. Similarly, an algorithm was used to generate all unique 2‐item combinations of the 20 items (190 unique pairs), which were compared to the reference. An error, no response, or an answer of I do not know by the patient was considered a positive screen for delirium. The 2‐item screeners were considered positive if 1 or both of the items were positive. Sensitivity and specificity were calculated along with 95% confidence intervals (CIs).

Subset analyses were performed to determine sensitivity and specificity of individual items and pairs of items stratified by the patient's baseline cognitive status. Two strata were createdpatients with dementia (N=56), and patients with normal baseline cognitive status or mild cognitive impairment (MCI) (N=145). We chose to group MCI with normal for 2 reasons: (1) dementia is a well‐established and strong risk factor for delirium, whereas the evidence for MCI being a risk factor for delirium is less established and (2) to achieve adequate allocation of delirious cases in both strata. Last, we report the sensitivity of altered level of consciousness (LOC), which included lethargy, stupor, coma, and hypervigilance as a single screening item for delirium in the overall sample and by cognitive status. Analyses were conducted using commercially available software (SAS version 9.3; SAS Institute, Inc., Cary, NC).

RESULTS

Characteristics of the patients are shown in Table 1. Subjects had a mean age of 84 years, 62% were female, and 28% had a baseline dementia. Forty‐two (21%) had delirium based on the clinical reference standard. Twenty (10%) had less than a high school education and 100 (49%) had at least a college education.

Sample Characteristics (N=201)
CharacteristicN (%)
  • NOTE: Abbreviations: ADL, activities of daily living; IADL, instrumental activities of daily living; MCI, mild cognitive impairment; MoCA, Montreal Cognitive Assessment; SD, standard deviation.

Age, y, mean (SD)84 (5.4)
Sex, n (%) female125 (62)
White, n (%)177 (88)
Education, n (%) 
Less than high school20 (10)
High school graduate75 (38)
College plus100 (49)
Vision interfered with interview, n (%)5 (2)
Hearing interfered with interview, n (%)18 (9)
English second language n (%)10 (5)
Charlson, mean (SD)3 (2.3)
ADL, n (% impaired)110 (55)
IADL, n (% impaired)163 (81)
MCI, n (%)50 (25)
Dementia, n (%)56 (28)
Delirium, n (%)42 (21)
MoCA, mean (SD)19 (6.6)
MoCA, median (range)20 (030)

Single Item Screens

Table 2 reports the results of single‐item screens for delirium with sensitivity, the ability to correctly identify delirium when it is present by the reference standard, and specificity, the ability to correctly identify patients without delirium when it is not present by reference standard and 95% CIs. Items are listed in descending order of sensitivity; in the case of ties, the item with the higher specificity is listed first. The screening items with the highest sensitivity for delirium are Months of the year backwards, and Four digits backwards, both with a sensitivity of 83% (95% CI: 69%‐93%). Of these 2 items, Months of the year backwards had a much better specificity of 69% (95% CI: 61%‐76%), whereas Four digits backwards had a specificity of 52% (95% CI: 44%‐60%). The item What is the day of the week? had lower sensitivity at 71% (95% CI: 55%‐84%), but excellent specificity at 92% (95% CI: 87%‐96%).

Top Ten Single‐Item Screen for Delirium (N=201)
Screen ItemScreen Positive (%)cSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Number of patients with delirium=42. Abbreviations: CI, confidence interval; LR, likelihood ratio.

  • There were 20 different items and 190 possible item pairs considered.

  • Top 10 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

Months of the year backwards420.83 (0.69‐0.93)0.69 (0.61‐0.76)2.70.24
Four digits backwards560.83 (0.69‐0.93)0.52 (0.44‐0.60)1.720.32
What is the day of the week?210.71 (0.55‐0.84)0.92 (0.87‐0.96)9.460.31
What is the year?160.55 (0.39‐0.70)0.94 (0.9‐0.97)9.670.48
Have you felt confused during the past day?140.50 (0.34‐0.66)0.95 (0.9‐0.98)9.940.53
Days of the week backwards150.50 (0.34‐0.66)0.94 (0.89‐0.97)7.950.53
During the past day, did you see things that were not really there?110.45 (0.3‐0.61)0.97 (0.94‐0.99)17.980.56
Three digits backwards150.45 (0.3‐0.61)0.92 (0.87‐0.96)5.990.59
What type of place is this?90.38 (0.24‐0.54)0.99 (0.96‐1)30.290.63
During the past day, did you think you were not in the hospital?100.38 (0.24‐0.54)0.97 (0.94‐0.99)15.140.64

We then examined performance of single‐item screeners in patients with and without dementia (Table 3). In persons with dementia, the best single item was also Months of the year backwards, with a sensitivity of 89% (95% CI: 72%‐98%) and a specificity of 61% (95% CI: 41%‐78%). In persons with normal baseline cognition or MCI, the best performing single item was Four digits backwards, with sensitivity of 79% (95% CI: 49%‐95%) and specificity of 51% (95% CI: 42%‐60%). Months of the year backwards also performed well, with sensitivity of 71% (95% CI: 42%‐92%) and specificity of 71% (95% CI: 62%‐79%).

Top Three Single‐Item Screen for Delirium Stratified by Baseline Cognition
Test ItemNormal/MCI Patients (n=145)Dementia Patients (n=56)
Screen Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLRScreen Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Participants with learning problems (1) grouped with dementia and MCI participants (44) grouped with normal. Number of patients with delirium=28. Abbreviations: CI, confidence interval; LR, likelihood ratio; MCI, mild cognitive impairment.

  • Top 3 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

Months backwards330.71 (0.42‐0.92)0.71 (0.62‐0.79)2.460.4640.89 (0.72‐0.98)0.61 (0.41‐0.78)2.270.18
Four digits backwards520.79 (0.49‐0.95)0.51 (0.42‐0.60)1.610.42660.86 (0.67‐0.96)0.54 (0.34‐0.72)1.850.27
What is the day of the week?100.64 (0.35‐0.87)0.96 (0.91‐0.99)16.840.37500.75 (0.55‐0.89)0.75 (0.55‐0.89)30.33

Two‐Item Screens

Table 4 reports the results of 2‐item screens for delirium with sensitivity, specificity, and 95% CIs. Item pairs are listed in descending order of sensitivity following the same convention as in Table 2. The 2‐item screen with the highest sensitivity for delirium is the combination of What is the day of the week? and Months of the year backwards, with a sensitivity of 93% (95% CI: 81%‐99%) and specificity of 64% (95% CI: 56%‐70%). This screen had a positive and negative likelihood ratio (LR) of 2.59 and 0.11, respectively. The combination of What is the day of the week? and Four digits backwards had the same sensitivity 93% (95% CI: 81%‐99%), but lower specificity of 48% (95% CI: 40%‐56%). The combination of What type of place is this? (hospital) and Four digits backwards had a sensitivity of 90% (95% CI: 77%‐97%) and specificity of 51% (95% CI: 43%‐50%).

Top Ten Two‐Item Screen for Delirium (N=201)
Screen Item 1Screen Item 2Screen Positive (%)cSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Number of patients with delirium=42. Abbreviations: CI, confidence interval; LR, likelihood ratio.

  • There were 20 different items and 190 possible item pairs considered.

  • Top 10 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

What is the day of the week?Months backwards480.93 (0.81‐0.99)0.64 (0.56‐0.70)2.590.11
What is the day of the week?Four digits backwards600.93 (0.81‐0.99)0.48 (0.4‐0.56)1.80.15
Four digits backwardsMonths backwards650.93 (0.81‐0.99)0.42 (0.34‐0.50)1.60.17
What type of place is this?Four digits backwards580.90 (0.77‐0.97)0.51 (0.43‐0.50)1.840.19
What is the year?Four digits backwards590.9 (0.77‐0.97)0.5 (0.42‐0.5)1.800.19
What is the day of the week?Three digits backwards300.88 (0.74‐0.96)0.86 (0.79‐0.90)6.090.14
What is the year?Months backwards440.88 (0.74‐0.96)0.68 (0.6‐0.75)2.750.18
What type of place is this?Months backwards430.86 (0.71‐0.95)0.69 (0.61‐0.70)2.730.21
During the past day, did you think you were not in the hospital?Months backwards430.86 (0.71‐0.95)0.69 (0.61‐0.70)2.730.21
Days of the week backwardsMonths backwards430.86 (0.71‐0.95)0.68 (0.6‐0.75)2.670.21

When subjects were stratified by baseline cognition, the best 2‐item screens for normal and MCI patients was What is the day of the week? and Four digits backwards, with 93% sensitivity (95% CI: 66%‐100%) and 50% specificity (95% CI: 42%‐59%). The best pair of items for patients with dementia (Table 5) was the same as the overall sample, What is the day of the week? and Months of the year backwards, but its performance differed with a higher sensitivity of 96% (95% CI: 82%‐100%) and lower specificity of 43% (95% CI: 24%‐63%). This same pair of items had 86% sensitivity (95% CI: 57%‐98%) and 69% (95% CI: 60%‐77%) specificity for persons with either normal cognition or MCI.

Top Three Two‐Item Screen for Normal/MCI and Persons With Dementia
Test Item 1Test Item 2Normal/MCI Patients (n=145)Dementia Patients (n=56) 
Item Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLRItem Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Participants with learning problems (1) grouped with dementia and MCI participants (44) grouped with normal. Number of patients with delirium=28. Abbreviations: CI, confidence interval; LR, likelihood ratio; MCI, mild cognitive impairment.

  • Top 3 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

What is the day of the week?Months backwards360.86 (0.57‐0.98)0.69 (0.60‐0.77)2.740.21770.96 (0.82‐1)0.43 (0.24‐0.63)1.690.08
What is the day of the week?Four digits backwards540.93 (0.66‐1)0.5 (0.42‐0.59)1.870.14770.93 (0.76‐0.99)0.39 (0.22‐0.59)1.530.18
Four digits backwardsMonths backwards610.93 (0.66‐1)0.43 (0.34‐0.52)1.620.17770.93 (0.76‐0.99)0.39 (0.22‐0.59)1.530.18

Altered Level of Consciousness as a Screener for Delirium

Altered level of consciousness (ALOC) was uncommon in our sample, with an overall prevalence of 10/201 (4.9%). When examined as a screening item for delirium, ALOC had very poor sensitivity of 19% (95% CI: 9%‐34%) but had excellent specificity 99% (95% CI: 96%‐100%). Altered LOC also demonstrated poor screening performance when stratified by cognitive status, with a sensitivity of 14% in the normal and MCI group (95% CI: 2%‐43%) and sensitivity of 21% (95% CI: 8%‐41%) in persons with dementia.

Positive and Negative Predictive Values

Although we focused on sensitivity and specificity in evaluating 1‐ and 2‐item screeners, we also examined positive and negative predictive values. These values will vary depending on the overall prevalence of delirium, which was 21% in this dataset. The best 1‐item screener, Months of the year backwards, had a positive predictive value of 31% and negative predictive value of 94%. The best 2‐item screener, Months of the year backwards with What is the day of the week?, had a positive predictive value of 41% and negative predictive value of 97% (see Supporting Tables 2 and 3 in the online version of this article) LRs for the items are in Tables 2 through 5.

DISCUSSION

Identifying simple, efficient, bedside case‐identification methods for delirium is an essential step toward improving recognition of this highly morbid syndrome in hospitalized older adults. In this study, we identified a single cognitive item, Months of the year backwards, that identified 83% of delirium cases when compared with a reference standard diagnosis. Furthermore, we identified 2 items, Months of the year backwards and What is the day of the week? which when used in combination identified 93% of delirium cases. The same 1 and 2 items also worked well in patients with dementia, in whom delirium is often missed. Although these items require further clinical validation, the development of an ultrabrief 2‐item test that identifies over 90% of delirium cases and can be completed in less than 1 minute (recently, we administered the best 2‐item screener to 20 consecutive general medicine patients over age 70 years, and it was completed in a median of 36.5 seconds), holds great potential for simplifying bedside delirium screening and improving the care of hospitalized older adults.

Our current findings both confirm and extend the emerging literature on best screening items for delirium. Sands and colleagues (2010)[26] tested a single test for delirium, Do you think (name of patient) has been more confused lately? in 21 subjects and achieved a sensitivity of 80%. Han and colleagues developed a screening tool in emergency‐department patients using the LOC question from the Richmond Agitation‐Sedation Scale and spelling the word lunch backwards, and achieved 98% sensitivity, but in a younger emergency department population with a low prevalence of dementia.[27] O'Regan et al. recently also found Months of the year backwards to be the best single‐screening item for delirium in a large sample, but only tested a 1‐item screen.[28] Our study extends these studies in several important ways by: (1) employing a rigorous clinical reference standard diagnosis of delirium, (2) having a large sample with a high prevalence of patients with dementia, (3) use of a general medical population, and (4) examining the best 2‐item screens in addition to the best single item.

Systematic intervention programs[29, 30, 31] that focus on improved delirium evaluation and management have the potential to improve patient outcomes and reduce costs. However, targeting these programs to patients with delirium has proven difficult, as only 12% to 35% of delirium cases are recognized in routine clinical practice.[11, 12, 13, 14, 15] The 1‐ and 2‐item screeners we identified could play an important role in future delirium identification. The 3D‐CAM combines high sensitivity (95%) with high specificity (94%)[16] and therefore would be an excellent choice as the second step after a positive screen. The feasibility, effectiveness, and cost of administering these screeners, followed by a brief diagnostic tool such as the 3D‐CAM, should be evaluated in future work.

Our study has noteworthy strengths, including the use of a large purposefully challenging clinical sample with advanced age that included a substantial proportion with dementia, a detailed assessment, and the testing of very brief and practical tools for bedside delirium screening.[25] This study also has several important limitations. Most importantly, we presented secondary analysis of individual items and pairs of items drawn from the 3D CAM assessment; therefore, the 2‐item bedside screen requires prospective clinical validation. The reference standard was based on the DSM‐IV, because this study was conducted prior to the release of DSM‐V. In addition, the ordering of the reference standard and 3D‐CAM assessments was not randomized due to feasibility constraints. In addition, this study was cross‐sectional, involved only a single hospital, and enrolled only older medical patients during the day shift. Our sample was older (aged 75 years and older), and a younger sample may have had a different prevalence of delirium, which could affect the positive predictive value of our ultrabrief screen. We plan to test this in a sample of patients aged 70 years and older in future studies. Finally, it should be noted that these best 1‐item and 2‐item screeners miss 17% and 7% of delirium cases, respectively. In cases where this is unacceptably high, alternative approaches might be necessary.

It is important to remember that these 1‐ and 2‐item screeners are not diagnostic tools and therefore should not be used in isolation. Optimally, they will be followed by a more specific evaluation, such as the 3D‐CAM, as part of a systematic delirium identification process. For instance, in our sample (with a delirium rate of 21%), the best 2‐item screener had a positive predictive value of 41%, meaning that positive screens are more likely to be false positives than true positives (see Supporting Tables 2 and 3 in the online version of this article).[32] Nevertheless, by reducing the total number of patients who require diagnostic instrument administration, use of these ultrabrief screeners can improve efficiency and result in a net benefit to delirium case‐identification efforts.[32]

Time has been demonstrated to be a barrier to delirium identification in previous studies, but there are likely others. These may include, for instance, staff nihilism about screening making a difference, ambiguous responsibility for delirium screening and management, unsupportive system leadership, and absent payment for these activities.[31] Moreover, it is possible that the 2‐step process we propose may create an incentive for staff to avoid positive screens as they see it creating more work for themselves. We plan to identify and address such barriers in our future work.

In conclusion, we identified a single screening item for delirium, Months of the year backwards, with 83% sensitivity, and a pair of items, Months of the year backwards and What is the day of the week?, with 93% sensitivity relative to a rigorous reference standard diagnosis. These ultrabrief screening items work well in patients with and without dementia, and should require very little training of staff. Future studies should further validate these tools, and determine their translatability and scalability into programs for systematic, widespread delirium detection. Developing efficient and accurate case identification strategies is a necessary prerequisite to appropriately target delirium management protocols, enabling healthcare systems to effectively address this costly and deadly condition.

Disclosures

Author contributionsD.M.F. conceived the study idea, participated in its design and coordination, and drafted the initial manuscript. S.K.I. contributed to the study design and conceptualization, supervision, funding, preliminary analysis, and interpretation of the data, and critical revision of the manuscript. J.G. conducted the analysis for the study and critically revised the manuscript. L.N. supervised the analysis for the study and critically revised the manuscript. R.J. contributed to the study design and critical revision of the manuscript. J.S.S. critically revised the manuscript. E.R.M. obtained funding for the study, supervised all data collection, assisted in drafting and critically revising the manuscript, and contributed to the conceptualization, design, and supervision of the study. All authors have seen and agree with the contents of the manuscript.

This work was supported by the National Institute of Aging grant number R01AG030618 and K24AG035075 to Dr. Marcantonio. Dr. Inouye's time was supported in part by grants P01AG031720, R01AG044518, and K07AG041835 from the National Institute on Aging. Dr. Inouye holds the Milton and Shirley F. Levy Family Chair (Hebrew Senior Life/Harvard Medical School). Dr. Fick is partially supported from National Institute of Nursing Research grant number R01 NR011042. Dr. Saczynski was supported in part by funding from the National Institute on Aging (K01AG33643) and from the National Heart Lung and Blood Institute (U01HL105268). The funding agencies had no role and the authors retained full autonomy in the preparation of this article. All authors and coauthors have no financial or nonfinancial conflicts of interest to disclose regarding this article.

This article was presented at the Presidential Poster Session at the American Geriatrics Society 2014 Annual Meeting in Orlando, Florida, May 14, 2014.

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References
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Delirium (acute confusion) is common in older adults and leads to poor outcomes, such as death, clinician and caregiver burden, and prolonged cognitive and functional decline.[1, 2, 3, 4] Delirium is extremely costly, with estimates ranging from $143 to $152 billion annually (2005 US$).[5, 6] Early detection and management may improve the poor outcomes and reduce costs attributable to delirium,[3, 7] yet delirium identification in clinical practice has been challenging, particularly when translating research tools to the bedside.[8, 9, 10]As a result, only 12% to 35% of delirium cases are detected in routine care, with hypoactive delirium and delirium superimposed on dementia most likely to be missed.[11, 12, 13, 14, 15]

To address these issues, we recently developed and published the three‐dimensional Confusion Assessment Method (3D‐CAM), the 3‐minute diagnostic assessment for CAM‐defined delirium.[16] The 3D‐CAM is a structured assessment tool that includes mental status testing, patient symptom probes, and guided interviewer observations for signs of delirium. 3D‐CAM items were selected through a rigorous process to determine the most informative items for the 4 CAM diagnostic features.[17] The 3D‐CAM can be completed in 3 minutes, and has 95% sensitivity and 94% specificity relative to a reference standard.[16]

Despite the capabilities of the 3D‐CAM, there are situations when even 3 minutes is too long to devote to delirium identification. Moreover, a 2‐step approach in which a sensitive ultrabrief screen is administered, followed by the 3D‐CAM in positives, may be the most efficient approach for large‐scale delirium case identification. The aim of the current study was to use the 3D‐CAM database to identify the most sensitive single item and pair of items in the diagnosis of delirium, using the reference standard in the diagnostic accuracy analysis. We hypothesized that we could identify a single item with greater than 80% sensitivity and a pair of items with greater than 90% sensitivity for detection of delirium.

METHODS

Study Sample and Design

We analyzed data from the 3D‐CAM validation study,[16] which prospectively enrolled participants from a large urban teaching hospital in Boston, Massachusetts, using a consecutive enrollment sampling strategy. Inclusion criteria were: (1) 75 years old, (2) admitted to general or geriatric medicine services, (3) able to communicate in English, (4) without terminal conditions, (5) expected hospital stay of 2 days, (6) not a previous study participant. Experienced clinicians screened patients for eligibility. If the patient lacked capacity to provide consent, the designated surrogate decision maker was contacted. The study was approved by the institutional review board.

Reference Standard Delirium Diagnosis

The reference standard delirium diagnosis was based on an extensive (45 minutes) face‐to‐face patient interview by experienced clinician assessors (neuropsychologists or advanced practice nurses), medical record review, and input from the nurse and family members. This comprehensive assessment included: (1) reason for hospital admission, hospital course, and presence of cognitive concerns, (2) family, social, and functional history, (3) Montreal Cognitive Assessment,[18] (4) Geriatric Depression Scale,[19] (5) medical record review including scoring of comorbidities using the Charlson index,[20] determination of functional status using the basic and Instrumental Activities of Daily Living,[21, 22] psychoactive medications administered, and (6) a family member interview to assess the patient's baseline cognitive status that included the Eight‐Item Interview to Differentiate Aging and Dementia,[23] to assess the presence of dementia. Using all of these data, an expert panel, including the clinical assessor, the study principal investigator (E.R.M.), a geriatrician, and an experienced neuropsychologist, adjudicated the final delirium diagnoses using Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM‐IV) criteria. The panel also adjudicated for the presence or absence of dementia and mild cognitive impairment based on National Institute on Aging‐Alzheimer's Association (NIA‐AA) criteria.[24] This approach has been used in other delirium studies.[25]

3D‐CAM Assessments

After the reference standard assessment, the 3D‐CAM was administered by trained research assistants (RAs) who were blinded to the results of the reference standard. To reduce the likelihood of fluctuations or temporal changes, all assessments were completed between 11:00 am and 2:00 pm and for each participant, within a 2‐hour time period (for example, 11:23 am to 1:23 pm).

Statistical Analyses to Determine the Best Single‐ and Two‐Item Screeners

To determine the best single 3D‐CAM item to identify delirium, the responses of the 20 individual items in the 3D‐CAM (see Supporting Table 1 in the online version of this article) were compared to the reference standard to determine their sensitivity and specificity. Similarly, an algorithm was used to generate all unique 2‐item combinations of the 20 items (190 unique pairs), which were compared to the reference. An error, no response, or an answer of I do not know by the patient was considered a positive screen for delirium. The 2‐item screeners were considered positive if 1 or both of the items were positive. Sensitivity and specificity were calculated along with 95% confidence intervals (CIs).

Subset analyses were performed to determine sensitivity and specificity of individual items and pairs of items stratified by the patient's baseline cognitive status. Two strata were createdpatients with dementia (N=56), and patients with normal baseline cognitive status or mild cognitive impairment (MCI) (N=145). We chose to group MCI with normal for 2 reasons: (1) dementia is a well‐established and strong risk factor for delirium, whereas the evidence for MCI being a risk factor for delirium is less established and (2) to achieve adequate allocation of delirious cases in both strata. Last, we report the sensitivity of altered level of consciousness (LOC), which included lethargy, stupor, coma, and hypervigilance as a single screening item for delirium in the overall sample and by cognitive status. Analyses were conducted using commercially available software (SAS version 9.3; SAS Institute, Inc., Cary, NC).

RESULTS

Characteristics of the patients are shown in Table 1. Subjects had a mean age of 84 years, 62% were female, and 28% had a baseline dementia. Forty‐two (21%) had delirium based on the clinical reference standard. Twenty (10%) had less than a high school education and 100 (49%) had at least a college education.

Sample Characteristics (N=201)
CharacteristicN (%)
  • NOTE: Abbreviations: ADL, activities of daily living; IADL, instrumental activities of daily living; MCI, mild cognitive impairment; MoCA, Montreal Cognitive Assessment; SD, standard deviation.

Age, y, mean (SD)84 (5.4)
Sex, n (%) female125 (62)
White, n (%)177 (88)
Education, n (%) 
Less than high school20 (10)
High school graduate75 (38)
College plus100 (49)
Vision interfered with interview, n (%)5 (2)
Hearing interfered with interview, n (%)18 (9)
English second language n (%)10 (5)
Charlson, mean (SD)3 (2.3)
ADL, n (% impaired)110 (55)
IADL, n (% impaired)163 (81)
MCI, n (%)50 (25)
Dementia, n (%)56 (28)
Delirium, n (%)42 (21)
MoCA, mean (SD)19 (6.6)
MoCA, median (range)20 (030)

Single Item Screens

Table 2 reports the results of single‐item screens for delirium with sensitivity, the ability to correctly identify delirium when it is present by the reference standard, and specificity, the ability to correctly identify patients without delirium when it is not present by reference standard and 95% CIs. Items are listed in descending order of sensitivity; in the case of ties, the item with the higher specificity is listed first. The screening items with the highest sensitivity for delirium are Months of the year backwards, and Four digits backwards, both with a sensitivity of 83% (95% CI: 69%‐93%). Of these 2 items, Months of the year backwards had a much better specificity of 69% (95% CI: 61%‐76%), whereas Four digits backwards had a specificity of 52% (95% CI: 44%‐60%). The item What is the day of the week? had lower sensitivity at 71% (95% CI: 55%‐84%), but excellent specificity at 92% (95% CI: 87%‐96%).

Top Ten Single‐Item Screen for Delirium (N=201)
Screen ItemScreen Positive (%)cSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Number of patients with delirium=42. Abbreviations: CI, confidence interval; LR, likelihood ratio.

  • There were 20 different items and 190 possible item pairs considered.

  • Top 10 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

Months of the year backwards420.83 (0.69‐0.93)0.69 (0.61‐0.76)2.70.24
Four digits backwards560.83 (0.69‐0.93)0.52 (0.44‐0.60)1.720.32
What is the day of the week?210.71 (0.55‐0.84)0.92 (0.87‐0.96)9.460.31
What is the year?160.55 (0.39‐0.70)0.94 (0.9‐0.97)9.670.48
Have you felt confused during the past day?140.50 (0.34‐0.66)0.95 (0.9‐0.98)9.940.53
Days of the week backwards150.50 (0.34‐0.66)0.94 (0.89‐0.97)7.950.53
During the past day, did you see things that were not really there?110.45 (0.3‐0.61)0.97 (0.94‐0.99)17.980.56
Three digits backwards150.45 (0.3‐0.61)0.92 (0.87‐0.96)5.990.59
What type of place is this?90.38 (0.24‐0.54)0.99 (0.96‐1)30.290.63
During the past day, did you think you were not in the hospital?100.38 (0.24‐0.54)0.97 (0.94‐0.99)15.140.64

We then examined performance of single‐item screeners in patients with and without dementia (Table 3). In persons with dementia, the best single item was also Months of the year backwards, with a sensitivity of 89% (95% CI: 72%‐98%) and a specificity of 61% (95% CI: 41%‐78%). In persons with normal baseline cognition or MCI, the best performing single item was Four digits backwards, with sensitivity of 79% (95% CI: 49%‐95%) and specificity of 51% (95% CI: 42%‐60%). Months of the year backwards also performed well, with sensitivity of 71% (95% CI: 42%‐92%) and specificity of 71% (95% CI: 62%‐79%).

Top Three Single‐Item Screen for Delirium Stratified by Baseline Cognition
Test ItemNormal/MCI Patients (n=145)Dementia Patients (n=56)
Screen Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLRScreen Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Participants with learning problems (1) grouped with dementia and MCI participants (44) grouped with normal. Number of patients with delirium=28. Abbreviations: CI, confidence interval; LR, likelihood ratio; MCI, mild cognitive impairment.

  • Top 3 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

Months backwards330.71 (0.42‐0.92)0.71 (0.62‐0.79)2.460.4640.89 (0.72‐0.98)0.61 (0.41‐0.78)2.270.18
Four digits backwards520.79 (0.49‐0.95)0.51 (0.42‐0.60)1.610.42660.86 (0.67‐0.96)0.54 (0.34‐0.72)1.850.27
What is the day of the week?100.64 (0.35‐0.87)0.96 (0.91‐0.99)16.840.37500.75 (0.55‐0.89)0.75 (0.55‐0.89)30.33

Two‐Item Screens

Table 4 reports the results of 2‐item screens for delirium with sensitivity, specificity, and 95% CIs. Item pairs are listed in descending order of sensitivity following the same convention as in Table 2. The 2‐item screen with the highest sensitivity for delirium is the combination of What is the day of the week? and Months of the year backwards, with a sensitivity of 93% (95% CI: 81%‐99%) and specificity of 64% (95% CI: 56%‐70%). This screen had a positive and negative likelihood ratio (LR) of 2.59 and 0.11, respectively. The combination of What is the day of the week? and Four digits backwards had the same sensitivity 93% (95% CI: 81%‐99%), but lower specificity of 48% (95% CI: 40%‐56%). The combination of What type of place is this? (hospital) and Four digits backwards had a sensitivity of 90% (95% CI: 77%‐97%) and specificity of 51% (95% CI: 43%‐50%).

Top Ten Two‐Item Screen for Delirium (N=201)
Screen Item 1Screen Item 2Screen Positive (%)cSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Number of patients with delirium=42. Abbreviations: CI, confidence interval; LR, likelihood ratio.

  • There were 20 different items and 190 possible item pairs considered.

  • Top 10 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

What is the day of the week?Months backwards480.93 (0.81‐0.99)0.64 (0.56‐0.70)2.590.11
What is the day of the week?Four digits backwards600.93 (0.81‐0.99)0.48 (0.4‐0.56)1.80.15
Four digits backwardsMonths backwards650.93 (0.81‐0.99)0.42 (0.34‐0.50)1.60.17
What type of place is this?Four digits backwards580.90 (0.77‐0.97)0.51 (0.43‐0.50)1.840.19
What is the year?Four digits backwards590.9 (0.77‐0.97)0.5 (0.42‐0.5)1.800.19
What is the day of the week?Three digits backwards300.88 (0.74‐0.96)0.86 (0.79‐0.90)6.090.14
What is the year?Months backwards440.88 (0.74‐0.96)0.68 (0.6‐0.75)2.750.18
What type of place is this?Months backwards430.86 (0.71‐0.95)0.69 (0.61‐0.70)2.730.21
During the past day, did you think you were not in the hospital?Months backwards430.86 (0.71‐0.95)0.69 (0.61‐0.70)2.730.21
Days of the week backwardsMonths backwards430.86 (0.71‐0.95)0.68 (0.6‐0.75)2.670.21

When subjects were stratified by baseline cognition, the best 2‐item screens for normal and MCI patients was What is the day of the week? and Four digits backwards, with 93% sensitivity (95% CI: 66%‐100%) and 50% specificity (95% CI: 42%‐59%). The best pair of items for patients with dementia (Table 5) was the same as the overall sample, What is the day of the week? and Months of the year backwards, but its performance differed with a higher sensitivity of 96% (95% CI: 82%‐100%) and lower specificity of 43% (95% CI: 24%‐63%). This same pair of items had 86% sensitivity (95% CI: 57%‐98%) and 69% (95% CI: 60%‐77%) specificity for persons with either normal cognition or MCI.

Top Three Two‐Item Screen for Normal/MCI and Persons With Dementia
Test Item 1Test Item 2Normal/MCI Patients (n=145)Dementia Patients (n=56) 
Item Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLRItem Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Participants with learning problems (1) grouped with dementia and MCI participants (44) grouped with normal. Number of patients with delirium=28. Abbreviations: CI, confidence interval; LR, likelihood ratio; MCI, mild cognitive impairment.

  • Top 3 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

What is the day of the week?Months backwards360.86 (0.57‐0.98)0.69 (0.60‐0.77)2.740.21770.96 (0.82‐1)0.43 (0.24‐0.63)1.690.08
What is the day of the week?Four digits backwards540.93 (0.66‐1)0.5 (0.42‐0.59)1.870.14770.93 (0.76‐0.99)0.39 (0.22‐0.59)1.530.18
Four digits backwardsMonths backwards610.93 (0.66‐1)0.43 (0.34‐0.52)1.620.17770.93 (0.76‐0.99)0.39 (0.22‐0.59)1.530.18

Altered Level of Consciousness as a Screener for Delirium

Altered level of consciousness (ALOC) was uncommon in our sample, with an overall prevalence of 10/201 (4.9%). When examined as a screening item for delirium, ALOC had very poor sensitivity of 19% (95% CI: 9%‐34%) but had excellent specificity 99% (95% CI: 96%‐100%). Altered LOC also demonstrated poor screening performance when stratified by cognitive status, with a sensitivity of 14% in the normal and MCI group (95% CI: 2%‐43%) and sensitivity of 21% (95% CI: 8%‐41%) in persons with dementia.

Positive and Negative Predictive Values

Although we focused on sensitivity and specificity in evaluating 1‐ and 2‐item screeners, we also examined positive and negative predictive values. These values will vary depending on the overall prevalence of delirium, which was 21% in this dataset. The best 1‐item screener, Months of the year backwards, had a positive predictive value of 31% and negative predictive value of 94%. The best 2‐item screener, Months of the year backwards with What is the day of the week?, had a positive predictive value of 41% and negative predictive value of 97% (see Supporting Tables 2 and 3 in the online version of this article) LRs for the items are in Tables 2 through 5.

DISCUSSION

Identifying simple, efficient, bedside case‐identification methods for delirium is an essential step toward improving recognition of this highly morbid syndrome in hospitalized older adults. In this study, we identified a single cognitive item, Months of the year backwards, that identified 83% of delirium cases when compared with a reference standard diagnosis. Furthermore, we identified 2 items, Months of the year backwards and What is the day of the week? which when used in combination identified 93% of delirium cases. The same 1 and 2 items also worked well in patients with dementia, in whom delirium is often missed. Although these items require further clinical validation, the development of an ultrabrief 2‐item test that identifies over 90% of delirium cases and can be completed in less than 1 minute (recently, we administered the best 2‐item screener to 20 consecutive general medicine patients over age 70 years, and it was completed in a median of 36.5 seconds), holds great potential for simplifying bedside delirium screening and improving the care of hospitalized older adults.

Our current findings both confirm and extend the emerging literature on best screening items for delirium. Sands and colleagues (2010)[26] tested a single test for delirium, Do you think (name of patient) has been more confused lately? in 21 subjects and achieved a sensitivity of 80%. Han and colleagues developed a screening tool in emergency‐department patients using the LOC question from the Richmond Agitation‐Sedation Scale and spelling the word lunch backwards, and achieved 98% sensitivity, but in a younger emergency department population with a low prevalence of dementia.[27] O'Regan et al. recently also found Months of the year backwards to be the best single‐screening item for delirium in a large sample, but only tested a 1‐item screen.[28] Our study extends these studies in several important ways by: (1) employing a rigorous clinical reference standard diagnosis of delirium, (2) having a large sample with a high prevalence of patients with dementia, (3) use of a general medical population, and (4) examining the best 2‐item screens in addition to the best single item.

Systematic intervention programs[29, 30, 31] that focus on improved delirium evaluation and management have the potential to improve patient outcomes and reduce costs. However, targeting these programs to patients with delirium has proven difficult, as only 12% to 35% of delirium cases are recognized in routine clinical practice.[11, 12, 13, 14, 15] The 1‐ and 2‐item screeners we identified could play an important role in future delirium identification. The 3D‐CAM combines high sensitivity (95%) with high specificity (94%)[16] and therefore would be an excellent choice as the second step after a positive screen. The feasibility, effectiveness, and cost of administering these screeners, followed by a brief diagnostic tool such as the 3D‐CAM, should be evaluated in future work.

Our study has noteworthy strengths, including the use of a large purposefully challenging clinical sample with advanced age that included a substantial proportion with dementia, a detailed assessment, and the testing of very brief and practical tools for bedside delirium screening.[25] This study also has several important limitations. Most importantly, we presented secondary analysis of individual items and pairs of items drawn from the 3D CAM assessment; therefore, the 2‐item bedside screen requires prospective clinical validation. The reference standard was based on the DSM‐IV, because this study was conducted prior to the release of DSM‐V. In addition, the ordering of the reference standard and 3D‐CAM assessments was not randomized due to feasibility constraints. In addition, this study was cross‐sectional, involved only a single hospital, and enrolled only older medical patients during the day shift. Our sample was older (aged 75 years and older), and a younger sample may have had a different prevalence of delirium, which could affect the positive predictive value of our ultrabrief screen. We plan to test this in a sample of patients aged 70 years and older in future studies. Finally, it should be noted that these best 1‐item and 2‐item screeners miss 17% and 7% of delirium cases, respectively. In cases where this is unacceptably high, alternative approaches might be necessary.

It is important to remember that these 1‐ and 2‐item screeners are not diagnostic tools and therefore should not be used in isolation. Optimally, they will be followed by a more specific evaluation, such as the 3D‐CAM, as part of a systematic delirium identification process. For instance, in our sample (with a delirium rate of 21%), the best 2‐item screener had a positive predictive value of 41%, meaning that positive screens are more likely to be false positives than true positives (see Supporting Tables 2 and 3 in the online version of this article).[32] Nevertheless, by reducing the total number of patients who require diagnostic instrument administration, use of these ultrabrief screeners can improve efficiency and result in a net benefit to delirium case‐identification efforts.[32]

Time has been demonstrated to be a barrier to delirium identification in previous studies, but there are likely others. These may include, for instance, staff nihilism about screening making a difference, ambiguous responsibility for delirium screening and management, unsupportive system leadership, and absent payment for these activities.[31] Moreover, it is possible that the 2‐step process we propose may create an incentive for staff to avoid positive screens as they see it creating more work for themselves. We plan to identify and address such barriers in our future work.

In conclusion, we identified a single screening item for delirium, Months of the year backwards, with 83% sensitivity, and a pair of items, Months of the year backwards and What is the day of the week?, with 93% sensitivity relative to a rigorous reference standard diagnosis. These ultrabrief screening items work well in patients with and without dementia, and should require very little training of staff. Future studies should further validate these tools, and determine their translatability and scalability into programs for systematic, widespread delirium detection. Developing efficient and accurate case identification strategies is a necessary prerequisite to appropriately target delirium management protocols, enabling healthcare systems to effectively address this costly and deadly condition.

Disclosures

Author contributionsD.M.F. conceived the study idea, participated in its design and coordination, and drafted the initial manuscript. S.K.I. contributed to the study design and conceptualization, supervision, funding, preliminary analysis, and interpretation of the data, and critical revision of the manuscript. J.G. conducted the analysis for the study and critically revised the manuscript. L.N. supervised the analysis for the study and critically revised the manuscript. R.J. contributed to the study design and critical revision of the manuscript. J.S.S. critically revised the manuscript. E.R.M. obtained funding for the study, supervised all data collection, assisted in drafting and critically revising the manuscript, and contributed to the conceptualization, design, and supervision of the study. All authors have seen and agree with the contents of the manuscript.

This work was supported by the National Institute of Aging grant number R01AG030618 and K24AG035075 to Dr. Marcantonio. Dr. Inouye's time was supported in part by grants P01AG031720, R01AG044518, and K07AG041835 from the National Institute on Aging. Dr. Inouye holds the Milton and Shirley F. Levy Family Chair (Hebrew Senior Life/Harvard Medical School). Dr. Fick is partially supported from National Institute of Nursing Research grant number R01 NR011042. Dr. Saczynski was supported in part by funding from the National Institute on Aging (K01AG33643) and from the National Heart Lung and Blood Institute (U01HL105268). The funding agencies had no role and the authors retained full autonomy in the preparation of this article. All authors and coauthors have no financial or nonfinancial conflicts of interest to disclose regarding this article.

This article was presented at the Presidential Poster Session at the American Geriatrics Society 2014 Annual Meeting in Orlando, Florida, May 14, 2014.

Delirium (acute confusion) is common in older adults and leads to poor outcomes, such as death, clinician and caregiver burden, and prolonged cognitive and functional decline.[1, 2, 3, 4] Delirium is extremely costly, with estimates ranging from $143 to $152 billion annually (2005 US$).[5, 6] Early detection and management may improve the poor outcomes and reduce costs attributable to delirium,[3, 7] yet delirium identification in clinical practice has been challenging, particularly when translating research tools to the bedside.[8, 9, 10]As a result, only 12% to 35% of delirium cases are detected in routine care, with hypoactive delirium and delirium superimposed on dementia most likely to be missed.[11, 12, 13, 14, 15]

To address these issues, we recently developed and published the three‐dimensional Confusion Assessment Method (3D‐CAM), the 3‐minute diagnostic assessment for CAM‐defined delirium.[16] The 3D‐CAM is a structured assessment tool that includes mental status testing, patient symptom probes, and guided interviewer observations for signs of delirium. 3D‐CAM items were selected through a rigorous process to determine the most informative items for the 4 CAM diagnostic features.[17] The 3D‐CAM can be completed in 3 minutes, and has 95% sensitivity and 94% specificity relative to a reference standard.[16]

Despite the capabilities of the 3D‐CAM, there are situations when even 3 minutes is too long to devote to delirium identification. Moreover, a 2‐step approach in which a sensitive ultrabrief screen is administered, followed by the 3D‐CAM in positives, may be the most efficient approach for large‐scale delirium case identification. The aim of the current study was to use the 3D‐CAM database to identify the most sensitive single item and pair of items in the diagnosis of delirium, using the reference standard in the diagnostic accuracy analysis. We hypothesized that we could identify a single item with greater than 80% sensitivity and a pair of items with greater than 90% sensitivity for detection of delirium.

METHODS

Study Sample and Design

We analyzed data from the 3D‐CAM validation study,[16] which prospectively enrolled participants from a large urban teaching hospital in Boston, Massachusetts, using a consecutive enrollment sampling strategy. Inclusion criteria were: (1) 75 years old, (2) admitted to general or geriatric medicine services, (3) able to communicate in English, (4) without terminal conditions, (5) expected hospital stay of 2 days, (6) not a previous study participant. Experienced clinicians screened patients for eligibility. If the patient lacked capacity to provide consent, the designated surrogate decision maker was contacted. The study was approved by the institutional review board.

Reference Standard Delirium Diagnosis

The reference standard delirium diagnosis was based on an extensive (45 minutes) face‐to‐face patient interview by experienced clinician assessors (neuropsychologists or advanced practice nurses), medical record review, and input from the nurse and family members. This comprehensive assessment included: (1) reason for hospital admission, hospital course, and presence of cognitive concerns, (2) family, social, and functional history, (3) Montreal Cognitive Assessment,[18] (4) Geriatric Depression Scale,[19] (5) medical record review including scoring of comorbidities using the Charlson index,[20] determination of functional status using the basic and Instrumental Activities of Daily Living,[21, 22] psychoactive medications administered, and (6) a family member interview to assess the patient's baseline cognitive status that included the Eight‐Item Interview to Differentiate Aging and Dementia,[23] to assess the presence of dementia. Using all of these data, an expert panel, including the clinical assessor, the study principal investigator (E.R.M.), a geriatrician, and an experienced neuropsychologist, adjudicated the final delirium diagnoses using Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM‐IV) criteria. The panel also adjudicated for the presence or absence of dementia and mild cognitive impairment based on National Institute on Aging‐Alzheimer's Association (NIA‐AA) criteria.[24] This approach has been used in other delirium studies.[25]

3D‐CAM Assessments

After the reference standard assessment, the 3D‐CAM was administered by trained research assistants (RAs) who were blinded to the results of the reference standard. To reduce the likelihood of fluctuations or temporal changes, all assessments were completed between 11:00 am and 2:00 pm and for each participant, within a 2‐hour time period (for example, 11:23 am to 1:23 pm).

Statistical Analyses to Determine the Best Single‐ and Two‐Item Screeners

To determine the best single 3D‐CAM item to identify delirium, the responses of the 20 individual items in the 3D‐CAM (see Supporting Table 1 in the online version of this article) were compared to the reference standard to determine their sensitivity and specificity. Similarly, an algorithm was used to generate all unique 2‐item combinations of the 20 items (190 unique pairs), which were compared to the reference. An error, no response, or an answer of I do not know by the patient was considered a positive screen for delirium. The 2‐item screeners were considered positive if 1 or both of the items were positive. Sensitivity and specificity were calculated along with 95% confidence intervals (CIs).

Subset analyses were performed to determine sensitivity and specificity of individual items and pairs of items stratified by the patient's baseline cognitive status. Two strata were createdpatients with dementia (N=56), and patients with normal baseline cognitive status or mild cognitive impairment (MCI) (N=145). We chose to group MCI with normal for 2 reasons: (1) dementia is a well‐established and strong risk factor for delirium, whereas the evidence for MCI being a risk factor for delirium is less established and (2) to achieve adequate allocation of delirious cases in both strata. Last, we report the sensitivity of altered level of consciousness (LOC), which included lethargy, stupor, coma, and hypervigilance as a single screening item for delirium in the overall sample and by cognitive status. Analyses were conducted using commercially available software (SAS version 9.3; SAS Institute, Inc., Cary, NC).

RESULTS

Characteristics of the patients are shown in Table 1. Subjects had a mean age of 84 years, 62% were female, and 28% had a baseline dementia. Forty‐two (21%) had delirium based on the clinical reference standard. Twenty (10%) had less than a high school education and 100 (49%) had at least a college education.

Sample Characteristics (N=201)
CharacteristicN (%)
  • NOTE: Abbreviations: ADL, activities of daily living; IADL, instrumental activities of daily living; MCI, mild cognitive impairment; MoCA, Montreal Cognitive Assessment; SD, standard deviation.

Age, y, mean (SD)84 (5.4)
Sex, n (%) female125 (62)
White, n (%)177 (88)
Education, n (%) 
Less than high school20 (10)
High school graduate75 (38)
College plus100 (49)
Vision interfered with interview, n (%)5 (2)
Hearing interfered with interview, n (%)18 (9)
English second language n (%)10 (5)
Charlson, mean (SD)3 (2.3)
ADL, n (% impaired)110 (55)
IADL, n (% impaired)163 (81)
MCI, n (%)50 (25)
Dementia, n (%)56 (28)
Delirium, n (%)42 (21)
MoCA, mean (SD)19 (6.6)
MoCA, median (range)20 (030)

Single Item Screens

Table 2 reports the results of single‐item screens for delirium with sensitivity, the ability to correctly identify delirium when it is present by the reference standard, and specificity, the ability to correctly identify patients without delirium when it is not present by reference standard and 95% CIs. Items are listed in descending order of sensitivity; in the case of ties, the item with the higher specificity is listed first. The screening items with the highest sensitivity for delirium are Months of the year backwards, and Four digits backwards, both with a sensitivity of 83% (95% CI: 69%‐93%). Of these 2 items, Months of the year backwards had a much better specificity of 69% (95% CI: 61%‐76%), whereas Four digits backwards had a specificity of 52% (95% CI: 44%‐60%). The item What is the day of the week? had lower sensitivity at 71% (95% CI: 55%‐84%), but excellent specificity at 92% (95% CI: 87%‐96%).

Top Ten Single‐Item Screen for Delirium (N=201)
Screen ItemScreen Positive (%)cSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Number of patients with delirium=42. Abbreviations: CI, confidence interval; LR, likelihood ratio.

  • There were 20 different items and 190 possible item pairs considered.

  • Top 10 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

Months of the year backwards420.83 (0.69‐0.93)0.69 (0.61‐0.76)2.70.24
Four digits backwards560.83 (0.69‐0.93)0.52 (0.44‐0.60)1.720.32
What is the day of the week?210.71 (0.55‐0.84)0.92 (0.87‐0.96)9.460.31
What is the year?160.55 (0.39‐0.70)0.94 (0.9‐0.97)9.670.48
Have you felt confused during the past day?140.50 (0.34‐0.66)0.95 (0.9‐0.98)9.940.53
Days of the week backwards150.50 (0.34‐0.66)0.94 (0.89‐0.97)7.950.53
During the past day, did you see things that were not really there?110.45 (0.3‐0.61)0.97 (0.94‐0.99)17.980.56
Three digits backwards150.45 (0.3‐0.61)0.92 (0.87‐0.96)5.990.59
What type of place is this?90.38 (0.24‐0.54)0.99 (0.96‐1)30.290.63
During the past day, did you think you were not in the hospital?100.38 (0.24‐0.54)0.97 (0.94‐0.99)15.140.64

We then examined performance of single‐item screeners in patients with and without dementia (Table 3). In persons with dementia, the best single item was also Months of the year backwards, with a sensitivity of 89% (95% CI: 72%‐98%) and a specificity of 61% (95% CI: 41%‐78%). In persons with normal baseline cognition or MCI, the best performing single item was Four digits backwards, with sensitivity of 79% (95% CI: 49%‐95%) and specificity of 51% (95% CI: 42%‐60%). Months of the year backwards also performed well, with sensitivity of 71% (95% CI: 42%‐92%) and specificity of 71% (95% CI: 62%‐79%).

Top Three Single‐Item Screen for Delirium Stratified by Baseline Cognition
Test ItemNormal/MCI Patients (n=145)Dementia Patients (n=56)
Screen Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLRScreen Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Participants with learning problems (1) grouped with dementia and MCI participants (44) grouped with normal. Number of patients with delirium=28. Abbreviations: CI, confidence interval; LR, likelihood ratio; MCI, mild cognitive impairment.

  • Top 3 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

Months backwards330.71 (0.42‐0.92)0.71 (0.62‐0.79)2.460.4640.89 (0.72‐0.98)0.61 (0.41‐0.78)2.270.18
Four digits backwards520.79 (0.49‐0.95)0.51 (0.42‐0.60)1.610.42660.86 (0.67‐0.96)0.54 (0.34‐0.72)1.850.27
What is the day of the week?100.64 (0.35‐0.87)0.96 (0.91‐0.99)16.840.37500.75 (0.55‐0.89)0.75 (0.55‐0.89)30.33

Two‐Item Screens

Table 4 reports the results of 2‐item screens for delirium with sensitivity, specificity, and 95% CIs. Item pairs are listed in descending order of sensitivity following the same convention as in Table 2. The 2‐item screen with the highest sensitivity for delirium is the combination of What is the day of the week? and Months of the year backwards, with a sensitivity of 93% (95% CI: 81%‐99%) and specificity of 64% (95% CI: 56%‐70%). This screen had a positive and negative likelihood ratio (LR) of 2.59 and 0.11, respectively. The combination of What is the day of the week? and Four digits backwards had the same sensitivity 93% (95% CI: 81%‐99%), but lower specificity of 48% (95% CI: 40%‐56%). The combination of What type of place is this? (hospital) and Four digits backwards had a sensitivity of 90% (95% CI: 77%‐97%) and specificity of 51% (95% CI: 43%‐50%).

Top Ten Two‐Item Screen for Delirium (N=201)
Screen Item 1Screen Item 2Screen Positive (%)cSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Number of patients with delirium=42. Abbreviations: CI, confidence interval; LR, likelihood ratio.

  • There were 20 different items and 190 possible item pairs considered.

  • Top 10 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

What is the day of the week?Months backwards480.93 (0.81‐0.99)0.64 (0.56‐0.70)2.590.11
What is the day of the week?Four digits backwards600.93 (0.81‐0.99)0.48 (0.4‐0.56)1.80.15
Four digits backwardsMonths backwards650.93 (0.81‐0.99)0.42 (0.34‐0.50)1.60.17
What type of place is this?Four digits backwards580.90 (0.77‐0.97)0.51 (0.43‐0.50)1.840.19
What is the year?Four digits backwards590.9 (0.77‐0.97)0.5 (0.42‐0.5)1.800.19
What is the day of the week?Three digits backwards300.88 (0.74‐0.96)0.86 (0.79‐0.90)6.090.14
What is the year?Months backwards440.88 (0.74‐0.96)0.68 (0.6‐0.75)2.750.18
What type of place is this?Months backwards430.86 (0.71‐0.95)0.69 (0.61‐0.70)2.730.21
During the past day, did you think you were not in the hospital?Months backwards430.86 (0.71‐0.95)0.69 (0.61‐0.70)2.730.21
Days of the week backwardsMonths backwards430.86 (0.71‐0.95)0.68 (0.6‐0.75)2.670.21

When subjects were stratified by baseline cognition, the best 2‐item screens for normal and MCI patients was What is the day of the week? and Four digits backwards, with 93% sensitivity (95% CI: 66%‐100%) and 50% specificity (95% CI: 42%‐59%). The best pair of items for patients with dementia (Table 5) was the same as the overall sample, What is the day of the week? and Months of the year backwards, but its performance differed with a higher sensitivity of 96% (95% CI: 82%‐100%) and lower specificity of 43% (95% CI: 24%‐63%). This same pair of items had 86% sensitivity (95% CI: 57%‐98%) and 69% (95% CI: 60%‐77%) specificity for persons with either normal cognition or MCI.

Top Three Two‐Item Screen for Normal/MCI and Persons With Dementia
Test Item 1Test Item 2Normal/MCI Patients (n=145)Dementia Patients (n=56) 
Item Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLRItem Positive (%)bSensitivity (95% CI)Specificity (95% CI)LRLR
  • NOTE: Participants with learning problems (1) grouped with dementia and MCI participants (44) grouped with normal. Number of patients with delirium=28. Abbreviations: CI, confidence interval; LR, likelihood ratio; MCI, mild cognitive impairment.

  • Top 3 items: our primary criterion for determining this was sensitivity, with a secondary criterion of specificity in the case of ties. Items are listed in descending order on this basis.

  • Screen positive: error, do not know, or no response.

What is the day of the week?Months backwards360.86 (0.57‐0.98)0.69 (0.60‐0.77)2.740.21770.96 (0.82‐1)0.43 (0.24‐0.63)1.690.08
What is the day of the week?Four digits backwards540.93 (0.66‐1)0.5 (0.42‐0.59)1.870.14770.93 (0.76‐0.99)0.39 (0.22‐0.59)1.530.18
Four digits backwardsMonths backwards610.93 (0.66‐1)0.43 (0.34‐0.52)1.620.17770.93 (0.76‐0.99)0.39 (0.22‐0.59)1.530.18

Altered Level of Consciousness as a Screener for Delirium

Altered level of consciousness (ALOC) was uncommon in our sample, with an overall prevalence of 10/201 (4.9%). When examined as a screening item for delirium, ALOC had very poor sensitivity of 19% (95% CI: 9%‐34%) but had excellent specificity 99% (95% CI: 96%‐100%). Altered LOC also demonstrated poor screening performance when stratified by cognitive status, with a sensitivity of 14% in the normal and MCI group (95% CI: 2%‐43%) and sensitivity of 21% (95% CI: 8%‐41%) in persons with dementia.

Positive and Negative Predictive Values

Although we focused on sensitivity and specificity in evaluating 1‐ and 2‐item screeners, we also examined positive and negative predictive values. These values will vary depending on the overall prevalence of delirium, which was 21% in this dataset. The best 1‐item screener, Months of the year backwards, had a positive predictive value of 31% and negative predictive value of 94%. The best 2‐item screener, Months of the year backwards with What is the day of the week?, had a positive predictive value of 41% and negative predictive value of 97% (see Supporting Tables 2 and 3 in the online version of this article) LRs for the items are in Tables 2 through 5.

DISCUSSION

Identifying simple, efficient, bedside case‐identification methods for delirium is an essential step toward improving recognition of this highly morbid syndrome in hospitalized older adults. In this study, we identified a single cognitive item, Months of the year backwards, that identified 83% of delirium cases when compared with a reference standard diagnosis. Furthermore, we identified 2 items, Months of the year backwards and What is the day of the week? which when used in combination identified 93% of delirium cases. The same 1 and 2 items also worked well in patients with dementia, in whom delirium is often missed. Although these items require further clinical validation, the development of an ultrabrief 2‐item test that identifies over 90% of delirium cases and can be completed in less than 1 minute (recently, we administered the best 2‐item screener to 20 consecutive general medicine patients over age 70 years, and it was completed in a median of 36.5 seconds), holds great potential for simplifying bedside delirium screening and improving the care of hospitalized older adults.

Our current findings both confirm and extend the emerging literature on best screening items for delirium. Sands and colleagues (2010)[26] tested a single test for delirium, Do you think (name of patient) has been more confused lately? in 21 subjects and achieved a sensitivity of 80%. Han and colleagues developed a screening tool in emergency‐department patients using the LOC question from the Richmond Agitation‐Sedation Scale and spelling the word lunch backwards, and achieved 98% sensitivity, but in a younger emergency department population with a low prevalence of dementia.[27] O'Regan et al. recently also found Months of the year backwards to be the best single‐screening item for delirium in a large sample, but only tested a 1‐item screen.[28] Our study extends these studies in several important ways by: (1) employing a rigorous clinical reference standard diagnosis of delirium, (2) having a large sample with a high prevalence of patients with dementia, (3) use of a general medical population, and (4) examining the best 2‐item screens in addition to the best single item.

Systematic intervention programs[29, 30, 31] that focus on improved delirium evaluation and management have the potential to improve patient outcomes and reduce costs. However, targeting these programs to patients with delirium has proven difficult, as only 12% to 35% of delirium cases are recognized in routine clinical practice.[11, 12, 13, 14, 15] The 1‐ and 2‐item screeners we identified could play an important role in future delirium identification. The 3D‐CAM combines high sensitivity (95%) with high specificity (94%)[16] and therefore would be an excellent choice as the second step after a positive screen. The feasibility, effectiveness, and cost of administering these screeners, followed by a brief diagnostic tool such as the 3D‐CAM, should be evaluated in future work.

Our study has noteworthy strengths, including the use of a large purposefully challenging clinical sample with advanced age that included a substantial proportion with dementia, a detailed assessment, and the testing of very brief and practical tools for bedside delirium screening.[25] This study also has several important limitations. Most importantly, we presented secondary analysis of individual items and pairs of items drawn from the 3D CAM assessment; therefore, the 2‐item bedside screen requires prospective clinical validation. The reference standard was based on the DSM‐IV, because this study was conducted prior to the release of DSM‐V. In addition, the ordering of the reference standard and 3D‐CAM assessments was not randomized due to feasibility constraints. In addition, this study was cross‐sectional, involved only a single hospital, and enrolled only older medical patients during the day shift. Our sample was older (aged 75 years and older), and a younger sample may have had a different prevalence of delirium, which could affect the positive predictive value of our ultrabrief screen. We plan to test this in a sample of patients aged 70 years and older in future studies. Finally, it should be noted that these best 1‐item and 2‐item screeners miss 17% and 7% of delirium cases, respectively. In cases where this is unacceptably high, alternative approaches might be necessary.

It is important to remember that these 1‐ and 2‐item screeners are not diagnostic tools and therefore should not be used in isolation. Optimally, they will be followed by a more specific evaluation, such as the 3D‐CAM, as part of a systematic delirium identification process. For instance, in our sample (with a delirium rate of 21%), the best 2‐item screener had a positive predictive value of 41%, meaning that positive screens are more likely to be false positives than true positives (see Supporting Tables 2 and 3 in the online version of this article).[32] Nevertheless, by reducing the total number of patients who require diagnostic instrument administration, use of these ultrabrief screeners can improve efficiency and result in a net benefit to delirium case‐identification efforts.[32]

Time has been demonstrated to be a barrier to delirium identification in previous studies, but there are likely others. These may include, for instance, staff nihilism about screening making a difference, ambiguous responsibility for delirium screening and management, unsupportive system leadership, and absent payment for these activities.[31] Moreover, it is possible that the 2‐step process we propose may create an incentive for staff to avoid positive screens as they see it creating more work for themselves. We plan to identify and address such barriers in our future work.

In conclusion, we identified a single screening item for delirium, Months of the year backwards, with 83% sensitivity, and a pair of items, Months of the year backwards and What is the day of the week?, with 93% sensitivity relative to a rigorous reference standard diagnosis. These ultrabrief screening items work well in patients with and without dementia, and should require very little training of staff. Future studies should further validate these tools, and determine their translatability and scalability into programs for systematic, widespread delirium detection. Developing efficient and accurate case identification strategies is a necessary prerequisite to appropriately target delirium management protocols, enabling healthcare systems to effectively address this costly and deadly condition.

Disclosures

Author contributionsD.M.F. conceived the study idea, participated in its design and coordination, and drafted the initial manuscript. S.K.I. contributed to the study design and conceptualization, supervision, funding, preliminary analysis, and interpretation of the data, and critical revision of the manuscript. J.G. conducted the analysis for the study and critically revised the manuscript. L.N. supervised the analysis for the study and critically revised the manuscript. R.J. contributed to the study design and critical revision of the manuscript. J.S.S. critically revised the manuscript. E.R.M. obtained funding for the study, supervised all data collection, assisted in drafting and critically revising the manuscript, and contributed to the conceptualization, design, and supervision of the study. All authors have seen and agree with the contents of the manuscript.

This work was supported by the National Institute of Aging grant number R01AG030618 and K24AG035075 to Dr. Marcantonio. Dr. Inouye's time was supported in part by grants P01AG031720, R01AG044518, and K07AG041835 from the National Institute on Aging. Dr. Inouye holds the Milton and Shirley F. Levy Family Chair (Hebrew Senior Life/Harvard Medical School). Dr. Fick is partially supported from National Institute of Nursing Research grant number R01 NR011042. Dr. Saczynski was supported in part by funding from the National Institute on Aging (K01AG33643) and from the National Heart Lung and Blood Institute (U01HL105268). The funding agencies had no role and the authors retained full autonomy in the preparation of this article. All authors and coauthors have no financial or nonfinancial conflicts of interest to disclose regarding this article.

This article was presented at the Presidential Poster Session at the American Geriatrics Society 2014 Annual Meeting in Orlando, Florida, May 14, 2014.

References
  1. Witlox J, Eurelings LS, Jonghe JF, Kalisvaart KJ, Eikelenboom P, Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta‐analysis. JAMA. 2010;304(4):443451.
  2. Saczynski JS, Marcantonio ER, Quach L, et al. Cognitive trajectories after postoperative delirium. N Engl J Med. 2012;367(1):3039.
  3. Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014;383:911922.
  4. Fick DM, Steis MR, Waller JL, Inouye SK. Delirium superimposed on dementia is associated with prolonged length of stay and poor outcomes in hospitalized older adults. J Hosp Med. 2013;8(9):500505.
  5. Leslie DL, Marcantonio ER, Zhang Y, Leo‐Summers L, Inouye SK. One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):2732.
  6. Leslie DL, Inouye SK. The importance of delirium: Economic and societal costs. J Am Geriatr Soc. 2011;59(suppl 2):S241S243.
  7. Marcantonio ER. Delirium. Ann Intern Med. 2011;154(11):ITC6.
  8. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
  9. Rice KL, Bennett MJ, Clesi T, Linville L. Mixed‐methods approach to understanding nurses' clinical reasoning in recognizing delirium in hospitalized older adults. J Contin Educ Nurs. 2014;45:1–13.
  10. Yanamadala M, Wieland D, Heflin MT. Educational interventions to improve recognition of delirium: a systematic review. J Am Geriatr Soc. 2013;61(11):19831993.
  11. Steis MR, Fick DM. Delirium superimposed on dementia: accuracy of nurse documentation. J Gerontol Nurs. 2012;38(1):3242.
  12. Lemiengre J, Nelis T, Joosten E, et al. Detection of delirium by bedside nurses using the confusion assessment method. J Am Geriatr Soc. 2006;54:685689.
  13. Milisen K, Foreman MD, Wouters B, et al. Documentation of delirium in elderly patients with hip fracture. J Gerontol Nurs. 2002;28(11):2329.
  14. Kales HC, Kamholz BA, Visnic SG, Blow FC. Recorded delirium in a national sample of elderly inpatients: potential implications for recognition. J Geriatr Psychiatry Neurol. 2003;16(1):3238.
  15. Saczynski JS, Kosar CM, Xu G, et al. A tale of two methods: chart and interview methods for identifying delirium. J Am Geriatr Soc. 2014;62(3):518524.
  16. Marcantonio E, Ngo L, Jones R, et al. 3D‐CAM: Derivation and validation of a 3‐minute diagnostic interview for CAM‐defined delirium: a cross‐sectional diagnostic test study. Ann Intern Med. 2014;161(8):554561.
  17. Yang FM, Jones RN, Inouye SK, et al. Selecting optimal screening items for delirium: an application of item response theory. BMC Med Res Methodol. 2013;13:8.
  18. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695699.
  19. Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull. 1988;24(4):709711.
  20. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.
  21. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185:914919.
  22. Lawton MP, Brody EM. Assessment of older people: self‐maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179186.
  23. Galvin J, Roe C, Powlishta K, et al. The AD8: a brief informant interview to detect dementia. Neurology. 2005;65(4):559564.
  24. McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):263269.
  25. Neufeld KJ, Nelliot A, Inouye SK, et al. Delirium diagnosis methodology used in research: a survey‐based study. Am J Geriatr Psychiatry. 2014;22(12):15131521.
  26. Sands M, Dantoc B, Hartshorn A, Ryan C, Lujic S. Single Question in Delirium (SQiD): testing its efficacy against psychiatrist interview, the Confusion Assessment Method and the Memorial Delirium Assessment Scale. Palliat Med. 2010;24(6):561565.
  27. Han JH, Wilson A, Vasilevskis EE, et al. Diagnosing delirium in older emergency department patients: validity and reliability of the delirium triage screen and the brief confusion assessment method. Ann Emerg Med. 2013;62(5):457465.
  28. O'Regan NA, Ryan DJ, Boland E, et al. Attention! A good bedside test for delirium? J Neurol Neurosurg Psychiatry. 2014;85(10):11221131.
  29. Bergmann MA, Murphy KM, Kiely DK, Jones RN, Marcantonio ER. A model for management of delirious postacute care patients. J Am Geriatr Soc. 2005;53(10):18171825.
  30. Fick DM, Steis MR, Mion LC, Walls JL. Computerized decision support for delirium superimposed on dementia in older adults: a pilot study. J Gerontol Nurs. 2011;37(4):3947.
  31. Yevchak AM, Fick DM, McDowell J, et al. Barriers and facilitators to implementing delirium rounds in a clinical trial across three diverse hospital settings. Clin Nurs Res. 2014;23(2):201215.
  32. Meehl PE, Rosen A. Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores. Psychol Bull. 1955;52(3):194.
References
  1. Witlox J, Eurelings LS, Jonghe JF, Kalisvaart KJ, Eikelenboom P, Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta‐analysis. JAMA. 2010;304(4):443451.
  2. Saczynski JS, Marcantonio ER, Quach L, et al. Cognitive trajectories after postoperative delirium. N Engl J Med. 2012;367(1):3039.
  3. Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014;383:911922.
  4. Fick DM, Steis MR, Waller JL, Inouye SK. Delirium superimposed on dementia is associated with prolonged length of stay and poor outcomes in hospitalized older adults. J Hosp Med. 2013;8(9):500505.
  5. Leslie DL, Marcantonio ER, Zhang Y, Leo‐Summers L, Inouye SK. One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):2732.
  6. Leslie DL, Inouye SK. The importance of delirium: Economic and societal costs. J Am Geriatr Soc. 2011;59(suppl 2):S241S243.
  7. Marcantonio ER. Delirium. Ann Intern Med. 2011;154(11):ITC6.
  8. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
  9. Rice KL, Bennett MJ, Clesi T, Linville L. Mixed‐methods approach to understanding nurses' clinical reasoning in recognizing delirium in hospitalized older adults. J Contin Educ Nurs. 2014;45:1–13.
  10. Yanamadala M, Wieland D, Heflin MT. Educational interventions to improve recognition of delirium: a systematic review. J Am Geriatr Soc. 2013;61(11):19831993.
  11. Steis MR, Fick DM. Delirium superimposed on dementia: accuracy of nurse documentation. J Gerontol Nurs. 2012;38(1):3242.
  12. Lemiengre J, Nelis T, Joosten E, et al. Detection of delirium by bedside nurses using the confusion assessment method. J Am Geriatr Soc. 2006;54:685689.
  13. Milisen K, Foreman MD, Wouters B, et al. Documentation of delirium in elderly patients with hip fracture. J Gerontol Nurs. 2002;28(11):2329.
  14. Kales HC, Kamholz BA, Visnic SG, Blow FC. Recorded delirium in a national sample of elderly inpatients: potential implications for recognition. J Geriatr Psychiatry Neurol. 2003;16(1):3238.
  15. Saczynski JS, Kosar CM, Xu G, et al. A tale of two methods: chart and interview methods for identifying delirium. J Am Geriatr Soc. 2014;62(3):518524.
  16. Marcantonio E, Ngo L, Jones R, et al. 3D‐CAM: Derivation and validation of a 3‐minute diagnostic interview for CAM‐defined delirium: a cross‐sectional diagnostic test study. Ann Intern Med. 2014;161(8):554561.
  17. Yang FM, Jones RN, Inouye SK, et al. Selecting optimal screening items for delirium: an application of item response theory. BMC Med Res Methodol. 2013;13:8.
  18. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695699.
  19. Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull. 1988;24(4):709711.
  20. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.
  21. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185:914919.
  22. Lawton MP, Brody EM. Assessment of older people: self‐maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179186.
  23. Galvin J, Roe C, Powlishta K, et al. The AD8: a brief informant interview to detect dementia. Neurology. 2005;65(4):559564.
  24. McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):263269.
  25. Neufeld KJ, Nelliot A, Inouye SK, et al. Delirium diagnosis methodology used in research: a survey‐based study. Am J Geriatr Psychiatry. 2014;22(12):15131521.
  26. Sands M, Dantoc B, Hartshorn A, Ryan C, Lujic S. Single Question in Delirium (SQiD): testing its efficacy against psychiatrist interview, the Confusion Assessment Method and the Memorial Delirium Assessment Scale. Palliat Med. 2010;24(6):561565.
  27. Han JH, Wilson A, Vasilevskis EE, et al. Diagnosing delirium in older emergency department patients: validity and reliability of the delirium triage screen and the brief confusion assessment method. Ann Emerg Med. 2013;62(5):457465.
  28. O'Regan NA, Ryan DJ, Boland E, et al. Attention! A good bedside test for delirium? J Neurol Neurosurg Psychiatry. 2014;85(10):11221131.
  29. Bergmann MA, Murphy KM, Kiely DK, Jones RN, Marcantonio ER. A model for management of delirious postacute care patients. J Am Geriatr Soc. 2005;53(10):18171825.
  30. Fick DM, Steis MR, Mion LC, Walls JL. Computerized decision support for delirium superimposed on dementia in older adults: a pilot study. J Gerontol Nurs. 2011;37(4):3947.
  31. Yevchak AM, Fick DM, McDowell J, et al. Barriers and facilitators to implementing delirium rounds in a clinical trial across three diverse hospital settings. Clin Nurs Res. 2014;23(2):201215.
  32. Meehl PE, Rosen A. Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores. Psychol Bull. 1955;52(3):194.
Issue
Journal of Hospital Medicine - 10(10)
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Journal of Hospital Medicine - 10(10)
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Preliminary development of an ultrabrief two‐item bedside test for delirium
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Address for correspondence and reprint requests: Donna M. Fick, PhD, Distinguished Professor, College of Nursing, Penn State University, Health and Human Development East, University Park, PA 16802; Telephone: 814‐865‐9325; Fax: 814‐865‐3779; E‐mail: [email protected]
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MedPAC to look at physician prescribing tools as a way to control drug spending

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MedPAC to look at physician prescribing tools as a way to control drug spending

WASHINGTON – The Medicare Payment Advisory Commission is going to look at physician prescribing tools as part of a broader examination of how to rein in Medicare drug spending.

Members acknowledged during the Sept. 11, 2015, meeting that when it comes to the prices of drugs in the Medicare programs, the tools are limited to keep the prices low. Between statutory requirements for coverage of drugs in protected classes and a prohibition against the secretary of Health and Human Services negotiating prices for the Part D prescription drug benefit and other statutory requirements, even for intermediaries such as plan providers and hospital groups, leverage in price negotiations is very limited.

©Kenishirotie/Thinkstock

However, commission member Dr. Craig Samitt, former partner at Oliver Wyman of Paradise Valley, Ariz., suggested that the focus should be more on what leverage providers might have when it comes to utilization.

“So if we feel that neither CMS nor the intermediaries have sufficient leverage, well then who has significant leverage? The prescribing clinician,” Dr. Samitt said. “How well have we aligned interests around utilization in particular, not so much price, with the clinicians?”

Dr. Samitt noted that on the commercial side, there is a focus on utilization as a more effective driver of price, rather than simply targeting price first in the negotiation process, and suggested there might be room in Medicare for that kind of focus.

He also suggested that perhaps including drug utilization within the context of accountable care organizations could result in “additional focus on more effective prescribing patterns.”

The conversation occurred against a backdrop of examination of drug spending in general. MedPAC staff noted that Medicare is becoming a more prominent payer for drugs in the wake of Part D’s launch.

MedPAC staff estimates that in 2013, retail drugs made up 13% of Medicare spending, versus 9% of national health expenditures. Additionally, of the $574 billion spent by Medicare in that year, 19% was drugs and pharmacy, with the majority of drug spending (57%) coming from Part D.

The discussion was just the first on the subject as the group will look at other aspects of drug pricing and spending in future meetings. A specific timetable for offering policy recommendations was not discussed.

Dr. William Hall, professor at the University of Rochester (N.Y.) School of Medicine, added that it is not the price of the drug per se, but its value that needs to be focused on. He noted that the prices of the latest hepatitis C drugs might be high, but the value they have to the health care system is much greater and needs to be taken into consideration.

“One of the big differences from 2004 is we know a great deal more about the efficacy of drugs,” Dr. Hall said, suggesting that more needs to be done to educate clinicians on the proper use of medications as part of finding the right way to use physician prescribing patterns as leverage in price negotiations.

[email protected]

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WASHINGTON – The Medicare Payment Advisory Commission is going to look at physician prescribing tools as part of a broader examination of how to rein in Medicare drug spending.

Members acknowledged during the Sept. 11, 2015, meeting that when it comes to the prices of drugs in the Medicare programs, the tools are limited to keep the prices low. Between statutory requirements for coverage of drugs in protected classes and a prohibition against the secretary of Health and Human Services negotiating prices for the Part D prescription drug benefit and other statutory requirements, even for intermediaries such as plan providers and hospital groups, leverage in price negotiations is very limited.

©Kenishirotie/Thinkstock

However, commission member Dr. Craig Samitt, former partner at Oliver Wyman of Paradise Valley, Ariz., suggested that the focus should be more on what leverage providers might have when it comes to utilization.

“So if we feel that neither CMS nor the intermediaries have sufficient leverage, well then who has significant leverage? The prescribing clinician,” Dr. Samitt said. “How well have we aligned interests around utilization in particular, not so much price, with the clinicians?”

Dr. Samitt noted that on the commercial side, there is a focus on utilization as a more effective driver of price, rather than simply targeting price first in the negotiation process, and suggested there might be room in Medicare for that kind of focus.

He also suggested that perhaps including drug utilization within the context of accountable care organizations could result in “additional focus on more effective prescribing patterns.”

The conversation occurred against a backdrop of examination of drug spending in general. MedPAC staff noted that Medicare is becoming a more prominent payer for drugs in the wake of Part D’s launch.

MedPAC staff estimates that in 2013, retail drugs made up 13% of Medicare spending, versus 9% of national health expenditures. Additionally, of the $574 billion spent by Medicare in that year, 19% was drugs and pharmacy, with the majority of drug spending (57%) coming from Part D.

The discussion was just the first on the subject as the group will look at other aspects of drug pricing and spending in future meetings. A specific timetable for offering policy recommendations was not discussed.

Dr. William Hall, professor at the University of Rochester (N.Y.) School of Medicine, added that it is not the price of the drug per se, but its value that needs to be focused on. He noted that the prices of the latest hepatitis C drugs might be high, but the value they have to the health care system is much greater and needs to be taken into consideration.

“One of the big differences from 2004 is we know a great deal more about the efficacy of drugs,” Dr. Hall said, suggesting that more needs to be done to educate clinicians on the proper use of medications as part of finding the right way to use physician prescribing patterns as leverage in price negotiations.

[email protected]

WASHINGTON – The Medicare Payment Advisory Commission is going to look at physician prescribing tools as part of a broader examination of how to rein in Medicare drug spending.

Members acknowledged during the Sept. 11, 2015, meeting that when it comes to the prices of drugs in the Medicare programs, the tools are limited to keep the prices low. Between statutory requirements for coverage of drugs in protected classes and a prohibition against the secretary of Health and Human Services negotiating prices for the Part D prescription drug benefit and other statutory requirements, even for intermediaries such as plan providers and hospital groups, leverage in price negotiations is very limited.

©Kenishirotie/Thinkstock

However, commission member Dr. Craig Samitt, former partner at Oliver Wyman of Paradise Valley, Ariz., suggested that the focus should be more on what leverage providers might have when it comes to utilization.

“So if we feel that neither CMS nor the intermediaries have sufficient leverage, well then who has significant leverage? The prescribing clinician,” Dr. Samitt said. “How well have we aligned interests around utilization in particular, not so much price, with the clinicians?”

Dr. Samitt noted that on the commercial side, there is a focus on utilization as a more effective driver of price, rather than simply targeting price first in the negotiation process, and suggested there might be room in Medicare for that kind of focus.

He also suggested that perhaps including drug utilization within the context of accountable care organizations could result in “additional focus on more effective prescribing patterns.”

The conversation occurred against a backdrop of examination of drug spending in general. MedPAC staff noted that Medicare is becoming a more prominent payer for drugs in the wake of Part D’s launch.

MedPAC staff estimates that in 2013, retail drugs made up 13% of Medicare spending, versus 9% of national health expenditures. Additionally, of the $574 billion spent by Medicare in that year, 19% was drugs and pharmacy, with the majority of drug spending (57%) coming from Part D.

The discussion was just the first on the subject as the group will look at other aspects of drug pricing and spending in future meetings. A specific timetable for offering policy recommendations was not discussed.

Dr. William Hall, professor at the University of Rochester (N.Y.) School of Medicine, added that it is not the price of the drug per se, but its value that needs to be focused on. He noted that the prices of the latest hepatitis C drugs might be high, but the value they have to the health care system is much greater and needs to be taken into consideration.

“One of the big differences from 2004 is we know a great deal more about the efficacy of drugs,” Dr. Hall said, suggesting that more needs to be done to educate clinicians on the proper use of medications as part of finding the right way to use physician prescribing patterns as leverage in price negotiations.

[email protected]

References

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MedPAC to look at physician prescribing tools as a way to control drug spending
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AT A MEETING OF THE MEDICARE PAYMENT ADVISORY COMMISSION

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Metronidazole and alcohol

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Metronidazole and alcohol

A 32-year-old man develops diarrhea after receiving amoxicillin/clavulanate to treat an infection following a dog bite. He is diagnosed with Clostridium difficile and prescribed a 10-day course of metronidazole. He has no other medical problems. He will be the best man at his brother’s wedding tomorrow. What advice should you give him about alcohol use at the reception?

A. Do not take metronidazole the day of the wedding if you will be drinking alcohol.

B. Take metronidazole, do not drink alcohol.

C. It’s okay to drink alcohol.

For years, we have advised patients to not use alcohol if they are taking metronidazole because of concern for a disulfiram-like reaction between alcohol and metronidazole. This has been a standard warning given by physicians and appears as a contraindication in the prescribing information. It has been well accepted as a true, proven reaction.

Is it true?

As early as the 1960s, case reports and an uncontrolled study suggested that combining metronidazole with alcohol produced a disulfiram-like reaction, with case reports of severe reactions, including death.1, 2, 3 This was initially considered an area that might be therapeutic in the treatment of alcoholism, but several studies showed no benefit.4, 5

Caroline S. Williams and Dr. Kevin R. Woodcock reviewed the case reports for evidence of proof of a true interaction between metronidazole and ethanol.6 The case reports referenced textbooks to substantiate the interaction, but they did not present clear evidence of an interaction as the cause of elevated acetaldehyde levels.

Researchers have shown in a rat model that metronidazole can increase intracolonic, but not blood, acetaldehyde levels in rats that have received a combination of ethanol and metronidazole.7 Metronidazole did not have any inhibitory effect on hepatic or colonic alcohol dehydrogenase or aldehyde dehydrogenase. What was found was that rats treated with metronidazole had increased growth of Enterobacteriaceae, an alcohol dehydrogenase–containing aerobe, which could be the cause of the higher intracolonic acetaldehyde levels.

Jukka-Pekka Visapää and his colleagues studied the effect of coadministration of metronidazole and ethanol in young, healthy male volunteers.8 The study was a placebo-controlled, randomized trial. The study was small, with 12 participants. One-half of the study participants received metronidazole three times a day for 5 days; the other half received placebo. All participants then received ethanol 0.4g/kg, with blood testing being done every 20 minutes for the next 4 hours. Blood was tested for ethanol concentrations and for acetaldehyde levels. The study participants also had blood pressure, pulse, skin temperature, and symptoms monitored during the study.

There was no difference in blood acetaldehyde levels, vital signs, or symptoms between patients who received metronidazole or placebo. None of the subjects in the study had any measurable symptoms.

Metronidazole has many side effects, including nausea, vomiting, headache, dizziness, and seizures. These symptoms have a great deal of overlap with the symptoms of alcohol-disulfiram interaction. It has been assumed in early case reports that metronidazole caused a similar interaction with alcohol and raised acetaldehyde levels by interfering with aldehyde dehydrogenase.

Animal models and the human study do not show this to be the case. It is possible that metronidazole side effects alone were the cause of the symptoms in case reports. The one human study done was on healthy male volunteers, so projecting the results to a population with liver disease or other serious illness is a bit of a stretch. I think that if a problem exists with alcohol and metronidazole, it is uncommon and unlikely to occur in healthy individuals.

So, what would I advise the patient in the case about whether he can drink alcohol? I think that the risk would be minimal and that it would be safe for him to drink alcohol.

References

1. Br J Clin Pract. 1985 Jul;39(7):292-3.

2. Psychiatr Neurol. 1966;152:395-401.

3. Am J Forensic Med Pathol. 1996 Dec;17(4):343-6.

4. Q J Stud Alcohol. 1972 Sep;33: 734-40.

5. Q J Stud Ethanol. 1969 Mar;30: 140-51.

6. Ann Pharmacother. 2000 Feb;34(2):255-7.

7. Alcohol Clin Exp Res. 2000 Apr;24(4):570-5.

8. Ann Pharmacother. 2002 Jun;36(6):971-4.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and he serves as third-year medical student clerkship director at the University of Washington. Contact Dr. Paauw at [email protected].

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A 32-year-old man develops diarrhea after receiving amoxicillin/clavulanate to treat an infection following a dog bite. He is diagnosed with Clostridium difficile and prescribed a 10-day course of metronidazole. He has no other medical problems. He will be the best man at his brother’s wedding tomorrow. What advice should you give him about alcohol use at the reception?

A. Do not take metronidazole the day of the wedding if you will be drinking alcohol.

B. Take metronidazole, do not drink alcohol.

C. It’s okay to drink alcohol.

For years, we have advised patients to not use alcohol if they are taking metronidazole because of concern for a disulfiram-like reaction between alcohol and metronidazole. This has been a standard warning given by physicians and appears as a contraindication in the prescribing information. It has been well accepted as a true, proven reaction.

Is it true?

As early as the 1960s, case reports and an uncontrolled study suggested that combining metronidazole with alcohol produced a disulfiram-like reaction, with case reports of severe reactions, including death.1, 2, 3 This was initially considered an area that might be therapeutic in the treatment of alcoholism, but several studies showed no benefit.4, 5

Caroline S. Williams and Dr. Kevin R. Woodcock reviewed the case reports for evidence of proof of a true interaction between metronidazole and ethanol.6 The case reports referenced textbooks to substantiate the interaction, but they did not present clear evidence of an interaction as the cause of elevated acetaldehyde levels.

Researchers have shown in a rat model that metronidazole can increase intracolonic, but not blood, acetaldehyde levels in rats that have received a combination of ethanol and metronidazole.7 Metronidazole did not have any inhibitory effect on hepatic or colonic alcohol dehydrogenase or aldehyde dehydrogenase. What was found was that rats treated with metronidazole had increased growth of Enterobacteriaceae, an alcohol dehydrogenase–containing aerobe, which could be the cause of the higher intracolonic acetaldehyde levels.

Jukka-Pekka Visapää and his colleagues studied the effect of coadministration of metronidazole and ethanol in young, healthy male volunteers.8 The study was a placebo-controlled, randomized trial. The study was small, with 12 participants. One-half of the study participants received metronidazole three times a day for 5 days; the other half received placebo. All participants then received ethanol 0.4g/kg, with blood testing being done every 20 minutes for the next 4 hours. Blood was tested for ethanol concentrations and for acetaldehyde levels. The study participants also had blood pressure, pulse, skin temperature, and symptoms monitored during the study.

There was no difference in blood acetaldehyde levels, vital signs, or symptoms between patients who received metronidazole or placebo. None of the subjects in the study had any measurable symptoms.

Metronidazole has many side effects, including nausea, vomiting, headache, dizziness, and seizures. These symptoms have a great deal of overlap with the symptoms of alcohol-disulfiram interaction. It has been assumed in early case reports that metronidazole caused a similar interaction with alcohol and raised acetaldehyde levels by interfering with aldehyde dehydrogenase.

Animal models and the human study do not show this to be the case. It is possible that metronidazole side effects alone were the cause of the symptoms in case reports. The one human study done was on healthy male volunteers, so projecting the results to a population with liver disease or other serious illness is a bit of a stretch. I think that if a problem exists with alcohol and metronidazole, it is uncommon and unlikely to occur in healthy individuals.

So, what would I advise the patient in the case about whether he can drink alcohol? I think that the risk would be minimal and that it would be safe for him to drink alcohol.

References

1. Br J Clin Pract. 1985 Jul;39(7):292-3.

2. Psychiatr Neurol. 1966;152:395-401.

3. Am J Forensic Med Pathol. 1996 Dec;17(4):343-6.

4. Q J Stud Alcohol. 1972 Sep;33: 734-40.

5. Q J Stud Ethanol. 1969 Mar;30: 140-51.

6. Ann Pharmacother. 2000 Feb;34(2):255-7.

7. Alcohol Clin Exp Res. 2000 Apr;24(4):570-5.

8. Ann Pharmacother. 2002 Jun;36(6):971-4.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and he serves as third-year medical student clerkship director at the University of Washington. Contact Dr. Paauw at [email protected].

A 32-year-old man develops diarrhea after receiving amoxicillin/clavulanate to treat an infection following a dog bite. He is diagnosed with Clostridium difficile and prescribed a 10-day course of metronidazole. He has no other medical problems. He will be the best man at his brother’s wedding tomorrow. What advice should you give him about alcohol use at the reception?

A. Do not take metronidazole the day of the wedding if you will be drinking alcohol.

B. Take metronidazole, do not drink alcohol.

C. It’s okay to drink alcohol.

For years, we have advised patients to not use alcohol if they are taking metronidazole because of concern for a disulfiram-like reaction between alcohol and metronidazole. This has been a standard warning given by physicians and appears as a contraindication in the prescribing information. It has been well accepted as a true, proven reaction.

Is it true?

As early as the 1960s, case reports and an uncontrolled study suggested that combining metronidazole with alcohol produced a disulfiram-like reaction, with case reports of severe reactions, including death.1, 2, 3 This was initially considered an area that might be therapeutic in the treatment of alcoholism, but several studies showed no benefit.4, 5

Caroline S. Williams and Dr. Kevin R. Woodcock reviewed the case reports for evidence of proof of a true interaction between metronidazole and ethanol.6 The case reports referenced textbooks to substantiate the interaction, but they did not present clear evidence of an interaction as the cause of elevated acetaldehyde levels.

Researchers have shown in a rat model that metronidazole can increase intracolonic, but not blood, acetaldehyde levels in rats that have received a combination of ethanol and metronidazole.7 Metronidazole did not have any inhibitory effect on hepatic or colonic alcohol dehydrogenase or aldehyde dehydrogenase. What was found was that rats treated with metronidazole had increased growth of Enterobacteriaceae, an alcohol dehydrogenase–containing aerobe, which could be the cause of the higher intracolonic acetaldehyde levels.

Jukka-Pekka Visapää and his colleagues studied the effect of coadministration of metronidazole and ethanol in young, healthy male volunteers.8 The study was a placebo-controlled, randomized trial. The study was small, with 12 participants. One-half of the study participants received metronidazole three times a day for 5 days; the other half received placebo. All participants then received ethanol 0.4g/kg, with blood testing being done every 20 minutes for the next 4 hours. Blood was tested for ethanol concentrations and for acetaldehyde levels. The study participants also had blood pressure, pulse, skin temperature, and symptoms monitored during the study.

There was no difference in blood acetaldehyde levels, vital signs, or symptoms between patients who received metronidazole or placebo. None of the subjects in the study had any measurable symptoms.

Metronidazole has many side effects, including nausea, vomiting, headache, dizziness, and seizures. These symptoms have a great deal of overlap with the symptoms of alcohol-disulfiram interaction. It has been assumed in early case reports that metronidazole caused a similar interaction with alcohol and raised acetaldehyde levels by interfering with aldehyde dehydrogenase.

Animal models and the human study do not show this to be the case. It is possible that metronidazole side effects alone were the cause of the symptoms in case reports. The one human study done was on healthy male volunteers, so projecting the results to a population with liver disease or other serious illness is a bit of a stretch. I think that if a problem exists with alcohol and metronidazole, it is uncommon and unlikely to occur in healthy individuals.

So, what would I advise the patient in the case about whether he can drink alcohol? I think that the risk would be minimal and that it would be safe for him to drink alcohol.

References

1. Br J Clin Pract. 1985 Jul;39(7):292-3.

2. Psychiatr Neurol. 1966;152:395-401.

3. Am J Forensic Med Pathol. 1996 Dec;17(4):343-6.

4. Q J Stud Alcohol. 1972 Sep;33: 734-40.

5. Q J Stud Ethanol. 1969 Mar;30: 140-51.

6. Ann Pharmacother. 2000 Feb;34(2):255-7.

7. Alcohol Clin Exp Res. 2000 Apr;24(4):570-5.

8. Ann Pharmacother. 2002 Jun;36(6):971-4.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and he serves as third-year medical student clerkship director at the University of Washington. Contact Dr. Paauw at [email protected].

References

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E-cigarette Smokers Less Exposed to Carbon Monoxide

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NEW YORK—Smokers who switch to e-cigarettes - even if only some of the time - may dramatically reduce their exposure to air pollutants including carbon monoxide and acrolein, a British study suggests.

Researchers gave e-cigarettes to 40 smokers who said they wanted to quit. After four weeks, the 16 participants using only e-cigarettes had about an 80% drop in exposure both to carbon monoxide and to acrolein, a harmful breakdown product that is also in some e-cigarettes' vapor. Acrolein is known to irritate exposed tissues and can destroy cilia.

The 17 participants who swapped some regular cigarettes for the electronic version had a 52% decline in carbon monoxide exposure and a 60% decline for acrolein, according to a report online September 3 in Cancer Prevention Research.

To get the most benefit from switching to e-cigarettes, smokers need to completely give up traditional cigarettes, lead study author Dr. Hayden McRobbie, of the Wolfson Institute of Preventive Medicine at Queen Mary University of London, said by email.

"Smokers may get some encouragement from the finding that there is some potential health benefit as soon as they start the process," Dr. McRobbie said.

While tobacco control advocates fear that e-cigarettes may give rise to a new generation of nicotine addicts who eventually transition to conventional cigarettes, the current study adds to a small but growing body of evidence suggesting the devices might benefit the health of people who already smoke.

An international analysis of published research by the Cochrane Review in December concluded the devices could help smokers quit but said much of the existing research on e-cigarettes was thin.

Even though the current study points to another potential benefit of e-cigarettes, more evidence is still needed from longer and larger trials before scientists can draw firm conclusions about any safety advantages, Dr. Nancy Rigotti, director of tobacco research at Massachusetts General Hospital in Boston, said by email.

"It is exactly the type of incremental, careful work that is needed but it is not yet a definitive study," Rigotti, who wasn't involved in the study, said.

Study participants were typically in their 40s and had attempted to quit at least twice before joining the trial. All of them were offered the same type of e-cigarette and encouraged to completely abandon traditional cigarettes.

Researchers measured carbon monoxide in participants' breath one week before switching to e-cigarettes, on the day they switched, and again four weeks later. They followed the same schedule for testing urine for exposure to acrolein.

A limitation of the study, the authors acknowledged, is that it only included people with a desire to quit smoking, making it possible the results would be different for smokers with no intention of quitting. It's also possible that the specific model of e-cigarette used in the study might not be representative of other devices.

Still, the findings suggest smokers should be told e-cigarettes may curb their exposure to toxic chemicals, Dr. Riccardo Polosa, head of the tobacco research center at the University of Catania in Italy, said by email.

"This study adds to the evidence that e-cigarettes are much less harmful compared to conventional cigarettes," said Polosa, who wasn't involved in the study.

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NEW YORK—Smokers who switch to e-cigarettes - even if only some of the time - may dramatically reduce their exposure to air pollutants including carbon monoxide and acrolein, a British study suggests.

Researchers gave e-cigarettes to 40 smokers who said they wanted to quit. After four weeks, the 16 participants using only e-cigarettes had about an 80% drop in exposure both to carbon monoxide and to acrolein, a harmful breakdown product that is also in some e-cigarettes' vapor. Acrolein is known to irritate exposed tissues and can destroy cilia.

The 17 participants who swapped some regular cigarettes for the electronic version had a 52% decline in carbon monoxide exposure and a 60% decline for acrolein, according to a report online September 3 in Cancer Prevention Research.

To get the most benefit from switching to e-cigarettes, smokers need to completely give up traditional cigarettes, lead study author Dr. Hayden McRobbie, of the Wolfson Institute of Preventive Medicine at Queen Mary University of London, said by email.

"Smokers may get some encouragement from the finding that there is some potential health benefit as soon as they start the process," Dr. McRobbie said.

While tobacco control advocates fear that e-cigarettes may give rise to a new generation of nicotine addicts who eventually transition to conventional cigarettes, the current study adds to a small but growing body of evidence suggesting the devices might benefit the health of people who already smoke.

An international analysis of published research by the Cochrane Review in December concluded the devices could help smokers quit but said much of the existing research on e-cigarettes was thin.

Even though the current study points to another potential benefit of e-cigarettes, more evidence is still needed from longer and larger trials before scientists can draw firm conclusions about any safety advantages, Dr. Nancy Rigotti, director of tobacco research at Massachusetts General Hospital in Boston, said by email.

"It is exactly the type of incremental, careful work that is needed but it is not yet a definitive study," Rigotti, who wasn't involved in the study, said.

Study participants were typically in their 40s and had attempted to quit at least twice before joining the trial. All of them were offered the same type of e-cigarette and encouraged to completely abandon traditional cigarettes.

Researchers measured carbon monoxide in participants' breath one week before switching to e-cigarettes, on the day they switched, and again four weeks later. They followed the same schedule for testing urine for exposure to acrolein.

A limitation of the study, the authors acknowledged, is that it only included people with a desire to quit smoking, making it possible the results would be different for smokers with no intention of quitting. It's also possible that the specific model of e-cigarette used in the study might not be representative of other devices.

Still, the findings suggest smokers should be told e-cigarettes may curb their exposure to toxic chemicals, Dr. Riccardo Polosa, head of the tobacco research center at the University of Catania in Italy, said by email.

"This study adds to the evidence that e-cigarettes are much less harmful compared to conventional cigarettes," said Polosa, who wasn't involved in the study.

NEW YORK—Smokers who switch to e-cigarettes - even if only some of the time - may dramatically reduce their exposure to air pollutants including carbon monoxide and acrolein, a British study suggests.

Researchers gave e-cigarettes to 40 smokers who said they wanted to quit. After four weeks, the 16 participants using only e-cigarettes had about an 80% drop in exposure both to carbon monoxide and to acrolein, a harmful breakdown product that is also in some e-cigarettes' vapor. Acrolein is known to irritate exposed tissues and can destroy cilia.

The 17 participants who swapped some regular cigarettes for the electronic version had a 52% decline in carbon monoxide exposure and a 60% decline for acrolein, according to a report online September 3 in Cancer Prevention Research.

To get the most benefit from switching to e-cigarettes, smokers need to completely give up traditional cigarettes, lead study author Dr. Hayden McRobbie, of the Wolfson Institute of Preventive Medicine at Queen Mary University of London, said by email.

"Smokers may get some encouragement from the finding that there is some potential health benefit as soon as they start the process," Dr. McRobbie said.

While tobacco control advocates fear that e-cigarettes may give rise to a new generation of nicotine addicts who eventually transition to conventional cigarettes, the current study adds to a small but growing body of evidence suggesting the devices might benefit the health of people who already smoke.

An international analysis of published research by the Cochrane Review in December concluded the devices could help smokers quit but said much of the existing research on e-cigarettes was thin.

Even though the current study points to another potential benefit of e-cigarettes, more evidence is still needed from longer and larger trials before scientists can draw firm conclusions about any safety advantages, Dr. Nancy Rigotti, director of tobacco research at Massachusetts General Hospital in Boston, said by email.

"It is exactly the type of incremental, careful work that is needed but it is not yet a definitive study," Rigotti, who wasn't involved in the study, said.

Study participants were typically in their 40s and had attempted to quit at least twice before joining the trial. All of them were offered the same type of e-cigarette and encouraged to completely abandon traditional cigarettes.

Researchers measured carbon monoxide in participants' breath one week before switching to e-cigarettes, on the day they switched, and again four weeks later. They followed the same schedule for testing urine for exposure to acrolein.

A limitation of the study, the authors acknowledged, is that it only included people with a desire to quit smoking, making it possible the results would be different for smokers with no intention of quitting. It's also possible that the specific model of e-cigarette used in the study might not be representative of other devices.

Still, the findings suggest smokers should be told e-cigarettes may curb their exposure to toxic chemicals, Dr. Riccardo Polosa, head of the tobacco research center at the University of Catania in Italy, said by email.

"This study adds to the evidence that e-cigarettes are much less harmful compared to conventional cigarettes," said Polosa, who wasn't involved in the study.

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Social factors may impact survival in AML

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Patient receiving chemotherapy

Photo by Rhoda Baer

A new study indicates that certain social factors may impact survival in adults with acute myelogenous leukemia (AML) who are under 65.

The research showed associations between patient survival and insurance status, marital status, and county-level income.

“We believe these 3 factors indicate lack of material and social support preventing young patients from successfully walking the long and difficult road towards a cure,” said Uma Borate, MD, of the University of Alabama at Birmingham.

To conduct this study, Dr Borate and her colleagues analyzed data on 5541 patients, ages 19 to 64, who were diagnosed with AML between 2007 and 2011.

The team reported their findings in Cancer.

Multivariable analysis showed that AML subtype, age, and sex were independently associated with patients’ survival. And the non-biological factors independently associated with survival were insurance status, marital status, and county-level median household income.

Specifically, there was a significantly increased risk of premature death among patients who were uninsured (P=0.005) or Medicaid beneficiaries (P<0.001), compared to patients with private insurance.

Single (P<0.001) or divorced (P=0.011) patients had a significantly higher risk of premature death than married patients. But there was no significant difference between married and widowed patients (P=0.206).

And patients who lived in areas with lower income—the lowest 3 of 5 income groups—had a significantly increased risk of premature death.

Compared to patients in the fifth income quintile ($58.3K-$79.9K), there was an increased risk of death in the first quintile ($16.2K-$38.8K, P=0.001), second quintile ($38.8K-$42.2K, P<0.001), and third quintile ($42.2K-$47.9K, P<0.001).

Early and late mortality

The researchers wanted to determine if the impact of non-biological factors on survival was related to early mortality (a possible surrogate for access to care or late presentation) or late mortality (a possible surrogate for access to post-remission therapy and hematopoietic stem cell transplant).

So they conducted an exploratory analysis of factors influencing the risk of death within the first 2 months of diagnosis.

Being a Medicaid beneficiary (P=0.01) or uninsured (P<0.001) was independently associated with an increased risk of death within the first 2 months.

The same was true for patients belonging to the first income quartile (P=0.001), second quartile (P=0.003), third quartile (P=0.02), and fourth quartile (P=0.028).

On the other hand, there was no significant difference in early death according to marital status.

The researchers also performed a landmark survival analysis including only patients who survived at least 2 months from diagnosis.

In this analysis, marital status (P<0.001), insurance status (P=0.001), and income (P=0.021) were all independent predictors of survival.

Implications

“As physicians, we often emphasize more of the biology of the cancer, especially with the recent focus on personalized medicine,” said study author Luciano Jose Costa, MD, PhD, also of the University of Alabama at Birmingham.

“But we need to pay the same attention to resources available to our patients, as this greatly impacts their chances to survive leukemia.”

The researchers believe this will be especially important as the US transitions to a healthcare system that ties physician and hospital payments to patient outcomes.

“Taking from the results of this study, factors that have nothing to do with quality of care need to be accounted for when comparing predicted with actual outcomes,” Dr Borate said. “Otherwise, we will create a disincentive for hospitals and doctors to care for less privileged patients.”

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Patient receiving chemotherapy

Photo by Rhoda Baer

A new study indicates that certain social factors may impact survival in adults with acute myelogenous leukemia (AML) who are under 65.

The research showed associations between patient survival and insurance status, marital status, and county-level income.

“We believe these 3 factors indicate lack of material and social support preventing young patients from successfully walking the long and difficult road towards a cure,” said Uma Borate, MD, of the University of Alabama at Birmingham.

To conduct this study, Dr Borate and her colleagues analyzed data on 5541 patients, ages 19 to 64, who were diagnosed with AML between 2007 and 2011.

The team reported their findings in Cancer.

Multivariable analysis showed that AML subtype, age, and sex were independently associated with patients’ survival. And the non-biological factors independently associated with survival were insurance status, marital status, and county-level median household income.

Specifically, there was a significantly increased risk of premature death among patients who were uninsured (P=0.005) or Medicaid beneficiaries (P<0.001), compared to patients with private insurance.

Single (P<0.001) or divorced (P=0.011) patients had a significantly higher risk of premature death than married patients. But there was no significant difference between married and widowed patients (P=0.206).

And patients who lived in areas with lower income—the lowest 3 of 5 income groups—had a significantly increased risk of premature death.

Compared to patients in the fifth income quintile ($58.3K-$79.9K), there was an increased risk of death in the first quintile ($16.2K-$38.8K, P=0.001), second quintile ($38.8K-$42.2K, P<0.001), and third quintile ($42.2K-$47.9K, P<0.001).

Early and late mortality

The researchers wanted to determine if the impact of non-biological factors on survival was related to early mortality (a possible surrogate for access to care or late presentation) or late mortality (a possible surrogate for access to post-remission therapy and hematopoietic stem cell transplant).

So they conducted an exploratory analysis of factors influencing the risk of death within the first 2 months of diagnosis.

Being a Medicaid beneficiary (P=0.01) or uninsured (P<0.001) was independently associated with an increased risk of death within the first 2 months.

The same was true for patients belonging to the first income quartile (P=0.001), second quartile (P=0.003), third quartile (P=0.02), and fourth quartile (P=0.028).

On the other hand, there was no significant difference in early death according to marital status.

The researchers also performed a landmark survival analysis including only patients who survived at least 2 months from diagnosis.

In this analysis, marital status (P<0.001), insurance status (P=0.001), and income (P=0.021) were all independent predictors of survival.

Implications

“As physicians, we often emphasize more of the biology of the cancer, especially with the recent focus on personalized medicine,” said study author Luciano Jose Costa, MD, PhD, also of the University of Alabama at Birmingham.

“But we need to pay the same attention to resources available to our patients, as this greatly impacts their chances to survive leukemia.”

The researchers believe this will be especially important as the US transitions to a healthcare system that ties physician and hospital payments to patient outcomes.

“Taking from the results of this study, factors that have nothing to do with quality of care need to be accounted for when comparing predicted with actual outcomes,” Dr Borate said. “Otherwise, we will create a disincentive for hospitals and doctors to care for less privileged patients.”

Patient receiving chemotherapy

Photo by Rhoda Baer

A new study indicates that certain social factors may impact survival in adults with acute myelogenous leukemia (AML) who are under 65.

The research showed associations between patient survival and insurance status, marital status, and county-level income.

“We believe these 3 factors indicate lack of material and social support preventing young patients from successfully walking the long and difficult road towards a cure,” said Uma Borate, MD, of the University of Alabama at Birmingham.

To conduct this study, Dr Borate and her colleagues analyzed data on 5541 patients, ages 19 to 64, who were diagnosed with AML between 2007 and 2011.

The team reported their findings in Cancer.

Multivariable analysis showed that AML subtype, age, and sex were independently associated with patients’ survival. And the non-biological factors independently associated with survival were insurance status, marital status, and county-level median household income.

Specifically, there was a significantly increased risk of premature death among patients who were uninsured (P=0.005) or Medicaid beneficiaries (P<0.001), compared to patients with private insurance.

Single (P<0.001) or divorced (P=0.011) patients had a significantly higher risk of premature death than married patients. But there was no significant difference between married and widowed patients (P=0.206).

And patients who lived in areas with lower income—the lowest 3 of 5 income groups—had a significantly increased risk of premature death.

Compared to patients in the fifth income quintile ($58.3K-$79.9K), there was an increased risk of death in the first quintile ($16.2K-$38.8K, P=0.001), second quintile ($38.8K-$42.2K, P<0.001), and third quintile ($42.2K-$47.9K, P<0.001).

Early and late mortality

The researchers wanted to determine if the impact of non-biological factors on survival was related to early mortality (a possible surrogate for access to care or late presentation) or late mortality (a possible surrogate for access to post-remission therapy and hematopoietic stem cell transplant).

So they conducted an exploratory analysis of factors influencing the risk of death within the first 2 months of diagnosis.

Being a Medicaid beneficiary (P=0.01) or uninsured (P<0.001) was independently associated with an increased risk of death within the first 2 months.

The same was true for patients belonging to the first income quartile (P=0.001), second quartile (P=0.003), third quartile (P=0.02), and fourth quartile (P=0.028).

On the other hand, there was no significant difference in early death according to marital status.

The researchers also performed a landmark survival analysis including only patients who survived at least 2 months from diagnosis.

In this analysis, marital status (P<0.001), insurance status (P=0.001), and income (P=0.021) were all independent predictors of survival.

Implications

“As physicians, we often emphasize more of the biology of the cancer, especially with the recent focus on personalized medicine,” said study author Luciano Jose Costa, MD, PhD, also of the University of Alabama at Birmingham.

“But we need to pay the same attention to resources available to our patients, as this greatly impacts their chances to survive leukemia.”

The researchers believe this will be especially important as the US transitions to a healthcare system that ties physician and hospital payments to patient outcomes.

“Taking from the results of this study, factors that have nothing to do with quality of care need to be accounted for when comparing predicted with actual outcomes,” Dr Borate said. “Otherwise, we will create a disincentive for hospitals and doctors to care for less privileged patients.”

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