As healthcare costs rise, physicians and other stakeholders are now seeking innovative and effective ways to reduce the provision of low-value services.^1,2 The Choosing Wisely^® campaign aims to further this goal by promoting lists of specific procedures, tests, and treatments that providers should avoid in selected clinical settings.³ On February 21, 2013, the Society of Hospital Medicine (SHM) released 2 Choosing Wisely^® lists consisting of adult and pediatric services that are seen as costly to consumers and to the healthcare system, but which are often nonbeneficial or even harmful.^4,5 A total of 80 physician and nurse specialty societies have joined in submitting additional lists.

Despite the growing enthusiasm for this effort, questions remain regarding the Choosing Wisely^® campaign’s ability to initiate the meaningful de-adoption of low-value services. Specifically, prior efforts to reduce the use of services deemed to be of questionable benefit have met several challenges.^2,6 Early analyses of the Choosing Wisely^® recommendations reveal similar roadblocks and variable uptakes of several recommendations.^7-10 While the reasons for difficulties in achieving de-adoption are broad, one important factor in whether clinicians are willing to follow guideline recommendations from such initiatives as Choosing Wisely^®is the extent to which they believe in the underlying evidence.¹¹ The current work seeks to formally evaluate the evidence supporting the Choosing Wisely^® recommendations, and to compare the quality of evidence supporting SHM lists to other published Choosing Wisely^® lists.

Data Sources

Using the online listing of published Choosing Wisely^® recommendations, a dataset was generated incorporating all 320 recommendations comprising the 58 lists published through August, 2014; these include both the adult and pediatric hospital medicine lists released by the SHM.^4,5,12 Although data collection ended at this point, this represents a majority of all 81 lists and 535 recommendations published through December, 2017. The reviewers (A.J.A., A.G., M.W., T.S.V., M.S., and C.R.C) extracted information about the references cited for each recommendation.

Data Analysis

The reviewers obtained each reference cited by a Choosing Wisely^® recommendation and categorized it by evidence strength along the following hierarchy: clinical practice guideline (CPG), primary research, review article, expert opinion, book, or others/unknown. CPGs were used as the highest level of evidence based on standard expectations for methodological rigor.¹³ Primary research was further rated as follows: systematic reviews and meta-analyses, randomized controlled trials (RCTs), observational studies, and case series. Each recommendation was graded using only the strongest piece of evidence cited.

Guideline Appraisal

We further sought to evaluate the strength of referenced CPGs. To accomplish this, a 10% random sample of the Choosing Wisely^® recommendations citing CPGs was selected, and the referenced CPGs were obtained. Separately, CPGs referenced by the SHM-published adult and pediatric lists were also obtained. For both groups, one CPG was randomly selected when a recommendation cited more than one CPG. These guidelines were assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, a widely used instrument designed to assess CPG quality.^14,15 AGREE II consists of 25 questions categorized into 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. Guidelines are also assigned an overall score. Two trained reviewers (A.J.A. and A.G.) assessed each of the sampled CPGs using a standardized form. Scores were then standardized using the method recommended by the instrument and reported as a percentage of available points. Although a standard interpretation of scores is not provided by the instrument, prior applications deemed scores below 50% as deficient^16,17. When a recommendation item cited multiple CPGs, one was randomly selected. We also abstracted data on the year of publication, the evidence grade assigned to specific items recommended by Choosing Wisely^®, and whether the CPG addressed the referring recommendation. All data management and analysis were conducted using Stata (V14.2, StataCorp, College Station, Texas).

A total of 320 recommendations were considered in our analysis, including 10 published across the 2 hospital medicine lists. When limited to the highest quality citation for each of the recommendations, 225 (70.3%) cited CPGs, whereas 71 (22.2%) cited primary research articles (Table 1). Specifically, 29 (9.1%) cited systematic reviews and meta-analyses, 28 (8.8%) cited observational studies, and 13 (4.1%) cited RCTs. One recommendation (0.3%) cited a case series as its highest level of evidence, 7 (2.2%) cited review articles, 7 (2.2%) cited editorials or opinion pieces, and 10 (3.1%) cited other types of documents, such as websites or books. Among hospital medicine recommendations, 9 (90%) referenced CPGs and 1 (10%) cited an observational study.

For the AGREE II assessment, we included 23 CPGs from the 225 referenced across all recommendations, after which we separately selected 6 CPGs from the hospital medicine recommendations. There was no overlap. Notably, 4 hospital medicine recommendations referenced a common CPG. Among the random sample of referenced CPGs, the median overall score obtained by using AGREE II was 54.2% (IQR 33.3%-70.8%, Table 2). This was similar to the median overall among hospital medicine guidelines (58.2%, IQR 50.0%-83.3%). Both hospital medicine and other sampled guidelines tended to score poorly in stakeholder involvement (48.6%, IQR 44.1%-61.1% and 47.2%, IQR 38.9%-61.1%, respectively). There were no significant differences between hospital medicine-referenced CPGs and the larger sample of CPGs in any AGREE II subdomains. The median age from the CPG publication to the list publication was 7 years (IQR 4–7) for hospital medicine recommendations and 3 years (IQR 2–6) for the nonhospital medicine recommendations. Substantial agreement was found between raters on the overall guideline assessment (ICC 0.80, 95% CI 0.58-0.91; Supplementary Table 1).

Given the rising costs and the potential for iatrogenic harm, curbing ineffective practices has become an urgent concern. To achieve this, the Choosing Wisely^® campaign has taken an important step by targeting certain low-value practices for de-adoption. However, the evidence supporting recommendations is variable. Specifically, 25 recommendations cited case series, review articles, or lower quality evidence as their highest level of support; moreover, among recommendations citing CPGs, quality, timeliness, and support for the recommendation item were variable. Although the hospital medicine lists tended to cite higher-quality evidence in the form of CPGs, these CPGs were often less recent than the guidelines referenced by other lists.

Our findings parallel those of other works that evaluate evidence among Choosing Wisely^® recommendations and, more broadly, among CPGs.^18–21 Lin and Yancey evaluated the quality of primary care-focused Choosing Wisely^® recommendations using the Strength of Recommendation Taxonomy, a ranking system that evaluates evidence quality, consistency, and patient-centeredness.¹⁸ In their analysis, the authors found that many recommendations were based on lower quality evidence or relied on nonpatent-centered intermediate outcomes. Several groups, meanwhile, have evaluated the quality of evidence supporting CPG recommendations, finding them to be highly variable as well.^19–21 These findings likely reflect inherent difficulties in the process, by which guideline development groups distill a broad evidence base into useful clinical recommendations, a reality that may have influenced the Choosing Wisely^® list development groups seeking to make similar recommendations on low-value services.

These data should be taken in context due to several limitations. First, our sample of referenced CPGs includes only a small sample of all CPGs cited; thus, it may not be representative of all referenced guidelines. Second, the AGREE II assessment is inherently subjective, despite the availability of training materials. Third, data collection ended in April, 2014. Although this represents a majority of published lists to date, it is possible that more recent Choosing Wisely^®lists include a stronger focus on evidence quality. Finally, references cited by Choosing Wisely^®may not be representative of the entirety of the dataset that was considered when formulating the recommendations.

Despite these limitations, our findings suggest that Choosing Wisely^®recommendations vary in terms of evidence strength. Although our results reveal that the majority of recommendations cite guidelines or high-quality original research, evidence gaps remain, with a small number citing low-quality evidence or low-quality CPGs as their highest form of support. Given the barriers to the successful de-implementation of low-value services, such campaigns as Choosing Wisely^®face an uphill battle in their attempt to prompt behavior changes among providers and consumers.^6-9 As a result, it is incumbent on funding agencies and medical journals to promote studies evaluating the harms and overall value of the care we deliver.

Although a majority of Choosing Wisely^® recommendations cite high-quality evidence, some reference low-quality evidence or low-quality CPGs as their highest form of support. To overcome clinical inertia and other barriers to the successful de-implementation of low-value services, a clear rationale for the impetus to eradicate entrenched practices is critical.^2,22 Choosing Wisely^® has provided visionary leadership and a powerful platform to question low-value care. To expand the campaign’s efforts, the medical field must be able to generate the high-quality evidence necessary to support these efforts; further, list development groups must consider the availability of strong evidence when targeting services for de-implementation.

This work was supported, in part, by a grant from the Agency for Healthcare Research and Quality (No. K08HS020672, Dr. Cooke).

Disclosures

The authors have nothing to disclose.

As healthcare costs rise, physicians and other stakeholders are now seeking innovative and effective ways to reduce the provision of low-value services.^1,2 The Choosing Wisely^® campaign aims to further this goal by promoting lists of specific procedures, tests, and treatments that providers should avoid in selected clinical settings.³ On February 21, 2013, the Society of Hospital Medicine (SHM) released 2 Choosing Wisely^® lists consisting of adult and pediatric services that are seen as costly to consumers and to the healthcare system, but which are often nonbeneficial or even harmful.^4,5 A total of 80 physician and nurse specialty societies have joined in submitting additional lists.

Despite the growing enthusiasm for this effort, questions remain regarding the Choosing Wisely^® campaign’s ability to initiate the meaningful de-adoption of low-value services. Specifically, prior efforts to reduce the use of services deemed to be of questionable benefit have met several challenges.^2,6 Early analyses of the Choosing Wisely^® recommendations reveal similar roadblocks and variable uptakes of several recommendations.^7-10 While the reasons for difficulties in achieving de-adoption are broad, one important factor in whether clinicians are willing to follow guideline recommendations from such initiatives as Choosing Wisely^®is the extent to which they believe in the underlying evidence.¹¹ The current work seeks to formally evaluate the evidence supporting the Choosing Wisely^® recommendations, and to compare the quality of evidence supporting SHM lists to other published Choosing Wisely^® lists.

Data Sources

Using the online listing of published Choosing Wisely^® recommendations, a dataset was generated incorporating all 320 recommendations comprising the 58 lists published through August, 2014; these include both the adult and pediatric hospital medicine lists released by the SHM.^4,5,12 Although data collection ended at this point, this represents a majority of all 81 lists and 535 recommendations published through December, 2017. The reviewers (A.J.A., A.G., M.W., T.S.V., M.S., and C.R.C) extracted information about the references cited for each recommendation.

Data Analysis

The reviewers obtained each reference cited by a Choosing Wisely^® recommendation and categorized it by evidence strength along the following hierarchy: clinical practice guideline (CPG), primary research, review article, expert opinion, book, or others/unknown. CPGs were used as the highest level of evidence based on standard expectations for methodological rigor.¹³ Primary research was further rated as follows: systematic reviews and meta-analyses, randomized controlled trials (RCTs), observational studies, and case series. Each recommendation was graded using only the strongest piece of evidence cited.

Guideline Appraisal

We further sought to evaluate the strength of referenced CPGs. To accomplish this, a 10% random sample of the Choosing Wisely^® recommendations citing CPGs was selected, and the referenced CPGs were obtained. Separately, CPGs referenced by the SHM-published adult and pediatric lists were also obtained. For both groups, one CPG was randomly selected when a recommendation cited more than one CPG. These guidelines were assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, a widely used instrument designed to assess CPG quality.^14,15 AGREE II consists of 25 questions categorized into 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. Guidelines are also assigned an overall score. Two trained reviewers (A.J.A. and A.G.) assessed each of the sampled CPGs using a standardized form. Scores were then standardized using the method recommended by the instrument and reported as a percentage of available points. Although a standard interpretation of scores is not provided by the instrument, prior applications deemed scores below 50% as deficient^16,17. When a recommendation item cited multiple CPGs, one was randomly selected. We also abstracted data on the year of publication, the evidence grade assigned to specific items recommended by Choosing Wisely^®, and whether the CPG addressed the referring recommendation. All data management and analysis were conducted using Stata (V14.2, StataCorp, College Station, Texas).

A total of 320 recommendations were considered in our analysis, including 10 published across the 2 hospital medicine lists. When limited to the highest quality citation for each of the recommendations, 225 (70.3%) cited CPGs, whereas 71 (22.2%) cited primary research articles (Table 1). Specifically, 29 (9.1%) cited systematic reviews and meta-analyses, 28 (8.8%) cited observational studies, and 13 (4.1%) cited RCTs. One recommendation (0.3%) cited a case series as its highest level of evidence, 7 (2.2%) cited review articles, 7 (2.2%) cited editorials or opinion pieces, and 10 (3.1%) cited other types of documents, such as websites or books. Among hospital medicine recommendations, 9 (90%) referenced CPGs and 1 (10%) cited an observational study.

For the AGREE II assessment, we included 23 CPGs from the 225 referenced across all recommendations, after which we separately selected 6 CPGs from the hospital medicine recommendations. There was no overlap. Notably, 4 hospital medicine recommendations referenced a common CPG. Among the random sample of referenced CPGs, the median overall score obtained by using AGREE II was 54.2% (IQR 33.3%-70.8%, Table 2). This was similar to the median overall among hospital medicine guidelines (58.2%, IQR 50.0%-83.3%). Both hospital medicine and other sampled guidelines tended to score poorly in stakeholder involvement (48.6%, IQR 44.1%-61.1% and 47.2%, IQR 38.9%-61.1%, respectively). There were no significant differences between hospital medicine-referenced CPGs and the larger sample of CPGs in any AGREE II subdomains. The median age from the CPG publication to the list publication was 7 years (IQR 4–7) for hospital medicine recommendations and 3 years (IQR 2–6) for the nonhospital medicine recommendations. Substantial agreement was found between raters on the overall guideline assessment (ICC 0.80, 95% CI 0.58-0.91; Supplementary Table 1).

Given the rising costs and the potential for iatrogenic harm, curbing ineffective practices has become an urgent concern. To achieve this, the Choosing Wisely^® campaign has taken an important step by targeting certain low-value practices for de-adoption. However, the evidence supporting recommendations is variable. Specifically, 25 recommendations cited case series, review articles, or lower quality evidence as their highest level of support; moreover, among recommendations citing CPGs, quality, timeliness, and support for the recommendation item were variable. Although the hospital medicine lists tended to cite higher-quality evidence in the form of CPGs, these CPGs were often less recent than the guidelines referenced by other lists.

Our findings parallel those of other works that evaluate evidence among Choosing Wisely^® recommendations and, more broadly, among CPGs.^18–21 Lin and Yancey evaluated the quality of primary care-focused Choosing Wisely^® recommendations using the Strength of Recommendation Taxonomy, a ranking system that evaluates evidence quality, consistency, and patient-centeredness.¹⁸ In their analysis, the authors found that many recommendations were based on lower quality evidence or relied on nonpatent-centered intermediate outcomes. Several groups, meanwhile, have evaluated the quality of evidence supporting CPG recommendations, finding them to be highly variable as well.^19–21 These findings likely reflect inherent difficulties in the process, by which guideline development groups distill a broad evidence base into useful clinical recommendations, a reality that may have influenced the Choosing Wisely^® list development groups seeking to make similar recommendations on low-value services.

These data should be taken in context due to several limitations. First, our sample of referenced CPGs includes only a small sample of all CPGs cited; thus, it may not be representative of all referenced guidelines. Second, the AGREE II assessment is inherently subjective, despite the availability of training materials. Third, data collection ended in April, 2014. Although this represents a majority of published lists to date, it is possible that more recent Choosing Wisely^®lists include a stronger focus on evidence quality. Finally, references cited by Choosing Wisely^®may not be representative of the entirety of the dataset that was considered when formulating the recommendations.

Despite these limitations, our findings suggest that Choosing Wisely^®recommendations vary in terms of evidence strength. Although our results reveal that the majority of recommendations cite guidelines or high-quality original research, evidence gaps remain, with a small number citing low-quality evidence or low-quality CPGs as their highest form of support. Given the barriers to the successful de-implementation of low-value services, such campaigns as Choosing Wisely^®face an uphill battle in their attempt to prompt behavior changes among providers and consumers.^6-9 As a result, it is incumbent on funding agencies and medical journals to promote studies evaluating the harms and overall value of the care we deliver.

Although a majority of Choosing Wisely^® recommendations cite high-quality evidence, some reference low-quality evidence or low-quality CPGs as their highest form of support. To overcome clinical inertia and other barriers to the successful de-implementation of low-value services, a clear rationale for the impetus to eradicate entrenched practices is critical.^2,22 Choosing Wisely^® has provided visionary leadership and a powerful platform to question low-value care. To expand the campaign’s efforts, the medical field must be able to generate the high-quality evidence necessary to support these efforts; further, list development groups must consider the availability of strong evidence when targeting services for de-implementation.

This work was supported, in part, by a grant from the Agency for Healthcare Research and Quality (No. K08HS020672, Dr. Cooke).

Disclosures

The authors have nothing to disclose.

As healthcare costs rise, physicians and other stakeholders are now seeking innovative and effective ways to reduce the provision of low-value services.^1,2 The Choosing Wisely^® campaign aims to further this goal by promoting lists of specific procedures, tests, and treatments that providers should avoid in selected clinical settings.³ On February 21, 2013, the Society of Hospital Medicine (SHM) released 2 Choosing Wisely^® lists consisting of adult and pediatric services that are seen as costly to consumers and to the healthcare system, but which are often nonbeneficial or even harmful.^4,5 A total of 80 physician and nurse specialty societies have joined in submitting additional lists.

Despite the growing enthusiasm for this effort, questions remain regarding the Choosing Wisely^® campaign’s ability to initiate the meaningful de-adoption of low-value services. Specifically, prior efforts to reduce the use of services deemed to be of questionable benefit have met several challenges.^2,6 Early analyses of the Choosing Wisely^® recommendations reveal similar roadblocks and variable uptakes of several recommendations.^7-10 While the reasons for difficulties in achieving de-adoption are broad, one important factor in whether clinicians are willing to follow guideline recommendations from such initiatives as Choosing Wisely^®is the extent to which they believe in the underlying evidence.¹¹ The current work seeks to formally evaluate the evidence supporting the Choosing Wisely^® recommendations, and to compare the quality of evidence supporting SHM lists to other published Choosing Wisely^® lists.

Data Sources

Using the online listing of published Choosing Wisely^® recommendations, a dataset was generated incorporating all 320 recommendations comprising the 58 lists published through August, 2014; these include both the adult and pediatric hospital medicine lists released by the SHM.^4,5,12 Although data collection ended at this point, this represents a majority of all 81 lists and 535 recommendations published through December, 2017. The reviewers (A.J.A., A.G., M.W., T.S.V., M.S., and C.R.C) extracted information about the references cited for each recommendation.

Data Analysis

The reviewers obtained each reference cited by a Choosing Wisely^® recommendation and categorized it by evidence strength along the following hierarchy: clinical practice guideline (CPG), primary research, review article, expert opinion, book, or others/unknown. CPGs were used as the highest level of evidence based on standard expectations for methodological rigor.¹³ Primary research was further rated as follows: systematic reviews and meta-analyses, randomized controlled trials (RCTs), observational studies, and case series. Each recommendation was graded using only the strongest piece of evidence cited.

Guideline Appraisal

We further sought to evaluate the strength of referenced CPGs. To accomplish this, a 10% random sample of the Choosing Wisely^® recommendations citing CPGs was selected, and the referenced CPGs were obtained. Separately, CPGs referenced by the SHM-published adult and pediatric lists were also obtained. For both groups, one CPG was randomly selected when a recommendation cited more than one CPG. These guidelines were assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, a widely used instrument designed to assess CPG quality.^14,15 AGREE II consists of 25 questions categorized into 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. Guidelines are also assigned an overall score. Two trained reviewers (A.J.A. and A.G.) assessed each of the sampled CPGs using a standardized form. Scores were then standardized using the method recommended by the instrument and reported as a percentage of available points. Although a standard interpretation of scores is not provided by the instrument, prior applications deemed scores below 50% as deficient^16,17. When a recommendation item cited multiple CPGs, one was randomly selected. We also abstracted data on the year of publication, the evidence grade assigned to specific items recommended by Choosing Wisely^®, and whether the CPG addressed the referring recommendation. All data management and analysis were conducted using Stata (V14.2, StataCorp, College Station, Texas).

A total of 320 recommendations were considered in our analysis, including 10 published across the 2 hospital medicine lists. When limited to the highest quality citation for each of the recommendations, 225 (70.3%) cited CPGs, whereas 71 (22.2%) cited primary research articles (Table 1). Specifically, 29 (9.1%) cited systematic reviews and meta-analyses, 28 (8.8%) cited observational studies, and 13 (4.1%) cited RCTs. One recommendation (0.3%) cited a case series as its highest level of evidence, 7 (2.2%) cited review articles, 7 (2.2%) cited editorials or opinion pieces, and 10 (3.1%) cited other types of documents, such as websites or books. Among hospital medicine recommendations, 9 (90%) referenced CPGs and 1 (10%) cited an observational study.

For the AGREE II assessment, we included 23 CPGs from the 225 referenced across all recommendations, after which we separately selected 6 CPGs from the hospital medicine recommendations. There was no overlap. Notably, 4 hospital medicine recommendations referenced a common CPG. Among the random sample of referenced CPGs, the median overall score obtained by using AGREE II was 54.2% (IQR 33.3%-70.8%, Table 2). This was similar to the median overall among hospital medicine guidelines (58.2%, IQR 50.0%-83.3%). Both hospital medicine and other sampled guidelines tended to score poorly in stakeholder involvement (48.6%, IQR 44.1%-61.1% and 47.2%, IQR 38.9%-61.1%, respectively). There were no significant differences between hospital medicine-referenced CPGs and the larger sample of CPGs in any AGREE II subdomains. The median age from the CPG publication to the list publication was 7 years (IQR 4–7) for hospital medicine recommendations and 3 years (IQR 2–6) for the nonhospital medicine recommendations. Substantial agreement was found between raters on the overall guideline assessment (ICC 0.80, 95% CI 0.58-0.91; Supplementary Table 1).

Given the rising costs and the potential for iatrogenic harm, curbing ineffective practices has become an urgent concern. To achieve this, the Choosing Wisely^® campaign has taken an important step by targeting certain low-value practices for de-adoption. However, the evidence supporting recommendations is variable. Specifically, 25 recommendations cited case series, review articles, or lower quality evidence as their highest level of support; moreover, among recommendations citing CPGs, quality, timeliness, and support for the recommendation item were variable. Although the hospital medicine lists tended to cite higher-quality evidence in the form of CPGs, these CPGs were often less recent than the guidelines referenced by other lists.

Our findings parallel those of other works that evaluate evidence among Choosing Wisely^® recommendations and, more broadly, among CPGs.^18–21 Lin and Yancey evaluated the quality of primary care-focused Choosing Wisely^® recommendations using the Strength of Recommendation Taxonomy, a ranking system that evaluates evidence quality, consistency, and patient-centeredness.¹⁸ In their analysis, the authors found that many recommendations were based on lower quality evidence or relied on nonpatent-centered intermediate outcomes. Several groups, meanwhile, have evaluated the quality of evidence supporting CPG recommendations, finding them to be highly variable as well.^19–21 These findings likely reflect inherent difficulties in the process, by which guideline development groups distill a broad evidence base into useful clinical recommendations, a reality that may have influenced the Choosing Wisely^® list development groups seeking to make similar recommendations on low-value services.

These data should be taken in context due to several limitations. First, our sample of referenced CPGs includes only a small sample of all CPGs cited; thus, it may not be representative of all referenced guidelines. Second, the AGREE II assessment is inherently subjective, despite the availability of training materials. Third, data collection ended in April, 2014. Although this represents a majority of published lists to date, it is possible that more recent Choosing Wisely^®lists include a stronger focus on evidence quality. Finally, references cited by Choosing Wisely^®may not be representative of the entirety of the dataset that was considered when formulating the recommendations.

Despite these limitations, our findings suggest that Choosing Wisely^®recommendations vary in terms of evidence strength. Although our results reveal that the majority of recommendations cite guidelines or high-quality original research, evidence gaps remain, with a small number citing low-quality evidence or low-quality CPGs as their highest form of support. Given the barriers to the successful de-implementation of low-value services, such campaigns as Choosing Wisely^®face an uphill battle in their attempt to prompt behavior changes among providers and consumers.^6-9 As a result, it is incumbent on funding agencies and medical journals to promote studies evaluating the harms and overall value of the care we deliver.

Although a majority of Choosing Wisely^® recommendations cite high-quality evidence, some reference low-quality evidence or low-quality CPGs as their highest form of support. To overcome clinical inertia and other barriers to the successful de-implementation of low-value services, a clear rationale for the impetus to eradicate entrenched practices is critical.^2,22 Choosing Wisely^® has provided visionary leadership and a powerful platform to question low-value care. To expand the campaign’s efforts, the medical field must be able to generate the high-quality evidence necessary to support these efforts; further, list development groups must consider the availability of strong evidence when targeting services for de-implementation.

This work was supported, in part, by a grant from the Agency for Healthcare Research and Quality (No. K08HS020672, Dr. Cooke).

Disclosures

The authors have nothing to disclose.

Recent years have seen a proliferation of programs designed to prevent readmissions, including patient education initiatives, financial assistance programs, postdischarge services, and clinical personnel assigned to help patients navigate their posthospitalization clinical care. Although some strategies do not require direct patient participation (such as timely and effective handoffs between inpatient and outpatient care teams), many rely upon a commitment by the patient to participate in the postdischarge care plan. At our hospital, we have found that only about 2/3 of patients who are offered transitional interventions (such as postdischarge phone calls by nurses or home nursing through a “transition guide” program) receive the intended interventions, and those who do not receive them are more likely to be readmitted.¹ While limited patient uptake may relate, in part, to factors that are difficult to overcome, such as inadequate housing or phone service, we have also encountered patients whose values, beliefs, or preferences about their care do not align with those of the care team. The purposes of this exploratory study were to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes are associated with actual readmission, and (3) determine whether patients can estimate their own risk of readmission.

From January 2014 to September 2016, we circulated surveys to patients on internal medicine nursing units who were being discharged home within 24 hours. Blank surveys were distributed to nursing units by the researchers. Unit clerks and support staff were educated on the purpose of the project and asked to distribute surveys to patients who were identified by unit case managers or nurses as slated for discharge. Staff members were not asked to help with or supervise survey completion. Surveys were generally filled out by patients, but we allowed family members to assist patients if needed, and to indicate so with a checkbox. There were no exclusion criteria. Because surveys were distributed by clinical staff, the received surveys can be considered a convenience sample. Patients were asked 5 questions with 4- or 5-point Likert scale responses:

(1) “How likely is it that you will be admitted to the hospital (have to stay in the hospital overnight) again within the next 30 days after you leave the hospital this time?” [answers ranging from “Very Unlikely (<5% chance)” to “Very Likely (>50% chance)”];

(2) “How would you feel about being rehospitalized in the next month?” [answers ranging from “Very sad, frustrated, or disappointed” to “Very happy or relieved”];

(3) “How much do you think that you personally can control whether or not you will be rehospitalized (based on what you do to take care of your body, take your medicines, and follow-up with your healthcare team)?” [answers ranging from “I have no control over whether I will be rehospitalized” to “I have complete control over whether I will be rehospitalized”];

(4) “Which of the options below best describes how you plan to follow the medical instructions after you leave the hospital?” [answers ranging from “I do NOT plan to do very much of what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I plan to do EVERYTHING I am being asked to do by the doctors, nurses, therapists and other members of the care team”]; and

(5) “Pick the item below that best describes YOUR OWN VIEW of the care team’s recommendations:” [answers ranging from “I DO NOT AGREE AT ALL that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I FULLY AGREE that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team”].

Responses were linked, based on discharge date and medical record number, to administrative data, including age, sex, race, payer, and clinical data. Subsequent hospitalizations to our hospital were ascertained from administrative data. We estimated expected risk of readmission using the all payer refined diagnosis related group coupled with the associated severity-of-illness (SOI) score, as we have reported previously.^2-5 We restricted our analysis to patients who answered the question related to the likelihood of readmission. Logistic regression models were constructed using actual 30-day readmission as the dependent variable to determine whether patients could predict their own readmissions and whether patient attitudes and beliefs about their care were predictive of subsequent readmission. Patient survey responses were entered as continuous independent variables (ranging from 1-4 or 1-5, as appropriate). Multivariable logistic regression was used to determine whether patients could predict their readmissions independent of demographic variables and expected readmission rate (modeled continuously); we repeated this model after dichotomizing the patient’s estimate of the likelihood of readmission as either “unlikely” or “likely.” Patients with missing survey responses were excluded from individual models without imputation. The study was approved by the Johns Hopkins institutional review board.

Responses were obtained from 895 patients. Their median age was 56 years [interquartile range, 43-67], 51.4% were female, and 41.7% were white. Mean SOI was 2.53 (on a 1-4 scale), and median length-of-stay was representative for our medical service at 5.2 days (range, 1-66 days). Family members reported filling out the survey in 57 cases. The primary payer was Medicare in 40.7%, Medicaid in 24.9%, and other in 34.4%. A total of 138 patients (15.4%) were readmitted within 30 days. The Table shows survey responses and associated readmission rates. None of the attitudes related to readmission were predictive of actual readmission. However, patients were able to predict their own readmissions (P = .002 for linear trend). After adjustment for expected readmission rate, race, sex, age, and payer, the trend remained significant (P = .005). Other significant predictors of readmissions in this model included expected readmission rate (P = .002), age (P = .02), and payer (P = .002). After dichotomizing the patient estimate of readmission rate as “unlikely” (N = 581) or “likely” (N = 314), the unadjusted odds ratio associating a patient-estimated risk of readmission as “likely” with actual readmission was 1.8 (95% confidence interval, 1.2-2.5). The adjusted odds ratio (including the variables above) was 1.6 (1.1-2.4).

Our findings demonstrate that patients are able to quantify their own readmission risk. This was true even after adjustment for expected readmission rate, age, sex, race, and payer. However, we did not identify any patient attitudes, beliefs, or preferences related to readmission or discharge instructions that were associated with subsequent rehospitalization. Reassuringly, more than 80% of patients who responded to the survey indicated that they would be sad, frustrated, or disappointed should readmission occur. This suggests that most patients are invested in preventing rehospitalization. Also reassuring was that patients indicated that they agreed with the discharge care plan and intended to follow their discharge instructions.

The major limitation of this study is that it was a convenience sample. Surveys were distributed inconsistently by nursing unit staff, preventing us from calculating a response rate. Further, it is possible, if not likely, that those patients with higher levels of engagement were more likely to take the time to respond, enriching our sample with activated patients. Although we allowed family members to fill out surveys on behalf of patients, this was done in fewer than 10% of instances; as such, our data may have limited applicability to patients who are physically or cognitively unable to participate in the discharge process. Finally, in this study, we did not capture readmissions to other facilities.

We conclude that patients are able to predict their own readmissions, even after accounting for other potential predictors of readmission. However, we found no evidence to support the possibility that low levels of engagement, limited trust in the healthcare team, or nonchalance about being readmitted are associated with subsequent rehospitalization. Whether asking patients about their perceived risk of readmission might help target readmission prevention programs deserves further study.

Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the study data and the accuracy of the data analysis. The authors also thank the following individuals for their contributions: Drafting the manuscript (Brotman); revising the manuscript for important intellectual content (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); acquiring the data (Brotman, Shihab, Tieu, Cheng, Bertram, Deutschendorf); interpreting the data (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); and analyzing the data (Brotman). The authors thank nursing leadership and nursing unit staff for their assistance in distributing surveys.

Funding support: Johns Hopkins Hospitalist Scholars Program

Disclosures: The authors have declared no conflicts of interest.

Recent years have seen a proliferation of programs designed to prevent readmissions, including patient education initiatives, financial assistance programs, postdischarge services, and clinical personnel assigned to help patients navigate their posthospitalization clinical care. Although some strategies do not require direct patient participation (such as timely and effective handoffs between inpatient and outpatient care teams), many rely upon a commitment by the patient to participate in the postdischarge care plan. At our hospital, we have found that only about 2/3 of patients who are offered transitional interventions (such as postdischarge phone calls by nurses or home nursing through a “transition guide” program) receive the intended interventions, and those who do not receive them are more likely to be readmitted.¹ While limited patient uptake may relate, in part, to factors that are difficult to overcome, such as inadequate housing or phone service, we have also encountered patients whose values, beliefs, or preferences about their care do not align with those of the care team. The purposes of this exploratory study were to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes are associated with actual readmission, and (3) determine whether patients can estimate their own risk of readmission.

From January 2014 to September 2016, we circulated surveys to patients on internal medicine nursing units who were being discharged home within 24 hours. Blank surveys were distributed to nursing units by the researchers. Unit clerks and support staff were educated on the purpose of the project and asked to distribute surveys to patients who were identified by unit case managers or nurses as slated for discharge. Staff members were not asked to help with or supervise survey completion. Surveys were generally filled out by patients, but we allowed family members to assist patients if needed, and to indicate so with a checkbox. There were no exclusion criteria. Because surveys were distributed by clinical staff, the received surveys can be considered a convenience sample. Patients were asked 5 questions with 4- or 5-point Likert scale responses:

(1) “How likely is it that you will be admitted to the hospital (have to stay in the hospital overnight) again within the next 30 days after you leave the hospital this time?” [answers ranging from “Very Unlikely (<5% chance)” to “Very Likely (>50% chance)”];

(2) “How would you feel about being rehospitalized in the next month?” [answers ranging from “Very sad, frustrated, or disappointed” to “Very happy or relieved”];

(3) “How much do you think that you personally can control whether or not you will be rehospitalized (based on what you do to take care of your body, take your medicines, and follow-up with your healthcare team)?” [answers ranging from “I have no control over whether I will be rehospitalized” to “I have complete control over whether I will be rehospitalized”];

(4) “Which of the options below best describes how you plan to follow the medical instructions after you leave the hospital?” [answers ranging from “I do NOT plan to do very much of what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I plan to do EVERYTHING I am being asked to do by the doctors, nurses, therapists and other members of the care team”]; and

(5) “Pick the item below that best describes YOUR OWN VIEW of the care team’s recommendations:” [answers ranging from “I DO NOT AGREE AT ALL that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I FULLY AGREE that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team”].

Responses were linked, based on discharge date and medical record number, to administrative data, including age, sex, race, payer, and clinical data. Subsequent hospitalizations to our hospital were ascertained from administrative data. We estimated expected risk of readmission using the all payer refined diagnosis related group coupled with the associated severity-of-illness (SOI) score, as we have reported previously.^2-5 We restricted our analysis to patients who answered the question related to the likelihood of readmission. Logistic regression models were constructed using actual 30-day readmission as the dependent variable to determine whether patients could predict their own readmissions and whether patient attitudes and beliefs about their care were predictive of subsequent readmission. Patient survey responses were entered as continuous independent variables (ranging from 1-4 or 1-5, as appropriate). Multivariable logistic regression was used to determine whether patients could predict their readmissions independent of demographic variables and expected readmission rate (modeled continuously); we repeated this model after dichotomizing the patient’s estimate of the likelihood of readmission as either “unlikely” or “likely.” Patients with missing survey responses were excluded from individual models without imputation. The study was approved by the Johns Hopkins institutional review board.

Responses were obtained from 895 patients. Their median age was 56 years [interquartile range, 43-67], 51.4% were female, and 41.7% were white. Mean SOI was 2.53 (on a 1-4 scale), and median length-of-stay was representative for our medical service at 5.2 days (range, 1-66 days). Family members reported filling out the survey in 57 cases. The primary payer was Medicare in 40.7%, Medicaid in 24.9%, and other in 34.4%. A total of 138 patients (15.4%) were readmitted within 30 days. The Table shows survey responses and associated readmission rates. None of the attitudes related to readmission were predictive of actual readmission. However, patients were able to predict their own readmissions (P = .002 for linear trend). After adjustment for expected readmission rate, race, sex, age, and payer, the trend remained significant (P = .005). Other significant predictors of readmissions in this model included expected readmission rate (P = .002), age (P = .02), and payer (P = .002). After dichotomizing the patient estimate of readmission rate as “unlikely” (N = 581) or “likely” (N = 314), the unadjusted odds ratio associating a patient-estimated risk of readmission as “likely” with actual readmission was 1.8 (95% confidence interval, 1.2-2.5). The adjusted odds ratio (including the variables above) was 1.6 (1.1-2.4).

Our findings demonstrate that patients are able to quantify their own readmission risk. This was true even after adjustment for expected readmission rate, age, sex, race, and payer. However, we did not identify any patient attitudes, beliefs, or preferences related to readmission or discharge instructions that were associated with subsequent rehospitalization. Reassuringly, more than 80% of patients who responded to the survey indicated that they would be sad, frustrated, or disappointed should readmission occur. This suggests that most patients are invested in preventing rehospitalization. Also reassuring was that patients indicated that they agreed with the discharge care plan and intended to follow their discharge instructions.

The major limitation of this study is that it was a convenience sample. Surveys were distributed inconsistently by nursing unit staff, preventing us from calculating a response rate. Further, it is possible, if not likely, that those patients with higher levels of engagement were more likely to take the time to respond, enriching our sample with activated patients. Although we allowed family members to fill out surveys on behalf of patients, this was done in fewer than 10% of instances; as such, our data may have limited applicability to patients who are physically or cognitively unable to participate in the discharge process. Finally, in this study, we did not capture readmissions to other facilities.

We conclude that patients are able to predict their own readmissions, even after accounting for other potential predictors of readmission. However, we found no evidence to support the possibility that low levels of engagement, limited trust in the healthcare team, or nonchalance about being readmitted are associated with subsequent rehospitalization. Whether asking patients about their perceived risk of readmission might help target readmission prevention programs deserves further study.

Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the study data and the accuracy of the data analysis. The authors also thank the following individuals for their contributions: Drafting the manuscript (Brotman); revising the manuscript for important intellectual content (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); acquiring the data (Brotman, Shihab, Tieu, Cheng, Bertram, Deutschendorf); interpreting the data (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); and analyzing the data (Brotman). The authors thank nursing leadership and nursing unit staff for their assistance in distributing surveys.

Funding support: Johns Hopkins Hospitalist Scholars Program

Disclosures: The authors have declared no conflicts of interest.

Recent years have seen a proliferation of programs designed to prevent readmissions, including patient education initiatives, financial assistance programs, postdischarge services, and clinical personnel assigned to help patients navigate their posthospitalization clinical care. Although some strategies do not require direct patient participation (such as timely and effective handoffs between inpatient and outpatient care teams), many rely upon a commitment by the patient to participate in the postdischarge care plan. At our hospital, we have found that only about 2/3 of patients who are offered transitional interventions (such as postdischarge phone calls by nurses or home nursing through a “transition guide” program) receive the intended interventions, and those who do not receive them are more likely to be readmitted.¹ While limited patient uptake may relate, in part, to factors that are difficult to overcome, such as inadequate housing or phone service, we have also encountered patients whose values, beliefs, or preferences about their care do not align with those of the care team. The purposes of this exploratory study were to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes are associated with actual readmission, and (3) determine whether patients can estimate their own risk of readmission.

From January 2014 to September 2016, we circulated surveys to patients on internal medicine nursing units who were being discharged home within 24 hours. Blank surveys were distributed to nursing units by the researchers. Unit clerks and support staff were educated on the purpose of the project and asked to distribute surveys to patients who were identified by unit case managers or nurses as slated for discharge. Staff members were not asked to help with or supervise survey completion. Surveys were generally filled out by patients, but we allowed family members to assist patients if needed, and to indicate so with a checkbox. There were no exclusion criteria. Because surveys were distributed by clinical staff, the received surveys can be considered a convenience sample. Patients were asked 5 questions with 4- or 5-point Likert scale responses:

(1) “How likely is it that you will be admitted to the hospital (have to stay in the hospital overnight) again within the next 30 days after you leave the hospital this time?” [answers ranging from “Very Unlikely (<5% chance)” to “Very Likely (>50% chance)”];

(2) “How would you feel about being rehospitalized in the next month?” [answers ranging from “Very sad, frustrated, or disappointed” to “Very happy or relieved”];

(3) “How much do you think that you personally can control whether or not you will be rehospitalized (based on what you do to take care of your body, take your medicines, and follow-up with your healthcare team)?” [answers ranging from “I have no control over whether I will be rehospitalized” to “I have complete control over whether I will be rehospitalized”];

(4) “Which of the options below best describes how you plan to follow the medical instructions after you leave the hospital?” [answers ranging from “I do NOT plan to do very much of what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I plan to do EVERYTHING I am being asked to do by the doctors, nurses, therapists and other members of the care team”]; and

(5) “Pick the item below that best describes YOUR OWN VIEW of the care team’s recommendations:” [answers ranging from “I DO NOT AGREE AT ALL that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I FULLY AGREE that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team”].

Responses were linked, based on discharge date and medical record number, to administrative data, including age, sex, race, payer, and clinical data. Subsequent hospitalizations to our hospital were ascertained from administrative data. We estimated expected risk of readmission using the all payer refined diagnosis related group coupled with the associated severity-of-illness (SOI) score, as we have reported previously.^2-5 We restricted our analysis to patients who answered the question related to the likelihood of readmission. Logistic regression models were constructed using actual 30-day readmission as the dependent variable to determine whether patients could predict their own readmissions and whether patient attitudes and beliefs about their care were predictive of subsequent readmission. Patient survey responses were entered as continuous independent variables (ranging from 1-4 or 1-5, as appropriate). Multivariable logistic regression was used to determine whether patients could predict their readmissions independent of demographic variables and expected readmission rate (modeled continuously); we repeated this model after dichotomizing the patient’s estimate of the likelihood of readmission as either “unlikely” or “likely.” Patients with missing survey responses were excluded from individual models without imputation. The study was approved by the Johns Hopkins institutional review board.

Responses were obtained from 895 patients. Their median age was 56 years [interquartile range, 43-67], 51.4% were female, and 41.7% were white. Mean SOI was 2.53 (on a 1-4 scale), and median length-of-stay was representative for our medical service at 5.2 days (range, 1-66 days). Family members reported filling out the survey in 57 cases. The primary payer was Medicare in 40.7%, Medicaid in 24.9%, and other in 34.4%. A total of 138 patients (15.4%) were readmitted within 30 days. The Table shows survey responses and associated readmission rates. None of the attitudes related to readmission were predictive of actual readmission. However, patients were able to predict their own readmissions (P = .002 for linear trend). After adjustment for expected readmission rate, race, sex, age, and payer, the trend remained significant (P = .005). Other significant predictors of readmissions in this model included expected readmission rate (P = .002), age (P = .02), and payer (P = .002). After dichotomizing the patient estimate of readmission rate as “unlikely” (N = 581) or “likely” (N = 314), the unadjusted odds ratio associating a patient-estimated risk of readmission as “likely” with actual readmission was 1.8 (95% confidence interval, 1.2-2.5). The adjusted odds ratio (including the variables above) was 1.6 (1.1-2.4).

Our findings demonstrate that patients are able to quantify their own readmission risk. This was true even after adjustment for expected readmission rate, age, sex, race, and payer. However, we did not identify any patient attitudes, beliefs, or preferences related to readmission or discharge instructions that were associated with subsequent rehospitalization. Reassuringly, more than 80% of patients who responded to the survey indicated that they would be sad, frustrated, or disappointed should readmission occur. This suggests that most patients are invested in preventing rehospitalization. Also reassuring was that patients indicated that they agreed with the discharge care plan and intended to follow their discharge instructions.

The major limitation of this study is that it was a convenience sample. Surveys were distributed inconsistently by nursing unit staff, preventing us from calculating a response rate. Further, it is possible, if not likely, that those patients with higher levels of engagement were more likely to take the time to respond, enriching our sample with activated patients. Although we allowed family members to fill out surveys on behalf of patients, this was done in fewer than 10% of instances; as such, our data may have limited applicability to patients who are physically or cognitively unable to participate in the discharge process. Finally, in this study, we did not capture readmissions to other facilities.

We conclude that patients are able to predict their own readmissions, even after accounting for other potential predictors of readmission. However, we found no evidence to support the possibility that low levels of engagement, limited trust in the healthcare team, or nonchalance about being readmitted are associated with subsequent rehospitalization. Whether asking patients about their perceived risk of readmission might help target readmission prevention programs deserves further study.

Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the study data and the accuracy of the data analysis. The authors also thank the following individuals for their contributions: Drafting the manuscript (Brotman); revising the manuscript for important intellectual content (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); acquiring the data (Brotman, Shihab, Tieu, Cheng, Bertram, Deutschendorf); interpreting the data (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); and analyzing the data (Brotman). The authors thank nursing leadership and nursing unit staff for their assistance in distributing surveys.

Funding support: Johns Hopkins Hospitalist Scholars Program

Disclosures: The authors have declared no conflicts of interest.

In addition to treating patients, physicians frequently have other time commitments that could include administrative, teaching, research, and family duties. Inpatient medicine is particularly unforgiving to these nonclinical duties since patients have to be assessed on a daily basis. Because of this characteristic, it is not uncommon for inpatient care responsibility to be switched between physicians to create time for nonclinical duties and personal health.

In contrast to the ambulatory setting, the influence of physician continuity of care on inpatient outcomes has not been studied frequently. Studies of inpatient continuity have primarily focused on patient discharge (likely because of its objective nature) over the weekends (likely because weekend cross-coverage is common) and have reported conflicting results.^1-3 However, discontinuity of care is not isolated to the weekend since hospitalist-switches can occur at any time. In addition, expressing hospitalist continuity of care as a dichotomous variable (Was there weekend cross-coverage?) could incompletely express continuity since discharge likelihood might change with the consecutive number of days that a hospitalist is on service. This study measured the influence of hospitalist continuity throughout the patient’s hospitalization (rather than just the weekend) on daily patient discharge.

Study Setting and Databases Used for Analysis

The study was conducted at The Ottawa Hospital, Ontario, Canada, a 1000-bed teaching hospital with 2 campuses and the primary referral center in our region. The division of general internal medicine has 6 patient services (or “teams”) at two campuses led by a staff hospitalist (exclusively general internists), a senior medical resident (2nd year of training), and various numbers of interns and medical students. Staff hospitalists do not treat more than one patient service even on the weekends.

Patients are admitted to each service on a daily basis and almost exclusively from the emergency room. Assignment of patients is essentially random since all services have the same clinical expertise. At a particular campus, the number of patients assigned daily to each service is usually equivalent between teams. Patients almost never switch between teams but may be transferred to another specialty. The study was approved by our local research ethics board.

The Patient Registry Database records for each patient the date and time of admissions (defined as the moment that a patient’s admission request is entered into the database), death or discharge from hospital (defined as the time when the patient’s discharge from hospital was entered into the database), or transfer to another specialty. It also records emergency visits, patient demographics, and location during admission. The Laboratory Database records all laboratory tests and their results.

Study Cohort

The Patient Registry Database was used to identify all individuals who were admitted to the general medicine services between January 1 and December 31, 2015. This time frame was selected to ensure that data were complete and current. General medicine services were analyzed because they are collectively the largest inpatient specialty in the hospital.

Study Outcome

The primary outcome was discharge from hospital as determined from the Patient Registry Database. Patients who died or were transferred to another service were not counted as outcomes.

Covariables

The primary exposure variable was the consecutive number of days (including weekends) that a particular hospitalist rounded on patients on a particular general medicine service. This was measured using call schedules. Other covariates included tomorrow’s expected number of discharges (TEND) daily discharge probability and its components. The TEND model4 used patient factors (age, Laboratory Abnormality Physiological Score [LAPS]5 calculated at admission) and hospitalization factors (hospital campus and service, admission urgency, day of the week, ICU status) to predict the daily discharge probability. In a validation population, these daily discharge probabilities (when summed over a particular day) strongly predicted the daily number of discharges (adjusted R2 of 89.2% [P < .001], median relative difference between observed and expected number of discharges of only 1.4% [Interquartile range,IQR: −5.5% to 7.1%]). The expected annual death risk was determined using the HOMR-now! model.6 This model used routinely collected data available at patient admission regarding the patient (sex, life-table-estimated 1-year death risk, Charlson score, current living location, previous cancer clinic status, and number of emergency department visits in the previous year) and the hospitalization (urgency, service, and LAPS score). The model explained more than half of the total variability in death likelihood of death (Nagelkirke’s R2 value of 0.53),7 was highly discriminative (C-statistic 0.92), and accurately predicted death risk (calibration slope 0.98).

Analysis

Logistic generalized estimating equation (GEE) methods were used to model the adjusted daily discharge probability.⁸ Data in the analytical dataset were expressed in a patient-day format (each dataset row represented one day for a particular patient). This permitted the inclusion of time-dependent covariates and allowed the GEE model to cluster hospitalization days within patients.

Model construction started with the TEND daily discharge probability and the HOMR-now! expected annual death risk (both expressed as log-odds). Then, hospitalist continuity was entered as a time-dependent covariate (ie, its value changed every day). Linear, square root, and natural logarithm forms of physician continuity were examined to determine the best fit (determined using the QIC statistic⁹). Finally, individual components of the TEND model were also offered to the model with those which significantly improving fit kept in the model. The GEE model used an independent correlation structure since this minimized the QIC statistic in the base model. All covariates in the final daily discharge probability model were used in the hospital death model. Analyses were conducted using SAS 9.4 (Cary, NC).

There were 6,405 general medicine admissions involving 5208 patients and 38,967 patient-days between January 1 and December 31, 2015 (Appendix A). Patients were elderly and were evenly divided in terms of gender, with 85% of them being admitted from the community. Comorbidities were common (median coded Charlson score was 2), with 6.0% of patients known to our cancer clinic. The median length of stay was 4 days (IQR, 2–7), with 378 admissions (5.9%) ending in death and 121 admissions (1.9%) ending in a transfer to another service.

There were 41 different staff people having at least 1 day on service. The median total service by physicians was 9 weeks (IQR 1.8–10.9 weeks). Changes in hospitalist coverage were common; hospitalizations had a median of 1 (IQR 1–2) physician switches and a median of 1 (IQR 1–2) different physicians. However, patients spent a median of 100% (IQR 66.7%–100%] of their total hospitalization with their primary hospitalist. The median duration of individual physician “stints” on service was 5 days (IQR 2–7, range 1–42).

The TEND model accurately estimated daily discharge probability for the entire cohort with 5833 and 5718.6 observed and expected discharges, respectively, during 38,967 patient-days (O/E 1.02, 95% CI 0.99–1.05). Discharge probability increased as hospitalist continuity increased, but this was statistically significant only when hospitalist continuity exceeded 4 days. Other covariables also significantly influenced discharge probability (Appendix B).

After adjusting for important covariables (Appendix C), hospitalist continuity was significantly associated with daily discharge probability (Figure). Discharge probability increased linearly with increasing consecutive days that hospitalists treated patients. For each additional consecutive day with the same hospitalist, the adjusted daily odds increased by 2% (Adj-odds ratio [OR] 1.02, 95% CI 1.01–1.02, Appendix C). When the consecutive number of days that hospitalists remained on service increased from 1 to 28 days, the adjusted discharge probability for the average patient increased from 18.1% to 25.7%, respectively. Discharge was significantly influenced by other factors (Appendix C). Continuity did not influence the risk of death in hospital (Appendix D).

In a general medicine service at a large teaching hospital, this study found that greater hospitalist continuity was associated with a significantly increased adjusted daily discharge probability, increasing (in the average patient) from 18.1% to 25.7% when the consecutive number of hospitalist days on service increased from 1 to 28 days, respectively.

The study demonstrated some interesting findings. First, it shows that shifting patient care between physicians can significantly influence patient outcomes. This could be a function of incomplete transfer of knowledge between physicians, a phenomenon that should be expected given the extensive amount of information–both explicit and implicit–that physicians collect about particular patients during their hospitalization. Second, continuity of care could increase a physician’s and a patient’s confidence in clinical decision-making. Perhaps physicians are subconsciously more trusting of their instincts (and the decisions based on those instincts) when they have been on service for a while. It is also possible that patients more readily trust recommendations of a physician they have had throughout their stay. Finally, people wishing to decrease patient length of stay might consider minimizing the extent that hospitalists sign over patient care to colleagues.

Several issues should be noted when interpreting the results of the study. First, the study examined only patient discharge and death. These are by no means the only or the most important outcomes that might be influenced by hospitalist continuity. Second, this study was limited to a single service at a single center. Third, the analysis did not account for house-staff continuity. Since hospitalist and house-staff at the study hospital invariably switched at different times, it is unlikely that hospitalist continuity was a surrogate for house-staff continuity.

Disclosures

This study was supported by the Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada. The author has nothing to disclose.

In addition to treating patients, physicians frequently have other time commitments that could include administrative, teaching, research, and family duties. Inpatient medicine is particularly unforgiving to these nonclinical duties since patients have to be assessed on a daily basis. Because of this characteristic, it is not uncommon for inpatient care responsibility to be switched between physicians to create time for nonclinical duties and personal health.

In contrast to the ambulatory setting, the influence of physician continuity of care on inpatient outcomes has not been studied frequently. Studies of inpatient continuity have primarily focused on patient discharge (likely because of its objective nature) over the weekends (likely because weekend cross-coverage is common) and have reported conflicting results.^1-3 However, discontinuity of care is not isolated to the weekend since hospitalist-switches can occur at any time. In addition, expressing hospitalist continuity of care as a dichotomous variable (Was there weekend cross-coverage?) could incompletely express continuity since discharge likelihood might change with the consecutive number of days that a hospitalist is on service. This study measured the influence of hospitalist continuity throughout the patient’s hospitalization (rather than just the weekend) on daily patient discharge.

Study Setting and Databases Used for Analysis

The study was conducted at The Ottawa Hospital, Ontario, Canada, a 1000-bed teaching hospital with 2 campuses and the primary referral center in our region. The division of general internal medicine has 6 patient services (or “teams”) at two campuses led by a staff hospitalist (exclusively general internists), a senior medical resident (2nd year of training), and various numbers of interns and medical students. Staff hospitalists do not treat more than one patient service even on the weekends.

Patients are admitted to each service on a daily basis and almost exclusively from the emergency room. Assignment of patients is essentially random since all services have the same clinical expertise. At a particular campus, the number of patients assigned daily to each service is usually equivalent between teams. Patients almost never switch between teams but may be transferred to another specialty. The study was approved by our local research ethics board.

The Patient Registry Database records for each patient the date and time of admissions (defined as the moment that a patient’s admission request is entered into the database), death or discharge from hospital (defined as the time when the patient’s discharge from hospital was entered into the database), or transfer to another specialty. It also records emergency visits, patient demographics, and location during admission. The Laboratory Database records all laboratory tests and their results.

Study Cohort

The Patient Registry Database was used to identify all individuals who were admitted to the general medicine services between January 1 and December 31, 2015. This time frame was selected to ensure that data were complete and current. General medicine services were analyzed because they are collectively the largest inpatient specialty in the hospital.

Study Outcome

The primary outcome was discharge from hospital as determined from the Patient Registry Database. Patients who died or were transferred to another service were not counted as outcomes.

Covariables

The primary exposure variable was the consecutive number of days (including weekends) that a particular hospitalist rounded on patients on a particular general medicine service. This was measured using call schedules. Other covariates included tomorrow’s expected number of discharges (TEND) daily discharge probability and its components. The TEND model4 used patient factors (age, Laboratory Abnormality Physiological Score [LAPS]5 calculated at admission) and hospitalization factors (hospital campus and service, admission urgency, day of the week, ICU status) to predict the daily discharge probability. In a validation population, these daily discharge probabilities (when summed over a particular day) strongly predicted the daily number of discharges (adjusted R2 of 89.2% [P < .001], median relative difference between observed and expected number of discharges of only 1.4% [Interquartile range,IQR: −5.5% to 7.1%]). The expected annual death risk was determined using the HOMR-now! model.6 This model used routinely collected data available at patient admission regarding the patient (sex, life-table-estimated 1-year death risk, Charlson score, current living location, previous cancer clinic status, and number of emergency department visits in the previous year) and the hospitalization (urgency, service, and LAPS score). The model explained more than half of the total variability in death likelihood of death (Nagelkirke’s R2 value of 0.53),7 was highly discriminative (C-statistic 0.92), and accurately predicted death risk (calibration slope 0.98).

Analysis

Logistic generalized estimating equation (GEE) methods were used to model the adjusted daily discharge probability.⁸ Data in the analytical dataset were expressed in a patient-day format (each dataset row represented one day for a particular patient). This permitted the inclusion of time-dependent covariates and allowed the GEE model to cluster hospitalization days within patients.

Model construction started with the TEND daily discharge probability and the HOMR-now! expected annual death risk (both expressed as log-odds). Then, hospitalist continuity was entered as a time-dependent covariate (ie, its value changed every day). Linear, square root, and natural logarithm forms of physician continuity were examined to determine the best fit (determined using the QIC statistic⁹). Finally, individual components of the TEND model were also offered to the model with those which significantly improving fit kept in the model. The GEE model used an independent correlation structure since this minimized the QIC statistic in the base model. All covariates in the final daily discharge probability model were used in the hospital death model. Analyses were conducted using SAS 9.4 (Cary, NC).

There were 6,405 general medicine admissions involving 5208 patients and 38,967 patient-days between January 1 and December 31, 2015 (Appendix A). Patients were elderly and were evenly divided in terms of gender, with 85% of them being admitted from the community. Comorbidities were common (median coded Charlson score was 2), with 6.0% of patients known to our cancer clinic. The median length of stay was 4 days (IQR, 2–7), with 378 admissions (5.9%) ending in death and 121 admissions (1.9%) ending in a transfer to another service.

There were 41 different staff people having at least 1 day on service. The median total service by physicians was 9 weeks (IQR 1.8–10.9 weeks). Changes in hospitalist coverage were common; hospitalizations had a median of 1 (IQR 1–2) physician switches and a median of 1 (IQR 1–2) different physicians. However, patients spent a median of 100% (IQR 66.7%–100%] of their total hospitalization with their primary hospitalist. The median duration of individual physician “stints” on service was 5 days (IQR 2–7, range 1–42).

The TEND model accurately estimated daily discharge probability for the entire cohort with 5833 and 5718.6 observed and expected discharges, respectively, during 38,967 patient-days (O/E 1.02, 95% CI 0.99–1.05). Discharge probability increased as hospitalist continuity increased, but this was statistically significant only when hospitalist continuity exceeded 4 days. Other covariables also significantly influenced discharge probability (Appendix B).

After adjusting for important covariables (Appendix C), hospitalist continuity was significantly associated with daily discharge probability (Figure). Discharge probability increased linearly with increasing consecutive days that hospitalists treated patients. For each additional consecutive day with the same hospitalist, the adjusted daily odds increased by 2% (Adj-odds ratio [OR] 1.02, 95% CI 1.01–1.02, Appendix C). When the consecutive number of days that hospitalists remained on service increased from 1 to 28 days, the adjusted discharge probability for the average patient increased from 18.1% to 25.7%, respectively. Discharge was significantly influenced by other factors (Appendix C). Continuity did not influence the risk of death in hospital (Appendix D).

In a general medicine service at a large teaching hospital, this study found that greater hospitalist continuity was associated with a significantly increased adjusted daily discharge probability, increasing (in the average patient) from 18.1% to 25.7% when the consecutive number of hospitalist days on service increased from 1 to 28 days, respectively.

The study demonstrated some interesting findings. First, it shows that shifting patient care between physicians can significantly influence patient outcomes. This could be a function of incomplete transfer of knowledge between physicians, a phenomenon that should be expected given the extensive amount of information–both explicit and implicit–that physicians collect about particular patients during their hospitalization. Second, continuity of care could increase a physician’s and a patient’s confidence in clinical decision-making. Perhaps physicians are subconsciously more trusting of their instincts (and the decisions based on those instincts) when they have been on service for a while. It is also possible that patients more readily trust recommendations of a physician they have had throughout their stay. Finally, people wishing to decrease patient length of stay might consider minimizing the extent that hospitalists sign over patient care to colleagues.

Several issues should be noted when interpreting the results of the study. First, the study examined only patient discharge and death. These are by no means the only or the most important outcomes that might be influenced by hospitalist continuity. Second, this study was limited to a single service at a single center. Third, the analysis did not account for house-staff continuity. Since hospitalist and house-staff at the study hospital invariably switched at different times, it is unlikely that hospitalist continuity was a surrogate for house-staff continuity.

Disclosures

This study was supported by the Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada. The author has nothing to disclose.

In addition to treating patients, physicians frequently have other time commitments that could include administrative, teaching, research, and family duties. Inpatient medicine is particularly unforgiving to these nonclinical duties since patients have to be assessed on a daily basis. Because of this characteristic, it is not uncommon for inpatient care responsibility to be switched between physicians to create time for nonclinical duties and personal health.

In contrast to the ambulatory setting, the influence of physician continuity of care on inpatient outcomes has not been studied frequently. Studies of inpatient continuity have primarily focused on patient discharge (likely because of its objective nature) over the weekends (likely because weekend cross-coverage is common) and have reported conflicting results.^1-3 However, discontinuity of care is not isolated to the weekend since hospitalist-switches can occur at any time. In addition, expressing hospitalist continuity of care as a dichotomous variable (Was there weekend cross-coverage?) could incompletely express continuity since discharge likelihood might change with the consecutive number of days that a hospitalist is on service. This study measured the influence of hospitalist continuity throughout the patient’s hospitalization (rather than just the weekend) on daily patient discharge.

Study Setting and Databases Used for Analysis

The study was conducted at The Ottawa Hospital, Ontario, Canada, a 1000-bed teaching hospital with 2 campuses and the primary referral center in our region. The division of general internal medicine has 6 patient services (or “teams”) at two campuses led by a staff hospitalist (exclusively general internists), a senior medical resident (2nd year of training), and various numbers of interns and medical students. Staff hospitalists do not treat more than one patient service even on the weekends.

Patients are admitted to each service on a daily basis and almost exclusively from the emergency room. Assignment of patients is essentially random since all services have the same clinical expertise. At a particular campus, the number of patients assigned daily to each service is usually equivalent between teams. Patients almost never switch between teams but may be transferred to another specialty. The study was approved by our local research ethics board.

The Patient Registry Database records for each patient the date and time of admissions (defined as the moment that a patient’s admission request is entered into the database), death or discharge from hospital (defined as the time when the patient’s discharge from hospital was entered into the database), or transfer to another specialty. It also records emergency visits, patient demographics, and location during admission. The Laboratory Database records all laboratory tests and their results.

Study Cohort

The Patient Registry Database was used to identify all individuals who were admitted to the general medicine services between January 1 and December 31, 2015. This time frame was selected to ensure that data were complete and current. General medicine services were analyzed because they are collectively the largest inpatient specialty in the hospital.

Study Outcome

The primary outcome was discharge from hospital as determined from the Patient Registry Database. Patients who died or were transferred to another service were not counted as outcomes.

Covariables

The primary exposure variable was the consecutive number of days (including weekends) that a particular hospitalist rounded on patients on a particular general medicine service. This was measured using call schedules. Other covariates included tomorrow’s expected number of discharges (TEND) daily discharge probability and its components. The TEND model4 used patient factors (age, Laboratory Abnormality Physiological Score [LAPS]5 calculated at admission) and hospitalization factors (hospital campus and service, admission urgency, day of the week, ICU status) to predict the daily discharge probability. In a validation population, these daily discharge probabilities (when summed over a particular day) strongly predicted the daily number of discharges (adjusted R2 of 89.2% [P < .001], median relative difference between observed and expected number of discharges of only 1.4% [Interquartile range,IQR: −5.5% to 7.1%]). The expected annual death risk was determined using the HOMR-now! model.6 This model used routinely collected data available at patient admission regarding the patient (sex, life-table-estimated 1-year death risk, Charlson score, current living location, previous cancer clinic status, and number of emergency department visits in the previous year) and the hospitalization (urgency, service, and LAPS score). The model explained more than half of the total variability in death likelihood of death (Nagelkirke’s R2 value of 0.53),7 was highly discriminative (C-statistic 0.92), and accurately predicted death risk (calibration slope 0.98).

Analysis

Logistic generalized estimating equation (GEE) methods were used to model the adjusted daily discharge probability.⁸ Data in the analytical dataset were expressed in a patient-day format (each dataset row represented one day for a particular patient). This permitted the inclusion of time-dependent covariates and allowed the GEE model to cluster hospitalization days within patients.

Model construction started with the TEND daily discharge probability and the HOMR-now! expected annual death risk (both expressed as log-odds). Then, hospitalist continuity was entered as a time-dependent covariate (ie, its value changed every day). Linear, square root, and natural logarithm forms of physician continuity were examined to determine the best fit (determined using the QIC statistic⁹). Finally, individual components of the TEND model were also offered to the model with those which significantly improving fit kept in the model. The GEE model used an independent correlation structure since this minimized the QIC statistic in the base model. All covariates in the final daily discharge probability model were used in the hospital death model. Analyses were conducted using SAS 9.4 (Cary, NC).

There were 6,405 general medicine admissions involving 5208 patients and 38,967 patient-days between January 1 and December 31, 2015 (Appendix A). Patients were elderly and were evenly divided in terms of gender, with 85% of them being admitted from the community. Comorbidities were common (median coded Charlson score was 2), with 6.0% of patients known to our cancer clinic. The median length of stay was 4 days (IQR, 2–7), with 378 admissions (5.9%) ending in death and 121 admissions (1.9%) ending in a transfer to another service.

There were 41 different staff people having at least 1 day on service. The median total service by physicians was 9 weeks (IQR 1.8–10.9 weeks). Changes in hospitalist coverage were common; hospitalizations had a median of 1 (IQR 1–2) physician switches and a median of 1 (IQR 1–2) different physicians. However, patients spent a median of 100% (IQR 66.7%–100%] of their total hospitalization with their primary hospitalist. The median duration of individual physician “stints” on service was 5 days (IQR 2–7, range 1–42).

The TEND model accurately estimated daily discharge probability for the entire cohort with 5833 and 5718.6 observed and expected discharges, respectively, during 38,967 patient-days (O/E 1.02, 95% CI 0.99–1.05). Discharge probability increased as hospitalist continuity increased, but this was statistically significant only when hospitalist continuity exceeded 4 days. Other covariables also significantly influenced discharge probability (Appendix B).

After adjusting for important covariables (Appendix C), hospitalist continuity was significantly associated with daily discharge probability (Figure). Discharge probability increased linearly with increasing consecutive days that hospitalists treated patients. For each additional consecutive day with the same hospitalist, the adjusted daily odds increased by 2% (Adj-odds ratio [OR] 1.02, 95% CI 1.01–1.02, Appendix C). When the consecutive number of days that hospitalists remained on service increased from 1 to 28 days, the adjusted discharge probability for the average patient increased from 18.1% to 25.7%, respectively. Discharge was significantly influenced by other factors (Appendix C). Continuity did not influence the risk of death in hospital (Appendix D).

In a general medicine service at a large teaching hospital, this study found that greater hospitalist continuity was associated with a significantly increased adjusted daily discharge probability, increasing (in the average patient) from 18.1% to 25.7% when the consecutive number of hospitalist days on service increased from 1 to 28 days, respectively.

The study demonstrated some interesting findings. First, it shows that shifting patient care between physicians can significantly influence patient outcomes. This could be a function of incomplete transfer of knowledge between physicians, a phenomenon that should be expected given the extensive amount of information–both explicit and implicit–that physicians collect about particular patients during their hospitalization. Second, continuity of care could increase a physician’s and a patient’s confidence in clinical decision-making. Perhaps physicians are subconsciously more trusting of their instincts (and the decisions based on those instincts) when they have been on service for a while. It is also possible that patients more readily trust recommendations of a physician they have had throughout their stay. Finally, people wishing to decrease patient length of stay might consider minimizing the extent that hospitalists sign over patient care to colleagues.

Several issues should be noted when interpreting the results of the study. First, the study examined only patient discharge and death. These are by no means the only or the most important outcomes that might be influenced by hospitalist continuity. Second, this study was limited to a single service at a single center. Third, the analysis did not account for house-staff continuity. Since hospitalist and house-staff at the study hospital invariably switched at different times, it is unlikely that hospitalist continuity was a surrogate for house-staff continuity.

Disclosures

This study was supported by the Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada. The author has nothing to disclose.

SAN DIEGO – Between 2009 and 2015, the number of emergency department visits related to mental health increased 56% among children and 41% among adults, results from an analysis of national hospital data showed.

[email protected]

SOURCE: Santillanes G et al. Ann Emerg Med. 2018 Oct. doi. 10.1016/j.annemergmed.2018.08.050.

SAN DIEGO – Between 2009 and 2015, the number of emergency department visits related to mental health increased 56% among children and 41% among adults, results from an analysis of national hospital data showed.

[email protected]

SOURCE: Santillanes G et al. Ann Emerg Med. 2018 Oct. doi. 10.1016/j.annemergmed.2018.08.050.

SAN DIEGO – Between 2009 and 2015, the number of emergency department visits related to mental health increased 56% among children and 41% among adults, results from an analysis of national hospital data showed.

[email protected]

SOURCE: Santillanes G et al. Ann Emerg Med. 2018 Oct. doi. 10.1016/j.annemergmed.2018.08.050.

Click here to access the October 2018 Digital Edition.

Table of Contents

• Comparison of Cardiovascular Outcomes Between Statin Monotherapy and Fish Oil and Statin Combination Therapy in a Veteran Population
• Improving Team-Based Care Coordination Delivery and Documentation in the Health Record
• An Overview of Pharmacotherapy Options for Alcohol Use Disorder
• An Imposter Twice Over: A Case of IgG4-Related Disease
• Happy Federal New Year
• FDA Update

Click here to access the October 2018 Digital Edition.

Table of Contents

• Comparison of Cardiovascular Outcomes Between Statin Monotherapy and Fish Oil and Statin Combination Therapy in a Veteran Population
• Improving Team-Based Care Coordination Delivery and Documentation in the Health Record
• An Overview of Pharmacotherapy Options for Alcohol Use Disorder
• An Imposter Twice Over: A Case of IgG4-Related Disease
• Happy Federal New Year
• FDA Update

Click here to access the October 2018 Digital Edition.

Table of Contents

• Comparison of Cardiovascular Outcomes Between Statin Monotherapy and Fish Oil and Statin Combination Therapy in a Veteran Population
• Improving Team-Based Care Coordination Delivery and Documentation in the Health Record
• An Overview of Pharmacotherapy Options for Alcohol Use Disorder
• An Imposter Twice Over: A Case of IgG4-Related Disease
• Happy Federal New Year
• FDA Update

AGA Research Foundation pilot awards are an invaluable tool for investigators – they provide seed funding to explore promising new lines of research and generate preliminary data for larger grants. So, when David L. Boone, PhD, received the 2017 AGA-Pfizer Young Investigator Pilot Research Award in Inflammatory Bowel Disease from the AGA Research Foundation, he was able to double-down on a very targeted project studying innate immunity in IBD. Based on his recent accomplishments – both in and out of the lab – we’re excited for you to get to know Dr. Boone, associate professor of microbiology and immunology at Indiana University School of Medicine-South Bend, and our AGA Research Foundation researcher of the month.

Bench to bedside: working toward new treatment options in IBD

The Boone lab AGA-funded project is specifically focused on JAK inhibitors, which are becoming a more popular treatment option for patients with IBD, especially for those patients who don’t respond to anti-TNF therapy. Dr. Boone is committed to enhancing our understanding of how these JAK inhibitors work at a cellular level. If we can understand this, Dr. Boone is optimistic it will lead to new approaches for treating inflammation in IBD.

With his AGA Research Foundation grant, Dr. Boone and his lab characterized a new robust mouse model of colitis that is entirely driven by innate immune mechanisms. With this model, his team is investigating the cellular and molecular mechanisms that drive innate immune-mediated inflammation in the intestine, which will provide important insights for future IBD drug development. You can read the specifics of Dr. Boone’s research in his recently published work in Mucosal Immunology.

Pilot award provides a stepping stone

Dr. Boone’s AGA Research Foundation pilot grant has paved the way for future success. Using the data from his AGA-funded project, as well as the constructive feedback he received from the AGA awards panel, Dr. Boone went on to successfully obtain new funding in the form of a Pfizer ASPIRE research grant. This work is building the foundation for Dr. Boone’s next big grant venture: an NIH R01 grant.

Two postdocs, a graduate student, a technician, and a dog named Boone

Dr. Boone shared with us that the best outcome from his AGA grant was that the additional funding made it possible to grow his lab by a postdoctoral researcher and lab technician. One of Dr. Boone’s great passions is training the next generation of scientists, both in the lab and through his role as a microbiology and immunology professor for first-year medical students at Indiana University Medical School.

Beyond the lab – a commitment to IBD patients

Dr. Boone wanted to do more to support patients with IBD. He had heard of Camp Oasis – the Crohn’s & Colitis Foundation regional camp for patients with IBD – and knew of physicians who provided medical services at the camp. After looking into making a donation to Camp Oasis Michigan, Dr. Boone learned that what the camp really needed was male counselors. So, despite being “older than an average camp counselor,” Dr. Boone packed his bags for Michigan. Participating in Camp Oasis the last 2 years has been a great joy for Dr. Boone and provides added inspiration and motivation for his work in the lab.

[email protected]

AGA Research Foundation pilot awards are an invaluable tool for investigators – they provide seed funding to explore promising new lines of research and generate preliminary data for larger grants. So, when David L. Boone, PhD, received the 2017 AGA-Pfizer Young Investigator Pilot Research Award in Inflammatory Bowel Disease from the AGA Research Foundation, he was able to double-down on a very targeted project studying innate immunity in IBD. Based on his recent accomplishments – both in and out of the lab – we’re excited for you to get to know Dr. Boone, associate professor of microbiology and immunology at Indiana University School of Medicine-South Bend, and our AGA Research Foundation researcher of the month.

Bench to bedside: working toward new treatment options in IBD

The Boone lab AGA-funded project is specifically focused on JAK inhibitors, which are becoming a more popular treatment option for patients with IBD, especially for those patients who don’t respond to anti-TNF therapy. Dr. Boone is committed to enhancing our understanding of how these JAK inhibitors work at a cellular level. If we can understand this, Dr. Boone is optimistic it will lead to new approaches for treating inflammation in IBD.

With his AGA Research Foundation grant, Dr. Boone and his lab characterized a new robust mouse model of colitis that is entirely driven by innate immune mechanisms. With this model, his team is investigating the cellular and molecular mechanisms that drive innate immune-mediated inflammation in the intestine, which will provide important insights for future IBD drug development. You can read the specifics of Dr. Boone’s research in his recently published work in Mucosal Immunology.

Pilot award provides a stepping stone

Dr. Boone’s AGA Research Foundation pilot grant has paved the way for future success. Using the data from his AGA-funded project, as well as the constructive feedback he received from the AGA awards panel, Dr. Boone went on to successfully obtain new funding in the form of a Pfizer ASPIRE research grant. This work is building the foundation for Dr. Boone’s next big grant venture: an NIH R01 grant.

Two postdocs, a graduate student, a technician, and a dog named Boone

Dr. Boone shared with us that the best outcome from his AGA grant was that the additional funding made it possible to grow his lab by a postdoctoral researcher and lab technician. One of Dr. Boone’s great passions is training the next generation of scientists, both in the lab and through his role as a microbiology and immunology professor for first-year medical students at Indiana University Medical School.

Beyond the lab – a commitment to IBD patients

Dr. Boone wanted to do more to support patients with IBD. He had heard of Camp Oasis – the Crohn’s & Colitis Foundation regional camp for patients with IBD – and knew of physicians who provided medical services at the camp. After looking into making a donation to Camp Oasis Michigan, Dr. Boone learned that what the camp really needed was male counselors. So, despite being “older than an average camp counselor,” Dr. Boone packed his bags for Michigan. Participating in Camp Oasis the last 2 years has been a great joy for Dr. Boone and provides added inspiration and motivation for his work in the lab.

[email protected]

AGA Research Foundation pilot awards are an invaluable tool for investigators – they provide seed funding to explore promising new lines of research and generate preliminary data for larger grants. So, when David L. Boone, PhD, received the 2017 AGA-Pfizer Young Investigator Pilot Research Award in Inflammatory Bowel Disease from the AGA Research Foundation, he was able to double-down on a very targeted project studying innate immunity in IBD. Based on his recent accomplishments – both in and out of the lab – we’re excited for you to get to know Dr. Boone, associate professor of microbiology and immunology at Indiana University School of Medicine-South Bend, and our AGA Research Foundation researcher of the month.

Bench to bedside: working toward new treatment options in IBD

The Boone lab AGA-funded project is specifically focused on JAK inhibitors, which are becoming a more popular treatment option for patients with IBD, especially for those patients who don’t respond to anti-TNF therapy. Dr. Boone is committed to enhancing our understanding of how these JAK inhibitors work at a cellular level. If we can understand this, Dr. Boone is optimistic it will lead to new approaches for treating inflammation in IBD.

With his AGA Research Foundation grant, Dr. Boone and his lab characterized a new robust mouse model of colitis that is entirely driven by innate immune mechanisms. With this model, his team is investigating the cellular and molecular mechanisms that drive innate immune-mediated inflammation in the intestine, which will provide important insights for future IBD drug development. You can read the specifics of Dr. Boone’s research in his recently published work in Mucosal Immunology.

Pilot award provides a stepping stone

Dr. Boone’s AGA Research Foundation pilot grant has paved the way for future success. Using the data from his AGA-funded project, as well as the constructive feedback he received from the AGA awards panel, Dr. Boone went on to successfully obtain new funding in the form of a Pfizer ASPIRE research grant. This work is building the foundation for Dr. Boone’s next big grant venture: an NIH R01 grant.

Two postdocs, a graduate student, a technician, and a dog named Boone

Dr. Boone shared with us that the best outcome from his AGA grant was that the additional funding made it possible to grow his lab by a postdoctoral researcher and lab technician. One of Dr. Boone’s great passions is training the next generation of scientists, both in the lab and through his role as a microbiology and immunology professor for first-year medical students at Indiana University Medical School.

Beyond the lab – a commitment to IBD patients

Dr. Boone wanted to do more to support patients with IBD. He had heard of Camp Oasis – the Crohn’s & Colitis Foundation regional camp for patients with IBD – and knew of physicians who provided medical services at the camp. After looking into making a donation to Camp Oasis Michigan, Dr. Boone learned that what the camp really needed was male counselors. So, despite being “older than an average camp counselor,” Dr. Boone packed his bags for Michigan. Participating in Camp Oasis the last 2 years has been a great joy for Dr. Boone and provides added inspiration and motivation for his work in the lab.

[email protected]

I recently had the privilege of treating a family who spoke my first language, Hindi. My patient, Ms. M, was 16 years old and struggling to adjust to her new life in the United States, having recently come from India. America’s schooling, culture, and “open society” was a contrast to her life in a semi-rural town, especially her close-knit family structure in which her parents and siblings are everything. Due to their cultural beliefs and religious faith in Islam, both Ms. M and her father were initially resistant to begin treatment for her depression and anxiety. “Let’s give it a trial” was the attitude I finally got from the father. But to me, there was a clear discordance in the communication among the family members in addition to the primary mental illness that led them to come for treatment. I was attracted to work with this family because I had a reasonable understanding of their faith, their culture, and their family system, and I have an inclination toward spirituality. Even though I recognized this family’s social isolation, I wondered why they were still in a state of unrest, given their deep commitment to their faith.

Ms. M was isolating herself at home, in an environment that wasn’t supportive of talking about her concerns. These included being bullied for being “different,” for how she dressed, and for having home-cooked traditional meals for lunch, and being unable to socialize with most of her male peers, except for those from her same community. This led her to dream of returning to India.

The family did not have a social life. Ms. M told me, “I wanted to socialize, but I cannot because of my faith and religion.” So she chose to wear attire to identify with her mother and her culture of origin. She also did this to hide her emotional pain from enduring trauma related to bullying at her school. It was a challenge to understand how faith, resilience, and trauma were intermingled in Ms. M and her family.

I saw Ms. M and her family for 12 one-hour family psychotherapy sessions. The initial session unfolded uneasily. It was a challenge to build rapport and help them understand how family therapy works. Circular inquiries to each family member, specifically to get the mother’s point of view, brought mourning, shame, and guilt to this family. The importance of marriage, education, and immigration were processed in reference to their culture and their incomplete acculturation to life in the United States.

I wondered if there were other families with different cultural backgrounds who struggled with similar conflicts. I also wondered if those families understood the value of family therapy or had ever experienced this therapeutic process.

The 3 key signs that made me believe that this family was making progress through our work together included:

• They complied with treatment; the family never missed a session.
• The parents acknowledged that their daughter was doing better.
• The mother brought me a dinner as a gesture of gratitude in our last session. This is a particularly meaningful gesture on the part of people with their cultural background.

I clearly remember our first meeting, when Ms. M asked me disapprovingly about family therapy, “Why do we need to come here? Can’t we do it at home?” The question itself gave me the answer, for our goal for family therapy was to get her to function better at home and school. Although we ended our work together after 12 sessions, I hope this family continues to participate in therapy, to resolve the difficulties they are now aware of as a result of our family work.

I recently had the privilege of treating a family who spoke my first language, Hindi. My patient, Ms. M, was 16 years old and struggling to adjust to her new life in the United States, having recently come from India. America’s schooling, culture, and “open society” was a contrast to her life in a semi-rural town, especially her close-knit family structure in which her parents and siblings are everything. Due to their cultural beliefs and religious faith in Islam, both Ms. M and her father were initially resistant to begin treatment for her depression and anxiety. “Let’s give it a trial” was the attitude I finally got from the father. But to me, there was a clear discordance in the communication among the family members in addition to the primary mental illness that led them to come for treatment. I was attracted to work with this family because I had a reasonable understanding of their faith, their culture, and their family system, and I have an inclination toward spirituality. Even though I recognized this family’s social isolation, I wondered why they were still in a state of unrest, given their deep commitment to their faith.

Ms. M was isolating herself at home, in an environment that wasn’t supportive of talking about her concerns. These included being bullied for being “different,” for how she dressed, and for having home-cooked traditional meals for lunch, and being unable to socialize with most of her male peers, except for those from her same community. This led her to dream of returning to India.

The family did not have a social life. Ms. M told me, “I wanted to socialize, but I cannot because of my faith and religion.” So she chose to wear attire to identify with her mother and her culture of origin. She also did this to hide her emotional pain from enduring trauma related to bullying at her school. It was a challenge to understand how faith, resilience, and trauma were intermingled in Ms. M and her family.

I saw Ms. M and her family for 12 one-hour family psychotherapy sessions. The initial session unfolded uneasily. It was a challenge to build rapport and help them understand how family therapy works. Circular inquiries to each family member, specifically to get the mother’s point of view, brought mourning, shame, and guilt to this family. The importance of marriage, education, and immigration were processed in reference to their culture and their incomplete acculturation to life in the United States.

I wondered if there were other families with different cultural backgrounds who struggled with similar conflicts. I also wondered if those families understood the value of family therapy or had ever experienced this therapeutic process.

The 3 key signs that made me believe that this family was making progress through our work together included:

• They complied with treatment; the family never missed a session.
• The parents acknowledged that their daughter was doing better.
• The mother brought me a dinner as a gesture of gratitude in our last session. This is a particularly meaningful gesture on the part of people with their cultural background.

I clearly remember our first meeting, when Ms. M asked me disapprovingly about family therapy, “Why do we need to come here? Can’t we do it at home?” The question itself gave me the answer, for our goal for family therapy was to get her to function better at home and school. Although we ended our work together after 12 sessions, I hope this family continues to participate in therapy, to resolve the difficulties they are now aware of as a result of our family work.

I recently had the privilege of treating a family who spoke my first language, Hindi. My patient, Ms. M, was 16 years old and struggling to adjust to her new life in the United States, having recently come from India. America’s schooling, culture, and “open society” was a contrast to her life in a semi-rural town, especially her close-knit family structure in which her parents and siblings are everything. Due to their cultural beliefs and religious faith in Islam, both Ms. M and her father were initially resistant to begin treatment for her depression and anxiety. “Let’s give it a trial” was the attitude I finally got from the father. But to me, there was a clear discordance in the communication among the family members in addition to the primary mental illness that led them to come for treatment. I was attracted to work with this family because I had a reasonable understanding of their faith, their culture, and their family system, and I have an inclination toward spirituality. Even though I recognized this family’s social isolation, I wondered why they were still in a state of unrest, given their deep commitment to their faith.

Ms. M was isolating herself at home, in an environment that wasn’t supportive of talking about her concerns. These included being bullied for being “different,” for how she dressed, and for having home-cooked traditional meals for lunch, and being unable to socialize with most of her male peers, except for those from her same community. This led her to dream of returning to India.

The family did not have a social life. Ms. M told me, “I wanted to socialize, but I cannot because of my faith and religion.” So she chose to wear attire to identify with her mother and her culture of origin. She also did this to hide her emotional pain from enduring trauma related to bullying at her school. It was a challenge to understand how faith, resilience, and trauma were intermingled in Ms. M and her family.

I saw Ms. M and her family for 12 one-hour family psychotherapy sessions. The initial session unfolded uneasily. It was a challenge to build rapport and help them understand how family therapy works. Circular inquiries to each family member, specifically to get the mother’s point of view, brought mourning, shame, and guilt to this family. The importance of marriage, education, and immigration were processed in reference to their culture and their incomplete acculturation to life in the United States.

I wondered if there were other families with different cultural backgrounds who struggled with similar conflicts. I also wondered if those families understood the value of family therapy or had ever experienced this therapeutic process.

The 3 key signs that made me believe that this family was making progress through our work together included:

• They complied with treatment; the family never missed a session.
• The parents acknowledged that their daughter was doing better.
• The mother brought me a dinner as a gesture of gratitude in our last session. This is a particularly meaningful gesture on the part of people with their cultural background.

I clearly remember our first meeting, when Ms. M asked me disapprovingly about family therapy, “Why do we need to come here? Can’t we do it at home?” The question itself gave me the answer, for our goal for family therapy was to get her to function better at home and school. Although we ended our work together after 12 sessions, I hope this family continues to participate in therapy, to resolve the difficulties they are now aware of as a result of our family work.

As psychiatrists treating an aging population, we frequently face the daunting challenges of managing medically complex and behaviorally unstable patients whose fragile condition tests the brightest among us. As our population enters late life, not only are physicians confronted with aging patients whose bodies have decreased renal and hepatic function, but we also face the challenges of the aging brain, severed neuronal networks, and neurotransmitter diminution. These physiological changes can alter treatment response, increase the frequency of adverse effects, and increase the likelihood of emergence of behavioral and psychological symptoms.

During the past decade, the number of people reaching age 65 has dramatically increased. As life expectancy improves, the “oldest old”—those age 85 and older—are the fastest-growing segment of the population. The prevalence of cognitive impairment, including mild cognitive impairment and dementia, in this cohort is >40%.¹ Roughly 90% of patients with dementia will develop clinically significant behavioral problems at some point in the course of their illness.²

Behavioral and psychological symptoms of dementia (BPSD) have a tremendous impact on the quality of life for both patients and their caregivers. We are experts in understanding these behaviors and crafting nonpharmacologic treatment plans to manage them. Understanding the context in which behaviors emerge allows us to modify the environment, communication strategies, and other potential triggers, in turn reducing the need for pharmacologic intervention.

However, when nonpharmacologic interventions have been exhausted, what are the options? Antipsychotics have been one of the approaches used to address the challenges of behavioral disturbances and psychosis occurring in dementia. Unfortunately, there is conflicting evidence regarding the risks and benefits associated with the use of antipsychotics in this population. In this article, we provide a roadmap for the judicious use of antipsychotics for patients with dementia.

Weighing the risks and benefits of antipsychotics

Until better treatment options become available, second-generation antipsychotics (SGAs) continue to have an important but limited role in the treatment of behavioral disturbances in dementia. Although safety risks exist, they can be minimized through the careful selection of appropriate patients for treatment, close monitoring, and effective communication with patients and caregivers before and during treatment.

Several studies examining the efficacy of antipsychotics in the treatment of BPSD have demonstrated an increased risk of cerebrovascular events, including stroke and death due to any cause.³ This evidence prompted the FDA to issue a “black-box” warning in 2005 to highlight the increased risk of mortality for patients with dementia who are treated with SGAs.⁴ Both first-generation antipsychotics (FGAs) and SGAs have been associated with higher rates of mortality than most other psychotropic classes, except anticonvulsants. This increased mortality risk has been shown to persist for at least 6 to 12 months.^5,6 FGAs appear to be associated with a greater mortality risk compared with SGAs. As a result, if antipsychotic treatment is necessary, the use of FGAs in this population is not recommended.

The potential mechanisms leading to stroke and death remain unclear. They could include orthostatic hypotension, anticholinergic adverse effects, QT prolongation, platelet aggregation effects, and venous thromboembolism. The presence of cardiovascular and vascular risk factors, electrolyte imbalances, cardiac arrhythmias, and concomitant use of medications that prolong the QTc interval may confer additional risks.

Continued to: Although the use of antipsychotics for patients with dementia...

Although the use of antipsychotics for patients with dementia may increase the risk of mortality, the absolute increased risk to a given individual, at least with short-term treatment, is likely small. The risk may also vary depending on the choice of SGA. Patients who were treated with quetiapine had a slightly lower risk of death than those who were treated risperidone.⁵ Death rates among patients prescribed aripiprazole, olanzapine, and ziprasidone were similar to the death rates of patients who were treated with risperidone. Compared with patients who were treated with risperidone, patients who were treated with the FGA haloperidol were twice as likely to die during a subsequent 6-month observation period. The largest number of deaths occurred during the first 40 days of treatment.⁵

While this increased risk of mortality is an important factor to discuss with patients and caregivers when deciding whether to initiate antipsychotic treatment, it is also important to put it into perspective. For example, the risk of suddenly dying from a stroke or heart attack for a person with dementia who is not taking an antipsychotic is approximately 2%. When an individual is started on one of these agents, that risk increases to approximately 4%. While the mortality risk is doubled, it remains relatively small.⁴ When faced with verbal or physical assaults, hostility, paranoid ideations, or other psychotic symptoms, many families feel that this relatively low risk does not outweigh the potential benefits of reducing caregiver and patient distress. If nonpharmacologic and/or other pharmacologic interventions have failed, the treatment has reached a point of no good alternatives and therapy should then focus on minimizing risk.

Informed consent is essential. A discussion of risks and benefits with the patient, family, or other decision-makers should focus on the risk of stroke, potential metabolic effects, and mortality, as well as potential worsening of cognitive decline associated with antipsychotic treatment. This should be weighed together with the evidence that suggests psychosis and agitation are associated with earlier nursing home admission and death.^7,8 Families should be given ample time and opportunity to ask questions. Alternatives to immediate initiation of antipsychotics should be thoroughly reviewed.

Despite the above-noted risks, expert consensus suggests that the use of antipsychotics in the treatment of individuals with dementia can be appropriate, particularly in individuals with dangerous agitation or psychosis.⁹ These agents can minimize the risk of violence, reduce patient distress, improve the patient’s quality of life, and reduce caregiver burden. In clinical trials, the benefits of antipsychotics have been modest. Nevertheless, evidence has shown that these agents can reduce psychosis, agitation, aggression, hostility, and suspiciousness, which makes them a valid option when other interventions have proven insufficient.

Target specific symptoms

Despite this article’s focus on the appropriate use of antipsychotics for patients with BPSD, it is important to emphasize that the first-line approach to the management of BPSD in this population should always be a person-centered, psychosocial, multidisciplinary, nonpharmacologic approach that focuses on identifying triggers and treating potentially modifiable contributors to behavioral symptoms. Table 1¹⁰ outlines common underlying causes of BPSD in dementia that should be assessed before prescribing an antipsychotic.

Continued to: Alternative psychopharmacologic treatments...

Alternative psychopharmacologic treatments based on a psychobehavioral metaphor should also be considered (Table 2¹¹). This approach matches the dominant target symptoms to the most relevant medication class.¹¹ For example, in the case of a verbally and physically agitated patient who is also irritable, negative, socially withdrawn, and appears dysphoric, we might first undertake a trial of an antidepressant. Conversely, if the patient shows agitation in the context of increased motor activity, loud and rapid speech, and affective lability, we might consider the use of a mood stabilizer. Pharmacologic treatment should be aimed at the modification of clearly identified and documented target behaviors.

• severe agitation and aggression associated with risk of harm
• delusions and hallucinations
• comorbid preexisting mental health conditions (eg, bipolar disorder, schizophrenia, treatment-resistant depression, etc.).

Symptoms that do not usually respond to an antipsychotic include wandering, social withdrawal, shouting, pacing, touching, cognitive defects, and incontinence.¹² These symptoms may respond to interventions such as changes to the environment.

Continued to: Choosing an antipsychotic

Choosing an antipsychotic

Once you have identified that an antipsychotic is truly indicated, the choice of an agent will focus on patient-related factors. Considerations such as frailty, comorbid medical conditions including diabetes, history of falls, hepatic insufficiency, cardiac arrhythmias, and cerebrovascular risk factors, should all be analyzed prior to initiating an antipsychotic. The presence of these conditions will increase the likelihood that adverse effects may occur. It will also guide the dose trajectory and the target dose for discontinuation. Antipsychotics differ with respect to their efficacy and adverse effect profile. For practical purposes, adverse effects typically guide the selection of these agents when used for patients with dementia.

Continued to: Gradual structural changes occur...

Gradual structural changes occur in the dopaminergic system with age and increase the propensity for antipsychotic adverse effects. The number of dopaminergic neurons and D2 receptors decreases approximately 10% per decade. In order to avoid the development of adverse effects related to extrapyramidal symptoms, approximately 20% of receptors need to be free. FGAs tend to block approximately 90% of D2 receptors, whereas SGAs block less than 70% to 80% and dissociate more rapidly from D2 receptors.¹³ FGAs should therefore be avoided, as they have been associated with numerous adverse effects, including parkinsonism, tardive dyskinesia, akathisia, sedation, peripheral and central anticholinergic effects, postural hypotension, cardiac conduction defects, and falls. As noted above, they have been linked to a greater risk of mortality (Figure¹⁴ ).

Dosing considerations

When beginning treatment with an antipsychotic, the starting dose should be as low as possible. This is particularly important for patients who are older, frail, cognitively impaired, or who carry a specific, significant risk that the antipsychotic may increase, such as a risk for falling. The starting dose can be divided or scheduled according to the behavior. For example, a lunchtime dose may be appropriate for patients exhibiting increased agitation towards the end of the day (“sundowning”). A good rule of thumb is to administer a dose approximately 2 hours before the behaviors typically occur. While there is no formal evidence from clinical trials to support this type of dosing schedule, clinical experience has shown it to have merit.

Dose increments should be modest and, in a nonemergent setting, may be adjusted at weekly intervals depending on response. Prior to starting a treatment trial, it is advisable to estimate what will constitute a worthwhile clinical response, the duration of treatment, and the maximum dose. Avoid high doses or prolonged use of antipsychotics that have not significantly improved the target behavior.

When the decision to use a SGA is made, choosing the initial starting dose is challenging given that none of these medications has an indication for use in this population. We propose doses that have been used in completed randomized trials that reflect the best information available about the dose likely to maximize benefit and minimize risk. On the basis of those trials, reasonable starting doses would be^15-22:

• quetiapine 25 to 50 mg/d
• risperidone 0.5 to 1 mg/d
• aripiprazole 2 to 10 mg/d
• olanzapine 2.5 to 5 mg/d
• ziprasidone 20 mg/d

Continued to: The highest doses tested...

The highest doses tested for each of these compounds in randomized clinical trials for this population were: risperidone 2 mg/d, olanzapine 10 mg/d, and aripiprazole 15 mg/d. A wide variety of maximum doses of quetiapine were studied in clinical trials, with a top dose of 200 mg being most common. It is worth noting that doses higher than these have been used for other indications.^15-22

Quetiapine. One of the most commonly prescribed antipsychotics for the treatment of BPSD in individuals with memory disorders is quetiapine. The reasons for this preference include a low risk of extrapyramidal adverse effects, flexibility of dosing, ability to use lower dosages, and evidence of the lower risk of mortality when compared with other second-generation agents.^5,15 If an antipsychotic is indicated, quetiapine should be considered as a first-line antipsychotic therapy. Quetiapine has well-established effects on mood, anxiety, and sleep, all of which can be disrupted in dementia and can act as drivers for agitation.^5,15 Starting quetiapine may mitigate the need for separate agents to treat insomnia, loss of appetite, or anxiety, although it is not FDA-indicated for these comorbid conditions. Quetiapine is also less likely to exacerbate motor symptoms compared with other SGAs but has the potential to increase the risk of falls, and orthostasis, and carries a considerable anticholinergic burden.^5,15

Risperidone has been shown to provide modest improvements in some people exhibiting symptoms of aggression, agitation, and psychosis.^5,15 There is no evidence that risperidone is any more effective than other SGAs, but it has been tested on more geriatric patients than other SGAs. The fact that it is also available in an orally disintegrating tablet makes it a practical treatment in certain populations of patients, such as those who have difficulty swallowing. Risperidone carries the highest extrapyramidal symptom burden among the SGAs due to its potent D2 receptor binding. ^5,15

Aripiprazole. There have been several studies of aripiprazole for the treatment of psychosis and agitation in Alzheimer’s dementia.¹⁵ This medication showed modest effect and was generally well tolerated. Aripiprazole appears to have less associated weight gain, which may be pertinent for some patients. It also appears to be less sedating than many of the other SGAs. However, some patients may experience activation or insomnia with this agent, particularly with doses <15 mg/d. This activating effect may be beneficial for treating comorbid depressive symptoms, although lower doses could theoretically worsen psychosis due to the activating effects.

Aripiprazole has also been studied in Parkinson’s disease. While some patients had favorable responses with improvement in psychosis and behavioral disturbances, this medication was also associated with worsening of motor symptoms. Certain individuals also experienced a worsening of their psychosis.²³ For this reason, it is unlikely to be a useful agent for patients displaying evidence of parkinsonism, Parkinson’s dementia, or dementia with Lewy bodies.

Olanzapine. Several studies have shown that low-dose olanzapine has been modestly effective in decreasing agitation and aggression in patients suffering from Alzheimer’s and vascular dementias.²⁴ The medication is also available in an orally disintegrating form, which may be beneficial when treating individuals whose swallowing abilities are compromised. Olanzapine also has been associated with significant weight gain and metabolic syndrome.²⁴

Continued to: Ziprasidone

Ziprasidone. There are no specific studies of ziprasidone for geriatric patients and none for patients with dementia. However, case reports have suggested both oral and injectable forms of the medication may be well tolerated and have some benefit in treating agitation in this population.²⁵ Based on evidence from younger populations, ziprasidone is less likely to be associated with weight gain or orthostatic hypotension. Medication has been associated with QTc prolongation and should be used with caution and monitored with an ECG.

The initial dosing and potential adverse effects of quetiapine, risperidone, aripiprazole, olanzapine, and ziprasidone are highlighted in Table 3.¹⁰

Other SGAs. Newer antipsychotics have recently become available and may serve as additional tools for managing BPSD in the future. Unfortunately, there are currently no available studies regarding their efficacy in the treatment of agitation and psychosis in dementia. One notable exception is pimavaserin, a serotonin 2A receptor inverse agonist. This medication has recently been FDA-approved for the treatment of Parkinson’s disease psychosis. The medication was extensively studied in older patients. It appeared to be effective in reducing delusions and hallucinations while not impairing motor function or causing sedation or hypotension.²³ Additional studies are currently ongoing for the treatment of Alzheimer’s dementia psychosis.

Monitor treatment, consider discontinuation

American Psychiatric Association guidelines on the use of antipsychotics to treat agitation or psychosis in patients with dementia currently recommend that clinicians use a quantitative measure to track symptoms and response to treatment.²⁶ These measures may be formal, such as an overall assessment of symptom severity on a Likert scale, or as simple as monitoring the changes in the frequency of periods of agitation.

After starting an antipsychotic, a follow-up appointment should typically take place within 1 month. If the patient is at high risk for developing adverse effects, or if the symptoms are severe, a follow-up appointment for monitoring the response to treatment and potential adverse effects should occur within 1 week. At a minimum, expert consensus suggests follow-up visits should occur every 3 months.

If there is no clinical response after 4 weeks of adequate dosing of an anti­psychotic, the medication should be tapered and withdrawn. Switching to an alternative agent may be appropriate.

Many patients will have only partial remission of target symptoms. Therefore, increasing the dose or switching to an alternative agent may be necessary. Concurrent use of multiple antipsychotic agents should be avoided.

Continued to: Maintenance treatment may be appropriate

How to approach discontinuation

Behavioral and psychological symptoms of dementia are frequently temporary. If the patient has been stable, gradual dose reduction and eventual discontinuation of antipsychotics should be attempted every 3 months. Studies have reported that most patients who were taken off antipsychotics for treating BPSD showed no worsening of behavioral symptoms.²⁷

Discontinuation of antipsychotics should be done gradually by reducing the dose by 50% every 2 weeks, and then stopping after 2 weeks on the minimum dose, with monitoring for recurrence of target symptoms or emergence of new ones. The longer a medication has been prescribed, the slower the withdrawal occurs. Thus, the possibility of emerging symptoms related to drug withdrawal will lessen.

A roadmap for judicious prescribing

When underlying treatable or reversible causes of BPSD in dementia have been ruled out or nonpharmacologic treatments have failed, a trial of an antipsychotic may be indicated. The choice of agent should focus on patient-related factors and on clearly identified target behaviors. Treatment should be started at a low dose and titrated cautiously to the lowest effective dose.

Behavioral and psychological symptoms of dementia are frequently temporary. Therefore, a gradual reduction and eventual withdrawal of antipsychotic medications should be attempted every 3 months. Studies indicate that most patients are able to tolerate elimination of antipsychotic medications with no worsening of behavioral symptoms.

Despite the limitations of treatment, SGAs remain a valid consideration when other interventions have proven insufficient. However, judicious use of these agents remains the cornerstone of therapy.

Bottom Line 

Until better treatment options become available, second-generation antipsychotics (SGAs) continue to have an important, albeit limited, role in the treatment of behavioral disturbances in dementia. Despite the limitations of treatment, SGAs remain a valid consideration when other interventions have proven insufficient. However, judicious use of these agents remains the cornerstone of therapy.

Related Resources

• Kales HC, Mulsant BH, Sajatovic MS. Prescribing antipsychotics in geriatric patients: Focus on dementia. Third of 3 parts. Current Psychiatry. 2017;16(12):24-30.
• Meeks TW, Jeste DV. Antipsychotics in dementia: Beyond ‘black-box’ warnings. Current Psychiatry. 2008;7(6):51-52, 55-58, 64-65.

Drug Brand Names

Aripiprazole • Abilify
Haloperidol • Haldol
Olanzapine • Zyprexa
Pimavanserin • Nuplazid
Risperidone • Risperdal
Quetiapine • Seroquel
Ziprasidone • Geodon

As psychiatrists treating an aging population, we frequently face the daunting challenges of managing medically complex and behaviorally unstable patients whose fragile condition tests the brightest among us. As our population enters late life, not only are physicians confronted with aging patients whose bodies have decreased renal and hepatic function, but we also face the challenges of the aging brain, severed neuronal networks, and neurotransmitter diminution. These physiological changes can alter treatment response, increase the frequency of adverse effects, and increase the likelihood of emergence of behavioral and psychological symptoms.

During the past decade, the number of people reaching age 65 has dramatically increased. As life expectancy improves, the “oldest old”—those age 85 and older—are the fastest-growing segment of the population. The prevalence of cognitive impairment, including mild cognitive impairment and dementia, in this cohort is >40%.¹ Roughly 90% of patients with dementia will develop clinically significant behavioral problems at some point in the course of their illness.²

Behavioral and psychological symptoms of dementia (BPSD) have a tremendous impact on the quality of life for both patients and their caregivers. We are experts in understanding these behaviors and crafting nonpharmacologic treatment plans to manage them. Understanding the context in which behaviors emerge allows us to modify the environment, communication strategies, and other potential triggers, in turn reducing the need for pharmacologic intervention.

However, when nonpharmacologic interventions have been exhausted, what are the options? Antipsychotics have been one of the approaches used to address the challenges of behavioral disturbances and psychosis occurring in dementia. Unfortunately, there is conflicting evidence regarding the risks and benefits associated with the use of antipsychotics in this population. In this article, we provide a roadmap for the judicious use of antipsychotics for patients with dementia.

Weighing the risks and benefits of antipsychotics

Until better treatment options become available, second-generation antipsychotics (SGAs) continue to have an important but limited role in the treatment of behavioral disturbances in dementia. Although safety risks exist, they can be minimized through the careful selection of appropriate patients for treatment, close monitoring, and effective communication with patients and caregivers before and during treatment.

Several studies examining the efficacy of antipsychotics in the treatment of BPSD have demonstrated an increased risk of cerebrovascular events, including stroke and death due to any cause.³ This evidence prompted the FDA to issue a “black-box” warning in 2005 to highlight the increased risk of mortality for patients with dementia who are treated with SGAs.⁴ Both first-generation antipsychotics (FGAs) and SGAs have been associated with higher rates of mortality than most other psychotropic classes, except anticonvulsants. This increased mortality risk has been shown to persist for at least 6 to 12 months.^5,6 FGAs appear to be associated with a greater mortality risk compared with SGAs. As a result, if antipsychotic treatment is necessary, the use of FGAs in this population is not recommended.

The potential mechanisms leading to stroke and death remain unclear. They could include orthostatic hypotension, anticholinergic adverse effects, QT prolongation, platelet aggregation effects, and venous thromboembolism. The presence of cardiovascular and vascular risk factors, electrolyte imbalances, cardiac arrhythmias, and concomitant use of medications that prolong the QTc interval may confer additional risks.

Continued to: Although the use of antipsychotics for patients with dementia...

Although the use of antipsychotics for patients with dementia may increase the risk of mortality, the absolute increased risk to a given individual, at least with short-term treatment, is likely small. The risk may also vary depending on the choice of SGA. Patients who were treated with quetiapine had a slightly lower risk of death than those who were treated risperidone.⁵ Death rates among patients prescribed aripiprazole, olanzapine, and ziprasidone were similar to the death rates of patients who were treated with risperidone. Compared with patients who were treated with risperidone, patients who were treated with the FGA haloperidol were twice as likely to die during a subsequent 6-month observation period. The largest number of deaths occurred during the first 40 days of treatment.⁵

While this increased risk of mortality is an important factor to discuss with patients and caregivers when deciding whether to initiate antipsychotic treatment, it is also important to put it into perspective. For example, the risk of suddenly dying from a stroke or heart attack for a person with dementia who is not taking an antipsychotic is approximately 2%. When an individual is started on one of these agents, that risk increases to approximately 4%. While the mortality risk is doubled, it remains relatively small.⁴ When faced with verbal or physical assaults, hostility, paranoid ideations, or other psychotic symptoms, many families feel that this relatively low risk does not outweigh the potential benefits of reducing caregiver and patient distress. If nonpharmacologic and/or other pharmacologic interventions have failed, the treatment has reached a point of no good alternatives and therapy should then focus on minimizing risk.

Informed consent is essential. A discussion of risks and benefits with the patient, family, or other decision-makers should focus on the risk of stroke, potential metabolic effects, and mortality, as well as potential worsening of cognitive decline associated with antipsychotic treatment. This should be weighed together with the evidence that suggests psychosis and agitation are associated with earlier nursing home admission and death.^7,8 Families should be given ample time and opportunity to ask questions. Alternatives to immediate initiation of antipsychotics should be thoroughly reviewed.

Despite the above-noted risks, expert consensus suggests that the use of antipsychotics in the treatment of individuals with dementia can be appropriate, particularly in individuals with dangerous agitation or psychosis.⁹ These agents can minimize the risk of violence, reduce patient distress, improve the patient’s quality of life, and reduce caregiver burden. In clinical trials, the benefits of antipsychotics have been modest. Nevertheless, evidence has shown that these agents can reduce psychosis, agitation, aggression, hostility, and suspiciousness, which makes them a valid option when other interventions have proven insufficient.

Target specific symptoms

Despite this article’s focus on the appropriate use of antipsychotics for patients with BPSD, it is important to emphasize that the first-line approach to the management of BPSD in this population should always be a person-centered, psychosocial, multidisciplinary, nonpharmacologic approach that focuses on identifying triggers and treating potentially modifiable contributors to behavioral symptoms. Table 1¹⁰ outlines common underlying causes of BPSD in dementia that should be assessed before prescribing an antipsychotic.

Continued to: Alternative psychopharmacologic treatments...

Alternative psychopharmacologic treatments based on a psychobehavioral metaphor should also be considered (Table 2¹¹). This approach matches the dominant target symptoms to the most relevant medication class.¹¹ For example, in the case of a verbally and physically agitated patient who is also irritable, negative, socially withdrawn, and appears dysphoric, we might first undertake a trial of an antidepressant. Conversely, if the patient shows agitation in the context of increased motor activity, loud and rapid speech, and affective lability, we might consider the use of a mood stabilizer. Pharmacologic treatment should be aimed at the modification of clearly identified and documented target behaviors.

• severe agitation and aggression associated with risk of harm
• delusions and hallucinations
• comorbid preexisting mental health conditions (eg, bipolar disorder, schizophrenia, treatment-resistant depression, etc.).

Symptoms that do not usually respond to an antipsychotic include wandering, social withdrawal, shouting, pacing, touching, cognitive defects, and incontinence.¹² These symptoms may respond to interventions such as changes to the environment.

Continued to: Choosing an antipsychotic

Choosing an antipsychotic

Once you have identified that an antipsychotic is truly indicated, the choice of an agent will focus on patient-related factors. Considerations such as frailty, comorbid medical conditions including diabetes, history of falls, hepatic insufficiency, cardiac arrhythmias, and cerebrovascular risk factors, should all be analyzed prior to initiating an antipsychotic. The presence of these conditions will increase the likelihood that adverse effects may occur. It will also guide the dose trajectory and the target dose for discontinuation. Antipsychotics differ with respect to their efficacy and adverse effect profile. For practical purposes, adverse effects typically guide the selection of these agents when used for patients with dementia.

Continued to: Gradual structural changes occur...

Gradual structural changes occur in the dopaminergic system with age and increase the propensity for antipsychotic adverse effects. The number of dopaminergic neurons and D2 receptors decreases approximately 10% per decade. In order to avoid the development of adverse effects related to extrapyramidal symptoms, approximately 20% of receptors need to be free. FGAs tend to block approximately 90% of D2 receptors, whereas SGAs block less than 70% to 80% and dissociate more rapidly from D2 receptors.¹³ FGAs should therefore be avoided, as they have been associated with numerous adverse effects, including parkinsonism, tardive dyskinesia, akathisia, sedation, peripheral and central anticholinergic effects, postural hypotension, cardiac conduction defects, and falls. As noted above, they have been linked to a greater risk of mortality (Figure¹⁴ ).

Dosing considerations

When beginning treatment with an antipsychotic, the starting dose should be as low as possible. This is particularly important for patients who are older, frail, cognitively impaired, or who carry a specific, significant risk that the antipsychotic may increase, such as a risk for falling. The starting dose can be divided or scheduled according to the behavior. For example, a lunchtime dose may be appropriate for patients exhibiting increased agitation towards the end of the day (“sundowning”). A good rule of thumb is to administer a dose approximately 2 hours before the behaviors typically occur. While there is no formal evidence from clinical trials to support this type of dosing schedule, clinical experience has shown it to have merit.

Dose increments should be modest and, in a nonemergent setting, may be adjusted at weekly intervals depending on response. Prior to starting a treatment trial, it is advisable to estimate what will constitute a worthwhile clinical response, the duration of treatment, and the maximum dose. Avoid high doses or prolonged use of antipsychotics that have not significantly improved the target behavior.

When the decision to use a SGA is made, choosing the initial starting dose is challenging given that none of these medications has an indication for use in this population. We propose doses that have been used in completed randomized trials that reflect the best information available about the dose likely to maximize benefit and minimize risk. On the basis of those trials, reasonable starting doses would be^15-22:

• quetiapine 25 to 50 mg/d
• risperidone 0.5 to 1 mg/d
• aripiprazole 2 to 10 mg/d
• olanzapine 2.5 to 5 mg/d
• ziprasidone 20 mg/d

Continued to: The highest doses tested...

The highest doses tested for each of these compounds in randomized clinical trials for this population were: risperidone 2 mg/d, olanzapine 10 mg/d, and aripiprazole 15 mg/d. A wide variety of maximum doses of quetiapine were studied in clinical trials, with a top dose of 200 mg being most common. It is worth noting that doses higher than these have been used for other indications.^15-22

Quetiapine. One of the most commonly prescribed antipsychotics for the treatment of BPSD in individuals with memory disorders is quetiapine. The reasons for this preference include a low risk of extrapyramidal adverse effects, flexibility of dosing, ability to use lower dosages, and evidence of the lower risk of mortality when compared with other second-generation agents.^5,15 If an antipsychotic is indicated, quetiapine should be considered as a first-line antipsychotic therapy. Quetiapine has well-established effects on mood, anxiety, and sleep, all of which can be disrupted in dementia and can act as drivers for agitation.^5,15 Starting quetiapine may mitigate the need for separate agents to treat insomnia, loss of appetite, or anxiety, although it is not FDA-indicated for these comorbid conditions. Quetiapine is also less likely to exacerbate motor symptoms compared with other SGAs but has the potential to increase the risk of falls, and orthostasis, and carries a considerable anticholinergic burden.^5,15

Risperidone has been shown to provide modest improvements in some people exhibiting symptoms of aggression, agitation, and psychosis.^5,15 There is no evidence that risperidone is any more effective than other SGAs, but it has been tested on more geriatric patients than other SGAs. The fact that it is also available in an orally disintegrating tablet makes it a practical treatment in certain populations of patients, such as those who have difficulty swallowing. Risperidone carries the highest extrapyramidal symptom burden among the SGAs due to its potent D2 receptor binding. ^5,15

Aripiprazole. There have been several studies of aripiprazole for the treatment of psychosis and agitation in Alzheimer’s dementia.¹⁵ This medication showed modest effect and was generally well tolerated. Aripiprazole appears to have less associated weight gain, which may be pertinent for some patients. It also appears to be less sedating than many of the other SGAs. However, some patients may experience activation or insomnia with this agent, particularly with doses <15 mg/d. This activating effect may be beneficial for treating comorbid depressive symptoms, although lower doses could theoretically worsen psychosis due to the activating effects.

Aripiprazole has also been studied in Parkinson’s disease. While some patients had favorable responses with improvement in psychosis and behavioral disturbances, this medication was also associated with worsening of motor symptoms. Certain individuals also experienced a worsening of their psychosis.²³ For this reason, it is unlikely to be a useful agent for patients displaying evidence of parkinsonism, Parkinson’s dementia, or dementia with Lewy bodies.

Olanzapine. Several studies have shown that low-dose olanzapine has been modestly effective in decreasing agitation and aggression in patients suffering from Alzheimer’s and vascular dementias.²⁴ The medication is also available in an orally disintegrating form, which may be beneficial when treating individuals whose swallowing abilities are compromised. Olanzapine also has been associated with significant weight gain and metabolic syndrome.²⁴

Continued to: Ziprasidone

Ziprasidone. There are no specific studies of ziprasidone for geriatric patients and none for patients with dementia. However, case reports have suggested both oral and injectable forms of the medication may be well tolerated and have some benefit in treating agitation in this population.²⁵ Based on evidence from younger populations, ziprasidone is less likely to be associated with weight gain or orthostatic hypotension. Medication has been associated with QTc prolongation and should be used with caution and monitored with an ECG.

The initial dosing and potential adverse effects of quetiapine, risperidone, aripiprazole, olanzapine, and ziprasidone are highlighted in Table 3.¹⁰

Other SGAs. Newer antipsychotics have recently become available and may serve as additional tools for managing BPSD in the future. Unfortunately, there are currently no available studies regarding their efficacy in the treatment of agitation and psychosis in dementia. One notable exception is pimavaserin, a serotonin 2A receptor inverse agonist. This medication has recently been FDA-approved for the treatment of Parkinson’s disease psychosis. The medication was extensively studied in older patients. It appeared to be effective in reducing delusions and hallucinations while not impairing motor function or causing sedation or hypotension.²³ Additional studies are currently ongoing for the treatment of Alzheimer’s dementia psychosis.

Monitor treatment, consider discontinuation

American Psychiatric Association guidelines on the use of antipsychotics to treat agitation or psychosis in patients with dementia currently recommend that clinicians use a quantitative measure to track symptoms and response to treatment.²⁶ These measures may be formal, such as an overall assessment of symptom severity on a Likert scale, or as simple as monitoring the changes in the frequency of periods of agitation.

After starting an antipsychotic, a follow-up appointment should typically take place within 1 month. If the patient is at high risk for developing adverse effects, or if the symptoms are severe, a follow-up appointment for monitoring the response to treatment and potential adverse effects should occur within 1 week. At a minimum, expert consensus suggests follow-up visits should occur every 3 months.

If there is no clinical response after 4 weeks of adequate dosing of an anti­psychotic, the medication should be tapered and withdrawn. Switching to an alternative agent may be appropriate.

Many patients will have only partial remission of target symptoms. Therefore, increasing the dose or switching to an alternative agent may be necessary. Concurrent use of multiple antipsychotic agents should be avoided.

Continued to: Maintenance treatment may be appropriate

How to approach discontinuation

Behavioral and psychological symptoms of dementia are frequently temporary. If the patient has been stable, gradual dose reduction and eventual discontinuation of antipsychotics should be attempted every 3 months. Studies have reported that most patients who were taken off antipsychotics for treating BPSD showed no worsening of behavioral symptoms.²⁷

Discontinuation of antipsychotics should be done gradually by reducing the dose by 50% every 2 weeks, and then stopping after 2 weeks on the minimum dose, with monitoring for recurrence of target symptoms or emergence of new ones. The longer a medication has been prescribed, the slower the withdrawal occurs. Thus, the possibility of emerging symptoms related to drug withdrawal will lessen.

A roadmap for judicious prescribing

When underlying treatable or reversible causes of BPSD in dementia have been ruled out or nonpharmacologic treatments have failed, a trial of an antipsychotic may be indicated. The choice of agent should focus on patient-related factors and on clearly identified target behaviors. Treatment should be started at a low dose and titrated cautiously to the lowest effective dose.

Behavioral and psychological symptoms of dementia are frequently temporary. Therefore, a gradual reduction and eventual withdrawal of antipsychotic medications should be attempted every 3 months. Studies indicate that most patients are able to tolerate elimination of antipsychotic medications with no worsening of behavioral symptoms.

Despite the limitations of treatment, SGAs remain a valid consideration when other interventions have proven insufficient. However, judicious use of these agents remains the cornerstone of therapy.

Bottom Line 

Until better treatment options become available, second-generation antipsychotics (SGAs) continue to have an important, albeit limited, role in the treatment of behavioral disturbances in dementia. Despite the limitations of treatment, SGAs remain a valid consideration when other interventions have proven insufficient. However, judicious use of these agents remains the cornerstone of therapy.

Related Resources

• Kales HC, Mulsant BH, Sajatovic MS. Prescribing antipsychotics in geriatric patients: Focus on dementia. Third of 3 parts. Current Psychiatry. 2017;16(12):24-30.
• Meeks TW, Jeste DV. Antipsychotics in dementia: Beyond ‘black-box’ warnings. Current Psychiatry. 2008;7(6):51-52, 55-58, 64-65.

Drug Brand Names

Aripiprazole • Abilify
Haloperidol • Haldol
Olanzapine • Zyprexa
Pimavanserin • Nuplazid
Risperidone • Risperdal
Quetiapine • Seroquel
Ziprasidone • Geodon

As psychiatrists treating an aging population, we frequently face the daunting challenges of managing medically complex and behaviorally unstable patients whose fragile condition tests the brightest among us. As our population enters late life, not only are physicians confronted with aging patients whose bodies have decreased renal and hepatic function, but we also face the challenges of the aging brain, severed neuronal networks, and neurotransmitter diminution. These physiological changes can alter treatment response, increase the frequency of adverse effects, and increase the likelihood of emergence of behavioral and psychological symptoms.

During the past decade, the number of people reaching age 65 has dramatically increased. As life expectancy improves, the “oldest old”—those age 85 and older—are the fastest-growing segment of the population. The prevalence of cognitive impairment, including mild cognitive impairment and dementia, in this cohort is >40%.¹ Roughly 90% of patients with dementia will develop clinically significant behavioral problems at some point in the course of their illness.²

Behavioral and psychological symptoms of dementia (BPSD) have a tremendous impact on the quality of life for both patients and their caregivers. We are experts in understanding these behaviors and crafting nonpharmacologic treatment plans to manage them. Understanding the context in which behaviors emerge allows us to modify the environment, communication strategies, and other potential triggers, in turn reducing the need for pharmacologic intervention.

However, when nonpharmacologic interventions have been exhausted, what are the options? Antipsychotics have been one of the approaches used to address the challenges of behavioral disturbances and psychosis occurring in dementia. Unfortunately, there is conflicting evidence regarding the risks and benefits associated with the use of antipsychotics in this population. In this article, we provide a roadmap for the judicious use of antipsychotics for patients with dementia.

Weighing the risks and benefits of antipsychotics

Until better treatment options become available, second-generation antipsychotics (SGAs) continue to have an important but limited role in the treatment of behavioral disturbances in dementia. Although safety risks exist, they can be minimized through the careful selection of appropriate patients for treatment, close monitoring, and effective communication with patients and caregivers before and during treatment.

Several studies examining the efficacy of antipsychotics in the treatment of BPSD have demonstrated an increased risk of cerebrovascular events, including stroke and death due to any cause.³ This evidence prompted the FDA to issue a “black-box” warning in 2005 to highlight the increased risk of mortality for patients with dementia who are treated with SGAs.⁴ Both first-generation antipsychotics (FGAs) and SGAs have been associated with higher rates of mortality than most other psychotropic classes, except anticonvulsants. This increased mortality risk has been shown to persist for at least 6 to 12 months.^5,6 FGAs appear to be associated with a greater mortality risk compared with SGAs. As a result, if antipsychotic treatment is necessary, the use of FGAs in this population is not recommended.

The potential mechanisms leading to stroke and death remain unclear. They could include orthostatic hypotension, anticholinergic adverse effects, QT prolongation, platelet aggregation effects, and venous thromboembolism. The presence of cardiovascular and vascular risk factors, electrolyte imbalances, cardiac arrhythmias, and concomitant use of medications that prolong the QTc interval may confer additional risks.

Continued to: Although the use of antipsychotics for patients with dementia...

Although the use of antipsychotics for patients with dementia may increase the risk of mortality, the absolute increased risk to a given individual, at least with short-term treatment, is likely small. The risk may also vary depending on the choice of SGA. Patients who were treated with quetiapine had a slightly lower risk of death than those who were treated risperidone.⁵ Death rates among patients prescribed aripiprazole, olanzapine, and ziprasidone were similar to the death rates of patients who were treated with risperidone. Compared with patients who were treated with risperidone, patients who were treated with the FGA haloperidol were twice as likely to die during a subsequent 6-month observation period. The largest number of deaths occurred during the first 40 days of treatment.⁵

While this increased risk of mortality is an important factor to discuss with patients and caregivers when deciding whether to initiate antipsychotic treatment, it is also important to put it into perspective. For example, the risk of suddenly dying from a stroke or heart attack for a person with dementia who is not taking an antipsychotic is approximately 2%. When an individual is started on one of these agents, that risk increases to approximately 4%. While the mortality risk is doubled, it remains relatively small.⁴ When faced with verbal or physical assaults, hostility, paranoid ideations, or other psychotic symptoms, many families feel that this relatively low risk does not outweigh the potential benefits of reducing caregiver and patient distress. If nonpharmacologic and/or other pharmacologic interventions have failed, the treatment has reached a point of no good alternatives and therapy should then focus on minimizing risk.

Informed consent is essential. A discussion of risks and benefits with the patient, family, or other decision-makers should focus on the risk of stroke, potential metabolic effects, and mortality, as well as potential worsening of cognitive decline associated with antipsychotic treatment. This should be weighed together with the evidence that suggests psychosis and agitation are associated with earlier nursing home admission and death.^7,8 Families should be given ample time and opportunity to ask questions. Alternatives to immediate initiation of antipsychotics should be thoroughly reviewed.

Despite the above-noted risks, expert consensus suggests that the use of antipsychotics in the treatment of individuals with dementia can be appropriate, particularly in individuals with dangerous agitation or psychosis.⁹ These agents can minimize the risk of violence, reduce patient distress, improve the patient’s quality of life, and reduce caregiver burden. In clinical trials, the benefits of antipsychotics have been modest. Nevertheless, evidence has shown that these agents can reduce psychosis, agitation, aggression, hostility, and suspiciousness, which makes them a valid option when other interventions have proven insufficient.

Target specific symptoms

Despite this article’s focus on the appropriate use of antipsychotics for patients with BPSD, it is important to emphasize that the first-line approach to the management of BPSD in this population should always be a person-centered, psychosocial, multidisciplinary, nonpharmacologic approach that focuses on identifying triggers and treating potentially modifiable contributors to behavioral symptoms. Table 1¹⁰ outlines common underlying causes of BPSD in dementia that should be assessed before prescribing an antipsychotic.

Continued to: Alternative psychopharmacologic treatments...

Alternative psychopharmacologic treatments based on a psychobehavioral metaphor should also be considered (Table 2¹¹). This approach matches the dominant target symptoms to the most relevant medication class.¹¹ For example, in the case of a verbally and physically agitated patient who is also irritable, negative, socially withdrawn, and appears dysphoric, we might first undertake a trial of an antidepressant. Conversely, if the patient shows agitation in the context of increased motor activity, loud and rapid speech, and affective lability, we might consider the use of a mood stabilizer. Pharmacologic treatment should be aimed at the modification of clearly identified and documented target behaviors.

• severe agitation and aggression associated with risk of harm
• delusions and hallucinations
• comorbid preexisting mental health conditions (eg, bipolar disorder, schizophrenia, treatment-resistant depression, etc.).

Symptoms that do not usually respond to an antipsychotic include wandering, social withdrawal, shouting, pacing, touching, cognitive defects, and incontinence.¹² These symptoms may respond to interventions such as changes to the environment.

Continued to: Choosing an antipsychotic

Choosing an antipsychotic

Once you have identified that an antipsychotic is truly indicated, the choice of an agent will focus on patient-related factors. Considerations such as frailty, comorbid medical conditions including diabetes, history of falls, hepatic insufficiency, cardiac arrhythmias, and cerebrovascular risk factors, should all be analyzed prior to initiating an antipsychotic. The presence of these conditions will increase the likelihood that adverse effects may occur. It will also guide the dose trajectory and the target dose for discontinuation. Antipsychotics differ with respect to their efficacy and adverse effect profile. For practical purposes, adverse effects typically guide the selection of these agents when used for patients with dementia.

Continued to: Gradual structural changes occur...

Gradual structural changes occur in the dopaminergic system with age and increase the propensity for antipsychotic adverse effects. The number of dopaminergic neurons and D2 receptors decreases approximately 10% per decade. In order to avoid the development of adverse effects related to extrapyramidal symptoms, approximately 20% of receptors need to be free. FGAs tend to block approximately 90% of D2 receptors, whereas SGAs block less than 70% to 80% and dissociate more rapidly from D2 receptors.¹³ FGAs should therefore be avoided, as they have been associated with numerous adverse effects, including parkinsonism, tardive dyskinesia, akathisia, sedation, peripheral and central anticholinergic effects, postural hypotension, cardiac conduction defects, and falls. As noted above, they have been linked to a greater risk of mortality (Figure¹⁴ ).

Dosing considerations

When beginning treatment with an antipsychotic, the starting dose should be as low as possible. This is particularly important for patients who are older, frail, cognitively impaired, or who carry a specific, significant risk that the antipsychotic may increase, such as a risk for falling. The starting dose can be divided or scheduled according to the behavior. For example, a lunchtime dose may be appropriate for patients exhibiting increased agitation towards the end of the day (“sundowning”). A good rule of thumb is to administer a dose approximately 2 hours before the behaviors typically occur. While there is no formal evidence from clinical trials to support this type of dosing schedule, clinical experience has shown it to have merit.

Dose increments should be modest and, in a nonemergent setting, may be adjusted at weekly intervals depending on response. Prior to starting a treatment trial, it is advisable to estimate what will constitute a worthwhile clinical response, the duration of treatment, and the maximum dose. Avoid high doses or prolonged use of antipsychotics that have not significantly improved the target behavior.

When the decision to use a SGA is made, choosing the initial starting dose is challenging given that none of these medications has an indication for use in this population. We propose doses that have been used in completed randomized trials that reflect the best information available about the dose likely to maximize benefit and minimize risk. On the basis of those trials, reasonable starting doses would be^15-22:

• quetiapine 25 to 50 mg/d
• risperidone 0.5 to 1 mg/d
• aripiprazole 2 to 10 mg/d
• olanzapine 2.5 to 5 mg/d
• ziprasidone 20 mg/d

Continued to: The highest doses tested...

The highest doses tested for each of these compounds in randomized clinical trials for this population were: risperidone 2 mg/d, olanzapine 10 mg/d, and aripiprazole 15 mg/d. A wide variety of maximum doses of quetiapine were studied in clinical trials, with a top dose of 200 mg being most common. It is worth noting that doses higher than these have been used for other indications.^15-22

Quetiapine. One of the most commonly prescribed antipsychotics for the treatment of BPSD in individuals with memory disorders is quetiapine. The reasons for this preference include a low risk of extrapyramidal adverse effects, flexibility of dosing, ability to use lower dosages, and evidence of the lower risk of mortality when compared with other second-generation agents.^5,15 If an antipsychotic is indicated, quetiapine should be considered as a first-line antipsychotic therapy. Quetiapine has well-established effects on mood, anxiety, and sleep, all of which can be disrupted in dementia and can act as drivers for agitation.^5,15 Starting quetiapine may mitigate the need for separate agents to treat insomnia, loss of appetite, or anxiety, although it is not FDA-indicated for these comorbid conditions. Quetiapine is also less likely to exacerbate motor symptoms compared with other SGAs but has the potential to increase the risk of falls, and orthostasis, and carries a considerable anticholinergic burden.^5,15

Risperidone has been shown to provide modest improvements in some people exhibiting symptoms of aggression, agitation, and psychosis.^5,15 There is no evidence that risperidone is any more effective than other SGAs, but it has been tested on more geriatric patients than other SGAs. The fact that it is also available in an orally disintegrating tablet makes it a practical treatment in certain populations of patients, such as those who have difficulty swallowing. Risperidone carries the highest extrapyramidal symptom burden among the SGAs due to its potent D2 receptor binding. ^5,15

Aripiprazole. There have been several studies of aripiprazole for the treatment of psychosis and agitation in Alzheimer’s dementia.¹⁵ This medication showed modest effect and was generally well tolerated. Aripiprazole appears to have less associated weight gain, which may be pertinent for some patients. It also appears to be less sedating than many of the other SGAs. However, some patients may experience activation or insomnia with this agent, particularly with doses <15 mg/d. This activating effect may be beneficial for treating comorbid depressive symptoms, although lower doses could theoretically worsen psychosis due to the activating effects.

Aripiprazole has also been studied in Parkinson’s disease. While some patients had favorable responses with improvement in psychosis and behavioral disturbances, this medication was also associated with worsening of motor symptoms. Certain individuals also experienced a worsening of their psychosis.²³ For this reason, it is unlikely to be a useful agent for patients displaying evidence of parkinsonism, Parkinson’s dementia, or dementia with Lewy bodies.

Olanzapine. Several studies have shown that low-dose olanzapine has been modestly effective in decreasing agitation and aggression in patients suffering from Alzheimer’s and vascular dementias.²⁴ The medication is also available in an orally disintegrating form, which may be beneficial when treating individuals whose swallowing abilities are compromised. Olanzapine also has been associated with significant weight gain and metabolic syndrome.²⁴

Continued to: Ziprasidone

Ziprasidone. There are no specific studies of ziprasidone for geriatric patients and none for patients with dementia. However, case reports have suggested both oral and injectable forms of the medication may be well tolerated and have some benefit in treating agitation in this population.²⁵ Based on evidence from younger populations, ziprasidone is less likely to be associated with weight gain or orthostatic hypotension. Medication has been associated with QTc prolongation and should be used with caution and monitored with an ECG.

The initial dosing and potential adverse effects of quetiapine, risperidone, aripiprazole, olanzapine, and ziprasidone are highlighted in Table 3.¹⁰

Other SGAs. Newer antipsychotics have recently become available and may serve as additional tools for managing BPSD in the future. Unfortunately, there are currently no available studies regarding their efficacy in the treatment of agitation and psychosis in dementia. One notable exception is pimavaserin, a serotonin 2A receptor inverse agonist. This medication has recently been FDA-approved for the treatment of Parkinson’s disease psychosis. The medication was extensively studied in older patients. It appeared to be effective in reducing delusions and hallucinations while not impairing motor function or causing sedation or hypotension.²³ Additional studies are currently ongoing for the treatment of Alzheimer’s dementia psychosis.

Monitor treatment, consider discontinuation

American Psychiatric Association guidelines on the use of antipsychotics to treat agitation or psychosis in patients with dementia currently recommend that clinicians use a quantitative measure to track symptoms and response to treatment.²⁶ These measures may be formal, such as an overall assessment of symptom severity on a Likert scale, or as simple as monitoring the changes in the frequency of periods of agitation.

After starting an antipsychotic, a follow-up appointment should typically take place within 1 month. If the patient is at high risk for developing adverse effects, or if the symptoms are severe, a follow-up appointment for monitoring the response to treatment and potential adverse effects should occur within 1 week. At a minimum, expert consensus suggests follow-up visits should occur every 3 months.

If there is no clinical response after 4 weeks of adequate dosing of an anti­psychotic, the medication should be tapered and withdrawn. Switching to an alternative agent may be appropriate.

Many patients will have only partial remission of target symptoms. Therefore, increasing the dose or switching to an alternative agent may be necessary. Concurrent use of multiple antipsychotic agents should be avoided.

Continued to: Maintenance treatment may be appropriate

How to approach discontinuation

Behavioral and psychological symptoms of dementia are frequently temporary. If the patient has been stable, gradual dose reduction and eventual discontinuation of antipsychotics should be attempted every 3 months. Studies have reported that most patients who were taken off antipsychotics for treating BPSD showed no worsening of behavioral symptoms.²⁷

Discontinuation of antipsychotics should be done gradually by reducing the dose by 50% every 2 weeks, and then stopping after 2 weeks on the minimum dose, with monitoring for recurrence of target symptoms or emergence of new ones. The longer a medication has been prescribed, the slower the withdrawal occurs. Thus, the possibility of emerging symptoms related to drug withdrawal will lessen.

A roadmap for judicious prescribing

When underlying treatable or reversible causes of BPSD in dementia have been ruled out or nonpharmacologic treatments have failed, a trial of an antipsychotic may be indicated. The choice of agent should focus on patient-related factors and on clearly identified target behaviors. Treatment should be started at a low dose and titrated cautiously to the lowest effective dose.

Behavioral and psychological symptoms of dementia are frequently temporary. Therefore, a gradual reduction and eventual withdrawal of antipsychotic medications should be attempted every 3 months. Studies indicate that most patients are able to tolerate elimination of antipsychotic medications with no worsening of behavioral symptoms.

Despite the limitations of treatment, SGAs remain a valid consideration when other interventions have proven insufficient. However, judicious use of these agents remains the cornerstone of therapy.

Bottom Line 

Until better treatment options become available, second-generation antipsychotics (SGAs) continue to have an important, albeit limited, role in the treatment of behavioral disturbances in dementia. Despite the limitations of treatment, SGAs remain a valid consideration when other interventions have proven insufficient. However, judicious use of these agents remains the cornerstone of therapy.

Related Resources

• Kales HC, Mulsant BH, Sajatovic MS. Prescribing antipsychotics in geriatric patients: Focus on dementia. Third of 3 parts. Current Psychiatry. 2017;16(12):24-30.
• Meeks TW, Jeste DV. Antipsychotics in dementia: Beyond ‘black-box’ warnings. Current Psychiatry. 2008;7(6):51-52, 55-58, 64-65.

Drug Brand Names

Aripiprazole • Abilify
Haloperidol • Haldol
Olanzapine • Zyprexa
Pimavanserin • Nuplazid
Risperidone • Risperdal
Quetiapine • Seroquel
Ziprasidone • Geodon

There is a well-established relationship between antipsychotic treatment and hyperprolactinemia. Most antipsychotics have been linked to increased prolactin levels, and the risk appears to be dose-related.¹ Antipsychotic-induced hyperprolactinemia can be asymptomatic, but it also has been associated with several adverse effects, including menstrual irregularity, osteoporosis, gynecomastia, and sexual dysfunction. Here I discuss what to do before starting a patient on an antipsychotic, and 5 treatment strategies for addressing antipsychotic-induced hyperprolactinemia.

Get a baseline prolactin level

Before starting a patient on an antipsychotic, obtain a baseline prolactin level measurement. If the patient later develops hyperprolactinemia, having a baseline measurement will make it easier to determine if the antipsychotic is a potential cause. Also, it is helpful to gather additional information regarding baseline psychosexual function and menstruation before starting an antipsychotic.

It is critical to determine if a temporal relationship exists between exposure to an antipsychotic and increase in prolactin levels.³ If the time course is unclear, laboratory tests need to be performed, including assessing liver, renal, and thyroid function or imaging of the pituitary gland. Also, hyperprolactinemia should not be diagnosed based on a single blood test result, because emotional and physical stress can elevate prolactin levels.

Continued to: 5 strategies for addressing hyperprolactinemia

 

 

5 strategies for addressing hyperprolactinemia

1. Reduce the antipsychotic dose. Because the risk of hyperprolactinemia is dose-dependent, reducing the antipsychotic dose could be helpful for some patients.

2. Switch to a prolactin-sparing antipsychotic, such as clozapine, quetiapine, olanzapine, or ziprasidone. However, it is often difficult to predict positive outcomes because switching antipsychotics may cause new adverse effects or trigger a psychotic relapse.

3. Consider sex hormone replacement therapy. A combined oral contraceptive could prevent osteoporosis and help estrogen deficiency symptoms in women who require antipsychotic medication. However, this treatment approach may worsen galactorrhea.

4. Use a dopamine receptor agonist. Dopamine receptor agonists, such as cabergoline or bromocriptine, have been shown to suppress prolactin secretion. Clinicians should always proceed cautiously because these medications can potentially increase the risk of psychosis.

5. Examine the potential benefits of adding aripiprazole because it can be used for augmentation to reduce prolactin levels in patients receiving other antipsychotics. In some cases, dopamine receptors can be exposed to competition between a partial agonist (aripiprazole) and an antagonist (the current antipsychotic). This competition may decrease the effectiveness of the current antipsychotic.¹ Also, adding another antipsychotic could increase overall adverse effects.

There is a well-established relationship between antipsychotic treatment and hyperprolactinemia. Most antipsychotics have been linked to increased prolactin levels, and the risk appears to be dose-related.¹ Antipsychotic-induced hyperprolactinemia can be asymptomatic, but it also has been associated with several adverse effects, including menstrual irregularity, osteoporosis, gynecomastia, and sexual dysfunction. Here I discuss what to do before starting a patient on an antipsychotic, and 5 treatment strategies for addressing antipsychotic-induced hyperprolactinemia.

Get a baseline prolactin level

Before starting a patient on an antipsychotic, obtain a baseline prolactin level measurement. If the patient later develops hyperprolactinemia, having a baseline measurement will make it easier to determine if the antipsychotic is a potential cause. Also, it is helpful to gather additional information regarding baseline psychosexual function and menstruation before starting an antipsychotic.

It is critical to determine if a temporal relationship exists between exposure to an antipsychotic and increase in prolactin levels.³ If the time course is unclear, laboratory tests need to be performed, including assessing liver, renal, and thyroid function or imaging of the pituitary gland. Also, hyperprolactinemia should not be diagnosed based on a single blood test result, because emotional and physical stress can elevate prolactin levels.

Continued to: 5 strategies for addressing hyperprolactinemia

 

 

5 strategies for addressing hyperprolactinemia

1. Reduce the antipsychotic dose. Because the risk of hyperprolactinemia is dose-dependent, reducing the antipsychotic dose could be helpful for some patients.

2. Switch to a prolactin-sparing antipsychotic, such as clozapine, quetiapine, olanzapine, or ziprasidone. However, it is often difficult to predict positive outcomes because switching antipsychotics may cause new adverse effects or trigger a psychotic relapse.

3. Consider sex hormone replacement therapy. A combined oral contraceptive could prevent osteoporosis and help estrogen deficiency symptoms in women who require antipsychotic medication. However, this treatment approach may worsen galactorrhea.

4. Use a dopamine receptor agonist. Dopamine receptor agonists, such as cabergoline or bromocriptine, have been shown to suppress prolactin secretion. Clinicians should always proceed cautiously because these medications can potentially increase the risk of psychosis.

5. Examine the potential benefits of adding aripiprazole because it can be used for augmentation to reduce prolactin levels in patients receiving other antipsychotics. In some cases, dopamine receptors can be exposed to competition between a partial agonist (aripiprazole) and an antagonist (the current antipsychotic). This competition may decrease the effectiveness of the current antipsychotic.¹ Also, adding another antipsychotic could increase overall adverse effects.

There is a well-established relationship between antipsychotic treatment and hyperprolactinemia. Most antipsychotics have been linked to increased prolactin levels, and the risk appears to be dose-related.¹ Antipsychotic-induced hyperprolactinemia can be asymptomatic, but it also has been associated with several adverse effects, including menstrual irregularity, osteoporosis, gynecomastia, and sexual dysfunction. Here I discuss what to do before starting a patient on an antipsychotic, and 5 treatment strategies for addressing antipsychotic-induced hyperprolactinemia.

Get a baseline prolactin level

Before starting a patient on an antipsychotic, obtain a baseline prolactin level measurement. If the patient later develops hyperprolactinemia, having a baseline measurement will make it easier to determine if the antipsychotic is a potential cause. Also, it is helpful to gather additional information regarding baseline psychosexual function and menstruation before starting an antipsychotic.

It is critical to determine if a temporal relationship exists between exposure to an antipsychotic and increase in prolactin levels.³ If the time course is unclear, laboratory tests need to be performed, including assessing liver, renal, and thyroid function or imaging of the pituitary gland. Also, hyperprolactinemia should not be diagnosed based on a single blood test result, because emotional and physical stress can elevate prolactin levels.

Continued to: 5 strategies for addressing hyperprolactinemia

 

 

5 strategies for addressing hyperprolactinemia

1. Reduce the antipsychotic dose. Because the risk of hyperprolactinemia is dose-dependent, reducing the antipsychotic dose could be helpful for some patients.

2. Switch to a prolactin-sparing antipsychotic, such as clozapine, quetiapine, olanzapine, or ziprasidone. However, it is often difficult to predict positive outcomes because switching antipsychotics may cause new adverse effects or trigger a psychotic relapse.

3. Consider sex hormone replacement therapy. A combined oral contraceptive could prevent osteoporosis and help estrogen deficiency symptoms in women who require antipsychotic medication. However, this treatment approach may worsen galactorrhea.

4. Use a dopamine receptor agonist. Dopamine receptor agonists, such as cabergoline or bromocriptine, have been shown to suppress prolactin secretion. Clinicians should always proceed cautiously because these medications can potentially increase the risk of psychosis.

5. Examine the potential benefits of adding aripiprazole because it can be used for augmentation to reduce prolactin levels in patients receiving other antipsychotics. In some cases, dopamine receptors can be exposed to competition between a partial agonist (aripiprazole) and an antagonist (the current antipsychotic). This competition may decrease the effectiveness of the current antipsychotic.¹ Also, adding another antipsychotic could increase overall adverse effects.

Mrs. J, age 49, presents to your psychiatric clinic. For the last few years, she has been experiencing night sweats and hot flashes, which she has attributed to being perimenopausal. Over the last year, she has noticed that her mood has declined; however, she has suffered several life events that she feels have contributed. Her mother was diagnosed with Alzheimer’s disease and had to move into a nursing home, which Mrs. J found very stressful. At the same time, her daughter left home for college, and her son is exploring his college options. Recently, Mrs. J has not been able to work due to her mood, and she is afraid she may lose her job as a consequence. She has struggled to talk to her husband about how she is feeling, and feels increasingly isolated. Over the last month, she has had increased problems sleeping and less energy; some days she struggles to get out of bed. She is finding it difficult to concentrate and is more forgetful. She has lost interest in her hobbies and is no longer meeting with her friends. She has no history of depression or anxiety, although she recalls feeling very low in mood for months after the birth of each of her children.

Are Mrs. J’s symptoms related to menopause or depression? What further investigations are necessary? Would you modify your treatment plan because of her menopausal status?

Women are at elevated risk of developing psychiatric symptoms and disorders throughout their reproductive lives, including during menopause. Menopause is a time of life transition, when women may experience multiple physical symptoms, including vasomotor symptoms (night sweats and hot flashes), sexual symptoms, and sleep difficulties. Depressive symptoms occur more frequently during menopause, and symptoms of schizophrenia may worsen.

Estrogen plays a role in mental illness throughout a woman’s life. In menopause, decreasing estrogen levels may correlate with increased mood symptoms, physical symptoms, and psychotic symptoms. As such, psychiatrists should consider whether collaboration regarding adjunctive hormone replacement therapy would be beneficial, and whether the benefits outweigh the potential risks. Otherwise, treatment of depression in menopause is similar to treatment outside of the menopausal transition, though serotonergic antidepressants may help target vasomotor symptoms while therapy may focus on role transition and loss. In this article, we review why women are at increased risk for mental illness during menopause, the role of estrogen, and treatment of mood and psychotic disorders during this phase of a woman’s life.

Increased vulnerability across the lifespan

Continued to: Why menopause?

Why menopause?

Perimenopausal mood disorders

However, one should keep in mind that new-onset mania in menopause is rare and should trigger a medical work-up and a dementia evaluation.¹³ Table 4¹⁴ provides recommendations for evaluation of women undergoing menopause.

Menopause and serious mental illnesses

A study of 91 perimenopausal and postmenopausal women (age 45 to 55) who were diagnosed with schizophrenia/schizoaffective disorder, bipolar disorder, or major depressive disorder (MDD) found that women with severe mental illness experienced significant vasomotor, physical, sexual, and psychosocial symptoms related to menopause.¹⁵ Furthermore, on 7 of 29 items on the Menopause Specific Quality of Life Scale, including hot flashes, women diagnosed with MDD reported problems significantly more often than women with other serious mental illnesses.¹⁵

Women with serious mental illness often have deficits in their knowledge about menopause.³ More than half of the 91 women in the study diagnosed with schizophrenia/schizoaffective disorder, bipolar disorder, or MDD felt more stressed related to menopause, and reported that menopause had a negative effect on their mental health.³ These women rated their top 5 symptoms potentially related to menopause as feeling depressed, anxious, or tired; lacking energy; and experiencing poor memory.³

Continued to: Role of estrogen on mood and psychosis

 

 

Role of estrogen on mood and psychosis

Women are at higher risk throughout their reproductive life than are men for MDD, anxiety disorders, and trauma-related disorders.¹² Factors associated with depression during the menopause transition are reproductive hormonal changes (rise of follicle-stimulating hormone [FSH] and luteinizing hormone levels, and variability in estrogen [E2] and FSH levels); menopausal symptoms, particularly vasomotor symptoms; prior depression; psychosocial factors (adverse life events, financial strain, poor social supports); high body mass index, smoking, and poor physical health.^6,7 Decreasing estrogen in the menopause transition may increase susceptibility to depression in some women.¹⁶ The Box^17,18 provides more information on the relationship between estrogen and brain function.

Depressive disorders, including premenstrual dysphoric disorder, postpartum depression, and perimenopausal depression, have been linked to changes in hormonal status in women. Symptomatic menopause transition occurs in at least 20% of women, and a retrospective cohort study suggests that symptomatic menopause transition might increase the risk of new-onset depressive disorders, bipolar disorders, anxiety disorders, and sleep disorders.¹⁹ Symptomatic menopause transition also is a vulnerable time for relapse of MDD. Among women experiencing menopausal symptoms, including hot flashes, one-third also report depression—which correlates with a poorer quality of life, less work productivity, and greater use of health care services.⁹

Women who undergo surgical menopause are at greater risk for depression.^8,10,11 This may be due to abrupt deprivation of estrogen—or related to a psychological reaction to the loss of fertility.

The observation that hormonal fluctuations related to women’s reproductive cycle have a significant impact on psychotic symptomatology has resulted in the “hypo-estrogenism hypothesis,” which proposes that gonadal dysfunction may increase vulnerability to schizophrenia, or that schizophrenia may lead to gonadal dysfunction.²⁰ The “estrogen protection hypothesis” proposes that estrogen may protect women from schizophrenia, and may be a factor in the delayed onset of schizophrenia compared with men, less severe psychopathology, better outcomes, and premenstrual and postmenopausal deterioration in women. Many women of reproductive age with schizophrenia experience improvement in symptoms during the high estrogen phase of their menstrual cycle.

Pope et al²¹ have suggested that a hormone sensitivity syndrome may underlie why some women experience physical, psychological, and emotional symptoms at times of hormonal shifts such as menopause. This may represent a critical window of vulnerability, and also an opportunity to consider E2 as a therapeutic intervention.

Continued to: Treating mental illness in menopause

 

 

Treating mental illness in menopause

Changes to drug pharmacokinetics occur because some metabolising enzymes are estrogen-dependent and their levels decline after menopause, which leads to greater variability in drug response, particularly for oral medications. Other factors that can contribute to variability in medication response are polypharmacy, alcohol, illicit drugs, liver mass, smoking, caffeine, and nutritional intake.

While antidepressants are the first-line treatment for MDD and anxiety disorders, some patients remain unresponsive or inadequately responsive to currently available medications. In perimenopausal women with MDD, there may be an indication for adjunctive therapy with transdermal E2 in refractory cases; estrogen may augment the effects of selective serotonin reuptake inhibitor (SSRI) antidepressants as well as hasten the onset of antidepressant action.²² Estrogen also may be worth considering in women with mild depressive symptoms. For MDD, SSRIs plus estrogen may be more beneficial in improving mood than either agent alone. The effectiveness of E2 is less certain in postmenopausal depression.

Hormonal therapy for mental health disorders has equivocal evidence. The individual’s history and risk factors (eg, cardiovascular and osteoporosis risks) must be considered. A recent trial found that treatment with either venlafaxine or low-dose estrogen improved quality of life in menopausal women with vasomotor symptoms.²³ Venlafaxine improved the psychosocial domain, while estrogen improved quality of life in other domains. Escitalopram, duloxetine, and citalopram have also been identified as having a possible positive impact on menopausal symptoms.²² SSRIs and serotonin-norepinephrine reuptake inhibitors may help reduce hot flashes and improve sleep.¹¹

Regarding schizophrenia and estrogen, there may be improved symptoms during the high estrogen phase of the menstrual cycle, followed by a premenstrual aggravation of symptoms. Recall that women have a second peak of onset of schizophrenia after age 45, around the age of the onset of menopause.²⁴ In a study of geropsychiatric hospital admissions, women were overrepresented among those with schizophrenia and schizoaffective disorder, compared with other psychiatric disorders.²⁵ Postmenopausally, some women experience a decreased responsiveness to antipsychotics and worsening symptoms. In menopausal women with schizophrenia, check prolactin levels to help determine whether they are experiencing a natural menopause or medication-induced amenorrhea. Gender differences in pharmacotherapy responses and the decreasing response to antipsychotics in women older than age 50 have been observed²⁶ and have led to exploration of the role of estrogen for treating schizophrenia in menopausal women. There have been contradictory results regarding use of estrogen as an adjunct to antipsychotics, with some reports finding this approach is effective and results in lower average doses of antipsychotics. Kulkarni et al^27,28 have reported improvements in positive symptoms of treatment-resistant schizophrenia with transdermal use of E2, 200 mcg, as an adjunct to antipsychotics in women of childbearing age. However, they expressed caution regarding the health risks associated with prolonged use of E2. Long-term risks of high-dose estrogen therapies include thromboembolism, endometrial hyperplasia, and breast cancer, and individual factors should be considered before starting any form of hormone therapy. Selective estrogen receptor modulators (SERMs), such as raloxifene, which can cause activation of E2 receptors in a tissue-specific fashion and have less estrogen-related adverse effects, offer hope for future development in this field.^27,28 While the use of adjunctive hormone therapy to manage psychotic symptoms in menopause is not routinely advised, the dosages of previously effective antipsychotics may need to be reviewed, or long-acting depot routes considered.²⁹ Increased risk of prolonged QTc interval and tardive dyskinesia in geriatric women also should be considered in decisions regarding changes to antipsychotics or dosages.³⁰

There are no guidelines regarding change in dosage of either individual antidepressants or antipsychotics in women at the time of menopause for managing pre-existing conditions. This may be due to the high variability in the effect of menopause on mental health and recognition that menopause is also a time for deterioration in physical health, as well as psychosocial changes for women, and thus other forms of intervention need to be considered.

Continued to: The biopsychosocial approach to treatment...

Although there are overlapping symptoms with both MDD and the perimenopause, these are typically restricted to impaired energy, sleep, and concentration, or changes in libido and weight.³² Therefore, it is vital to obtain a clear history and explore these symptoms in greater depth, as well as collect further information related to additional criteria such as appetite, agitation, feelings of worthlessness or guilt, and suicidal ideation.

Starting an antidepressant

On evaluation, Mrs. J discloses that she had experienced thoughts of wanting to end her life by overdose, although she had not acted on these thoughts. She appears subdued with poor eye contact, latency of response, and a slowed thought process. Mrs. J has blood tests to rule out thyroid abnormality or anemia. FSH and LH levels also are measured; these could provide a useful reference for later.

After a discussion with Mrs. J, she agrees to start an antidepressant. She also plans to speak to her gynecologist about the possibility of hormone replacement therapy. She is referred for psychotherapy to help support her with current life stressors. Mrs. J is started on escitalopram, 10 mg/d, and, after a month, she notices some improvement in her mood, psychomotor symptoms, sleep, and energy levels.

Bottom Line

Menopause is an important transition in our patients’ lives—both biologically and psychosocially. Women’s symptom patterns and medication needs may change during menopause.

Related Resource

• The North American Menopause Society. Depression & menopause. https://www.menopause.org/for-women/menopauseflashes/mental-health-at-menopause/depressionmenopause.

Drug Brand Names
Citalopram • Celexa
Duloxetine • Cymbalta
Escitalopram • Lexapro
Raloxifene • Evista
Venlafaxine • Effexor

Mrs. J, age 49, presents to your psychiatric clinic. For the last few years, she has been experiencing night sweats and hot flashes, which she has attributed to being perimenopausal. Over the last year, she has noticed that her mood has declined; however, she has suffered several life events that she feels have contributed. Her mother was diagnosed with Alzheimer’s disease and had to move into a nursing home, which Mrs. J found very stressful. At the same time, her daughter left home for college, and her son is exploring his college options. Recently, Mrs. J has not been able to work due to her mood, and she is afraid she may lose her job as a consequence. She has struggled to talk to her husband about how she is feeling, and feels increasingly isolated. Over the last month, she has had increased problems sleeping and less energy; some days she struggles to get out of bed. She is finding it difficult to concentrate and is more forgetful. She has lost interest in her hobbies and is no longer meeting with her friends. She has no history of depression or anxiety, although she recalls feeling very low in mood for months after the birth of each of her children.

Are Mrs. J’s symptoms related to menopause or depression? What further investigations are necessary? Would you modify your treatment plan because of her menopausal status?

Women are at elevated risk of developing psychiatric symptoms and disorders throughout their reproductive lives, including during menopause. Menopause is a time of life transition, when women may experience multiple physical symptoms, including vasomotor symptoms (night sweats and hot flashes), sexual symptoms, and sleep difficulties. Depressive symptoms occur more frequently during menopause, and symptoms of schizophrenia may worsen.

Estrogen plays a role in mental illness throughout a woman’s life. In menopause, decreasing estrogen levels may correlate with increased mood symptoms, physical symptoms, and psychotic symptoms. As such, psychiatrists should consider whether collaboration regarding adjunctive hormone replacement therapy would be beneficial, and whether the benefits outweigh the potential risks. Otherwise, treatment of depression in menopause is similar to treatment outside of the menopausal transition, though serotonergic antidepressants may help target vasomotor symptoms while therapy may focus on role transition and loss. In this article, we review why women are at increased risk for mental illness during menopause, the role of estrogen, and treatment of mood and psychotic disorders during this phase of a woman’s life.

Increased vulnerability across the lifespan

Continued to: Why menopause?

Why menopause?

Perimenopausal mood disorders

However, one should keep in mind that new-onset mania in menopause is rare and should trigger a medical work-up and a dementia evaluation.¹³ Table 4¹⁴ provides recommendations for evaluation of women undergoing menopause.

Menopause and serious mental illnesses

A study of 91 perimenopausal and postmenopausal women (age 45 to 55) who were diagnosed with schizophrenia/schizoaffective disorder, bipolar disorder, or major depressive disorder (MDD) found that women with severe mental illness experienced significant vasomotor, physical, sexual, and psychosocial symptoms related to menopause.¹⁵ Furthermore, on 7 of 29 items on the Menopause Specific Quality of Life Scale, including hot flashes, women diagnosed with MDD reported problems significantly more often than women with other serious mental illnesses.¹⁵

Women with serious mental illness often have deficits in their knowledge about menopause.³ More than half of the 91 women in the study diagnosed with schizophrenia/schizoaffective disorder, bipolar disorder, or MDD felt more stressed related to menopause, and reported that menopause had a negative effect on their mental health.³ These women rated their top 5 symptoms potentially related to menopause as feeling depressed, anxious, or tired; lacking energy; and experiencing poor memory.³

Continued to: Role of estrogen on mood and psychosis

 

 

Role of estrogen on mood and psychosis

Women are at higher risk throughout their reproductive life than are men for MDD, anxiety disorders, and trauma-related disorders.¹² Factors associated with depression during the menopause transition are reproductive hormonal changes (rise of follicle-stimulating hormone [FSH] and luteinizing hormone levels, and variability in estrogen [E2] and FSH levels); menopausal symptoms, particularly vasomotor symptoms; prior depression; psychosocial factors (adverse life events, financial strain, poor social supports); high body mass index, smoking, and poor physical health.^6,7 Decreasing estrogen in the menopause transition may increase susceptibility to depression in some women.¹⁶ The Box^17,18 provides more information on the relationship between estrogen and brain function.

Depressive disorders, including premenstrual dysphoric disorder, postpartum depression, and perimenopausal depression, have been linked to changes in hormonal status in women. Symptomatic menopause transition occurs in at least 20% of women, and a retrospective cohort study suggests that symptomatic menopause transition might increase the risk of new-onset depressive disorders, bipolar disorders, anxiety disorders, and sleep disorders.¹⁹ Symptomatic menopause transition also is a vulnerable time for relapse of MDD. Among women experiencing menopausal symptoms, including hot flashes, one-third also report depression—which correlates with a poorer quality of life, less work productivity, and greater use of health care services.⁹

Women who undergo surgical menopause are at greater risk for depression.^8,10,11 This may be due to abrupt deprivation of estrogen—or related to a psychological reaction to the loss of fertility.

The observation that hormonal fluctuations related to women’s reproductive cycle have a significant impact on psychotic symptomatology has resulted in the “hypo-estrogenism hypothesis,” which proposes that gonadal dysfunction may increase vulnerability to schizophrenia, or that schizophrenia may lead to gonadal dysfunction.²⁰ The “estrogen protection hypothesis” proposes that estrogen may protect women from schizophrenia, and may be a factor in the delayed onset of schizophrenia compared with men, less severe psychopathology, better outcomes, and premenstrual and postmenopausal deterioration in women. Many women of reproductive age with schizophrenia experience improvement in symptoms during the high estrogen phase of their menstrual cycle.

Pope et al²¹ have suggested that a hormone sensitivity syndrome may underlie why some women experience physical, psychological, and emotional symptoms at times of hormonal shifts such as menopause. This may represent a critical window of vulnerability, and also an opportunity to consider E2 as a therapeutic intervention.

Continued to: Treating mental illness in menopause

 

 

Treating mental illness in menopause

Changes to drug pharmacokinetics occur because some metabolising enzymes are estrogen-dependent and their levels decline after menopause, which leads to greater variability in drug response, particularly for oral medications. Other factors that can contribute to variability in medication response are polypharmacy, alcohol, illicit drugs, liver mass, smoking, caffeine, and nutritional intake.

While antidepressants are the first-line treatment for MDD and anxiety disorders, some patients remain unresponsive or inadequately responsive to currently available medications. In perimenopausal women with MDD, there may be an indication for adjunctive therapy with transdermal E2 in refractory cases; estrogen may augment the effects of selective serotonin reuptake inhibitor (SSRI) antidepressants as well as hasten the onset of antidepressant action.²² Estrogen also may be worth considering in women with mild depressive symptoms. For MDD, SSRIs plus estrogen may be more beneficial in improving mood than either agent alone. The effectiveness of E2 is less certain in postmenopausal depression.

Hormonal therapy for mental health disorders has equivocal evidence. The individual’s history and risk factors (eg, cardiovascular and osteoporosis risks) must be considered. A recent trial found that treatment with either venlafaxine or low-dose estrogen improved quality of life in menopausal women with vasomotor symptoms.²³ Venlafaxine improved the psychosocial domain, while estrogen improved quality of life in other domains. Escitalopram, duloxetine, and citalopram have also been identified as having a possible positive impact on menopausal symptoms.²² SSRIs and serotonin-norepinephrine reuptake inhibitors may help reduce hot flashes and improve sleep.¹¹

Regarding schizophrenia and estrogen, there may be improved symptoms during the high estrogen phase of the menstrual cycle, followed by a premenstrual aggravation of symptoms. Recall that women have a second peak of onset of schizophrenia after age 45, around the age of the onset of menopause.²⁴ In a study of geropsychiatric hospital admissions, women were overrepresented among those with schizophrenia and schizoaffective disorder, compared with other psychiatric disorders.²⁵ Postmenopausally, some women experience a decreased responsiveness to antipsychotics and worsening symptoms. In menopausal women with schizophrenia, check prolactin levels to help determine whether they are experiencing a natural menopause or medication-induced amenorrhea. Gender differences in pharmacotherapy responses and the decreasing response to antipsychotics in women older than age 50 have been observed²⁶ and have led to exploration of the role of estrogen for treating schizophrenia in menopausal women. There have been contradictory results regarding use of estrogen as an adjunct to antipsychotics, with some reports finding this approach is effective and results in lower average doses of antipsychotics. Kulkarni et al^27,28 have reported improvements in positive symptoms of treatment-resistant schizophrenia with transdermal use of E2, 200 mcg, as an adjunct to antipsychotics in women of childbearing age. However, they expressed caution regarding the health risks associated with prolonged use of E2. Long-term risks of high-dose estrogen therapies include thromboembolism, endometrial hyperplasia, and breast cancer, and individual factors should be considered before starting any form of hormone therapy. Selective estrogen receptor modulators (SERMs), such as raloxifene, which can cause activation of E2 receptors in a tissue-specific fashion and have less estrogen-related adverse effects, offer hope for future development in this field.^27,28 While the use of adjunctive hormone therapy to manage psychotic symptoms in menopause is not routinely advised, the dosages of previously effective antipsychotics may need to be reviewed, or long-acting depot routes considered.²⁹ Increased risk of prolonged QTc interval and tardive dyskinesia in geriatric women also should be considered in decisions regarding changes to antipsychotics or dosages.³⁰

There are no guidelines regarding change in dosage of either individual antidepressants or antipsychotics in women at the time of menopause for managing pre-existing conditions. This may be due to the high variability in the effect of menopause on mental health and recognition that menopause is also a time for deterioration in physical health, as well as psychosocial changes for women, and thus other forms of intervention need to be considered.

Continued to: The biopsychosocial approach to treatment...

Although there are overlapping symptoms with both MDD and the perimenopause, these are typically restricted to impaired energy, sleep, and concentration, or changes in libido and weight.³² Therefore, it is vital to obtain a clear history and explore these symptoms in greater depth, as well as collect further information related to additional criteria such as appetite, agitation, feelings of worthlessness or guilt, and suicidal ideation.

Starting an antidepressant

On evaluation, Mrs. J discloses that she had experienced thoughts of wanting to end her life by overdose, although she had not acted on these thoughts. She appears subdued with poor eye contact, latency of response, and a slowed thought process. Mrs. J has blood tests to rule out thyroid abnormality or anemia. FSH and LH levels also are measured; these could provide a useful reference for later.

After a discussion with Mrs. J, she agrees to start an antidepressant. She also plans to speak to her gynecologist about the possibility of hormone replacement therapy. She is referred for psychotherapy to help support her with current life stressors. Mrs. J is started on escitalopram, 10 mg/d, and, after a month, she notices some improvement in her mood, psychomotor symptoms, sleep, and energy levels.

Bottom Line

Menopause is an important transition in our patients’ lives—both biologically and psychosocially. Women’s symptom patterns and medication needs may change during menopause.

Related Resource

• The North American Menopause Society. Depression & menopause. https://www.menopause.org/for-women/menopauseflashes/mental-health-at-menopause/depressionmenopause.

Drug Brand Names
Citalopram • Celexa
Duloxetine • Cymbalta
Escitalopram • Lexapro
Raloxifene • Evista
Venlafaxine • Effexor

Mrs. J, age 49, presents to your psychiatric clinic. For the last few years, she has been experiencing night sweats and hot flashes, which she has attributed to being perimenopausal. Over the last year, she has noticed that her mood has declined; however, she has suffered several life events that she feels have contributed. Her mother was diagnosed with Alzheimer’s disease and had to move into a nursing home, which Mrs. J found very stressful. At the same time, her daughter left home for college, and her son is exploring his college options. Recently, Mrs. J has not been able to work due to her mood, and she is afraid she may lose her job as a consequence. She has struggled to talk to her husband about how she is feeling, and feels increasingly isolated. Over the last month, she has had increased problems sleeping and less energy; some days she struggles to get out of bed. She is finding it difficult to concentrate and is more forgetful. She has lost interest in her hobbies and is no longer meeting with her friends. She has no history of depression or anxiety, although she recalls feeling very low in mood for months after the birth of each of her children.

Are Mrs. J’s symptoms related to menopause or depression? What further investigations are necessary? Would you modify your treatment plan because of her menopausal status?

Women are at elevated risk of developing psychiatric symptoms and disorders throughout their reproductive lives, including during menopause. Menopause is a time of life transition, when women may experience multiple physical symptoms, including vasomotor symptoms (night sweats and hot flashes), sexual symptoms, and sleep difficulties. Depressive symptoms occur more frequently during menopause, and symptoms of schizophrenia may worsen.

Estrogen plays a role in mental illness throughout a woman’s life. In menopause, decreasing estrogen levels may correlate with increased mood symptoms, physical symptoms, and psychotic symptoms. As such, psychiatrists should consider whether collaboration regarding adjunctive hormone replacement therapy would be beneficial, and whether the benefits outweigh the potential risks. Otherwise, treatment of depression in menopause is similar to treatment outside of the menopausal transition, though serotonergic antidepressants may help target vasomotor symptoms while therapy may focus on role transition and loss. In this article, we review why women are at increased risk for mental illness during menopause, the role of estrogen, and treatment of mood and psychotic disorders during this phase of a woman’s life.

Increased vulnerability across the lifespan

Continued to: Why menopause?

Why menopause?

Perimenopausal mood disorders

However, one should keep in mind that new-onset mania in menopause is rare and should trigger a medical work-up and a dementia evaluation.¹³ Table 4¹⁴ provides recommendations for evaluation of women undergoing menopause.

Menopause and serious mental illnesses

A study of 91 perimenopausal and postmenopausal women (age 45 to 55) who were diagnosed with schizophrenia/schizoaffective disorder, bipolar disorder, or major depressive disorder (MDD) found that women with severe mental illness experienced significant vasomotor, physical, sexual, and psychosocial symptoms related to menopause.¹⁵ Furthermore, on 7 of 29 items on the Menopause Specific Quality of Life Scale, including hot flashes, women diagnosed with MDD reported problems significantly more often than women with other serious mental illnesses.¹⁵

Women with serious mental illness often have deficits in their knowledge about menopause.³ More than half of the 91 women in the study diagnosed with schizophrenia/schizoaffective disorder, bipolar disorder, or MDD felt more stressed related to menopause, and reported that menopause had a negative effect on their mental health.³ These women rated their top 5 symptoms potentially related to menopause as feeling depressed, anxious, or tired; lacking energy; and experiencing poor memory.³

Continued to: Role of estrogen on mood and psychosis

 

 

Role of estrogen on mood and psychosis

Women are at higher risk throughout their reproductive life than are men for MDD, anxiety disorders, and trauma-related disorders.¹² Factors associated with depression during the menopause transition are reproductive hormonal changes (rise of follicle-stimulating hormone [FSH] and luteinizing hormone levels, and variability in estrogen [E2] and FSH levels); menopausal symptoms, particularly vasomotor symptoms; prior depression; psychosocial factors (adverse life events, financial strain, poor social supports); high body mass index, smoking, and poor physical health.^6,7 Decreasing estrogen in the menopause transition may increase susceptibility to depression in some women.¹⁶ The Box^17,18 provides more information on the relationship between estrogen and brain function.

Depressive disorders, including premenstrual dysphoric disorder, postpartum depression, and perimenopausal depression, have been linked to changes in hormonal status in women. Symptomatic menopause transition occurs in at least 20% of women, and a retrospective cohort study suggests that symptomatic menopause transition might increase the risk of new-onset depressive disorders, bipolar disorders, anxiety disorders, and sleep disorders.¹⁹ Symptomatic menopause transition also is a vulnerable time for relapse of MDD. Among women experiencing menopausal symptoms, including hot flashes, one-third also report depression—which correlates with a poorer quality of life, less work productivity, and greater use of health care services.⁹

Women who undergo surgical menopause are at greater risk for depression.^8,10,11 This may be due to abrupt deprivation of estrogen—or related to a psychological reaction to the loss of fertility.

The observation that hormonal fluctuations related to women’s reproductive cycle have a significant impact on psychotic symptomatology has resulted in the “hypo-estrogenism hypothesis,” which proposes that gonadal dysfunction may increase vulnerability to schizophrenia, or that schizophrenia may lead to gonadal dysfunction.²⁰ The “estrogen protection hypothesis” proposes that estrogen may protect women from schizophrenia, and may be a factor in the delayed onset of schizophrenia compared with men, less severe psychopathology, better outcomes, and premenstrual and postmenopausal deterioration in women. Many women of reproductive age with schizophrenia experience improvement in symptoms during the high estrogen phase of their menstrual cycle.

Pope et al²¹ have suggested that a hormone sensitivity syndrome may underlie why some women experience physical, psychological, and emotional symptoms at times of hormonal shifts such as menopause. This may represent a critical window of vulnerability, and also an opportunity to consider E2 as a therapeutic intervention.

Continued to: Treating mental illness in menopause

 

 

Treating mental illness in menopause

Changes to drug pharmacokinetics occur because some metabolising enzymes are estrogen-dependent and their levels decline after menopause, which leads to greater variability in drug response, particularly for oral medications. Other factors that can contribute to variability in medication response are polypharmacy, alcohol, illicit drugs, liver mass, smoking, caffeine, and nutritional intake.

While antidepressants are the first-line treatment for MDD and anxiety disorders, some patients remain unresponsive or inadequately responsive to currently available medications. In perimenopausal women with MDD, there may be an indication for adjunctive therapy with transdermal E2 in refractory cases; estrogen may augment the effects of selective serotonin reuptake inhibitor (SSRI) antidepressants as well as hasten the onset of antidepressant action.²² Estrogen also may be worth considering in women with mild depressive symptoms. For MDD, SSRIs plus estrogen may be more beneficial in improving mood than either agent alone. The effectiveness of E2 is less certain in postmenopausal depression.

Hormonal therapy for mental health disorders has equivocal evidence. The individual’s history and risk factors (eg, cardiovascular and osteoporosis risks) must be considered. A recent trial found that treatment with either venlafaxine or low-dose estrogen improved quality of life in menopausal women with vasomotor symptoms.²³ Venlafaxine improved the psychosocial domain, while estrogen improved quality of life in other domains. Escitalopram, duloxetine, and citalopram have also been identified as having a possible positive impact on menopausal symptoms.²² SSRIs and serotonin-norepinephrine reuptake inhibitors may help reduce hot flashes and improve sleep.¹¹

Regarding schizophrenia and estrogen, there may be improved symptoms during the high estrogen phase of the menstrual cycle, followed by a premenstrual aggravation of symptoms. Recall that women have a second peak of onset of schizophrenia after age 45, around the age of the onset of menopause.²⁴ In a study of geropsychiatric hospital admissions, women were overrepresented among those with schizophrenia and schizoaffective disorder, compared with other psychiatric disorders.²⁵ Postmenopausally, some women experience a decreased responsiveness to antipsychotics and worsening symptoms. In menopausal women with schizophrenia, check prolactin levels to help determine whether they are experiencing a natural menopause or medication-induced amenorrhea. Gender differences in pharmacotherapy responses and the decreasing response to antipsychotics in women older than age 50 have been observed²⁶ and have led to exploration of the role of estrogen for treating schizophrenia in menopausal women. There have been contradictory results regarding use of estrogen as an adjunct to antipsychotics, with some reports finding this approach is effective and results in lower average doses of antipsychotics. Kulkarni et al^27,28 have reported improvements in positive symptoms of treatment-resistant schizophrenia with transdermal use of E2, 200 mcg, as an adjunct to antipsychotics in women of childbearing age. However, they expressed caution regarding the health risks associated with prolonged use of E2. Long-term risks of high-dose estrogen therapies include thromboembolism, endometrial hyperplasia, and breast cancer, and individual factors should be considered before starting any form of hormone therapy. Selective estrogen receptor modulators (SERMs), such as raloxifene, which can cause activation of E2 receptors in a tissue-specific fashion and have less estrogen-related adverse effects, offer hope for future development in this field.^27,28 While the use of adjunctive hormone therapy to manage psychotic symptoms in menopause is not routinely advised, the dosages of previously effective antipsychotics may need to be reviewed, or long-acting depot routes considered.²⁹ Increased risk of prolonged QTc interval and tardive dyskinesia in geriatric women also should be considered in decisions regarding changes to antipsychotics or dosages.³⁰

There are no guidelines regarding change in dosage of either individual antidepressants or antipsychotics in women at the time of menopause for managing pre-existing conditions. This may be due to the high variability in the effect of menopause on mental health and recognition that menopause is also a time for deterioration in physical health, as well as psychosocial changes for women, and thus other forms of intervention need to be considered.

Continued to: The biopsychosocial approach to treatment...

Although there are overlapping symptoms with both MDD and the perimenopause, these are typically restricted to impaired energy, sleep, and concentration, or changes in libido and weight.³² Therefore, it is vital to obtain a clear history and explore these symptoms in greater depth, as well as collect further information related to additional criteria such as appetite, agitation, feelings of worthlessness or guilt, and suicidal ideation.

Starting an antidepressant

On evaluation, Mrs. J discloses that she had experienced thoughts of wanting to end her life by overdose, although she had not acted on these thoughts. She appears subdued with poor eye contact, latency of response, and a slowed thought process. Mrs. J has blood tests to rule out thyroid abnormality or anemia. FSH and LH levels also are measured; these could provide a useful reference for later.

After a discussion with Mrs. J, she agrees to start an antidepressant. She also plans to speak to her gynecologist about the possibility of hormone replacement therapy. She is referred for psychotherapy to help support her with current life stressors. Mrs. J is started on escitalopram, 10 mg/d, and, after a month, she notices some improvement in her mood, psychomotor symptoms, sleep, and energy levels.

Bottom Line

Menopause is an important transition in our patients’ lives—both biologically and psychosocially. Women’s symptom patterns and medication needs may change during menopause.

Related Resource

• The North American Menopause Society. Depression & menopause. https://www.menopause.org/for-women/menopauseflashes/mental-health-at-menopause/depressionmenopause.

Drug Brand Names
Citalopram • Celexa
Duloxetine • Cymbalta
Escitalopram • Lexapro
Raloxifene • Evista
Venlafaxine • Effexor