Antifungal shows promise in hematologic conditions

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Antifungal shows promise in hematologic conditions

Aspergillus fumigatus

WASHINGTON, DC—A new antifungal agent is as effective as an existing drug against invasive mold infection in patients with hematologic disorders, results of a phase 3 trial suggest.

Overall response and all-cause mortality rates were comparable with the newer drug, isavuconazole (ISA), and the existing drug, voriconazole (VRC).

The overall rates of treatment-emergent adverse events were comparable as well, but ISA was associated with a significantly lower incidence of several events.

Kieren Marr, MD, of Johns Hopkins University in Baltimore, Maryland, and her colleagues presented these results in a subset of patients from the SECURE trial at the 54th Interscience Conference on Antimicrobial Agents and Chemotherapy (poster M-1757).

Patient characteristics and safety outcomes

Of the 433 patients with a hematologic disorder enrolled in the trial, 217 had proven or probable invasive mold infection. The researchers divided patients into 2 groups according to disease: those with acute myeloid leukemia (AML) and those with acute lymphoblastic leukemia (ALL) or other conditions.

In all, 102 patients had AML, and 115 had ALL (n=28) or other conditions, including non-Hodgkin lymphoma (n=19), chronic lymphocytic leukemia (n=15), refractory anemia with excess blasts (n=9), myelodysplastic syndrome (n=8), chronic myeloid leukemia (n=6), and “other” underlying conditions (n=30).

Patients were randomized to receive VRC (n=105) or ISA (N=112). Thirty patients in the ISA arm and 26 in the VRC arm had undergone allogeneic transplant prior to therapy.

The primary outcome was all-cause mortality at day 42. Overall response and safety were secondary endpoints.

The overall rates of treatment-emergent adverse events were similar between ISA and VRC arms. Ninety-seven percent of patients in the ISA arm and 98% of patients in the VRC arm reported at least 1 treatment-emergent adverse event.

However, patients in the ISA arm had significantly fewer (P<0.05) events for the cardiac disorders, eye, psychiatric disorders, renal and urinary, and vascular system organ classes.

Response and mortality

All-cause mortality rates were comparable between the ISA and VRC arms—22% and 24%, respectively—as were overall response rates—39% and 34%, respectively—and complete response rates—13% and 10%, respectively.

All-cause mortality rates among patients with AML were 18% in the ISA arm and 15% in the VRC arm. Overall response rates were 36% and 48%, respectively.

For patients with ALL or other hematologic conditions, all-cause mortality rates were 26% in the ISA arm and 32% in the VRC arm. Overall response rates were 42% and 21%, respectively.

In transplant patients, the all-cause mortality rate was 27% for both the ISA and VRC arms. The overall response rate was 27% for both arms as well.

“These results show the potential of isavuconazole as a potent antifungal in the fight against invasive mold disease,” Dr Marr said.

ISA is an investigational antifungal under development by Astellas and Basilea Pharmaceutica International Ltd. The SECURE trial was funded by Astellas.

The US Food and Drug Administration recently accepted a new drug application seeking approval for ISA for the treatment of invasive aspergillosis and invasive mucormycosis.

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Topics

Aspergillus fumigatus

WASHINGTON, DC—A new antifungal agent is as effective as an existing drug against invasive mold infection in patients with hematologic disorders, results of a phase 3 trial suggest.

Overall response and all-cause mortality rates were comparable with the newer drug, isavuconazole (ISA), and the existing drug, voriconazole (VRC).

The overall rates of treatment-emergent adverse events were comparable as well, but ISA was associated with a significantly lower incidence of several events.

Kieren Marr, MD, of Johns Hopkins University in Baltimore, Maryland, and her colleagues presented these results in a subset of patients from the SECURE trial at the 54th Interscience Conference on Antimicrobial Agents and Chemotherapy (poster M-1757).

Patient characteristics and safety outcomes

Of the 433 patients with a hematologic disorder enrolled in the trial, 217 had proven or probable invasive mold infection. The researchers divided patients into 2 groups according to disease: those with acute myeloid leukemia (AML) and those with acute lymphoblastic leukemia (ALL) or other conditions.

In all, 102 patients had AML, and 115 had ALL (n=28) or other conditions, including non-Hodgkin lymphoma (n=19), chronic lymphocytic leukemia (n=15), refractory anemia with excess blasts (n=9), myelodysplastic syndrome (n=8), chronic myeloid leukemia (n=6), and “other” underlying conditions (n=30).

Patients were randomized to receive VRC (n=105) or ISA (N=112). Thirty patients in the ISA arm and 26 in the VRC arm had undergone allogeneic transplant prior to therapy.

The primary outcome was all-cause mortality at day 42. Overall response and safety were secondary endpoints.

The overall rates of treatment-emergent adverse events were similar between ISA and VRC arms. Ninety-seven percent of patients in the ISA arm and 98% of patients in the VRC arm reported at least 1 treatment-emergent adverse event.

However, patients in the ISA arm had significantly fewer (P<0.05) events for the cardiac disorders, eye, psychiatric disorders, renal and urinary, and vascular system organ classes.

Response and mortality

All-cause mortality rates were comparable between the ISA and VRC arms—22% and 24%, respectively—as were overall response rates—39% and 34%, respectively—and complete response rates—13% and 10%, respectively.

All-cause mortality rates among patients with AML were 18% in the ISA arm and 15% in the VRC arm. Overall response rates were 36% and 48%, respectively.

For patients with ALL or other hematologic conditions, all-cause mortality rates were 26% in the ISA arm and 32% in the VRC arm. Overall response rates were 42% and 21%, respectively.

In transplant patients, the all-cause mortality rate was 27% for both the ISA and VRC arms. The overall response rate was 27% for both arms as well.

“These results show the potential of isavuconazole as a potent antifungal in the fight against invasive mold disease,” Dr Marr said.

ISA is an investigational antifungal under development by Astellas and Basilea Pharmaceutica International Ltd. The SECURE trial was funded by Astellas.

The US Food and Drug Administration recently accepted a new drug application seeking approval for ISA for the treatment of invasive aspergillosis and invasive mucormycosis.

Aspergillus fumigatus

WASHINGTON, DC—A new antifungal agent is as effective as an existing drug against invasive mold infection in patients with hematologic disorders, results of a phase 3 trial suggest.

Overall response and all-cause mortality rates were comparable with the newer drug, isavuconazole (ISA), and the existing drug, voriconazole (VRC).

The overall rates of treatment-emergent adverse events were comparable as well, but ISA was associated with a significantly lower incidence of several events.

Kieren Marr, MD, of Johns Hopkins University in Baltimore, Maryland, and her colleagues presented these results in a subset of patients from the SECURE trial at the 54th Interscience Conference on Antimicrobial Agents and Chemotherapy (poster M-1757).

Patient characteristics and safety outcomes

Of the 433 patients with a hematologic disorder enrolled in the trial, 217 had proven or probable invasive mold infection. The researchers divided patients into 2 groups according to disease: those with acute myeloid leukemia (AML) and those with acute lymphoblastic leukemia (ALL) or other conditions.

In all, 102 patients had AML, and 115 had ALL (n=28) or other conditions, including non-Hodgkin lymphoma (n=19), chronic lymphocytic leukemia (n=15), refractory anemia with excess blasts (n=9), myelodysplastic syndrome (n=8), chronic myeloid leukemia (n=6), and “other” underlying conditions (n=30).

Patients were randomized to receive VRC (n=105) or ISA (N=112). Thirty patients in the ISA arm and 26 in the VRC arm had undergone allogeneic transplant prior to therapy.

The primary outcome was all-cause mortality at day 42. Overall response and safety were secondary endpoints.

The overall rates of treatment-emergent adverse events were similar between ISA and VRC arms. Ninety-seven percent of patients in the ISA arm and 98% of patients in the VRC arm reported at least 1 treatment-emergent adverse event.

However, patients in the ISA arm had significantly fewer (P<0.05) events for the cardiac disorders, eye, psychiatric disorders, renal and urinary, and vascular system organ classes.

Response and mortality

All-cause mortality rates were comparable between the ISA and VRC arms—22% and 24%, respectively—as were overall response rates—39% and 34%, respectively—and complete response rates—13% and 10%, respectively.

All-cause mortality rates among patients with AML were 18% in the ISA arm and 15% in the VRC arm. Overall response rates were 36% and 48%, respectively.

For patients with ALL or other hematologic conditions, all-cause mortality rates were 26% in the ISA arm and 32% in the VRC arm. Overall response rates were 42% and 21%, respectively.

In transplant patients, the all-cause mortality rate was 27% for both the ISA and VRC arms. The overall response rate was 27% for both arms as well.

“These results show the potential of isavuconazole as a potent antifungal in the fight against invasive mold disease,” Dr Marr said.

ISA is an investigational antifungal under development by Astellas and Basilea Pharmaceutica International Ltd. The SECURE trial was funded by Astellas.

The US Food and Drug Administration recently accepted a new drug application seeking approval for ISA for the treatment of invasive aspergillosis and invasive mucormycosis.

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Team identifies risk factors for vitiligo, AA in cGVHD

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Team identifies risk factors for vitiligo, AA in cGVHD

Skin biopsy showing GVHD

Credit: PLOS ONE

Results of a retrospective study have revealed factors that appear to increase the risk of vitiligo and alopecia areata (AA) in patients who develop chronic graft-vs-host disease (cGVHD) after a stem cell transplant.

Multivariable analysis suggested that a female donor to male recipient sex mismatch and positive test results for anticardiolipin immunoglobulin G (ACA-IgG) were both significantly associated with vitiligo and/or AA.

This research was published in JAMA Dermatology.

Edward W. Cowen, MD, of the National Cancer Institute in Bethesda, Maryland, and his colleagues conducted the study in 282 adult and pediatric patients with cGVHD.

Fifteen of the patients (5.3%) had vitiligo and/or AA. One patient had only AA, 1 had vitiligo and AA, and the rest had vitiligo alone. The median age of these patients at enrollment was 38 years (range, 9-69 years), and most were male (n=10).

Most patients had received a transplant to treat chronic myelogenous leukemia (n=5) or acute leukemia/myelodysplastic syndrome (n=5). Most patients (n=13) had an HLA-identical donor and received a peripheral blood stem cell transplant (n=9).

Eleven patients had concomitant skin cGVHD at the time of evaluation, and it was most often sclerotic-type cGVHD (n=9).

For the 5 vitiligo patients in whom the onset of skin depigmentation was documented, pigment changes occurred at a median of 41 months (range, 24-84) after transplant.

Depigmentation was classic periorbital, perioral, acrofacial involvement in 6 patients, generalized in 6 patients, and torso-predominant in 2 patients. Trichrome vitiligo was present in 3 patients, and poliosis occurred in 5 patients. In both AA patients, hair loss was localized to the scalp.

The researchers evaluated demographic, clinical, and laboratory data from these patients, and used univariate and multivariable logistic regression analyses to identify risk factors for vitiligo and AA.

Univariate analysis suggested the following factors were significantly associated with vitiligo and/or AA: female donor to male recipient sex mismatch (P=0.003), positive test results for ACA-IgG (P=0.03) or antiparietal antibody (P=0.049), elevated CD19 (P=0.045), and normal or elevated IgG (P=0.02).

However, only positive ACA-IgG results and female donor to male recipient mismatch retained significance in multivariable analysis (P=0.01 and P=0.003, respectively).

The researchers said additional studies are needed to clarify whether these risk factors can lead to a better understanding of the pathomechanisms of cGVHD.

dermatology

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Skin biopsy showing GVHD

Credit: PLOS ONE

Results of a retrospective study have revealed factors that appear to increase the risk of vitiligo and alopecia areata (AA) in patients who develop chronic graft-vs-host disease (cGVHD) after a stem cell transplant.

Multivariable analysis suggested that a female donor to male recipient sex mismatch and positive test results for anticardiolipin immunoglobulin G (ACA-IgG) were both significantly associated with vitiligo and/or AA.

This research was published in JAMA Dermatology.

Edward W. Cowen, MD, of the National Cancer Institute in Bethesda, Maryland, and his colleagues conducted the study in 282 adult and pediatric patients with cGVHD.

Fifteen of the patients (5.3%) had vitiligo and/or AA. One patient had only AA, 1 had vitiligo and AA, and the rest had vitiligo alone. The median age of these patients at enrollment was 38 years (range, 9-69 years), and most were male (n=10).

Most patients had received a transplant to treat chronic myelogenous leukemia (n=5) or acute leukemia/myelodysplastic syndrome (n=5). Most patients (n=13) had an HLA-identical donor and received a peripheral blood stem cell transplant (n=9).

Eleven patients had concomitant skin cGVHD at the time of evaluation, and it was most often sclerotic-type cGVHD (n=9).

For the 5 vitiligo patients in whom the onset of skin depigmentation was documented, pigment changes occurred at a median of 41 months (range, 24-84) after transplant.

Depigmentation was classic periorbital, perioral, acrofacial involvement in 6 patients, generalized in 6 patients, and torso-predominant in 2 patients. Trichrome vitiligo was present in 3 patients, and poliosis occurred in 5 patients. In both AA patients, hair loss was localized to the scalp.

The researchers evaluated demographic, clinical, and laboratory data from these patients, and used univariate and multivariable logistic regression analyses to identify risk factors for vitiligo and AA.

Univariate analysis suggested the following factors were significantly associated with vitiligo and/or AA: female donor to male recipient sex mismatch (P=0.003), positive test results for ACA-IgG (P=0.03) or antiparietal antibody (P=0.049), elevated CD19 (P=0.045), and normal or elevated IgG (P=0.02).

However, only positive ACA-IgG results and female donor to male recipient mismatch retained significance in multivariable analysis (P=0.01 and P=0.003, respectively).

The researchers said additional studies are needed to clarify whether these risk factors can lead to a better understanding of the pathomechanisms of cGVHD.

dermatology

Skin biopsy showing GVHD

Credit: PLOS ONE

Results of a retrospective study have revealed factors that appear to increase the risk of vitiligo and alopecia areata (AA) in patients who develop chronic graft-vs-host disease (cGVHD) after a stem cell transplant.

Multivariable analysis suggested that a female donor to male recipient sex mismatch and positive test results for anticardiolipin immunoglobulin G (ACA-IgG) were both significantly associated with vitiligo and/or AA.

This research was published in JAMA Dermatology.

Edward W. Cowen, MD, of the National Cancer Institute in Bethesda, Maryland, and his colleagues conducted the study in 282 adult and pediatric patients with cGVHD.

Fifteen of the patients (5.3%) had vitiligo and/or AA. One patient had only AA, 1 had vitiligo and AA, and the rest had vitiligo alone. The median age of these patients at enrollment was 38 years (range, 9-69 years), and most were male (n=10).

Most patients had received a transplant to treat chronic myelogenous leukemia (n=5) or acute leukemia/myelodysplastic syndrome (n=5). Most patients (n=13) had an HLA-identical donor and received a peripheral blood stem cell transplant (n=9).

Eleven patients had concomitant skin cGVHD at the time of evaluation, and it was most often sclerotic-type cGVHD (n=9).

For the 5 vitiligo patients in whom the onset of skin depigmentation was documented, pigment changes occurred at a median of 41 months (range, 24-84) after transplant.

Depigmentation was classic periorbital, perioral, acrofacial involvement in 6 patients, generalized in 6 patients, and torso-predominant in 2 patients. Trichrome vitiligo was present in 3 patients, and poliosis occurred in 5 patients. In both AA patients, hair loss was localized to the scalp.

The researchers evaluated demographic, clinical, and laboratory data from these patients, and used univariate and multivariable logistic regression analyses to identify risk factors for vitiligo and AA.

Univariate analysis suggested the following factors were significantly associated with vitiligo and/or AA: female donor to male recipient sex mismatch (P=0.003), positive test results for ACA-IgG (P=0.03) or antiparietal antibody (P=0.049), elevated CD19 (P=0.045), and normal or elevated IgG (P=0.02).

However, only positive ACA-IgG results and female donor to male recipient mismatch retained significance in multivariable analysis (P=0.01 and P=0.003, respectively).

The researchers said additional studies are needed to clarify whether these risk factors can lead to a better understanding of the pathomechanisms of cGVHD.

dermatology

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The pediatrician’s role in mental health: An interview with Dr. Joseph Hagan

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The pediatrician’s role in mental health: An interview with Dr. Joseph Hagan

Between the ongoing shortage of child psychiatrists, ever-evolving changes in health care policy and medical insurance, and documented increases in the rates of many psychiatric disorders, it can be difficult for pediatricians to define their role in delivering quality mental health care. To get some perspective on these issues, I talked with Dr. Joseph F. Hagan Jr., a pediatrician from Burlington, Vt. Dr. Hagan has been involved in shaping pediatric mental health care policy for years as the former chair of the American Academy of Pediatrics’ (AAP) Committee on the Psychosocial Aspects of Child & Family Health and current member of the Bright Futures Steering Committee. He is also running this year to be the president-elect of the national AAP.

 

Dr. Joseph F. Hagan Jr.

Q: What do you see as some of the key issues affecting child mental health care?

A: One of the things I haven’t heard a lot about is that there are not enough therapists to see children. The system has traditionally been based upon procedures and not on time, and that’s a problem. Therapists get paid less than the shop rate of your local auto mechanic, and of course, anyone who sees children has to talk with schools and parents outside of the session. That’s nonbillable, and we wonder why nobody will see children. Mental health is part of health, and the earlier we invest, the bigger the return. Because our practice was certified as a Family Centered Medical Home and now has access to a Community Health Team, my life has changed because we now have services that we didn’t have before. The problem with screening in the past has been "What if you find something?" Now we have so much more to offer.

Q: How much should a pediatrician really be expected to know and do when it comes to child behavioral problems? Is there a floor of knowledge and skills when it comes to mental health that all pediatricians should attain?

A: I think there definitely is. I would say that this could happen in steps. The AAP’s Taskforce for Mental Health really helped lay this out, but we already knew this. Behavioral and mental health problems can be managed in our offices, and everyone ought to be able to manage the majority of children with attention-deficit/hyperactivity disorder (ADHD), but also those with oppositional defiant disorder, anxiety, and depression. There are certain mental health problems that are part of pediatrics. To refer a standard ADHD child is absurd, because it really is a day-to-day problem that needs to be managed in your primary care medical home. Everybody needs to know how to do that and do it well. It is a chronic illness, and you need to hang in there with these children. That’s the basic floor. I think the floor is extended in being able to identify postpartum depression because we know that’s crucial and to be able to identify families who are really struggling with social determinants of health. This is going to be a big push in the forthcoming edition of Bright Futures. I think you also need to be able to identify anxiety and depression and be able to take the first steps in that. And maybe you should know how to treat them with selective serotonin reuptake inhibitors (SSRIs) if that should become important. I think you also should be able to talk about preventive things and ought to know that there is this thing called CBT (cognitive-behavioral therapy), and which therapists are in town who do CBT. You’ve got to know your community nonmedication options and access them before you decide upon meds.

Q: Psychiatric medications certainly have become even more controversial lately. What advice do you have for pediatricians when they prescribe them?

A: Tell families the expected effects and potential side effects. If you don’t, Dr. Google will. Start low and go slow, but titrate until desired effect of recovery. Remember if you are 100% anxious and miserable, you’ll look and feel great when you’re only 50% anxious, but you’re still only halfway better! It’s also important to discuss with your patient when you start meds, how long you are going to continue them, lest they feel good and stop prematurely.

Q: There are a lot of efforts these days to extend the education of pediatricians and provide consulting back up while the patient remains directly in the care of pediatrician. Do you think those efforts are enough or should we be more focused on providing more psychiatrists and other mental health clinicians that pediatricians can refer to?

 

 

A: We need to be able to do this (mental health) work, but part of being successful is having someone to consult with and someone to refer to. Just like with cardiac or GI problems, there are cases we can take care of all by ourselves, cases in which we will need to reach out to a consultant for help, and cases that need referral. Yes, we need more child psychiatrists. Co-located and collaborative care are the best-case scenarios.

More information about mental health care from the American Academy of Pediatrics can be found if you click here.

Dr. Rettew is an associate professor of psychiatry and pediatrics at the University of Vermont, Burlington. He is the author of "Child Temperament: New Thinking About the Boundary between Traits and Illness." Follow him on Twitter @pedipsych.

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Between the ongoing shortage of child psychiatrists, ever-evolving changes in health care policy and medical insurance, and documented increases in the rates of many psychiatric disorders, it can be difficult for pediatricians to define their role in delivering quality mental health care. To get some perspective on these issues, I talked with Dr. Joseph F. Hagan Jr., a pediatrician from Burlington, Vt. Dr. Hagan has been involved in shaping pediatric mental health care policy for years as the former chair of the American Academy of Pediatrics’ (AAP) Committee on the Psychosocial Aspects of Child & Family Health and current member of the Bright Futures Steering Committee. He is also running this year to be the president-elect of the national AAP.

 

Dr. Joseph F. Hagan Jr.

Q: What do you see as some of the key issues affecting child mental health care?

A: One of the things I haven’t heard a lot about is that there are not enough therapists to see children. The system has traditionally been based upon procedures and not on time, and that’s a problem. Therapists get paid less than the shop rate of your local auto mechanic, and of course, anyone who sees children has to talk with schools and parents outside of the session. That’s nonbillable, and we wonder why nobody will see children. Mental health is part of health, and the earlier we invest, the bigger the return. Because our practice was certified as a Family Centered Medical Home and now has access to a Community Health Team, my life has changed because we now have services that we didn’t have before. The problem with screening in the past has been "What if you find something?" Now we have so much more to offer.

Q: How much should a pediatrician really be expected to know and do when it comes to child behavioral problems? Is there a floor of knowledge and skills when it comes to mental health that all pediatricians should attain?

A: I think there definitely is. I would say that this could happen in steps. The AAP’s Taskforce for Mental Health really helped lay this out, but we already knew this. Behavioral and mental health problems can be managed in our offices, and everyone ought to be able to manage the majority of children with attention-deficit/hyperactivity disorder (ADHD), but also those with oppositional defiant disorder, anxiety, and depression. There are certain mental health problems that are part of pediatrics. To refer a standard ADHD child is absurd, because it really is a day-to-day problem that needs to be managed in your primary care medical home. Everybody needs to know how to do that and do it well. It is a chronic illness, and you need to hang in there with these children. That’s the basic floor. I think the floor is extended in being able to identify postpartum depression because we know that’s crucial and to be able to identify families who are really struggling with social determinants of health. This is going to be a big push in the forthcoming edition of Bright Futures. I think you also need to be able to identify anxiety and depression and be able to take the first steps in that. And maybe you should know how to treat them with selective serotonin reuptake inhibitors (SSRIs) if that should become important. I think you also should be able to talk about preventive things and ought to know that there is this thing called CBT (cognitive-behavioral therapy), and which therapists are in town who do CBT. You’ve got to know your community nonmedication options and access them before you decide upon meds.

Q: Psychiatric medications certainly have become even more controversial lately. What advice do you have for pediatricians when they prescribe them?

A: Tell families the expected effects and potential side effects. If you don’t, Dr. Google will. Start low and go slow, but titrate until desired effect of recovery. Remember if you are 100% anxious and miserable, you’ll look and feel great when you’re only 50% anxious, but you’re still only halfway better! It’s also important to discuss with your patient when you start meds, how long you are going to continue them, lest they feel good and stop prematurely.

Q: There are a lot of efforts these days to extend the education of pediatricians and provide consulting back up while the patient remains directly in the care of pediatrician. Do you think those efforts are enough or should we be more focused on providing more psychiatrists and other mental health clinicians that pediatricians can refer to?

 

 

A: We need to be able to do this (mental health) work, but part of being successful is having someone to consult with and someone to refer to. Just like with cardiac or GI problems, there are cases we can take care of all by ourselves, cases in which we will need to reach out to a consultant for help, and cases that need referral. Yes, we need more child psychiatrists. Co-located and collaborative care are the best-case scenarios.

More information about mental health care from the American Academy of Pediatrics can be found if you click here.

Dr. Rettew is an associate professor of psychiatry and pediatrics at the University of Vermont, Burlington. He is the author of "Child Temperament: New Thinking About the Boundary between Traits and Illness." Follow him on Twitter @pedipsych.

Between the ongoing shortage of child psychiatrists, ever-evolving changes in health care policy and medical insurance, and documented increases in the rates of many psychiatric disorders, it can be difficult for pediatricians to define their role in delivering quality mental health care. To get some perspective on these issues, I talked with Dr. Joseph F. Hagan Jr., a pediatrician from Burlington, Vt. Dr. Hagan has been involved in shaping pediatric mental health care policy for years as the former chair of the American Academy of Pediatrics’ (AAP) Committee on the Psychosocial Aspects of Child & Family Health and current member of the Bright Futures Steering Committee. He is also running this year to be the president-elect of the national AAP.

 

Dr. Joseph F. Hagan Jr.

Q: What do you see as some of the key issues affecting child mental health care?

A: One of the things I haven’t heard a lot about is that there are not enough therapists to see children. The system has traditionally been based upon procedures and not on time, and that’s a problem. Therapists get paid less than the shop rate of your local auto mechanic, and of course, anyone who sees children has to talk with schools and parents outside of the session. That’s nonbillable, and we wonder why nobody will see children. Mental health is part of health, and the earlier we invest, the bigger the return. Because our practice was certified as a Family Centered Medical Home and now has access to a Community Health Team, my life has changed because we now have services that we didn’t have before. The problem with screening in the past has been "What if you find something?" Now we have so much more to offer.

Q: How much should a pediatrician really be expected to know and do when it comes to child behavioral problems? Is there a floor of knowledge and skills when it comes to mental health that all pediatricians should attain?

A: I think there definitely is. I would say that this could happen in steps. The AAP’s Taskforce for Mental Health really helped lay this out, but we already knew this. Behavioral and mental health problems can be managed in our offices, and everyone ought to be able to manage the majority of children with attention-deficit/hyperactivity disorder (ADHD), but also those with oppositional defiant disorder, anxiety, and depression. There are certain mental health problems that are part of pediatrics. To refer a standard ADHD child is absurd, because it really is a day-to-day problem that needs to be managed in your primary care medical home. Everybody needs to know how to do that and do it well. It is a chronic illness, and you need to hang in there with these children. That’s the basic floor. I think the floor is extended in being able to identify postpartum depression because we know that’s crucial and to be able to identify families who are really struggling with social determinants of health. This is going to be a big push in the forthcoming edition of Bright Futures. I think you also need to be able to identify anxiety and depression and be able to take the first steps in that. And maybe you should know how to treat them with selective serotonin reuptake inhibitors (SSRIs) if that should become important. I think you also should be able to talk about preventive things and ought to know that there is this thing called CBT (cognitive-behavioral therapy), and which therapists are in town who do CBT. You’ve got to know your community nonmedication options and access them before you decide upon meds.

Q: Psychiatric medications certainly have become even more controversial lately. What advice do you have for pediatricians when they prescribe them?

A: Tell families the expected effects and potential side effects. If you don’t, Dr. Google will. Start low and go slow, but titrate until desired effect of recovery. Remember if you are 100% anxious and miserable, you’ll look and feel great when you’re only 50% anxious, but you’re still only halfway better! It’s also important to discuss with your patient when you start meds, how long you are going to continue them, lest they feel good and stop prematurely.

Q: There are a lot of efforts these days to extend the education of pediatricians and provide consulting back up while the patient remains directly in the care of pediatrician. Do you think those efforts are enough or should we be more focused on providing more psychiatrists and other mental health clinicians that pediatricians can refer to?

 

 

A: We need to be able to do this (mental health) work, but part of being successful is having someone to consult with and someone to refer to. Just like with cardiac or GI problems, there are cases we can take care of all by ourselves, cases in which we will need to reach out to a consultant for help, and cases that need referral. Yes, we need more child psychiatrists. Co-located and collaborative care are the best-case scenarios.

More information about mental health care from the American Academy of Pediatrics can be found if you click here.

Dr. Rettew is an associate professor of psychiatry and pediatrics at the University of Vermont, Burlington. He is the author of "Child Temperament: New Thinking About the Boundary between Traits and Illness." Follow him on Twitter @pedipsych.

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VIDEO: Breast cancer symposium take-home messages, Day 1

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SAN FRANCISCO – Dr. Eleftherios (Terry) Mamounas reviews the first day of the annual breast cancer symposium sponsored by the American Society of Clinical Oncology.

Key sessions covered the increasingly complex topic of genetic risk assessment and stirred up the debate about management of ductal carcinoma in situ (DCIS). Dr. Mamounas, professor of surgery at the University of Central Florida and medical director of the comprehensive breast program at the University of Florida Health Cancer Center, both in Orlando, discusses the significance of atypical hyperplasia, including new data suggesting that the fourfold increased risk of developing breast cancer in women with ductal carcinoma in situ (DCIS) is not further worsened by having a family history of DCIS.

Among the top oral presentations, one study suggested that a nomogram helped predict the risk of locoregional recurrence in patients treated for breast cancer using accelerated partial-breast irradiation. Another study examined the effect of hormone receptor status and local treatment on overall survival for patients with early-stage breast cancer.

Dr. Mamounas also discusses his own study, which he presented at the meeting, showing lower rates of locoregional recurrence in patients who have a pathologic complete response to neoadjuvant therapy. He puts the findings in context with tips on how to incorporate pathologic complete response data into clinical practice.

A separate study reported some of the first data on complication rates after unilateral or bilateral mastectomy and reconstruction. Dr. Mamounas wraps up the day’s review by discussing sessions on the effect of luteinizing hormone-releasing hormone agonists during chemotherapy in preserving ovarian function, and on breast cancer prevention, including the use of aromatase inhibitors.

For more of the meeting’s highlights, see our video interviews with Dr. Hope S. Rugo discussing the events of the second and third days of the Breast Cancer Symposium. Dr. Rugo is director of the Breast Oncology Clinical Trials Program at the University of California, San Francisco, Helen Diller Family Comprehensive Cancer Center.

Dr. Mamounas reported financial associations with Genomic Health, Genentech/Roche, Pfizer, GlaxoSmithKline, Eisai, Celgene, and GE Healthcare.

The video associated with this article is no longer available on this site. Please view all of our videos on the MDedge YouTube channel

[email protected]

On Twitter @sherryboschert

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SAN FRANCISCO – Dr. Eleftherios (Terry) Mamounas reviews the first day of the annual breast cancer symposium sponsored by the American Society of Clinical Oncology.

Key sessions covered the increasingly complex topic of genetic risk assessment and stirred up the debate about management of ductal carcinoma in situ (DCIS). Dr. Mamounas, professor of surgery at the University of Central Florida and medical director of the comprehensive breast program at the University of Florida Health Cancer Center, both in Orlando, discusses the significance of atypical hyperplasia, including new data suggesting that the fourfold increased risk of developing breast cancer in women with ductal carcinoma in situ (DCIS) is not further worsened by having a family history of DCIS.

Among the top oral presentations, one study suggested that a nomogram helped predict the risk of locoregional recurrence in patients treated for breast cancer using accelerated partial-breast irradiation. Another study examined the effect of hormone receptor status and local treatment on overall survival for patients with early-stage breast cancer.

Dr. Mamounas also discusses his own study, which he presented at the meeting, showing lower rates of locoregional recurrence in patients who have a pathologic complete response to neoadjuvant therapy. He puts the findings in context with tips on how to incorporate pathologic complete response data into clinical practice.

A separate study reported some of the first data on complication rates after unilateral or bilateral mastectomy and reconstruction. Dr. Mamounas wraps up the day’s review by discussing sessions on the effect of luteinizing hormone-releasing hormone agonists during chemotherapy in preserving ovarian function, and on breast cancer prevention, including the use of aromatase inhibitors.

For more of the meeting’s highlights, see our video interviews with Dr. Hope S. Rugo discussing the events of the second and third days of the Breast Cancer Symposium. Dr. Rugo is director of the Breast Oncology Clinical Trials Program at the University of California, San Francisco, Helen Diller Family Comprehensive Cancer Center.

Dr. Mamounas reported financial associations with Genomic Health, Genentech/Roche, Pfizer, GlaxoSmithKline, Eisai, Celgene, and GE Healthcare.

The video associated with this article is no longer available on this site. Please view all of our videos on the MDedge YouTube channel

[email protected]

On Twitter @sherryboschert

SAN FRANCISCO – Dr. Eleftherios (Terry) Mamounas reviews the first day of the annual breast cancer symposium sponsored by the American Society of Clinical Oncology.

Key sessions covered the increasingly complex topic of genetic risk assessment and stirred up the debate about management of ductal carcinoma in situ (DCIS). Dr. Mamounas, professor of surgery at the University of Central Florida and medical director of the comprehensive breast program at the University of Florida Health Cancer Center, both in Orlando, discusses the significance of atypical hyperplasia, including new data suggesting that the fourfold increased risk of developing breast cancer in women with ductal carcinoma in situ (DCIS) is not further worsened by having a family history of DCIS.

Among the top oral presentations, one study suggested that a nomogram helped predict the risk of locoregional recurrence in patients treated for breast cancer using accelerated partial-breast irradiation. Another study examined the effect of hormone receptor status and local treatment on overall survival for patients with early-stage breast cancer.

Dr. Mamounas also discusses his own study, which he presented at the meeting, showing lower rates of locoregional recurrence in patients who have a pathologic complete response to neoadjuvant therapy. He puts the findings in context with tips on how to incorporate pathologic complete response data into clinical practice.

A separate study reported some of the first data on complication rates after unilateral or bilateral mastectomy and reconstruction. Dr. Mamounas wraps up the day’s review by discussing sessions on the effect of luteinizing hormone-releasing hormone agonists during chemotherapy in preserving ovarian function, and on breast cancer prevention, including the use of aromatase inhibitors.

For more of the meeting’s highlights, see our video interviews with Dr. Hope S. Rugo discussing the events of the second and third days of the Breast Cancer Symposium. Dr. Rugo is director of the Breast Oncology Clinical Trials Program at the University of California, San Francisco, Helen Diller Family Comprehensive Cancer Center.

Dr. Mamounas reported financial associations with Genomic Health, Genentech/Roche, Pfizer, GlaxoSmithKline, Eisai, Celgene, and GE Healthcare.

The video associated with this article is no longer available on this site. Please view all of our videos on the MDedge YouTube channel

[email protected]

On Twitter @sherryboschert

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Resilience and Reintegration

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Active-duty military personnel, members of the National Guard and reserve, veterans, military families, and health professionals all are provided unique resources for ongoing support when they visit the Real Warriors Campaign at http://www.realwarriors.net, launched by the Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury. The public awareness campaign is intended “to promote the processes of building resilience, facilitating recovery, and supporting reintegration of returning service members, veterans, and their families.”

The Active Duty menu addresses broad topics, such as Signs and Symptoms of Combat Stress, Building Resilience, and After Deployment. Additional topics are drilled down for active-duty members of the Army, Navy, Marine Corps, and Air Force.

National Guard and reserve members are provided advice to assist in preparation for deployment, a reintegration guide for communicating with employers and family members, and coping and support.

The Veterans page directs users to resources provided through the VA; meanwhile, health care professionals are directed to TRICARE information, evidence-based treatment guidelines for PTSD and TBI, and original presentations of tools and tips, many of which can be ordered online at no cost.

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Active-duty military personnel, members of the National Guard and reserve, veterans, military families, and health professionals all are provided unique resources for ongoing support when they visit the Real Warriors Campaign at http://www.realwarriors.net, launched by the Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury. The public awareness campaign is intended “to promote the processes of building resilience, facilitating recovery, and supporting reintegration of returning service members, veterans, and their families.”

The Active Duty menu addresses broad topics, such as Signs and Symptoms of Combat Stress, Building Resilience, and After Deployment. Additional topics are drilled down for active-duty members of the Army, Navy, Marine Corps, and Air Force.

National Guard and reserve members are provided advice to assist in preparation for deployment, a reintegration guide for communicating with employers and family members, and coping and support.

The Veterans page directs users to resources provided through the VA; meanwhile, health care professionals are directed to TRICARE information, evidence-based treatment guidelines for PTSD and TBI, and original presentations of tools and tips, many of which can be ordered online at no cost.

Active-duty military personnel, members of the National Guard and reserve, veterans, military families, and health professionals all are provided unique resources for ongoing support when they visit the Real Warriors Campaign at http://www.realwarriors.net, launched by the Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury. The public awareness campaign is intended “to promote the processes of building resilience, facilitating recovery, and supporting reintegration of returning service members, veterans, and their families.”

The Active Duty menu addresses broad topics, such as Signs and Symptoms of Combat Stress, Building Resilience, and After Deployment. Additional topics are drilled down for active-duty members of the Army, Navy, Marine Corps, and Air Force.

National Guard and reserve members are provided advice to assist in preparation for deployment, a reintegration guide for communicating with employers and family members, and coping and support.

The Veterans page directs users to resources provided through the VA; meanwhile, health care professionals are directed to TRICARE information, evidence-based treatment guidelines for PTSD and TBI, and original presentations of tools and tips, many of which can be ordered online at no cost.

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Mutations linked to population disparities in cancers

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Genome testing

Credit: NIGMS

Researchers have identified mutations in microRNAs (miRNAs) that are closely associated with certain global populations and have been implicated in cancers.

The group discovered 31 miRNAs containing variants that occur with different frequencies in African and non-African populations.

Seven of these miRNAs have been linked to the onset, progression, and spread of cancers with known health disparities between patients of European and African descent.

And a variant in one of these miRNAs is associated with a significantly increased risk of non-Hodgkin lymphoma (NHL).

These findings appear in BMC Medical Genomics.

To better understand miRNA diversity across the world, the researchers searched for miRNA variants in the genome sequences of 69 individuals from 14 populations in Europe, Asia, the Americas, and Africa. The samples included genetic material from diverse African populations, including 3 hunter-gatherer populations.

“We wanted to try to see if there was variability in miRNA that hadn’t been identified before,” said study author Renata A. Rawlings-Goss, PhD, of the University of Pennsylvania’s Perelman School of Medicine in Philadelphia.

Overall, the researchers found that miRNA sequences were similar across the populations they sampled. But they did identify 33 novel variants and found that variants in 31 miRNAs were population-differentiated.

The team searched available databases to see which genes these miRNAs were known to inhibit. Their query turned up a large proportion of genes involved in glucose and insulin metabolism, indicating a possible connection between diabetes risk and possessing one of these variants. The search also pointed to effects on genes implicated in cancers.

Specifically, 7 of the population-differentiated miRNAs are currently implicated as cancer biomarkers: hsa-mir-202, hsa-mir-423, hsa-mir-196a-2, hsa-mir-520h, hsa-mir-647, hsa-mir-943, and hsa-mir-1908.

Of particular interest was hsa-mir-202, which contained one of the most highly population-differentiated variants in the dataset and is under investigation as a marker for NHL and early stage breast cancer.

Recent research suggested that a T allele at SNP rs12355840 in hsa-mir-202 helps protect against death from breast cancer by increasing mature hsa-mir-202 expression levels, which leads to downregulation of its gene targets.

On the other hand, diminished expression of mature hsa-mir-202 in subjects harboring at least 1 non-T allele resulted in a significantly elevated risk of NHL (odds ratio=1.83, P=0.008).

Dr Rawlings-Goss and her colleagues found that African/African-American populations had a lower frequency of the T allele compared to European/Asian populations—26% vs 65%, on average. And this suggests decreased baseline expression levels of mature hsa-mir-202 in African populations.

“It’s becoming more and more apparent that miRNAs can have a broad-reaching and global effect on our health and adaptation to disease,” Dr Rawlings-Goss said. “Learning more about differences across populations could be helpful to doing early diagnostics and treating disease across diverse populations.”

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Genome testing

Credit: NIGMS

Researchers have identified mutations in microRNAs (miRNAs) that are closely associated with certain global populations and have been implicated in cancers.

The group discovered 31 miRNAs containing variants that occur with different frequencies in African and non-African populations.

Seven of these miRNAs have been linked to the onset, progression, and spread of cancers with known health disparities between patients of European and African descent.

And a variant in one of these miRNAs is associated with a significantly increased risk of non-Hodgkin lymphoma (NHL).

These findings appear in BMC Medical Genomics.

To better understand miRNA diversity across the world, the researchers searched for miRNA variants in the genome sequences of 69 individuals from 14 populations in Europe, Asia, the Americas, and Africa. The samples included genetic material from diverse African populations, including 3 hunter-gatherer populations.

“We wanted to try to see if there was variability in miRNA that hadn’t been identified before,” said study author Renata A. Rawlings-Goss, PhD, of the University of Pennsylvania’s Perelman School of Medicine in Philadelphia.

Overall, the researchers found that miRNA sequences were similar across the populations they sampled. But they did identify 33 novel variants and found that variants in 31 miRNAs were population-differentiated.

The team searched available databases to see which genes these miRNAs were known to inhibit. Their query turned up a large proportion of genes involved in glucose and insulin metabolism, indicating a possible connection between diabetes risk and possessing one of these variants. The search also pointed to effects on genes implicated in cancers.

Specifically, 7 of the population-differentiated miRNAs are currently implicated as cancer biomarkers: hsa-mir-202, hsa-mir-423, hsa-mir-196a-2, hsa-mir-520h, hsa-mir-647, hsa-mir-943, and hsa-mir-1908.

Of particular interest was hsa-mir-202, which contained one of the most highly population-differentiated variants in the dataset and is under investigation as a marker for NHL and early stage breast cancer.

Recent research suggested that a T allele at SNP rs12355840 in hsa-mir-202 helps protect against death from breast cancer by increasing mature hsa-mir-202 expression levels, which leads to downregulation of its gene targets.

On the other hand, diminished expression of mature hsa-mir-202 in subjects harboring at least 1 non-T allele resulted in a significantly elevated risk of NHL (odds ratio=1.83, P=0.008).

Dr Rawlings-Goss and her colleagues found that African/African-American populations had a lower frequency of the T allele compared to European/Asian populations—26% vs 65%, on average. And this suggests decreased baseline expression levels of mature hsa-mir-202 in African populations.

“It’s becoming more and more apparent that miRNAs can have a broad-reaching and global effect on our health and adaptation to disease,” Dr Rawlings-Goss said. “Learning more about differences across populations could be helpful to doing early diagnostics and treating disease across diverse populations.”

Genome testing

Credit: NIGMS

Researchers have identified mutations in microRNAs (miRNAs) that are closely associated with certain global populations and have been implicated in cancers.

The group discovered 31 miRNAs containing variants that occur with different frequencies in African and non-African populations.

Seven of these miRNAs have been linked to the onset, progression, and spread of cancers with known health disparities between patients of European and African descent.

And a variant in one of these miRNAs is associated with a significantly increased risk of non-Hodgkin lymphoma (NHL).

These findings appear in BMC Medical Genomics.

To better understand miRNA diversity across the world, the researchers searched for miRNA variants in the genome sequences of 69 individuals from 14 populations in Europe, Asia, the Americas, and Africa. The samples included genetic material from diverse African populations, including 3 hunter-gatherer populations.

“We wanted to try to see if there was variability in miRNA that hadn’t been identified before,” said study author Renata A. Rawlings-Goss, PhD, of the University of Pennsylvania’s Perelman School of Medicine in Philadelphia.

Overall, the researchers found that miRNA sequences were similar across the populations they sampled. But they did identify 33 novel variants and found that variants in 31 miRNAs were population-differentiated.

The team searched available databases to see which genes these miRNAs were known to inhibit. Their query turned up a large proportion of genes involved in glucose and insulin metabolism, indicating a possible connection between diabetes risk and possessing one of these variants. The search also pointed to effects on genes implicated in cancers.

Specifically, 7 of the population-differentiated miRNAs are currently implicated as cancer biomarkers: hsa-mir-202, hsa-mir-423, hsa-mir-196a-2, hsa-mir-520h, hsa-mir-647, hsa-mir-943, and hsa-mir-1908.

Of particular interest was hsa-mir-202, which contained one of the most highly population-differentiated variants in the dataset and is under investigation as a marker for NHL and early stage breast cancer.

Recent research suggested that a T allele at SNP rs12355840 in hsa-mir-202 helps protect against death from breast cancer by increasing mature hsa-mir-202 expression levels, which leads to downregulation of its gene targets.

On the other hand, diminished expression of mature hsa-mir-202 in subjects harboring at least 1 non-T allele resulted in a significantly elevated risk of NHL (odds ratio=1.83, P=0.008).

Dr Rawlings-Goss and her colleagues found that African/African-American populations had a lower frequency of the T allele compared to European/Asian populations—26% vs 65%, on average. And this suggests decreased baseline expression levels of mature hsa-mir-202 in African populations.

“It’s becoming more and more apparent that miRNAs can have a broad-reaching and global effect on our health and adaptation to disease,” Dr Rawlings-Goss said. “Learning more about differences across populations could be helpful to doing early diagnostics and treating disease across diverse populations.”

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Database details international research regulations

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The National Institutes of Health has launched an online public database called ClinRegs, which includes country-specific information on

clinical research regulations.

ClinRegs currently provides information for 12 countries, but additional countries will likely be added in the future.

The goal of ClinRegs is to make it easier for investigators to find and understand country-specific requirements on topics such as clinical trial application submission and ethics committee approvals.

The database allows users to review regulatory requirements in 7 topic areas, including informed consent practices and trial sponsorship.

The site was created—and will be updated—by the National Institute of Allergy and Infectious Diseases.

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The National Institutes of Health has launched an online public database called ClinRegs, which includes country-specific information on

clinical research regulations.

ClinRegs currently provides information for 12 countries, but additional countries will likely be added in the future.

The goal of ClinRegs is to make it easier for investigators to find and understand country-specific requirements on topics such as clinical trial application submission and ethics committee approvals.

The database allows users to review regulatory requirements in 7 topic areas, including informed consent practices and trial sponsorship.

The site was created—and will be updated—by the National Institute of Allergy and Infectious Diseases.

The National Institutes of Health has launched an online public database called ClinRegs, which includes country-specific information on

clinical research regulations.

ClinRegs currently provides information for 12 countries, but additional countries will likely be added in the future.

The goal of ClinRegs is to make it easier for investigators to find and understand country-specific requirements on topics such as clinical trial application submission and ethics committee approvals.

The database allows users to review regulatory requirements in 7 topic areas, including informed consent practices and trial sponsorship.

The site was created—and will be updated—by the National Institute of Allergy and Infectious Diseases.

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Discovery could lead to better proteasome inhibitors

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Bone marrow aspirate

showing multiple myeloma

A newly discovered mechanism has paved the way for the next generation of proteasome inhibitors, according to a paper published in Chemistry & Biology.

Investigators developed a series of molecules that employ this mechanism, inhibiting the proteasome in 2 ways.

They are now planning to synthesize related compounds that may offer improved proteasome inhibition, target cancer cells more selectivity, and eliminate the resistance problems that occur with current drugs.

The group’s research began with epoxyketone, a molecule isolated from a cyanobacterium called carmaphycin, whose reactive group is the same as that of the proteasome inhibitor carfilzomib.

“Epoxyketones are very potent, selective inhibitors of the proteasome because they interact with this enzyme in 2 stages—the first reversible, and the second irreversible,” noted study author Daniela Trivella, PhD, of the Brazilian Biosciences National Laboratory at the Brazilian Center for Research in Energy and Materials in Campinas.

To optimize epoxyketone’s effects and find new reactive groups, the investigators developed and tested a series of synthetic analogs with slight structural modifications.

One of the molecules had an enone as a reactive group and had characteristics of carmaphycin and another natural molecule called syringolin, which was isolated from plant pathogens.

By investigating the reaction mechanisms of the new molecule, called carmaphycin-syringolin enone, the team verified that the enone interacts with the proteasome in 2 stages, with the second stage being irreversible.

The investigators also observed that, in the case of the enone, the second reaction occurs more slowly, increasing the duration of the reversible phase of carmaphycin-syringolin enone inhibition.

“Because the irreversible inactivation of the proteasome has toxic effects, the best window of reversibility observed for the carmaphycin-syringolin enone will potentially reduce the toxicity of this new class of proteasome inhibitors,” Dr Trivella said. “The compound would therefore present a balance between selectivity and potency.”

Toxicity tests are still underway. But the investigators have already conducted studies to determine exactly how the interaction between the enzyme target and the carmaphycin-syringolin enone target occurs.

“We discovered that a chemical reaction called hydroamination occurs, which had never before [been] seen under physiological conditions,” Dr Trivella said.

“This type of reaction is frequently used by synthetic chemists in preparing substances, but, normally, it requires very specific temperature and pH conditions and the use of catalysts to occur. It has never been reported as a mechanism of enzyme inhibition.”

Inspired by this new mechanism for proteasome inhibition, the investigators plan to synthesize and test a new series of carmaphycin-syringolin enone analogs to determine their effects on the therapeutic window and assess whether they are also capable of reacting with proteasomes that are resistant to traditional inhibitors.

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Bone marrow aspirate

showing multiple myeloma

A newly discovered mechanism has paved the way for the next generation of proteasome inhibitors, according to a paper published in Chemistry & Biology.

Investigators developed a series of molecules that employ this mechanism, inhibiting the proteasome in 2 ways.

They are now planning to synthesize related compounds that may offer improved proteasome inhibition, target cancer cells more selectivity, and eliminate the resistance problems that occur with current drugs.

The group’s research began with epoxyketone, a molecule isolated from a cyanobacterium called carmaphycin, whose reactive group is the same as that of the proteasome inhibitor carfilzomib.

“Epoxyketones are very potent, selective inhibitors of the proteasome because they interact with this enzyme in 2 stages—the first reversible, and the second irreversible,” noted study author Daniela Trivella, PhD, of the Brazilian Biosciences National Laboratory at the Brazilian Center for Research in Energy and Materials in Campinas.

To optimize epoxyketone’s effects and find new reactive groups, the investigators developed and tested a series of synthetic analogs with slight structural modifications.

One of the molecules had an enone as a reactive group and had characteristics of carmaphycin and another natural molecule called syringolin, which was isolated from plant pathogens.

By investigating the reaction mechanisms of the new molecule, called carmaphycin-syringolin enone, the team verified that the enone interacts with the proteasome in 2 stages, with the second stage being irreversible.

The investigators also observed that, in the case of the enone, the second reaction occurs more slowly, increasing the duration of the reversible phase of carmaphycin-syringolin enone inhibition.

“Because the irreversible inactivation of the proteasome has toxic effects, the best window of reversibility observed for the carmaphycin-syringolin enone will potentially reduce the toxicity of this new class of proteasome inhibitors,” Dr Trivella said. “The compound would therefore present a balance between selectivity and potency.”

Toxicity tests are still underway. But the investigators have already conducted studies to determine exactly how the interaction between the enzyme target and the carmaphycin-syringolin enone target occurs.

“We discovered that a chemical reaction called hydroamination occurs, which had never before [been] seen under physiological conditions,” Dr Trivella said.

“This type of reaction is frequently used by synthetic chemists in preparing substances, but, normally, it requires very specific temperature and pH conditions and the use of catalysts to occur. It has never been reported as a mechanism of enzyme inhibition.”

Inspired by this new mechanism for proteasome inhibition, the investigators plan to synthesize and test a new series of carmaphycin-syringolin enone analogs to determine their effects on the therapeutic window and assess whether they are also capable of reacting with proteasomes that are resistant to traditional inhibitors.

Bone marrow aspirate

showing multiple myeloma

A newly discovered mechanism has paved the way for the next generation of proteasome inhibitors, according to a paper published in Chemistry & Biology.

Investigators developed a series of molecules that employ this mechanism, inhibiting the proteasome in 2 ways.

They are now planning to synthesize related compounds that may offer improved proteasome inhibition, target cancer cells more selectivity, and eliminate the resistance problems that occur with current drugs.

The group’s research began with epoxyketone, a molecule isolated from a cyanobacterium called carmaphycin, whose reactive group is the same as that of the proteasome inhibitor carfilzomib.

“Epoxyketones are very potent, selective inhibitors of the proteasome because they interact with this enzyme in 2 stages—the first reversible, and the second irreversible,” noted study author Daniela Trivella, PhD, of the Brazilian Biosciences National Laboratory at the Brazilian Center for Research in Energy and Materials in Campinas.

To optimize epoxyketone’s effects and find new reactive groups, the investigators developed and tested a series of synthetic analogs with slight structural modifications.

One of the molecules had an enone as a reactive group and had characteristics of carmaphycin and another natural molecule called syringolin, which was isolated from plant pathogens.

By investigating the reaction mechanisms of the new molecule, called carmaphycin-syringolin enone, the team verified that the enone interacts with the proteasome in 2 stages, with the second stage being irreversible.

The investigators also observed that, in the case of the enone, the second reaction occurs more slowly, increasing the duration of the reversible phase of carmaphycin-syringolin enone inhibition.

“Because the irreversible inactivation of the proteasome has toxic effects, the best window of reversibility observed for the carmaphycin-syringolin enone will potentially reduce the toxicity of this new class of proteasome inhibitors,” Dr Trivella said. “The compound would therefore present a balance between selectivity and potency.”

Toxicity tests are still underway. But the investigators have already conducted studies to determine exactly how the interaction between the enzyme target and the carmaphycin-syringolin enone target occurs.

“We discovered that a chemical reaction called hydroamination occurs, which had never before [been] seen under physiological conditions,” Dr Trivella said.

“This type of reaction is frequently used by synthetic chemists in preparing substances, but, normally, it requires very specific temperature and pH conditions and the use of catalysts to occur. It has never been reported as a mechanism of enzyme inhibition.”

Inspired by this new mechanism for proteasome inhibition, the investigators plan to synthesize and test a new series of carmaphycin-syringolin enone analogs to determine their effects on the therapeutic window and assess whether they are also capable of reacting with proteasomes that are resistant to traditional inhibitors.

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Discrimination may prompt non-adherence in SCD patients

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Discrimination may prompt non-adherence in SCD patients

Doctor examines patient while

attending physician looks on

Credit: NCI

Research has shown that many patients with sickle cell disease (SCD) do not consistently follow their doctor’s orders, and a new study suggests discrimination may be partly to blame.

Patients who felt they experienced discrimination because of their race or health status were 53% more likely than their peers to disregard physician recommendations.

However, reports of discrimination were also common among patients who said they followed doctors’ orders to the letter.

Carlton Haywood Jr, PhD, of the Johns Hopkins School of Medicine in Baltimore, Maryland, and his colleagues conducted this research and detailed the results in the Journal of General Internal Medicine.

Dr Haywood’s team monitored the experiences of 291 SCD patients (aged 15 and older) who were participating in the Improving Patient Outcomes with Respect and Trust (IMPORT) study.

Patients completed surveys to report perceived discrimination from healthcare providers and their adherence to physician recommendations.

More than a third of patients (36%) reported non-adherence to a doctor’s recommendations in the 2 years prior to completing the survey.

Fifty-eight percent of the non-adherent patients and 43% of the adherent group reported at least 1 incident of discrimination due to their race or health status.

Patients who had experienced discrimination were 53% more likely than their peers to follow physicians’ recommendations inconsistently.

Trust in medical professionals appeared to mediate the discrimination/non-adherence relationship. It accounted for 50% of the excess prevalence of non-adherence among patients who reported incidents of discrimination.

The researchers said these findings are consistent with previous studies among other chronically ill patient groups. They also show how discrimination affects a patient’s trust in the healthcare system, as well as the person’s subsequent willingness to follow prescribed treatment regimens.

Dr Haywood believes the perceptions and experiences of discrimination may increase the chances that SCD patients will not fully benefit from the care available to them.

“A good relationship between the patient and provider can facilitate adherence, while a problematic relationship can negatively impact patient adherence,” he said.

“Improving relationships between healthcare providers and such patients may improve their trust in medical professionals, which, in turn, may improve other outcomes among this underserved patient population.”

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Doctor examines patient while

attending physician looks on

Credit: NCI

Research has shown that many patients with sickle cell disease (SCD) do not consistently follow their doctor’s orders, and a new study suggests discrimination may be partly to blame.

Patients who felt they experienced discrimination because of their race or health status were 53% more likely than their peers to disregard physician recommendations.

However, reports of discrimination were also common among patients who said they followed doctors’ orders to the letter.

Carlton Haywood Jr, PhD, of the Johns Hopkins School of Medicine in Baltimore, Maryland, and his colleagues conducted this research and detailed the results in the Journal of General Internal Medicine.

Dr Haywood’s team monitored the experiences of 291 SCD patients (aged 15 and older) who were participating in the Improving Patient Outcomes with Respect and Trust (IMPORT) study.

Patients completed surveys to report perceived discrimination from healthcare providers and their adherence to physician recommendations.

More than a third of patients (36%) reported non-adherence to a doctor’s recommendations in the 2 years prior to completing the survey.

Fifty-eight percent of the non-adherent patients and 43% of the adherent group reported at least 1 incident of discrimination due to their race or health status.

Patients who had experienced discrimination were 53% more likely than their peers to follow physicians’ recommendations inconsistently.

Trust in medical professionals appeared to mediate the discrimination/non-adherence relationship. It accounted for 50% of the excess prevalence of non-adherence among patients who reported incidents of discrimination.

The researchers said these findings are consistent with previous studies among other chronically ill patient groups. They also show how discrimination affects a patient’s trust in the healthcare system, as well as the person’s subsequent willingness to follow prescribed treatment regimens.

Dr Haywood believes the perceptions and experiences of discrimination may increase the chances that SCD patients will not fully benefit from the care available to them.

“A good relationship between the patient and provider can facilitate adherence, while a problematic relationship can negatively impact patient adherence,” he said.

“Improving relationships between healthcare providers and such patients may improve their trust in medical professionals, which, in turn, may improve other outcomes among this underserved patient population.”

Doctor examines patient while

attending physician looks on

Credit: NCI

Research has shown that many patients with sickle cell disease (SCD) do not consistently follow their doctor’s orders, and a new study suggests discrimination may be partly to blame.

Patients who felt they experienced discrimination because of their race or health status were 53% more likely than their peers to disregard physician recommendations.

However, reports of discrimination were also common among patients who said they followed doctors’ orders to the letter.

Carlton Haywood Jr, PhD, of the Johns Hopkins School of Medicine in Baltimore, Maryland, and his colleagues conducted this research and detailed the results in the Journal of General Internal Medicine.

Dr Haywood’s team monitored the experiences of 291 SCD patients (aged 15 and older) who were participating in the Improving Patient Outcomes with Respect and Trust (IMPORT) study.

Patients completed surveys to report perceived discrimination from healthcare providers and their adherence to physician recommendations.

More than a third of patients (36%) reported non-adherence to a doctor’s recommendations in the 2 years prior to completing the survey.

Fifty-eight percent of the non-adherent patients and 43% of the adherent group reported at least 1 incident of discrimination due to their race or health status.

Patients who had experienced discrimination were 53% more likely than their peers to follow physicians’ recommendations inconsistently.

Trust in medical professionals appeared to mediate the discrimination/non-adherence relationship. It accounted for 50% of the excess prevalence of non-adherence among patients who reported incidents of discrimination.

The researchers said these findings are consistent with previous studies among other chronically ill patient groups. They also show how discrimination affects a patient’s trust in the healthcare system, as well as the person’s subsequent willingness to follow prescribed treatment regimens.

Dr Haywood believes the perceptions and experiences of discrimination may increase the chances that SCD patients will not fully benefit from the care available to them.

“A good relationship between the patient and provider can facilitate adherence, while a problematic relationship can negatively impact patient adherence,” he said.

“Improving relationships between healthcare providers and such patients may improve their trust in medical professionals, which, in turn, may improve other outcomes among this underserved patient population.”

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HAC Diagnosis Code and MS‐DRG Assignment

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Association of the position of a hospital‐acquired condition diagnosis code with changes in medicare severity diagnosis‐related group assignment

One financial incentive to improve quality of care is the Centers for Medicare and Medicaid Services' (CMS) policy to not pay additionally for certain adverse events that are classified as hospital‐acquired conditions (HACs).[1, 2, 3] HACs are specific conditions that occur during the hospital stay and presumably could have been prevented.[4, 5, 6] Under the CMS policy, if an HAC occurs during a patient's stay, that condition is not included in the Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment.

The MS‐DRG assigned to a patient discharge determines reimbursement. Each MS‐DRG is assigned a weight, which is used to adjust for the fact that the treatment of different conditions consume different resources and have difference costs. Groups of patients who are expected to require above‐average resources have a higher weight than those who require fewer resources, and higher‐weighted MS‐DRG assignment results in a higher payment. In some cases, the inclusion of the diagnosis code of an HAC in the determination of the MS‐DRG results in a higher complexity level and higher DRG weight. The policy is designed to shift the incremental costs associated with treating the HAC to the hospital. As of October 2009, there were 10 HACs included in the CMS nonpayment program (see Supporting Table 1 in the online version of this article). CMS expanded the list of HACs to include 13 conditions in 2013.

Characteristics of Patients With a Hospital‐Acquired Condition Discharged Between October 2007 and April 2008 (N=7,027)
VariableMS‐DRG Change, No. (%) or MSD, N=980No MS‐DRG Change, No. (%) or MSD, N=6,047P Value
  • NOTE: Abbreviations: DVT, deep venous thrombosis; HAC, hospital‐acquired conditions; ICD‐9, International Classification of Diseases, 9th Revision; M, mean; MS‐DRG, Medicare Severity Diagnosis‐Related Group; SD, standard deviation; UTI, urinary tract infection.

Patient sociodemographic characteristics
Age, y62.718.957.521.9<0.001
Race   
White687 (70.1)4,006 (66.3)0.024
Black166 (16.9)1,100 (18.2) 
Hispanic45 (4.6)416 (6.9) 
Other82 (8.4)525 (8.7) 
Sex  <0.001
Male441 (45.0)3,298 (54.5) 
Female539 (55.0)2,749 (45.5) 
Payer  <0.001
Commercial279 (28.5)1,609 (26.6) 
Medicaid88 (9.0)910 (15.1) 
Medicare532 (54.3)3,003 (49.7) 
Self‐pay/charity52 (5.3)331 (5.5) 
Other29 (3.0)194 (3.2) 
Severity of illness  <0.001
Minor50 (5.1)71 (1.2) 
Moderate216 (22.0)359 (5.9) 
Major599 (61.1)1,318 (21.8) 
Extreme115 (11.7)4,299 (71.1) 
Patient clinical characteristics
Number of ICD‐9 diagnosis codes per patient13.76.020.26.6<0.001
MS‐DRG weight2.92.15.96.1<0.001
Hospital characteristics
Mean number of ICD‐9 diagnosis codes per patient per hospital8.51.48.61.40.280
Total hospital discharges15,9576,55316,8576,634<0.001
HACs per 1,000 discharges9.83.710.23.7<0.001
Hospital‐acquired condition
Type of HAC  <0.001
Pressure ulcer334 (34.1)1,599 (26.4) 
Falls/trauma96 (9.8)440 (7.3) 
Catheter‐associated UTI19 (1.9)215 (3.6) 
Vascular catheter infection26 (2.7)1,179 (19.5) 
DVT/pulmonary embolism448 (45.7)2,145 (35.5) 
Other conditions57 (5.8)469 (7.8) 
HAC position  <0.001
2nd code850 (86.7)697 (11.5) 
3rd code45 (4.6)739 (12.2) 
4th code30 (3.1)641 (10.6) 
5th code15 (1.5)569 (9.4) 
6th code or higher40 (4.1)3,401 (56.2) 

Withholding additional reimbursement for an HAC has been controversial. One area of debate is that the assignment of an HAC may be imprecise, in part due to the variation in how physicians document in the medical record.[1, 2, 6, 7, 8, 9] Coding is derived from documentation in physician notes and is the primary mechanism for assigning International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9) diagnosis codes to the patient's encounter. The coding process begins with health information technicians (ie, medical record coders) reviewing all medical record documentation to assign diagnosis and procedure codes using the ICD‐9 codes.[10] Primary and secondary diagnoses are determined by certain definitions in the hospital setting. Secondary diagnoses can be further separated into complications or comorbidities in the MS‐DRG system, which can affect reimbursement. The MS‐DRG is then determined using these diagnosis and procedure codes. Physician documentation is the principal source of data for hospital billing, because health information technicians (ie, medical record coders) must assign a code based on what is documented in the chart. If key medical detail is missing or language is ambiguous, then coding can be inaccurate, which may lead to inappropriate compensation.[11]

Accurate and complete ICD‐9 diagnosis and procedure coding is essential for correct MS‐DRG assignment and reimbursement.[12] Physicians may influence coding prioritization by either over‐emphasizing a patient diagnosis or by downplaying the significance of new findings. In addition, unless the physician uses specific, accurate, and accepted terminology, the diagnosis may not even appear in the list of diagnosis codes. Medical records with nonstandard abbreviations may result in coder‐omission of key diagnoses. Finally, when clinicians use qualified diagnoses such as rule‐out or probable, the final diagnosis coded may not be accurate.[10]

Although the CMS policy creates a financial incentive for hospitals to improve quality, the extent to which the policy actually impacts reimbursement across multiple HACs has not been quantified. Additionally, if HACsas a policy initiativereflect actual quality of care, then the position of the ICD‐9 code should not affect MS‐DRG assignment. In this study we evaluated the extent to which MS‐DRG assignment would have been influenced by the presence of an HAC and tested the association of the position of an HAC in the list of ICD‐9 diagnosis codes with changes in MS‐DRG assignment.

METHODS

Study Population

This study was a retrospective analysis of all patients discharged from hospital members of the University HealthSystem Consortium's (UHC) Clinical Data Base between October 2007 and April 2008. The data set was limited to patient discharge records with at least 1 of 10 HACs for which CMS no longer provides additional reimbursement (see Supporting Table 1 in the online version of this article). The presence of an HAC was indicated by the corresponding diagnosis code using the ICD‐9 diagnosis and procedure codes.

Data Source

UHC's Clinical Data Base is a database of patient discharge‐level administrative data used primarily for billing purposes. UHC's Clinical Data Base provides comparative data for in‐hospital healthcare outcomes using encounter‐level and line‐item transactional information from each member organization. UHC is a nonprofit alliance of 116 academic medical centers and 276 of their affiliated hospitals.

Dependent Variable: Change in MS‐DRG Assignment

The dependent variable was a change in MS‐DRG assignment. MS‐DRG assignment was calculated by comparing the MS‐DRG assigned when the HAC's ICD‐9 diagnosis code was considered a no‐payment event and was not included in the determination (ie, post‐policy DRG) with the MS‐DRG that would have been assigned when the HAC was not included in the determination (ie, pre‐policy DRG). The list of ICD‐9 diagnosis codes was entered into MS‐DRG grouping software with the ICD‐9 diagnosis code for each HAC in the identical position presented to CMS. Up to 29 secondary ICD‐9 diagnosis and procedure codes were entered, but the analyses of association on the position of the HAC used the first 9 diagnosis and 6 procedure codes processed by CMS, as only codes in these positions would have changed the MS‐DRG assigned during the study time period. If the 2 MS‐DRGs (pre‐policy DRG and post‐policy DRG) did not match, the case was classified as having a change in MS‐DRG assignment (MS‐DRG change).

Independent variables included in this analysis were coding variables and patient characteristics. Coding variables included the total number of ICD‐9 diagnosis codes recorded in the discharge record, absolute position of the HAC ICD‐9 diagnosis code in the order of all diagnosis codes, weight for the actual MS‐DRG, and specific type of HAC. The absolute position of the HAC was included in the analysis as a categorical variable (second position, third, fourth, fifth, and sixth position and higher). In addition, patient‐level characteristics including sociodemographic characteristics, clinical factors and severity of illness (minor, moderate, major, extreme),[6] and hospital‐level characteristics.

Statistical Analysis

Means and standard deviations or frequencies and percentages were used to describe the variables. A 2 test was used to test for differences in the absolute position of the HAC with change in MS‐DRG assignment (change/no change). In addition, 2 tests were used to test for differences in each of the other categorical independent variables with change in MS‐DRG assignment; t tests were used to test for differences in the continuous variables with change in MS‐DRG assignment.

Two multivariable binary logistic regression models were fit to test the relationship between change in MS‐DRG assignment with the absolute position of the HAC, adjusting for coding variables, patient characteristics, and hospital characteristics that were associated with change in MS‐DRG assignment in the bivariate analysis. The first model tested the relationship between change in MS‐DRG and position of the HAC, without accounting for the specific type of HAC, and the second tested the relationship including both position and the specific type of HAC. Receiver operating characteristic (ROC) curves were developed for each model to evaluate the predictive accuracy. Additionally, analyses were stratified by severity of illness, and the areas under the ROC curves for 3 models were compared to determine whether the predictive accuracy increased with the inclusion of variables other than HAC position. The first model included HAC position only, the second model added type of HAC, and the third model added other coding variables and patient‐ and hospital‐level variables.

Two sensitivity analyses were performed to test the robustness of the results. The first analysis tested the sensitivity of the results to the specification of comorbid disease burden, as measured by number of diagnosis codes. We used Elixhauser's method[13] for identifying comorbid conditions to create binary variables indicating the presence or absence of 29 distinct comorbid conditions, then calculated the total number of comorbid conditions. The binary logistic regression model was refit, with the total number of comorbid conditions in place of the number of diagnosis codes. An additional binary logistic regression model was fit that included the individual comorbid conditions that were associated with change in MS‐DRG assignment in a bivariate analysis (P<0.05). The second sensitivity analysis evaluated whether hospital‐level variation in coding practices explained change in MS‐DRG assignment using a hierarchical binary logistic regression model that included hospital as a random effect.

All statistical analyses were conducted using the SAS version 9.2 statistical software package (SAS Institute Inc., Cary, NC). The Rush University Medical Center Institutional Review Board approved the study protocol.

RESULTS

Of the 954,946 discharges from UHC academic medical centers, 7027 patients (0.7%) had an HAC. Of the patients with an HAC, 6047 did not change MS‐DRG assignment, whereas 980 patients (13.8%) had a change in MS‐DRG assignment. Patients with a change in MS‐DRG assignment were significantly different from those without a change in MS‐DRG assignment on all patient‐level characteristics and all but 1 hospital characteristic (Table 1). The variable with the largest absolute difference between those with and without a change in MS‐DRG was the actual position of the HAC; 86.7% of those with an MS‐DRG change had their HAC in the second position, whereas those without a change had only 11.5% in the second position.

After controlling for patient and hospital characteristics, an HAC in the second position in the list of ICD‐9 codes was associated with the greatest likelihood of a change in MS‐DRG assignment (P<0.001) (Table 2). Each additional ICD‐9 code decreased the odds of an MS‐DRG change (P=0.004), demonstrating that having more secondary diagnosis codes was associated with a lesser likelihood of an MS‐DRG change. After including the individual HACs in the regression model, the second position remained associated with the likelihood of a change in MS‐DRG assignment (results not shown). The predictive accuracy of our model did not improve, however, with the addition of type of HAC. The area under the ROC curve was 0.94 in both models, indicating high predictive power.

Results of Binary Logistic Regression Model for Change in MS‐DRG Assignment (N=7,027)
InterceptOdds RatioP Value
  • NOTE: The reference category for includes extreme severity of illness and HAC ICD‐9 code in the 6th position or higher. The model controls for patient age, sex, race/ethnicity, primary payer, hospital HAC rate, and total number of discharges per hospital. Abbreviations: HAC, hospital‐acquired conditions; ICD‐9, International Classification of Diseases, 9th Revision; MS‐DRG, Medicare Severity Diagnosis‐Related Group; ROC, receiver operating characteristic. *Compared to the model with patient sociodemographic characteristics only.

Minor severity of illness6.80<0.001
Moderate severity of illness5.52<0.001
Major severity of illness8.02<0.001
Number of ICD‐9 diagnosis codes per patient0.970.004
HAC ICD‐9 diagnosis code in 2nd position40.52<0.001
HAC ICD‐9 diagnosis code in 3rd position1.820.009
HAC ICD‐9 diagnosis code in 4th position1.720.032
HAC ICD‐9 diagnosis code in 5th position1.150.662
Area under the ROC curve0.94<0.001*
Area under the ROC curve, model with patient socio‐demographic characteristics only0.85 

The proportion of cases with a change in MS‐DRG by severity of illness is reported in Table 3. The largest proportion of cases with a change in MS‐DRG was in the minor severity of illness category (41.3%), whereas only 2.6% of cases with an extreme severity of illness had a change in MS‐DRG. Figure 1 shows ROC curves stratified by severity of illness. Figure 1A illustrates the ROC curves for the 121 (1.7%) patients with minor severity of illness. The area under the ROC curve for the model including HAC position only was 0.74, indicating moderate predictive power. The inclusion of HAC type increased the predictive power to 0.91, and inclusion of sociodemographic characteristics further increased the predictive power to 0.95. Figure 1BD illustrates the ROC curves for moderate, major, and extreme severities of illness. For more severe illnesses, the predictive accuracy of the models with only HAC position were similar to the full models, demonstrating that HAC position alone had a high predictive power for change in MS‐DRG assignment.

Percentage of Patients With a Change in MS‐DRG by Severity of Illness, Discharges Between October 2007 and April 2008 (N=7,027)
VariableNo.Within Category Percent With MS‐DRG Change
  • NOTE: Abbreviations: MS‐DRG, Medicare Severity Diagnosis‐Related Group.

Severity of illness  
Minor12141.3
Moderate57537.6
Major1,91731.3
Extreme4,4142.6
Figure 1
Receiver operating characteristic (ROC) curves stratified by severity of illness. ROC curves by severity of illness. Abbreviations: AUC, area under the curve; HAC, hospital‐acquired conditions.

In a sensitivity analysis that evaluated the robustness of our results to the specification of disease burden, inclusion of the number of comorbid conditions did not improve the predictive accuracy of the model. Although inclusion of individual comorbid conditions rather than number of diagnosis codes attenuated the odds ratio (OR) for HAC position (OR: 40.5 in the original model vs OR: 32.9 in the model with individual comorbid conditions), the improvement of the predictive accuracy of the model was small (area under the ROC curve=0.936 in the original model vs 0.943 in the model with individual conditions, P<0.001) (results not shown). In a sensitivity analysis using a hierarchical logistic regression model that included hospital random effects, hospital‐level variation in coding practices did not attenuate the relationship between HAC position and MS‐DRG change (results not shown).

DISCUSSION

This study investigated the association of a change in MS‐DRG assignment and position of the ICD‐9 diagnosis codes for HACs in a sample of patients discharged from US academic medical centers. We found that only 14% of the MS‐DRGs for patients with an HAC would have experienced a change in DRG assignment. Our results are consistent with those of Teufack et al.,[14] who estimated the economic impact of CMS' HAC policy for neurosurgery services at a single hospital to be 0.007% of overall net revenues. Nevertheless, the majority of hospitals have increased their efforts to prevent HACs that are included in CMS' policy.[15] At the same time, most hospitals have not increased their budgets for preventing HACs, and instead have reallocated resources from nontargeted HACs to those included in CMS' policy.

The low proportion of records that are impacted by the policy may be partially explained by the fact that CMS' policy only has an impact on reimbursement for MS‐DRGs with multiple levels. For example, heart failure has 3 levels of reimbursement in the MS‐DRG system (Table 4). Prior to CMS' policy, a heart failure patient with an air embolism as an HAC would have been classified in the most severe MS‐DRG (291), whereas after implementation the patient would be classified in the least severe MS‐DRG, if no other complication or comorbidity (CC) or a major complication or comorbidity (MCC) were present. Chest pain has only 1 level, and reimbursement for a patient with an HAC and classified in the chest pain MS‐DRG would not be impacted by CMS' policy. Most hospitalized patients are complicated, and the proportion of patients who are complicated will continue to increase over time as less complex care shifts to the ambulatory setting. The relative effectiveness of CMS' policy is likely to diminish with the continued shift of care to the ambulatory setting.

Example of MS‐DRG Codes and Weights, Fiscal Year 2014
VariableMS‐DRGDRG Weight
  • NOTE: Abbreviations: DRG, Diagnosis‐Related Group; MS‐DRG, Medicare Severity Diagnosis‐Related Group.

Heart failure and shock  
With major complications and comorbidities (MS‐DRG 291)2911.5062
With complications and comorbidities2920.9952
Without major complications or comorbidities2930.6718
Chest pain3130.5992

Patient discharges with a diagnosis code for as HAC in the second position were substantially more likely to have a change in MS‐DRG assignment compared to cases with an HAC listed lower in the final list of diagnosis codes. Perhaps it is not surprising that MS‐DRG assignment is most likely to change when the HAC is in the second position, because an ICD‐9 diagnosis code in this position is more likely to be a major complication or comorbidity. For HACs listed in a lower position of the list of ICD‐9 diagnosis codes, it is likely that the patient had another major complication or comorbidity listed in the second position that would have maintained classification in the same MS‐DRG. Our results suggest that physicians and hospitals caring for patients with lower complexity of illness will sustain a higher financial burden as a result of an HAC under CMS' policy compared to providers whose patients sustain the exact same HAC but have underlying medical care of greater complexity.

These results raise further concerns about the ability of CMS' payment policy to improve quality. One criticism of CMS' policy is that all HACs are not universally preventable. If they are not preventable, payment reductions promulgated via the policy would be punitive rather than incentivizing. In their study of central catheter‐associated bloodstream infections and catheter‐associated urinary tract infections, for example, Lee et al. found no change in infection rates after implementation of CMS' policy.[16] As such, some have suggested HACs should not be used to determine reimbursement, and CMS should abandon its current nonpayment policy.[4, 17] Our findings echo this criticism given that the financial penalty for an HAC depends on whether a patient is more or less complex.

Because coding emanates from physician documentation, a uniform documentation process must exist to ensure nonvariable coding practices.[1, 2, 7, 9] This is not the case, however, and some hospitals comanage documentation to refine or maximize the number of ICD‐9 diagnosis and procedure codes. Furthermore, there are certain differences in the documentation practices of individual physicians. If physician documentation and coding variation leads to fewer ICD‐9 codes during an encounter, the chance that an HAC will influence MS‐DRG change increases.

Another source of variation in coding practices found in this study was code sequencing. Although guidelines for appropriate ICD‐9 diagnosis coding currently exist, individual subjectivity remains. The most essential step in the coding process is identifying the principal diagnosis by extrapolating from physician documentation and clinical data. For example, when a patient is admitted for chest pain, and after some evaluation it is determined that the patient experienced a myocardial infarction, then myocardial infarction becomes the principal diagnosis. Based on that principal diagnosis, coders must select the relevant secondary diagnoses. The process involves a series of steps that must be followed exactly in order to ensure accurate coding.[12] There are no guidelines by which coding personnel must follow to sequence secondary diagnoses, with the exception of listed MCCs and CCs prior to other secondary diagnoses. Ultimately, the order by which these codes are assigned may result in unfavorable variation in MS‐DRG assignment.[1, 2, 4, 7, 8, 9, 17]

There are a number of limitations to this study. First, our cohort included only UHC‐affiliated academic medical centers, which may not represent all acute‐care hospitals and their coding practices. Although our data are for discharges prior to implementation of the policy, we were able to analyze the anticipated impact of the policy prior to any direct or indirect changes in coding that may have occurred in response to CMS' policy. Additionally, the number of diagnosis codes accepted by CMS was expanded from 9 to 25 in 2011. Future analyses that include MS‐DRG classifications with the expanded number of diagnosis codes should be conducted to validate our findings and determine whether any changes have occurred over time. It is not known whether low illness severity scores signify patient or hospital characteristics. If they represent patient characteristics, then CMS' policy will disproportionately affect hospitals taking care of less severely ill patients. Alternatively, if hospital coding practice explains more of the variation in the number of ICD‐9 codes (and thus severity of illness), then the system of adjudicating reimbursement via HACs to incentivize quality of care will be flawed, as there is no standard position for HACs on a more lengthy diagnosis list. Finally, we did not evaluate the change in DRG weight with the reassignment of MS‐DRG if the HAC had been included in the calculation. Future work should evaluate whether there is a differential impact of the policy by change in MS‐DRG weight.

CONCLUSION

Under CMS' current policy, hospitals and physicians caring for patients with lower severity of illness and have an HAC will be penalized by CMS disproportionately more than those caring for more complex, sicker patients with the identical HAC. If, in fact, HACs are indicators of a hospital's quality of care, then the CMS policy will likely do little to foster improved quality unless there is a reduction in coding practice variation and modifications to ensure that the policy impacts reimbursement, independent of severity of illness.

Disclosures

The authors acknowledge the financial support for data acquisition from the Rush University College of Health Sciences. The authors report no conflicts of interest.

Files
References
  1. Centers for Medicare and Medicaid Services. Hospital‐acquired conditions (present on admission indicator). Available at: http://www.cms.hhs.gov/HospitalAcqCond/05_Coding.asp#TopOfPage. Updated 2012. Accessed September 20, 2012.
  2. Centers for Medicare and Medicaid Services. Hospital‐acquired conditions: coding. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/HospitalAcqCond/Coding.html. Updated 2012. Accessed February 2, 2012.
  3. ICD‐9‐CM 2009 Coders' Desk Reference for Procedures. Eden Prairie, MN: Ingenix; 2009.
  4. Averill RF, Hughes JS, Goldfield NI, McCullough EC. Hospital complications: linking payment reduction to preventability. Jt Comm J Qual Patient Saf. 2009;35(5):283285.
  5. McNutt R, Johnson TJ, Odwazny R, et al. Change in MS‐DRG assignment and hospital reimbursement as a result of Centers for Medicare
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One financial incentive to improve quality of care is the Centers for Medicare and Medicaid Services' (CMS) policy to not pay additionally for certain adverse events that are classified as hospital‐acquired conditions (HACs).[1, 2, 3] HACs are specific conditions that occur during the hospital stay and presumably could have been prevented.[4, 5, 6] Under the CMS policy, if an HAC occurs during a patient's stay, that condition is not included in the Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment.

The MS‐DRG assigned to a patient discharge determines reimbursement. Each MS‐DRG is assigned a weight, which is used to adjust for the fact that the treatment of different conditions consume different resources and have difference costs. Groups of patients who are expected to require above‐average resources have a higher weight than those who require fewer resources, and higher‐weighted MS‐DRG assignment results in a higher payment. In some cases, the inclusion of the diagnosis code of an HAC in the determination of the MS‐DRG results in a higher complexity level and higher DRG weight. The policy is designed to shift the incremental costs associated with treating the HAC to the hospital. As of October 2009, there were 10 HACs included in the CMS nonpayment program (see Supporting Table 1 in the online version of this article). CMS expanded the list of HACs to include 13 conditions in 2013.

Characteristics of Patients With a Hospital‐Acquired Condition Discharged Between October 2007 and April 2008 (N=7,027)
VariableMS‐DRG Change, No. (%) or MSD, N=980No MS‐DRG Change, No. (%) or MSD, N=6,047P Value
  • NOTE: Abbreviations: DVT, deep venous thrombosis; HAC, hospital‐acquired conditions; ICD‐9, International Classification of Diseases, 9th Revision; M, mean; MS‐DRG, Medicare Severity Diagnosis‐Related Group; SD, standard deviation; UTI, urinary tract infection.

Patient sociodemographic characteristics
Age, y62.718.957.521.9<0.001
Race   
White687 (70.1)4,006 (66.3)0.024
Black166 (16.9)1,100 (18.2) 
Hispanic45 (4.6)416 (6.9) 
Other82 (8.4)525 (8.7) 
Sex  <0.001
Male441 (45.0)3,298 (54.5) 
Female539 (55.0)2,749 (45.5) 
Payer  <0.001
Commercial279 (28.5)1,609 (26.6) 
Medicaid88 (9.0)910 (15.1) 
Medicare532 (54.3)3,003 (49.7) 
Self‐pay/charity52 (5.3)331 (5.5) 
Other29 (3.0)194 (3.2) 
Severity of illness  <0.001
Minor50 (5.1)71 (1.2) 
Moderate216 (22.0)359 (5.9) 
Major599 (61.1)1,318 (21.8) 
Extreme115 (11.7)4,299 (71.1) 
Patient clinical characteristics
Number of ICD‐9 diagnosis codes per patient13.76.020.26.6<0.001
MS‐DRG weight2.92.15.96.1<0.001
Hospital characteristics
Mean number of ICD‐9 diagnosis codes per patient per hospital8.51.48.61.40.280
Total hospital discharges15,9576,55316,8576,634<0.001
HACs per 1,000 discharges9.83.710.23.7<0.001
Hospital‐acquired condition
Type of HAC  <0.001
Pressure ulcer334 (34.1)1,599 (26.4) 
Falls/trauma96 (9.8)440 (7.3) 
Catheter‐associated UTI19 (1.9)215 (3.6) 
Vascular catheter infection26 (2.7)1,179 (19.5) 
DVT/pulmonary embolism448 (45.7)2,145 (35.5) 
Other conditions57 (5.8)469 (7.8) 
HAC position  <0.001
2nd code850 (86.7)697 (11.5) 
3rd code45 (4.6)739 (12.2) 
4th code30 (3.1)641 (10.6) 
5th code15 (1.5)569 (9.4) 
6th code or higher40 (4.1)3,401 (56.2) 

Withholding additional reimbursement for an HAC has been controversial. One area of debate is that the assignment of an HAC may be imprecise, in part due to the variation in how physicians document in the medical record.[1, 2, 6, 7, 8, 9] Coding is derived from documentation in physician notes and is the primary mechanism for assigning International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9) diagnosis codes to the patient's encounter. The coding process begins with health information technicians (ie, medical record coders) reviewing all medical record documentation to assign diagnosis and procedure codes using the ICD‐9 codes.[10] Primary and secondary diagnoses are determined by certain definitions in the hospital setting. Secondary diagnoses can be further separated into complications or comorbidities in the MS‐DRG system, which can affect reimbursement. The MS‐DRG is then determined using these diagnosis and procedure codes. Physician documentation is the principal source of data for hospital billing, because health information technicians (ie, medical record coders) must assign a code based on what is documented in the chart. If key medical detail is missing or language is ambiguous, then coding can be inaccurate, which may lead to inappropriate compensation.[11]

Accurate and complete ICD‐9 diagnosis and procedure coding is essential for correct MS‐DRG assignment and reimbursement.[12] Physicians may influence coding prioritization by either over‐emphasizing a patient diagnosis or by downplaying the significance of new findings. In addition, unless the physician uses specific, accurate, and accepted terminology, the diagnosis may not even appear in the list of diagnosis codes. Medical records with nonstandard abbreviations may result in coder‐omission of key diagnoses. Finally, when clinicians use qualified diagnoses such as rule‐out or probable, the final diagnosis coded may not be accurate.[10]

Although the CMS policy creates a financial incentive for hospitals to improve quality, the extent to which the policy actually impacts reimbursement across multiple HACs has not been quantified. Additionally, if HACsas a policy initiativereflect actual quality of care, then the position of the ICD‐9 code should not affect MS‐DRG assignment. In this study we evaluated the extent to which MS‐DRG assignment would have been influenced by the presence of an HAC and tested the association of the position of an HAC in the list of ICD‐9 diagnosis codes with changes in MS‐DRG assignment.

METHODS

Study Population

This study was a retrospective analysis of all patients discharged from hospital members of the University HealthSystem Consortium's (UHC) Clinical Data Base between October 2007 and April 2008. The data set was limited to patient discharge records with at least 1 of 10 HACs for which CMS no longer provides additional reimbursement (see Supporting Table 1 in the online version of this article). The presence of an HAC was indicated by the corresponding diagnosis code using the ICD‐9 diagnosis and procedure codes.

Data Source

UHC's Clinical Data Base is a database of patient discharge‐level administrative data used primarily for billing purposes. UHC's Clinical Data Base provides comparative data for in‐hospital healthcare outcomes using encounter‐level and line‐item transactional information from each member organization. UHC is a nonprofit alliance of 116 academic medical centers and 276 of their affiliated hospitals.

Dependent Variable: Change in MS‐DRG Assignment

The dependent variable was a change in MS‐DRG assignment. MS‐DRG assignment was calculated by comparing the MS‐DRG assigned when the HAC's ICD‐9 diagnosis code was considered a no‐payment event and was not included in the determination (ie, post‐policy DRG) with the MS‐DRG that would have been assigned when the HAC was not included in the determination (ie, pre‐policy DRG). The list of ICD‐9 diagnosis codes was entered into MS‐DRG grouping software with the ICD‐9 diagnosis code for each HAC in the identical position presented to CMS. Up to 29 secondary ICD‐9 diagnosis and procedure codes were entered, but the analyses of association on the position of the HAC used the first 9 diagnosis and 6 procedure codes processed by CMS, as only codes in these positions would have changed the MS‐DRG assigned during the study time period. If the 2 MS‐DRGs (pre‐policy DRG and post‐policy DRG) did not match, the case was classified as having a change in MS‐DRG assignment (MS‐DRG change).

Independent variables included in this analysis were coding variables and patient characteristics. Coding variables included the total number of ICD‐9 diagnosis codes recorded in the discharge record, absolute position of the HAC ICD‐9 diagnosis code in the order of all diagnosis codes, weight for the actual MS‐DRG, and specific type of HAC. The absolute position of the HAC was included in the analysis as a categorical variable (second position, third, fourth, fifth, and sixth position and higher). In addition, patient‐level characteristics including sociodemographic characteristics, clinical factors and severity of illness (minor, moderate, major, extreme),[6] and hospital‐level characteristics.

Statistical Analysis

Means and standard deviations or frequencies and percentages were used to describe the variables. A 2 test was used to test for differences in the absolute position of the HAC with change in MS‐DRG assignment (change/no change). In addition, 2 tests were used to test for differences in each of the other categorical independent variables with change in MS‐DRG assignment; t tests were used to test for differences in the continuous variables with change in MS‐DRG assignment.

Two multivariable binary logistic regression models were fit to test the relationship between change in MS‐DRG assignment with the absolute position of the HAC, adjusting for coding variables, patient characteristics, and hospital characteristics that were associated with change in MS‐DRG assignment in the bivariate analysis. The first model tested the relationship between change in MS‐DRG and position of the HAC, without accounting for the specific type of HAC, and the second tested the relationship including both position and the specific type of HAC. Receiver operating characteristic (ROC) curves were developed for each model to evaluate the predictive accuracy. Additionally, analyses were stratified by severity of illness, and the areas under the ROC curves for 3 models were compared to determine whether the predictive accuracy increased with the inclusion of variables other than HAC position. The first model included HAC position only, the second model added type of HAC, and the third model added other coding variables and patient‐ and hospital‐level variables.

Two sensitivity analyses were performed to test the robustness of the results. The first analysis tested the sensitivity of the results to the specification of comorbid disease burden, as measured by number of diagnosis codes. We used Elixhauser's method[13] for identifying comorbid conditions to create binary variables indicating the presence or absence of 29 distinct comorbid conditions, then calculated the total number of comorbid conditions. The binary logistic regression model was refit, with the total number of comorbid conditions in place of the number of diagnosis codes. An additional binary logistic regression model was fit that included the individual comorbid conditions that were associated with change in MS‐DRG assignment in a bivariate analysis (P<0.05). The second sensitivity analysis evaluated whether hospital‐level variation in coding practices explained change in MS‐DRG assignment using a hierarchical binary logistic regression model that included hospital as a random effect.

All statistical analyses were conducted using the SAS version 9.2 statistical software package (SAS Institute Inc., Cary, NC). The Rush University Medical Center Institutional Review Board approved the study protocol.

RESULTS

Of the 954,946 discharges from UHC academic medical centers, 7027 patients (0.7%) had an HAC. Of the patients with an HAC, 6047 did not change MS‐DRG assignment, whereas 980 patients (13.8%) had a change in MS‐DRG assignment. Patients with a change in MS‐DRG assignment were significantly different from those without a change in MS‐DRG assignment on all patient‐level characteristics and all but 1 hospital characteristic (Table 1). The variable with the largest absolute difference between those with and without a change in MS‐DRG was the actual position of the HAC; 86.7% of those with an MS‐DRG change had their HAC in the second position, whereas those without a change had only 11.5% in the second position.

After controlling for patient and hospital characteristics, an HAC in the second position in the list of ICD‐9 codes was associated with the greatest likelihood of a change in MS‐DRG assignment (P<0.001) (Table 2). Each additional ICD‐9 code decreased the odds of an MS‐DRG change (P=0.004), demonstrating that having more secondary diagnosis codes was associated with a lesser likelihood of an MS‐DRG change. After including the individual HACs in the regression model, the second position remained associated with the likelihood of a change in MS‐DRG assignment (results not shown). The predictive accuracy of our model did not improve, however, with the addition of type of HAC. The area under the ROC curve was 0.94 in both models, indicating high predictive power.

Results of Binary Logistic Regression Model for Change in MS‐DRG Assignment (N=7,027)
InterceptOdds RatioP Value
  • NOTE: The reference category for includes extreme severity of illness and HAC ICD‐9 code in the 6th position or higher. The model controls for patient age, sex, race/ethnicity, primary payer, hospital HAC rate, and total number of discharges per hospital. Abbreviations: HAC, hospital‐acquired conditions; ICD‐9, International Classification of Diseases, 9th Revision; MS‐DRG, Medicare Severity Diagnosis‐Related Group; ROC, receiver operating characteristic. *Compared to the model with patient sociodemographic characteristics only.

Minor severity of illness6.80<0.001
Moderate severity of illness5.52<0.001
Major severity of illness8.02<0.001
Number of ICD‐9 diagnosis codes per patient0.970.004
HAC ICD‐9 diagnosis code in 2nd position40.52<0.001
HAC ICD‐9 diagnosis code in 3rd position1.820.009
HAC ICD‐9 diagnosis code in 4th position1.720.032
HAC ICD‐9 diagnosis code in 5th position1.150.662
Area under the ROC curve0.94<0.001*
Area under the ROC curve, model with patient socio‐demographic characteristics only0.85 

The proportion of cases with a change in MS‐DRG by severity of illness is reported in Table 3. The largest proportion of cases with a change in MS‐DRG was in the minor severity of illness category (41.3%), whereas only 2.6% of cases with an extreme severity of illness had a change in MS‐DRG. Figure 1 shows ROC curves stratified by severity of illness. Figure 1A illustrates the ROC curves for the 121 (1.7%) patients with minor severity of illness. The area under the ROC curve for the model including HAC position only was 0.74, indicating moderate predictive power. The inclusion of HAC type increased the predictive power to 0.91, and inclusion of sociodemographic characteristics further increased the predictive power to 0.95. Figure 1BD illustrates the ROC curves for moderate, major, and extreme severities of illness. For more severe illnesses, the predictive accuracy of the models with only HAC position were similar to the full models, demonstrating that HAC position alone had a high predictive power for change in MS‐DRG assignment.

Percentage of Patients With a Change in MS‐DRG by Severity of Illness, Discharges Between October 2007 and April 2008 (N=7,027)
VariableNo.Within Category Percent With MS‐DRG Change
  • NOTE: Abbreviations: MS‐DRG, Medicare Severity Diagnosis‐Related Group.

Severity of illness  
Minor12141.3
Moderate57537.6
Major1,91731.3
Extreme4,4142.6
Figure 1
Receiver operating characteristic (ROC) curves stratified by severity of illness. ROC curves by severity of illness. Abbreviations: AUC, area under the curve; HAC, hospital‐acquired conditions.

In a sensitivity analysis that evaluated the robustness of our results to the specification of disease burden, inclusion of the number of comorbid conditions did not improve the predictive accuracy of the model. Although inclusion of individual comorbid conditions rather than number of diagnosis codes attenuated the odds ratio (OR) for HAC position (OR: 40.5 in the original model vs OR: 32.9 in the model with individual comorbid conditions), the improvement of the predictive accuracy of the model was small (area under the ROC curve=0.936 in the original model vs 0.943 in the model with individual conditions, P<0.001) (results not shown). In a sensitivity analysis using a hierarchical logistic regression model that included hospital random effects, hospital‐level variation in coding practices did not attenuate the relationship between HAC position and MS‐DRG change (results not shown).

DISCUSSION

This study investigated the association of a change in MS‐DRG assignment and position of the ICD‐9 diagnosis codes for HACs in a sample of patients discharged from US academic medical centers. We found that only 14% of the MS‐DRGs for patients with an HAC would have experienced a change in DRG assignment. Our results are consistent with those of Teufack et al.,[14] who estimated the economic impact of CMS' HAC policy for neurosurgery services at a single hospital to be 0.007% of overall net revenues. Nevertheless, the majority of hospitals have increased their efforts to prevent HACs that are included in CMS' policy.[15] At the same time, most hospitals have not increased their budgets for preventing HACs, and instead have reallocated resources from nontargeted HACs to those included in CMS' policy.

The low proportion of records that are impacted by the policy may be partially explained by the fact that CMS' policy only has an impact on reimbursement for MS‐DRGs with multiple levels. For example, heart failure has 3 levels of reimbursement in the MS‐DRG system (Table 4). Prior to CMS' policy, a heart failure patient with an air embolism as an HAC would have been classified in the most severe MS‐DRG (291), whereas after implementation the patient would be classified in the least severe MS‐DRG, if no other complication or comorbidity (CC) or a major complication or comorbidity (MCC) were present. Chest pain has only 1 level, and reimbursement for a patient with an HAC and classified in the chest pain MS‐DRG would not be impacted by CMS' policy. Most hospitalized patients are complicated, and the proportion of patients who are complicated will continue to increase over time as less complex care shifts to the ambulatory setting. The relative effectiveness of CMS' policy is likely to diminish with the continued shift of care to the ambulatory setting.

Example of MS‐DRG Codes and Weights, Fiscal Year 2014
VariableMS‐DRGDRG Weight
  • NOTE: Abbreviations: DRG, Diagnosis‐Related Group; MS‐DRG, Medicare Severity Diagnosis‐Related Group.

Heart failure and shock  
With major complications and comorbidities (MS‐DRG 291)2911.5062
With complications and comorbidities2920.9952
Without major complications or comorbidities2930.6718
Chest pain3130.5992

Patient discharges with a diagnosis code for as HAC in the second position were substantially more likely to have a change in MS‐DRG assignment compared to cases with an HAC listed lower in the final list of diagnosis codes. Perhaps it is not surprising that MS‐DRG assignment is most likely to change when the HAC is in the second position, because an ICD‐9 diagnosis code in this position is more likely to be a major complication or comorbidity. For HACs listed in a lower position of the list of ICD‐9 diagnosis codes, it is likely that the patient had another major complication or comorbidity listed in the second position that would have maintained classification in the same MS‐DRG. Our results suggest that physicians and hospitals caring for patients with lower complexity of illness will sustain a higher financial burden as a result of an HAC under CMS' policy compared to providers whose patients sustain the exact same HAC but have underlying medical care of greater complexity.

These results raise further concerns about the ability of CMS' payment policy to improve quality. One criticism of CMS' policy is that all HACs are not universally preventable. If they are not preventable, payment reductions promulgated via the policy would be punitive rather than incentivizing. In their study of central catheter‐associated bloodstream infections and catheter‐associated urinary tract infections, for example, Lee et al. found no change in infection rates after implementation of CMS' policy.[16] As such, some have suggested HACs should not be used to determine reimbursement, and CMS should abandon its current nonpayment policy.[4, 17] Our findings echo this criticism given that the financial penalty for an HAC depends on whether a patient is more or less complex.

Because coding emanates from physician documentation, a uniform documentation process must exist to ensure nonvariable coding practices.[1, 2, 7, 9] This is not the case, however, and some hospitals comanage documentation to refine or maximize the number of ICD‐9 diagnosis and procedure codes. Furthermore, there are certain differences in the documentation practices of individual physicians. If physician documentation and coding variation leads to fewer ICD‐9 codes during an encounter, the chance that an HAC will influence MS‐DRG change increases.

Another source of variation in coding practices found in this study was code sequencing. Although guidelines for appropriate ICD‐9 diagnosis coding currently exist, individual subjectivity remains. The most essential step in the coding process is identifying the principal diagnosis by extrapolating from physician documentation and clinical data. For example, when a patient is admitted for chest pain, and after some evaluation it is determined that the patient experienced a myocardial infarction, then myocardial infarction becomes the principal diagnosis. Based on that principal diagnosis, coders must select the relevant secondary diagnoses. The process involves a series of steps that must be followed exactly in order to ensure accurate coding.[12] There are no guidelines by which coding personnel must follow to sequence secondary diagnoses, with the exception of listed MCCs and CCs prior to other secondary diagnoses. Ultimately, the order by which these codes are assigned may result in unfavorable variation in MS‐DRG assignment.[1, 2, 4, 7, 8, 9, 17]

There are a number of limitations to this study. First, our cohort included only UHC‐affiliated academic medical centers, which may not represent all acute‐care hospitals and their coding practices. Although our data are for discharges prior to implementation of the policy, we were able to analyze the anticipated impact of the policy prior to any direct or indirect changes in coding that may have occurred in response to CMS' policy. Additionally, the number of diagnosis codes accepted by CMS was expanded from 9 to 25 in 2011. Future analyses that include MS‐DRG classifications with the expanded number of diagnosis codes should be conducted to validate our findings and determine whether any changes have occurred over time. It is not known whether low illness severity scores signify patient or hospital characteristics. If they represent patient characteristics, then CMS' policy will disproportionately affect hospitals taking care of less severely ill patients. Alternatively, if hospital coding practice explains more of the variation in the number of ICD‐9 codes (and thus severity of illness), then the system of adjudicating reimbursement via HACs to incentivize quality of care will be flawed, as there is no standard position for HACs on a more lengthy diagnosis list. Finally, we did not evaluate the change in DRG weight with the reassignment of MS‐DRG if the HAC had been included in the calculation. Future work should evaluate whether there is a differential impact of the policy by change in MS‐DRG weight.

CONCLUSION

Under CMS' current policy, hospitals and physicians caring for patients with lower severity of illness and have an HAC will be penalized by CMS disproportionately more than those caring for more complex, sicker patients with the identical HAC. If, in fact, HACs are indicators of a hospital's quality of care, then the CMS policy will likely do little to foster improved quality unless there is a reduction in coding practice variation and modifications to ensure that the policy impacts reimbursement, independent of severity of illness.

Disclosures

The authors acknowledge the financial support for data acquisition from the Rush University College of Health Sciences. The authors report no conflicts of interest.

One financial incentive to improve quality of care is the Centers for Medicare and Medicaid Services' (CMS) policy to not pay additionally for certain adverse events that are classified as hospital‐acquired conditions (HACs).[1, 2, 3] HACs are specific conditions that occur during the hospital stay and presumably could have been prevented.[4, 5, 6] Under the CMS policy, if an HAC occurs during a patient's stay, that condition is not included in the Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment.

The MS‐DRG assigned to a patient discharge determines reimbursement. Each MS‐DRG is assigned a weight, which is used to adjust for the fact that the treatment of different conditions consume different resources and have difference costs. Groups of patients who are expected to require above‐average resources have a higher weight than those who require fewer resources, and higher‐weighted MS‐DRG assignment results in a higher payment. In some cases, the inclusion of the diagnosis code of an HAC in the determination of the MS‐DRG results in a higher complexity level and higher DRG weight. The policy is designed to shift the incremental costs associated with treating the HAC to the hospital. As of October 2009, there were 10 HACs included in the CMS nonpayment program (see Supporting Table 1 in the online version of this article). CMS expanded the list of HACs to include 13 conditions in 2013.

Characteristics of Patients With a Hospital‐Acquired Condition Discharged Between October 2007 and April 2008 (N=7,027)
VariableMS‐DRG Change, No. (%) or MSD, N=980No MS‐DRG Change, No. (%) or MSD, N=6,047P Value
  • NOTE: Abbreviations: DVT, deep venous thrombosis; HAC, hospital‐acquired conditions; ICD‐9, International Classification of Diseases, 9th Revision; M, mean; MS‐DRG, Medicare Severity Diagnosis‐Related Group; SD, standard deviation; UTI, urinary tract infection.

Patient sociodemographic characteristics
Age, y62.718.957.521.9<0.001
Race   
White687 (70.1)4,006 (66.3)0.024
Black166 (16.9)1,100 (18.2) 
Hispanic45 (4.6)416 (6.9) 
Other82 (8.4)525 (8.7) 
Sex  <0.001
Male441 (45.0)3,298 (54.5) 
Female539 (55.0)2,749 (45.5) 
Payer  <0.001
Commercial279 (28.5)1,609 (26.6) 
Medicaid88 (9.0)910 (15.1) 
Medicare532 (54.3)3,003 (49.7) 
Self‐pay/charity52 (5.3)331 (5.5) 
Other29 (3.0)194 (3.2) 
Severity of illness  <0.001
Minor50 (5.1)71 (1.2) 
Moderate216 (22.0)359 (5.9) 
Major599 (61.1)1,318 (21.8) 
Extreme115 (11.7)4,299 (71.1) 
Patient clinical characteristics
Number of ICD‐9 diagnosis codes per patient13.76.020.26.6<0.001
MS‐DRG weight2.92.15.96.1<0.001
Hospital characteristics
Mean number of ICD‐9 diagnosis codes per patient per hospital8.51.48.61.40.280
Total hospital discharges15,9576,55316,8576,634<0.001
HACs per 1,000 discharges9.83.710.23.7<0.001
Hospital‐acquired condition
Type of HAC  <0.001
Pressure ulcer334 (34.1)1,599 (26.4) 
Falls/trauma96 (9.8)440 (7.3) 
Catheter‐associated UTI19 (1.9)215 (3.6) 
Vascular catheter infection26 (2.7)1,179 (19.5) 
DVT/pulmonary embolism448 (45.7)2,145 (35.5) 
Other conditions57 (5.8)469 (7.8) 
HAC position  <0.001
2nd code850 (86.7)697 (11.5) 
3rd code45 (4.6)739 (12.2) 
4th code30 (3.1)641 (10.6) 
5th code15 (1.5)569 (9.4) 
6th code or higher40 (4.1)3,401 (56.2) 

Withholding additional reimbursement for an HAC has been controversial. One area of debate is that the assignment of an HAC may be imprecise, in part due to the variation in how physicians document in the medical record.[1, 2, 6, 7, 8, 9] Coding is derived from documentation in physician notes and is the primary mechanism for assigning International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9) diagnosis codes to the patient's encounter. The coding process begins with health information technicians (ie, medical record coders) reviewing all medical record documentation to assign diagnosis and procedure codes using the ICD‐9 codes.[10] Primary and secondary diagnoses are determined by certain definitions in the hospital setting. Secondary diagnoses can be further separated into complications or comorbidities in the MS‐DRG system, which can affect reimbursement. The MS‐DRG is then determined using these diagnosis and procedure codes. Physician documentation is the principal source of data for hospital billing, because health information technicians (ie, medical record coders) must assign a code based on what is documented in the chart. If key medical detail is missing or language is ambiguous, then coding can be inaccurate, which may lead to inappropriate compensation.[11]

Accurate and complete ICD‐9 diagnosis and procedure coding is essential for correct MS‐DRG assignment and reimbursement.[12] Physicians may influence coding prioritization by either over‐emphasizing a patient diagnosis or by downplaying the significance of new findings. In addition, unless the physician uses specific, accurate, and accepted terminology, the diagnosis may not even appear in the list of diagnosis codes. Medical records with nonstandard abbreviations may result in coder‐omission of key diagnoses. Finally, when clinicians use qualified diagnoses such as rule‐out or probable, the final diagnosis coded may not be accurate.[10]

Although the CMS policy creates a financial incentive for hospitals to improve quality, the extent to which the policy actually impacts reimbursement across multiple HACs has not been quantified. Additionally, if HACsas a policy initiativereflect actual quality of care, then the position of the ICD‐9 code should not affect MS‐DRG assignment. In this study we evaluated the extent to which MS‐DRG assignment would have been influenced by the presence of an HAC and tested the association of the position of an HAC in the list of ICD‐9 diagnosis codes with changes in MS‐DRG assignment.

METHODS

Study Population

This study was a retrospective analysis of all patients discharged from hospital members of the University HealthSystem Consortium's (UHC) Clinical Data Base between October 2007 and April 2008. The data set was limited to patient discharge records with at least 1 of 10 HACs for which CMS no longer provides additional reimbursement (see Supporting Table 1 in the online version of this article). The presence of an HAC was indicated by the corresponding diagnosis code using the ICD‐9 diagnosis and procedure codes.

Data Source

UHC's Clinical Data Base is a database of patient discharge‐level administrative data used primarily for billing purposes. UHC's Clinical Data Base provides comparative data for in‐hospital healthcare outcomes using encounter‐level and line‐item transactional information from each member organization. UHC is a nonprofit alliance of 116 academic medical centers and 276 of their affiliated hospitals.

Dependent Variable: Change in MS‐DRG Assignment

The dependent variable was a change in MS‐DRG assignment. MS‐DRG assignment was calculated by comparing the MS‐DRG assigned when the HAC's ICD‐9 diagnosis code was considered a no‐payment event and was not included in the determination (ie, post‐policy DRG) with the MS‐DRG that would have been assigned when the HAC was not included in the determination (ie, pre‐policy DRG). The list of ICD‐9 diagnosis codes was entered into MS‐DRG grouping software with the ICD‐9 diagnosis code for each HAC in the identical position presented to CMS. Up to 29 secondary ICD‐9 diagnosis and procedure codes were entered, but the analyses of association on the position of the HAC used the first 9 diagnosis and 6 procedure codes processed by CMS, as only codes in these positions would have changed the MS‐DRG assigned during the study time period. If the 2 MS‐DRGs (pre‐policy DRG and post‐policy DRG) did not match, the case was classified as having a change in MS‐DRG assignment (MS‐DRG change).

Independent variables included in this analysis were coding variables and patient characteristics. Coding variables included the total number of ICD‐9 diagnosis codes recorded in the discharge record, absolute position of the HAC ICD‐9 diagnosis code in the order of all diagnosis codes, weight for the actual MS‐DRG, and specific type of HAC. The absolute position of the HAC was included in the analysis as a categorical variable (second position, third, fourth, fifth, and sixth position and higher). In addition, patient‐level characteristics including sociodemographic characteristics, clinical factors and severity of illness (minor, moderate, major, extreme),[6] and hospital‐level characteristics.

Statistical Analysis

Means and standard deviations or frequencies and percentages were used to describe the variables. A 2 test was used to test for differences in the absolute position of the HAC with change in MS‐DRG assignment (change/no change). In addition, 2 tests were used to test for differences in each of the other categorical independent variables with change in MS‐DRG assignment; t tests were used to test for differences in the continuous variables with change in MS‐DRG assignment.

Two multivariable binary logistic regression models were fit to test the relationship between change in MS‐DRG assignment with the absolute position of the HAC, adjusting for coding variables, patient characteristics, and hospital characteristics that were associated with change in MS‐DRG assignment in the bivariate analysis. The first model tested the relationship between change in MS‐DRG and position of the HAC, without accounting for the specific type of HAC, and the second tested the relationship including both position and the specific type of HAC. Receiver operating characteristic (ROC) curves were developed for each model to evaluate the predictive accuracy. Additionally, analyses were stratified by severity of illness, and the areas under the ROC curves for 3 models were compared to determine whether the predictive accuracy increased with the inclusion of variables other than HAC position. The first model included HAC position only, the second model added type of HAC, and the third model added other coding variables and patient‐ and hospital‐level variables.

Two sensitivity analyses were performed to test the robustness of the results. The first analysis tested the sensitivity of the results to the specification of comorbid disease burden, as measured by number of diagnosis codes. We used Elixhauser's method[13] for identifying comorbid conditions to create binary variables indicating the presence or absence of 29 distinct comorbid conditions, then calculated the total number of comorbid conditions. The binary logistic regression model was refit, with the total number of comorbid conditions in place of the number of diagnosis codes. An additional binary logistic regression model was fit that included the individual comorbid conditions that were associated with change in MS‐DRG assignment in a bivariate analysis (P<0.05). The second sensitivity analysis evaluated whether hospital‐level variation in coding practices explained change in MS‐DRG assignment using a hierarchical binary logistic regression model that included hospital as a random effect.

All statistical analyses were conducted using the SAS version 9.2 statistical software package (SAS Institute Inc., Cary, NC). The Rush University Medical Center Institutional Review Board approved the study protocol.

RESULTS

Of the 954,946 discharges from UHC academic medical centers, 7027 patients (0.7%) had an HAC. Of the patients with an HAC, 6047 did not change MS‐DRG assignment, whereas 980 patients (13.8%) had a change in MS‐DRG assignment. Patients with a change in MS‐DRG assignment were significantly different from those without a change in MS‐DRG assignment on all patient‐level characteristics and all but 1 hospital characteristic (Table 1). The variable with the largest absolute difference between those with and without a change in MS‐DRG was the actual position of the HAC; 86.7% of those with an MS‐DRG change had their HAC in the second position, whereas those without a change had only 11.5% in the second position.

After controlling for patient and hospital characteristics, an HAC in the second position in the list of ICD‐9 codes was associated with the greatest likelihood of a change in MS‐DRG assignment (P<0.001) (Table 2). Each additional ICD‐9 code decreased the odds of an MS‐DRG change (P=0.004), demonstrating that having more secondary diagnosis codes was associated with a lesser likelihood of an MS‐DRG change. After including the individual HACs in the regression model, the second position remained associated with the likelihood of a change in MS‐DRG assignment (results not shown). The predictive accuracy of our model did not improve, however, with the addition of type of HAC. The area under the ROC curve was 0.94 in both models, indicating high predictive power.

Results of Binary Logistic Regression Model for Change in MS‐DRG Assignment (N=7,027)
InterceptOdds RatioP Value
  • NOTE: The reference category for includes extreme severity of illness and HAC ICD‐9 code in the 6th position or higher. The model controls for patient age, sex, race/ethnicity, primary payer, hospital HAC rate, and total number of discharges per hospital. Abbreviations: HAC, hospital‐acquired conditions; ICD‐9, International Classification of Diseases, 9th Revision; MS‐DRG, Medicare Severity Diagnosis‐Related Group; ROC, receiver operating characteristic. *Compared to the model with patient sociodemographic characteristics only.

Minor severity of illness6.80<0.001
Moderate severity of illness5.52<0.001
Major severity of illness8.02<0.001
Number of ICD‐9 diagnosis codes per patient0.970.004
HAC ICD‐9 diagnosis code in 2nd position40.52<0.001
HAC ICD‐9 diagnosis code in 3rd position1.820.009
HAC ICD‐9 diagnosis code in 4th position1.720.032
HAC ICD‐9 diagnosis code in 5th position1.150.662
Area under the ROC curve0.94<0.001*
Area under the ROC curve, model with patient socio‐demographic characteristics only0.85 

The proportion of cases with a change in MS‐DRG by severity of illness is reported in Table 3. The largest proportion of cases with a change in MS‐DRG was in the minor severity of illness category (41.3%), whereas only 2.6% of cases with an extreme severity of illness had a change in MS‐DRG. Figure 1 shows ROC curves stratified by severity of illness. Figure 1A illustrates the ROC curves for the 121 (1.7%) patients with minor severity of illness. The area under the ROC curve for the model including HAC position only was 0.74, indicating moderate predictive power. The inclusion of HAC type increased the predictive power to 0.91, and inclusion of sociodemographic characteristics further increased the predictive power to 0.95. Figure 1BD illustrates the ROC curves for moderate, major, and extreme severities of illness. For more severe illnesses, the predictive accuracy of the models with only HAC position were similar to the full models, demonstrating that HAC position alone had a high predictive power for change in MS‐DRG assignment.

Percentage of Patients With a Change in MS‐DRG by Severity of Illness, Discharges Between October 2007 and April 2008 (N=7,027)
VariableNo.Within Category Percent With MS‐DRG Change
  • NOTE: Abbreviations: MS‐DRG, Medicare Severity Diagnosis‐Related Group.

Severity of illness  
Minor12141.3
Moderate57537.6
Major1,91731.3
Extreme4,4142.6
Figure 1
Receiver operating characteristic (ROC) curves stratified by severity of illness. ROC curves by severity of illness. Abbreviations: AUC, area under the curve; HAC, hospital‐acquired conditions.

In a sensitivity analysis that evaluated the robustness of our results to the specification of disease burden, inclusion of the number of comorbid conditions did not improve the predictive accuracy of the model. Although inclusion of individual comorbid conditions rather than number of diagnosis codes attenuated the odds ratio (OR) for HAC position (OR: 40.5 in the original model vs OR: 32.9 in the model with individual comorbid conditions), the improvement of the predictive accuracy of the model was small (area under the ROC curve=0.936 in the original model vs 0.943 in the model with individual conditions, P<0.001) (results not shown). In a sensitivity analysis using a hierarchical logistic regression model that included hospital random effects, hospital‐level variation in coding practices did not attenuate the relationship between HAC position and MS‐DRG change (results not shown).

DISCUSSION

This study investigated the association of a change in MS‐DRG assignment and position of the ICD‐9 diagnosis codes for HACs in a sample of patients discharged from US academic medical centers. We found that only 14% of the MS‐DRGs for patients with an HAC would have experienced a change in DRG assignment. Our results are consistent with those of Teufack et al.,[14] who estimated the economic impact of CMS' HAC policy for neurosurgery services at a single hospital to be 0.007% of overall net revenues. Nevertheless, the majority of hospitals have increased their efforts to prevent HACs that are included in CMS' policy.[15] At the same time, most hospitals have not increased their budgets for preventing HACs, and instead have reallocated resources from nontargeted HACs to those included in CMS' policy.

The low proportion of records that are impacted by the policy may be partially explained by the fact that CMS' policy only has an impact on reimbursement for MS‐DRGs with multiple levels. For example, heart failure has 3 levels of reimbursement in the MS‐DRG system (Table 4). Prior to CMS' policy, a heart failure patient with an air embolism as an HAC would have been classified in the most severe MS‐DRG (291), whereas after implementation the patient would be classified in the least severe MS‐DRG, if no other complication or comorbidity (CC) or a major complication or comorbidity (MCC) were present. Chest pain has only 1 level, and reimbursement for a patient with an HAC and classified in the chest pain MS‐DRG would not be impacted by CMS' policy. Most hospitalized patients are complicated, and the proportion of patients who are complicated will continue to increase over time as less complex care shifts to the ambulatory setting. The relative effectiveness of CMS' policy is likely to diminish with the continued shift of care to the ambulatory setting.

Example of MS‐DRG Codes and Weights, Fiscal Year 2014
VariableMS‐DRGDRG Weight
  • NOTE: Abbreviations: DRG, Diagnosis‐Related Group; MS‐DRG, Medicare Severity Diagnosis‐Related Group.

Heart failure and shock  
With major complications and comorbidities (MS‐DRG 291)2911.5062
With complications and comorbidities2920.9952
Without major complications or comorbidities2930.6718
Chest pain3130.5992

Patient discharges with a diagnosis code for as HAC in the second position were substantially more likely to have a change in MS‐DRG assignment compared to cases with an HAC listed lower in the final list of diagnosis codes. Perhaps it is not surprising that MS‐DRG assignment is most likely to change when the HAC is in the second position, because an ICD‐9 diagnosis code in this position is more likely to be a major complication or comorbidity. For HACs listed in a lower position of the list of ICD‐9 diagnosis codes, it is likely that the patient had another major complication or comorbidity listed in the second position that would have maintained classification in the same MS‐DRG. Our results suggest that physicians and hospitals caring for patients with lower complexity of illness will sustain a higher financial burden as a result of an HAC under CMS' policy compared to providers whose patients sustain the exact same HAC but have underlying medical care of greater complexity.

These results raise further concerns about the ability of CMS' payment policy to improve quality. One criticism of CMS' policy is that all HACs are not universally preventable. If they are not preventable, payment reductions promulgated via the policy would be punitive rather than incentivizing. In their study of central catheter‐associated bloodstream infections and catheter‐associated urinary tract infections, for example, Lee et al. found no change in infection rates after implementation of CMS' policy.[16] As such, some have suggested HACs should not be used to determine reimbursement, and CMS should abandon its current nonpayment policy.[4, 17] Our findings echo this criticism given that the financial penalty for an HAC depends on whether a patient is more or less complex.

Because coding emanates from physician documentation, a uniform documentation process must exist to ensure nonvariable coding practices.[1, 2, 7, 9] This is not the case, however, and some hospitals comanage documentation to refine or maximize the number of ICD‐9 diagnosis and procedure codes. Furthermore, there are certain differences in the documentation practices of individual physicians. If physician documentation and coding variation leads to fewer ICD‐9 codes during an encounter, the chance that an HAC will influence MS‐DRG change increases.

Another source of variation in coding practices found in this study was code sequencing. Although guidelines for appropriate ICD‐9 diagnosis coding currently exist, individual subjectivity remains. The most essential step in the coding process is identifying the principal diagnosis by extrapolating from physician documentation and clinical data. For example, when a patient is admitted for chest pain, and after some evaluation it is determined that the patient experienced a myocardial infarction, then myocardial infarction becomes the principal diagnosis. Based on that principal diagnosis, coders must select the relevant secondary diagnoses. The process involves a series of steps that must be followed exactly in order to ensure accurate coding.[12] There are no guidelines by which coding personnel must follow to sequence secondary diagnoses, with the exception of listed MCCs and CCs prior to other secondary diagnoses. Ultimately, the order by which these codes are assigned may result in unfavorable variation in MS‐DRG assignment.[1, 2, 4, 7, 8, 9, 17]

There are a number of limitations to this study. First, our cohort included only UHC‐affiliated academic medical centers, which may not represent all acute‐care hospitals and their coding practices. Although our data are for discharges prior to implementation of the policy, we were able to analyze the anticipated impact of the policy prior to any direct or indirect changes in coding that may have occurred in response to CMS' policy. Additionally, the number of diagnosis codes accepted by CMS was expanded from 9 to 25 in 2011. Future analyses that include MS‐DRG classifications with the expanded number of diagnosis codes should be conducted to validate our findings and determine whether any changes have occurred over time. It is not known whether low illness severity scores signify patient or hospital characteristics. If they represent patient characteristics, then CMS' policy will disproportionately affect hospitals taking care of less severely ill patients. Alternatively, if hospital coding practice explains more of the variation in the number of ICD‐9 codes (and thus severity of illness), then the system of adjudicating reimbursement via HACs to incentivize quality of care will be flawed, as there is no standard position for HACs on a more lengthy diagnosis list. Finally, we did not evaluate the change in DRG weight with the reassignment of MS‐DRG if the HAC had been included in the calculation. Future work should evaluate whether there is a differential impact of the policy by change in MS‐DRG weight.

CONCLUSION

Under CMS' current policy, hospitals and physicians caring for patients with lower severity of illness and have an HAC will be penalized by CMS disproportionately more than those caring for more complex, sicker patients with the identical HAC. If, in fact, HACs are indicators of a hospital's quality of care, then the CMS policy will likely do little to foster improved quality unless there is a reduction in coding practice variation and modifications to ensure that the policy impacts reimbursement, independent of severity of illness.

Disclosures

The authors acknowledge the financial support for data acquisition from the Rush University College of Health Sciences. The authors report no conflicts of interest.

References
  1. Centers for Medicare and Medicaid Services. Hospital‐acquired conditions (present on admission indicator). Available at: http://www.cms.hhs.gov/HospitalAcqCond/05_Coding.asp#TopOfPage. Updated 2012. Accessed September 20, 2012.
  2. Centers for Medicare and Medicaid Services. Hospital‐acquired conditions: coding. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/HospitalAcqCond/Coding.html. Updated 2012. Accessed February 2, 2012.
  3. ICD‐9‐CM 2009 Coders' Desk Reference for Procedures. Eden Prairie, MN: Ingenix; 2009.
  4. Averill RF, Hughes JS, Goldfield NI, McCullough EC. Hospital complications: linking payment reduction to preventability. Jt Comm J Qual Patient Saf. 2009;35(5):283285.
  5. McNutt R, Johnson TJ, Odwazny R, et al. Change in MS‐DRG assignment and hospital reimbursement as a result of Centers for Medicare
References
  1. Centers for Medicare and Medicaid Services. Hospital‐acquired conditions (present on admission indicator). Available at: http://www.cms.hhs.gov/HospitalAcqCond/05_Coding.asp#TopOfPage. Updated 2012. Accessed September 20, 2012.
  2. Centers for Medicare and Medicaid Services. Hospital‐acquired conditions: coding. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/HospitalAcqCond/Coding.html. Updated 2012. Accessed February 2, 2012.
  3. ICD‐9‐CM 2009 Coders' Desk Reference for Procedures. Eden Prairie, MN: Ingenix; 2009.
  4. Averill RF, Hughes JS, Goldfield NI, McCullough EC. Hospital complications: linking payment reduction to preventability. Jt Comm J Qual Patient Saf. 2009;35(5):283285.
  5. McNutt R, Johnson TJ, Odwazny R, et al. Change in MS‐DRG assignment and hospital reimbursement as a result of Centers for Medicare
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Journal of Hospital Medicine - 9(11)
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Journal of Hospital Medicine - 9(11)
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707-713
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Association of the position of a hospital‐acquired condition diagnosis code with changes in medicare severity diagnosis‐related group assignment
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Association of the position of a hospital‐acquired condition diagnosis code with changes in medicare severity diagnosis‐related group assignment
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Address for correspondence and reprint requests: Tricia Johnson, PhD, Department of Health Systems Management, Rush University Medical Center, 1700 West Van Buren Street, TOB Suite 126B, Chicago, IL 60612; Telephone: 312‐942‐7107; Fax: 312‐942‐4957; E‐mail: [email protected]
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