Predictors of psychiatric readmission among patients with bipolar disorder at an academic safety-net hospital

Clinical, sociodemographic and environmental predicting factors for relapse in bipolar disorder: A systematic review

INTRODUCTION

Bipolar disorder (BD) is a chronic and recurrent illness characterized by manic, mixed or depressive episodes, alternated with periods of euthymia. Several prognostic factors are associated with higher rates of relapse, which is crucial for the identification of high-risk individuals. This study aimed at systematically reviewing the existing literature regarding the impact of sociodemographic, clinical and environmental factors, in clinical relapses, recurrences and hospitalizations due to mood episodes in BD.

METHODS

A systematic search of electronic databases (PubMed, Cochrane library and Web of Science) was conducted to integrate current evidence about the impact of specific risk factors in these outcomes.

RESULTS

Fifty-eight articles met the inclusion criteria. Studies were grouped by the type of factors assessed. Family and personal psychiatric history, more severe previous episodes, earlier age of onset, and history of rapid cycling are associated with clinical relapses, along with lower global functioning and cognitive impairments. Unemployment, low educational status, poorer social adjustment and life events are also associated with higher frequency of episodes, and cannabis with a higher likelihood for rehospitalization.

LIMITATIONS

Small sample sizes, absence of randomized clinical trials, diverse follow-up periods, lack of control for some confounding factors, heterogeneous study designs and diverse clinical outcomes.

CONCLUSIONS

Although current evidence remains controversial, several factors have been associated with an impaired prognosis, which might allow clinicians to identify patients at higher risk for adverse clinical outcomes and find modifiable factors. Further research is needed to elucidate the impact of each risk factor in the mentioned outcomes.

Do bipolar disorder symptom profiles matter for suicide risk? A latent class approach to investigating differences in suicidal desire and acquired capability

INTRODUCTION

Depressive and mixed symptoms in bipolar disorder (BD) have been linked to higher suicide risk. Based on Klonsky and May's three-step theory and Joiner's Interpersonal Psychological Theory of Suicide, we hypothesized that patients diagnosed with BD who reported severe levels of depressive symptoms and mixed depressive and manic symptoms would also report higher levels of suicidal desire and acquired capability of suicide, as well as suicidal thoughts and behaviors.

METHODS

The sample included 177 outpatients diagnosed with BD. Latent class analysis was conducted to replicate the identified groups of a previously conducted study using a smaller but overlapping dataset. Between-class and pairwise analyses with measures of suicidal desire and acquired capability were conducted.

RESULTS

As expected, the classes characterized by severe depressive symptoms and mixed symptoms reported higher levels of suicidal desire. However, the results regarding acquired capability were less consistent.

CONCLUSION

Given the overall elevated suicide risk of BD and the consistent relationship between depressive symptoms and other strong correlates of suicide, clinicians who work with patients diagnosed with BD should closely monitor changes in their depressive symptoms.

Lithium treatment versus hospitalization in bipolar disorder and major depression patients

BACKGROUND

Preventing hospitalization of major affective disorder patients is a fundamental clinical challenge for which lithium is expected to be helpful.

METHODS

We compared hospitalization rates and morbidity of 260 patients with DSM-5 bipolar or major depressive disorder in the 12 months before starting lithium versus 12 months of its use. We evaluated duration of untreated illness, previous treatments, substance abuse, suicidal status, lithium dose, and use of other medicines for association with new episodes of illness or of symptomatic worsening as well as hospitalization, using bivariate and multivariate analyses.

RESULTS

Within 12 months before lithium, 40.4 % of patients were hospitalized versus 11.2 % during lithium treatment; other measures of morbidity also improved. Benefits were similar with bipolar and major depressive disorders. Independently associated with hospitalization during lithium treatment were: receiving an antipsychotic with lithium, suicide attempt during lithium treatment, lifetime substance abuse, and psychiatric hospitalization in the year before starting lithium, but not diagnosis.

LIMITATIONS

Participants and observation times were limited. The study was retrospective regarding clinical history, lacked strict control of treatments and was not blinded.

CONCLUSIONS

This naturalistic study adds support to the effectiveness of lithium treatment in preventing hospitalization in patients with episodic major mood disorders.

Does the Ranking Matter? A Retrospective Cohort Study Investigating the Impact of the 2018 CANMAT and ISBD Guidelines for the Management of Patients with Bipolar Disorder Treatment Recommendations for Acute Mania on Rehospitalization Rates

OBJECTIVE

There is limited data about the impact of mood disorders treatment guidelines on clinical outcomes. The objective of this study was to investigate the impact of prescribers' adherence to the 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) treatment guidelines recommendations on the readmission rates of patients hospitalized for mania.

METHOD

A retrospective cohort of all individuals admitted due to acute mania to Kingston General Hospital, Kingston, ON, from January 2018 to July 2021 was included in this study. Patient variables and data regarding index admission and subsequent hospitalizations were extracted from medical records up to December 31, 2021. Treatment regimens were classified as first-line, second-line, noncompliant, or no treatment. We explored the associations between treatment regimens and the risk of readmissions using univariate, multivariate, and survival analysis.

RESULTS

We identified 211 hospitalizations related to 165 patients. The mean time-to-readmission was 211.8 days (standard deviation [SD]  =  247.1); the 30-day rehospitalization rate was 13.7%, and any rehospitalization rate was 40.3%. Compared to no treatment, only first-line treatments were associated with a statistically significant decreased risk of 30-day readmission (odds ratio [OR] = 0.209; 95% CI, 0.058 to 0.670). The risk of any readmission was reduced by first-line (OR = 0.387; 95% CI, 0.173 to 0.848) and noncompliant regimens (OR = 0.414; 95% CI, 0.174 to 0.982) compared to no treatment. On survival analysis, no treatment group was associated with shorter time-to-readmission (log-rank test, p  =  0.014) and increased risk of readmission (hazard ratio = 2.27; 95% CI, 1.30 to 3.96) when compared to first-line medications.

CONCLUSIONS

Treatment with first-line medications was associated with lower 30-day rehospitalization rates and longer time-to-readmission. Physicians' adherence to treatments with higher-ranked evidence for efficacy, safety, and tolerability may improve bipolar disorder outcomes.

Readmission and associated clinical factors among individuals admitted with bipolar affective disorder at a psychiatry facility in Uganda

BACKGROUND

Bipolar affective disorder (BAD) is a common severe mental health condition with a relapsing course that may include periods of hospital re-admissions. With recurrent relapses and admissions, the course, prognosis, and patient's overall quality of life can be affected negatively. This study aims to explore the rates and clinical factors associated with re-admission among individuals with BAD.

METHOD

This study used data from a retrospective chart review of all records of patients with BAD admitted in 2018 and followed up their hospital records for four years till 2021 at a large psychiatric unit in Uganda. Cox regression analysis was used to determine the clinical characteristics associated with readmission among patients diagnosed with BAD.

RESULTS

A total of 206 patients living with BAD were admitted in 2018 and followed up for four years. The average number of months to readmission was 9.4 (standard deviation = 8.6). The incidence of readmission was 23.8% (n = 49/206). Of those readmitted during the study period, 46.9% (n = 23/49) and 28.6% (n = 14/49) individuals were readmitted twice and three times or more, respectively. The readmission rate in the first 12 months following discharge was 69.4% (n = 34/49) at first readmission, 78.3% (n = 18/23) at second readmission, and 87.5% (n = 12/14) at third or more times. For the next 12 months, the readmission rate was 22.5% (n = 11/49) for the first, 21.7% (n = 5/23) for the second, and 7.1% (n = 1/14) for more than two readmissions. Between 25 and 36 months, the readmission rate was 4.1% (n = 2/49) for the first readmission and 7.1% (n = 1/14) for the third or more times. Between 37 and 48 months, the readmission rate was 4.1% (n = 2/49) for those readmitted the first time. Patients who presented with poor appetite and undressed in public before admission were at increased risk of being readmitted with time. However, the following symptoms/clinical presentations, were protective against having a readmission with time, increased number of days with symptoms before admission, mood lability, and high energy levels.

CONCLUSION

The incidence of readmission among individuals living with BAD is high, and readmission was associated with patients' symptoms presentation on previous admission. Future studies looking at BAD using a prospective design, standardized scales, and robust explanatory model are warranted to understand causal factors for hospital re-admission and inform management strategies.

Factors associated with hospitalization times and length of stay in patients with bipolar disorder

Aim

Appraise the clinical features and influencing factors of the hospitalization times and length of stay in bipolar disorder (BD) patients.

Methods

This is a multicenter, observational, cohort study of patients diagnosed of type I or type II bipolar disorder. Five hundred twenty outpatients in seven hospitals from six cities in China were recruited from February 2013 to June 2014 and followed up using a continuous sampling pattern. The research included a retrospective period of 12 months and the prospective period of 9 months. The demographic and clinical features of the patients were collected. The influencing factors that could affect the length of stay (number of days spent in the hospital in the prospective period) were analyzed by poisson's regression and the hospitalization times (times of hospitalization in the prospective and retrospective period) was analyzed by general linear model. The selected variables included gender, age, years of education, occupational status, residence status, family history of mental disease, comorbid substance abuse, comorbid anxiety disorder, times of suicide (total suicide times that occurred in the retrospective and prospective period), polarity of the first mood episode, and BD type(I/II).

Results

Poisson's regression analysis showed that suicide times [Incidence Rate Ratio (IRR) = 1.20, p < 0.001], use of antipsychotic (IRR = 0.62, p = 0.011), and use of antidepressant (IRR = 0.56, p < 0.001) were correlated to more hospitalization times. Linear regression analysis showed that BD type II (β = 0.28, p = 0.005) and unemployment (β = 0.16, p = 0.039) which might mean longer duration of depression and poor function were correlated to longer length of stay. However, patients who experienced more suicide times (β = -0.21, p = 0.007) tended to have a shorter length of stay.

Conclusion

Overall, better management of the depressive episode and functional rehabilitation may help to reduce the length of stay. BD patients with more hospitalization times were characterized by higher risk of suicide and complex polypharmacy. Patients at high risk of suicide tended to have inadequate therapy and poor compliance, which should be assessed and treated adequately during hospitalization.

Clinical trial registration

www.ClinicalTrials.gov, Identifier: NCT01770704.

Risk of childhood trauma exposure and severity of bipolar disorder in Colombia

BACKGROUND

Bipolar disorder (BD) is higher in developing countries. Childhood trauma exposure is a common environmental risk factor in Colombia and might be associated with a more severe course of bipolar disorder in Low-Middle Income-Countries. We carried out the first case-control study in Colombia using a structural clinical interview and the Childhood Trauma Questionnaire-Short Form (CTQ-SF) to describe the prevalence and association between trauma exposure during childhood with a severe course of illness (early age onset, rapid cycling, ideation or suicide attempt, or ≥ 3 hospitalization) in a sample of BD patients.

RESULTS

A total of 114 cases and 191 controls evaluated showed the following results. Cases included 61.4% BD type I and 38.6% BD type II. The median age was 31.5 years (IQR, 75-24) for BD patients and 31 years old (IQR, 38-24) for healthy controls. A higher prevalence of childhood trauma was evidenced in cases compared to controls. Emotional abuse, physical abuse, sexual abuse, physical neglect and emotional neglect evidenced a strong association with severe bipolar disorder (OR = 3.42, p < .001; OR = 4.68, p < .001; OR = 4.30, p = .003; OR = 5.10, p < .001; OR = 5.64, p < .001, respectively).

CONCLUSIONS

This is the first association study between childhood trauma exposure as a higher risk for a severe course of illness in BD patients in Colombian. Our findings highlight the higher prevalence of childhood trauma in bipolar patients and the strong association of childhood trauma with severe bipolar disorder. These findings are relevant for screening and evaluating childhood trauma exposure during the course of BD patients.

Risk factors associated with 30-day unplanned hospital readmission for patients with mental illness

Unplanned hospital readmission rate is up to 43% in mental health settings, which is higher than in general health settings. Unplanned readmissions delay the recovery of patients with mental illness and add financial burden on families and healthcare services. There have been efforts to reduce readmissions with a particular interest in identifying patients at higher readmission risk after index admission; however, the results have been inconsistent. This systematic review synthesized risk factors associated with 30-day unplanned hospital readmissions for patients with mental illness. Eleven electronic databases were searched from 2010 to 30 September 2021 using key terms of 'mental illness', 'readmission' and 'risk factors'. Sixteen studies met the selection criteria for this review. Data were synthesized using content analysis and presented in narrative and tabular form because the extracted risk factors could not be pooled statistically due to methodological heterogeneity of the included studies. Consistently cited readmission predictors were patients with lower educational background, unemployment, previous mental illness hospital admission and more than 7 days of the index hospitalization. Results revealed the complexity of identifying unplanned hospital readmission predictors for people with mental illness. Policymakers need to specify the expected standards that written discharge summary must reach general practitioners concurrently at discharge. Hospital clinicians should ensure that discharge summary summaries are distributed to general practitioners for effective ongoing patient care and management. Having an advanced mental health nurse for patients during their transition period needs to be explored to understand how this role could ensure referrals to the general practitioner are eventuated.

Real-world effectiveness of long-acting injectable antipsychotics to reduce 90-day and annual readmission in psychotic disorders: insights from a state psychiatric hospital

BACKGROUND

To evaluate the effectiveness of long-acting injectable antipsychotics (LAI-a) in reducing the 90-day and annual readmission rates in schizophrenia inpatients.

METHODS

We conducted a cross-sectional study and included 180 adult patients with psychotic disorders discharged from 2018 to 2019 at a state psychiatric hospital. Descriptive statistics were used to measure the differences between the readmit and nonreadmit cohorts. Logistic regression model was used to measure the odds ratio (OR) for 90-day and annual readmission and was controlled for potential readmission risk factors.

RESULTS

A lower proportion of patients receiving LAI-a were readmitted within 90-day (28.6%) and 1-year (32.4%) periods. Patients receiving LAI-a had lower odds of association for 90-day (OR 0.36, 95% confidence intervals [CI] 0.139-0.921) and annual (OR 0.35, 95% CI 0.131-0.954) readmissions compared to those discharged on oral antipsychotics. A higher proportion of inpatients who received fluphenazine LAI had 90-day (25%) and annual (18.2%) readmissions compared to other LAI-a.

CONCLUSION

Utilization of LAI-a in patients with psychotic disorders can decrease both 90-day and annual psychiatric readmissions by 64% to 65%. Physicians should prefer LAI-a to reduce the readmission rate and improve the quality of life, and decrease the healthcare-related financial burden.

Direct medical cost of bipolar disorder: Insights from the FACE-BD longitudinal cohort

BACKGROUND

Bipolar disorder (BD) is a severe chronic psychiatric disorder affecting 0.5 to 1% of the population worldwide. To date, most studies have estimated the cost of BD via information sourced from insurance claims with limited information on clinical characteristics and course of BD. The aims of this study are (i) to estimate the direct healthcare cost associated with BD and to identify contributing factors and (ii) to study the evolution of cost during a two-year follow-up period.

METHOD

We analyzed a sample of 1116 individuals with BD included in the Advanced Centers of Expertise in Bipolar Disorder cohort. We estimated the direct healthcare cost per year and per patient, and we identified the clinical features of patients with BD associated with higher direct healthcare costs. In a subsample of patients followed up for two years centers of expertise for BD, we studied the evolution of direct healthcare cost.

RESULTS

The average cost of bipolar disorder was € 6910 per year and per patient. Clinical features of BD, sociodemographic characteristics, and associated addiction were associated with higher direct healthcare costs. In the subsample of patients followed-up for two years, direct healthcare cost dropped by more than 50%, strongly suggesting the beneficial effect of specialized care organization.

LIMITATION

We did not estimate indirect healthcare and intangible costs.

CONCLUSION

Our study investigates the cost of BD and its evolution in a deeply phenotyped longitudinal sample. Cost-utility and cost-effectiveness analyses are required to inform resource allocation decisions and to promote innovative healthcare organizations.

Development and Calibration of the PREMIUM Item Bank for Measuring Respect and Dignity for Patients with Severe Mental Illness

Most patient-reported experience measures (PREMs) are paper-based, leading to a high burden for patients and care providers. The aim of this study was to (1) calibrate an item bank to measure patients’ experience of respect and dignity for adult patients with serious mental illnesses and (2) develop computerized adaptive testing (CAT) to improve the use of this PREM in routine practice. Patients with schizophrenia, bipolar disorder, and major depressive disorder were enrolled in this multicenter and cross-sectional study. Psychometric analyses were based on classical test and item response theories and included evaluations of unidimensionality, local independence, and monotonicity; calibration and evaluation of model fit; analyses of differential item functioning (DIF); testing of external validity; and finally, CAT development. A total of 458 patients participated in the study. Of the 24 items, 2 highly inter-correlated items were deleted. Factor analysis showed that the remaining items met the unidimensional assumption (RMSEA = 0.054, CFI = 0.988, TLI = 0.986). DIF analyses revealed no biases by sex, age, care setting, or diagnosis. External validity testing has generally supported our assumptions. CAT showed satisfactory accuracy and precision. This work provides a more accurate and flexible measure of patients’ experience of respect and dignity than that obtained from standard questionnaires.

Inpatient Early Intervention for Serious Mental Illnesses Is Associated With Fewer Rehospitalizations Compared With Treatment as Usual in a High-volume Public Psychiatric Hospital Setting

OBJECTIVE

High-acuity publicly funded inpatient psychiatric settings usually feature short lengths of stay and high readmission rates. This study examined the influence of an early intervention program for serious mental illnesses (SMI) on readmissions at 6 and 12 months postdischarge at a high-volume, urban public inpatient psychiatric hospital.

METHODS

The Early Onset Treatment Program (EOTP) is a cost-free, 90-day inpatient multidisciplinary service intervention program for uninsured patients who are within 5 years of SMI onset, funded as a pilot program by the Texas state legislature. Rehospitalization rates at 6 and 12 months were extracted from electronic medical records for EOTP participants (n=165) and comparison patients matched on demographics and diagnosis (n=155). The comparison group received treatment as usual at the same psychiatric hospital. Group re-admission rates were compared using logistic and Poisson regression analyses.

RESULTS

Group membership was a significant predictor of rehospitalization (P<0.0001) at both 6 and 12 months. Expressed as 1/odds ratio (OR), the EOTP group was less likely to readmit once and more than once at 6 months postdischarge (1/OR=3.82 and 4.74, respectively) compared with the non-EOTP group. The EOTP group was also less likely to readmit once and more than once at 12 months postdischarge (1/OR=2.96 and 3.51, respectively).

CONCLUSIONS

The results suggest that participation in the EOTP service in this high-acuity setting was significantly related to reduced likelihood of rehospitalization at 6 and 12 months. Several variables may account for this observation, including length of stay, longer medication adherence, environmental stability, and more individualized and extensive psychotherapy treatment.

Genetic risk for psychiatric illness is associated with the number of hospitalizations of bipolar disorder patients

OBJECTIVES

Bipolar disorder (BD) has a highly heterogeneous clinical course that is characterized by relapses and increased health care utilization in a significant fraction of patients. A thorough understanding of factors influencing illness course is essential for predicting disorder severity and developing targeted therapies.

METHODS

We performed polygenic score analyses in four cohorts (N = 954) to test whether the genetic risk for BD, schizophrenia, or major depression is associated with a severe course of BD. We analyzed BD patients with a minimum illness duration of five years. The severity of the disease course was assessed by using the number of hospitalizations in a mental health facility and a composite measure of longitudinal illness severity (OPCRIT item 90).

RESULTS

Our analyses showed that higher polygenic scores for BD (β = 0.11, SE = 0.03, p = 1.17 × 10-3) and schizophrenia (β = 0.09, SE = 0.03, p = 4.24 × 10-3), but not for major depression, were associated with more hospitalizations. None of the investigated polygenic scores was associated with the composite measure of longitudinal illness severity (OPCRIT item 90).

LIMITATIONS

We could not account for non-genetic influences on disease course. Our clinical sample contained more severe cases.

CONCLUSIONS

This study demonstrates that the genetic risk burden for psychiatric illness is associated with increased health care utilization, a proxy for disease severity, in BD patients. The findings are in line with previous observations made for patients diagnosed with schizophrenia or major depression. Therefore, in the future psychiatric disorder polygenic scores might become helpful for stratifying patients with high risk of a chronic manifestation and predicting disease course.

Effects of temporary psychiatric holds on length of stay and readmission risk among persons admitted for psychotic disorders

The practice of involuntary psychiatric commitment is central to the acute treatment of persons with severe mental illness and others in psychiatric crisis. Deciding whether a patient should be admitted involuntarily requires weighing respect for autonomy against beneficence, considering the clinical needs of the patient, and navigating ambiguous legal standards. The relative dearth of information about the impact of involuntary commitment on objective patient outcomes complicates matters ethically, legally, and clinically. To address this gap in the literature, we sought to determine the association between temporary psychiatric holds and length of stay and readmission rates among a retrospective sample of adult patients admitted to a large psychiatric hospital with diagnoses of schizophrenia, schizoaffective disorder, mania, and other psychotic disorders. In total, we identified 460 patients and 559 unique encounters meeting our inclusion criteria; 90 of the encounters were voluntary (involving a temporary psychiatric hold) and 469 were involuntary. Univariable and multivariable analyses suggested that temporary psychiatric holds were not significantly associated with either length of stay or readmission rate. These findings are relevant to clinicians who must decide whether to admit a patient involuntarily, as they suggest that making a patient involuntary is not associated with differences in length of stay or readmission risk.

Utility of the READMIT Index to Identify Community Hospital 30-Day Psychiatric Readmissions

This case-controlled study determined the utility of the READMIT index to identify the risk for 30-day readmission of patients discharged from an urban community hospital psychiatric inpatient unit. Data was collected from 118 matched patient pairs from 2017 to 2018. Findings demonstrated the READMIT index did not effectively discriminate those patients who were likely to readmit within 30 days. However, the following factors were associated with likelihood of 30-day readmission: the inability to care for self, number of lifetime readmissions, the comorbidity of liver disease, as well as a history of substance abuse.

A conceptual framework to develop a patient-reported experience measure of the quality of mental health care: a qualitative study of the PREMIUM project in France

Background: The objective of this study was to develop a conceptual framework to define a domain map describing the experience of patients with severe mental illnesses (SMIs) on the quality of mental health care. Methods: This study used an exploratory qualitative approach to examine the subjective experience of adult patients (18-65 years old) with SMIs, including schizophrenia (SZ), bipolar disorder (BD) and major depressive disorder (MDD). Participants were selected using a purposeful sampling method. Semistructured interviews were conducted with 37 psychiatric inpatients and outpatients recruited from the largest public hospital in southeastern France. Transcripts were subjected to an inductive analysis by using two complementary approaches (thematic analysis and computerized text analysis) to identify themes and subthemes. Results: Our analysis generated a conceptual model composed of 7 main themes, ranked from most important to least important as follows: interpersonal relationships, care environment, drug therapy, access and care coordination, respect and dignity, information and psychological care. The interpersonal relationships theme was divided into 3 subthemes: patient-staff relationships, relations with other patients and involvement of family and friends. All themes were spontaneously raised by respondents. Conclusion: This work provides a conceptual framework that will inform the subsequent development of a patient-reported experience measure to monitor and improve the performance of the mental health care system in France. The findings showed that patients with SMIs place an emphasis on the interpersonal component, which is one of the important predictors of therapeutic alliance. Trial registration: NCT02491866.

Time to rehospitalization in patients with bipolar mania discharged on long-acting injectable or oral antipsychotics

OBJECTIVE

This study aimed to analyze time to rehospitalization in patients with bipolar mania discharged on long-acting injectable antipsychotics (LAIs) or oral antipsychotics (OAPs). Additionally, temporal trends in LAI prescription were investigated.

METHODS

Patients with bipolar mania discharged from the study hospital on antipsychotics between 2006 and 2018 were included. Survival analysis was used to compare time to rehospitalization within one year of discharge between patients discharged on LAIs and OAPs, and between FGA-LAIs (first- generation antipsychotic) and SGA-LAIs (second-generation antipsychotic). The Cochrane-Armitage trend test was used to test whether a temporal trend existed for LAI prescription rates during the study period.

RESULTS

The LAI group (n = 224) had a significantly lower rehospitalization rate and a significantly longer time to rehospitalization than the OAP group (n = 3836). Rehospitalization rate and time to rehospitalization were not significantly different between patients discharged on FGA-LAIs or SGA-LAIs. The LAI prescription rate grew significantly from 2.20% in 2006 to 11.58% in 2018 (Z = 5.5843, p < 0.0001). The prescription rate of SGA-LAIs also increased significantly (Z = 7.7141, p < 0.0001), but not the prescription rate of FGA-LAIs.

LIMITATIONS

The treatment allocation is not randomized in this retrospective study. Furthermore, various clinical characteristics were unavailable in our analysis, such as symptom severity, functional impairment, and others.

CONCLUSIONS

LAIs were significantly superior to OAPs in reducing rehospitalization risk. However, SGA-LAIs were comparable with FGA-LAIs in reducing rehospitalization risk. Use of LAIs increased significantly in discharged patients with bipolar disorder during the study period, especially SGA-LAIs.

Factors Related to 30-day Readmission following Hospitalization for Any Medical Reason among Patients with Mental Disorders: Facteurs liés à la réhospitalisation à 30 jours suivant une hospitalisation pour une raison médicale chez des patients souffrant de troubles mentaux

OBJECTIVE

This study evaluated the contributions of clinical, sociodemographic, and service use variables to the risk of early readmission, defined as readmission within 30 days of discharge following hospitalization for any medical reason (mental or physical illnesses), among patients with mental disorders in Quebec (Canada).

METHODS

In this longitudinal study, 2,954 hospitalized patients who had visited 1 of 6 Quebec emergency departments (ED) in 2014 to 2015 (index year) were identified through clinical administrative databanks. The first hospitalization was considered that may have occurred at any Quebec hospital. Data collected between 2012 and 2013 and 2013 and 2014 on clinical, sociodemographic, and service use variables were assessed as related to readmission/no readmission within 30 days of discharge using hierarchical binary logistic regression.

RESULTS

Patients with co-occurring substance-related disorders/chronic physical illnesses, serious mental disorders, or adjustment disorders (clinical variables); 4+ outpatient psychiatric consultations with the same psychiatrist; and patients hospitalized for any medical reason within 12 months prior to index hospitalization (service use variables) were more likely to be readmitted within 30 days of discharge. Patients who made 1 to 3 ED visits within 1 year prior to the index hospitalization, had their index hospitalization stay of 16 to 29 days, or consulted a physician for any medical reason within 30 days after discharge or prior to the readmission (service use variables) were less likely to be rehospitalized.

CONCLUSIONS

Early hospital readmission was more strongly associated with clinical variables, followed by service use variables, both playing a key role in preventing early readmission. Results suggest the importance of developing specific interventions for patients at high risk of readmission such as better discharge planning, integrated and collaborative care, and case management. Overall, better access to services and continuity of care before and after hospital discharge should be provided to prevent early hospital readmission.

Predicting hospitalization following psychiatric crisis care using machine learning

BACKGROUND

Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization.

METHODS

Data from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients' socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking.

RESULTS

All models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use.

CONCLUSIONS

Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.

Sociodemographic and clinical characteristics of patients with recurrent psychiatric readmissions in Qatar

OBJECTIVE

To examine the sociodemographic and clinical characteristics of psychiatric patients with recurrent psychiatric readmissions (RPR).

METHODS

A retrospective study was conducted at Hamad General Hospital in Qatar on psychiatric patients with recurrent readmissions from August 2018 to January 2019.

RESULTS

Of 380 psychiatric patients admitted during the study period, 40 (10.5%) were readmitted within 30 days of discharge. Most of the patients who were readmitted were single, male and unemployed. Psychotic spectrum disorder was the most frequent psychiatric condition and was diagnosed in 18 (45%) patients. A total of 30% of the patients were receiving treatment with anti-psychotics, and a similar number received more than one medication. Most patients showed poor or no compliance. Only 12.5% of patients stayed in the hospital for more than 5 weeks in their last admission during the study period.

CONCLUSIONS

Poor compliance, male sex and single status were the most common demographic and clinical features of patients with RPR. Post-discharge psychiatric care should be tailored to meet the requirements of patients prone to RPR.

Predictors of 1-year rehospitalization in inpatients with bipolar I disorder treated with atypical antipsychotics

Bipolar disorder (BPD) is debilitating disorder, and patients can experience multiple relapses and subsequent hospitalizations. Since pharmacotherapy is the mainstay of treatment for patients with BPD, investigations on the effects of atypical antipsychotics (AAP) on reducing rehospitalization risk are crucial. The objective of study is to explore predictors of 1-year rehospitalization in patients with bipolar I disorder treated with AAP. A retrospective chart review on inpatients with bipolar I disorder was conducted. All participants were followed up for 1 year, and they were subdivided into three AAP treatment groups (olanzapine, risperidone, and quetiapine group). Kaplan-Meier survival analysis was implemented to detect time to rehospitalization due to any mood episodes within 1 year after discharge. Cox proportional regression model was adopted to find predictors of 1-year hospitalization in patients who experienced rehospitalization. One hundred thirty-eight participants were included in the study, and a 1-year rehospitalization rate was 18.1%. Time to rehospitalization did not differ between three AAP treatment groups. Predictors of rehospitalization due to any episode within 1 year were family history of depression and number of previous admission. Our findings can be conducive to understanding prognosis, and predicting rehospitalization risk in patients with BPD on AAP.

Race, History of Abuse, and Homelessness Are Associated With Forced Medication Administration During Psychiatric Inpatient Care

OBJECTIVE

Although previous research has suggested that racial disparities exist in the administration of forced medication (FM) in psychiatric inpatients, data remain scarce regarding other contributing variables. Therefore, this study examined sociodemographic and clinical variables associated with FM administration in psychiatric inpatients.

METHODS

Electronic medical records from 57,615 patients admitted to an academic psychiatric hospital between 2010 and 2018 were reviewed to identify patients who received FM. These records indicated that FM petitions were requested and approved for ∼6200 patients. Patients were excluded from the analysis if they met the following exclusion criteria: under 18 years of age, presence of intellectual/developmental disability, dementia, or other neurological condition, or primary diagnosis of a nonpsychiatric medical condition or a substance-induced mood or psychotic disorder. After data on those patients were excluded, the final sample included records from 2569 patients (4.5% of the total records) in which the administration of FM was petitioned for and approved. The FM group was compared with a control group of 2569 patients matched in terms of age, sex, and admission date (no-forced medication group; NFM) via propensity scoring matching. Group comparisons (FM vs. NFM group) examined sociodemographic factors (race, age, sex, living situation), clinical features (diagnosis, substance abuse, history of abuse), and outcomes (length of stay, readmission rate). Regression analyses examined the association between FM and sociodemographic, clinical, and outcome variables.

RESULTS

Compared with the NFM group, the FM group contained significantly more African Americans (P<0.001), homeless individuals (P<0.001), and individuals with histories of abuse (P<0.001). Having received FM was a significant predictor of a longer length of stay (P<0.001) and higher readmission rates (P<0.001).

DISCUSSION

These results suggest that FM is more likely to be instituted in psychiatric inpatients who are of a minority race (African American), are in a homeless living situation, and/or have a history of abuse. Moreover, FM may be associated with poorer clinical outcomes at least as measured by the length of stay and higher readmission rates. We discuss possible reasons for these results and the importance of culturally competent and trauma-focused care.

Clinical risk model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders

BACKGROUND

Unplanned readmissions rates are an important indicator of the quality of care provided in a psychiatric unit. However, there is no validated risk model to predict this outcome in patients with psychotic spectrum disorders.

AIMS

This paper aims to establish a clinical risk prediction model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders.

METHOD

Adult patients with psychotic spectrum disorders discharged within a 5-year period from all psychiatric units in Hong Kong were included in this study. Information on the socioeconomic background, past medical and psychiatric history, current discharge episode and Health of the Nation Outcome Scales (HoNOS) scores were used in a logistic regression to derive the risk model and the predictive variables. The sample was randomly split into two to derive (n = 10 219) and validate (n = 10 643) the model.

RESULTS

The rate of unplanned readmission was 7.09%. The risk factors for unplanned readmission include higher number of previous admissions, comorbid substance misuse, history of violence and a score of one or more in the discharge HoNOS overactivity or aggression item. Protective factors include older age, prescribing clozapine, living with family and relatives after discharge and imposition of conditional discharge. The model had moderate discriminative power with a c-statistic of 0.705 and 0.684 on the derivation and validation data-set.

CONCLUSIONS

The risk of readmission for each patient can be identified and adjustments in the treatment for those with a high risk may be implemented to prevent this undesirable outcome.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force

OBJECTIVES

The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.

METHOD

A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.

RESULTS

The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.

CONCLUSION

Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Predictors of 30-day rehospitalization in a sample of hospitalized patients with Bipolar I disorder

The transition from psychiatric hospitalization to home is marked by high clinical vulnerability, characterized by risk of symptom rebound, exposure to preexisting stressors, and challenges with outpatient treatment linkage. Rates of rehospitalization during this post-discharge period, particularly for those with bipolar disorder, are reported to be high. This study evaluated demographic and clinical predictors of early rehospitalization (within 30 days) in a sample of hospitalized adults with Bipolar I disorder (BD-I). A chart review was conducted for 215 patients with BD-I admitted to an academically-affiliated psychiatric hospital within one calendar year. A computer algorithm was used to extract relevant demographic, clinical, and treatment information. Univariate and multivariate logistic regression models were used to examine predictors of early rehospitalization. Overall, 12% of participants were readmitted within 30 days of discharge. Controlling for other clinical and demographic variables, patient functioning and pre-admission psychiatric polypharmacy, but not comorbid psychiatric diagnoses, predicted early readmission in patients with BD-I. Findings highlight the relative importance of considering low psychosocial functioning, and medication regimens containing multiple psychiatric medications, during hospitalizations. These features may indicate a subset of patients with BD-I who require more comprehensive discharge planning and support to transition to the community following a psychiatric hospitalization.

Hospital admission and readmission among homeless patients with neurologic disease

OBJECTIVE

To characterize the most common neurologic diagnoses leading to hospitalization for homeless compared to housed individuals and to assess whether homelessness is an independent risk factor for 30-day readmission after an admission for a neurologic illness.

METHODS

We performed a retrospective serial cross-sectional study using data from the Healthcare Cost and Utilization Project California State Inpatient Database from 2006 to 2011. Adult patients with a primary neurologic discharge diagnosis were included. The primary outcome was 30-day readmission. We used multilevel logistic regression to examine the association between homelessness and readmission after adjustment for patient factors.

RESULTS

We identified 1,082,347 patients with a neurologic primary diagnosis. The rate of homelessness was 0.37%. The most common indications for hospitalization among homeless patients were seizure and traumatic brain injury, both of which were more common in the homeless compared to housed population (19.3% vs 8.1% and 31.9% vs 9.2%, respectively, p < 0.001). A multilevel mixed-effects model controlling for patient age, sex, race, insurance type, comorbid conditions, and clustering on the hospital level found that homelessness was associated with increased 30-day readmission (odds ratio 1.5, 95% confidence interval 1.4-1.6, p < 0.001). This association persisted after this analysis was repeated within specific diagnoses (patients with epilepsy, trauma, encephalopathy, and neuromuscular disease).

CONCLUSION

The most common neurologic reasons for admission among homeless patients are seizure and traumatic brain injury; these patients are at high risk for readmission. Future interventions should target the drivers of readmissions in this vulnerable population.

Risk factors for early unplanned readmission in patients with bipolar disorder: A retrospective observational study

OBJECTIVES

Evidence regarding the relationships between patient, hospital, and regional factors and early unplanned readmission (short-term outcome) in patients with bipolar disorder is lacking. This study aimed to examine risk factors associated with early unplanned readmission in patients with bipolar disorder.

METHOD

We retrospectively analyzed adult bipolar patients (ICD-10; F31) between April 2012 and March 2014 in the Japanese Diagnosis Procedure Combination database. We examined factors affecting the 30-day unplanned readmission using multivariable logistic regression analysis.

RESULTS

A total of 2688 patients admitted to psychiatric beds were included. Multivariate analysis showed that unchanged or exacerbation discharge outcome (adjusted odds ratio [aOR]: 1.93; 95% confidence interval [CI]: 1.06-3.51, p = 0.031), unplanned or urgent admission settings (aOR: 1.51; 95% CI: 1.00-2.26, p = 0.048), physical comorbidity (chronic pulmonary disease) (aOR: 4.74; 95% CI: 1.30-17.29, p = 0.018), presence of psychiatric acute-care beds (aOR: 1.72; 95% CI: 1.02-2.87, p = 0.040), and intermediate-level hospital psychiatric staffing (aOR: 1.82; 95% CI: 1.14-2.91, p = 0.012) were significantly associated with higher early unplanned readmission, while higher density of psychiatrists in the area (aOR: 0.50; 95% CI: 0.29-0.87, p = 0.014) was significantly associated with lower early unplanned readmission.

CONCLUSIONS

The results suggest that not only careful management of high-risk patients but also consideration of functional differentiation in psychiatric inpatient care, psychiatric resource allocation, and follow-up support for patients with bipolar disorder are needed for reducing the early unplanned readmission rate.

A new inner-city specialist programme reduces readmission rates in frequently admitted patients with bipolar disorder

Aims and methodThe OPTIMA mood disorders service is a newly established specialist programme for people with bipolar disorder requiring frequent admissions. This audit compared data on hospital admissions and home treatment team (HTT) spells in patients before entry to and after discharge from the core programme. We included patients admitted between April 2015 and March 2017 who were subsequently discharged. Basic demographic data and numbers of admissions and HTT spells three years before and after discharge were collected and analysed. RESULTS: Thirty patients who completed the programme were included in the analyses. The median monthly rate of hospital admissions after OPTIMA was significantly reduced compared with the rate prior to the programme. HTT utilisation was numerically reduced, but this difference was not statistically significant.Clinical implicationsThese results highlight the effectiveness and importance of individually tailored, specialist care for patients with bipolar disorder following discharge from hospital.Declaration of interestNone.

Hospitalization risk factors in antipsychotic-treated schizophrenia, bipolar I disorder or major depressive disorder

Aim: To examine hospitalization risk factors in antipsychotic-treated patients with schizophrenia, bipolar I disorder (BD-I) or major depressive disorder (MDD). Patients & methods: Using Truven Health MarketScan® Commercial, Medicaid and Medicare Supplemental data (01/01/2012–06/30/2016), logistic regression models were performed to identify risk factors for both psychiatric and all-cause hospitalization in three separate analyses. Results: Significant risk factors included prior hospitalization (schizophrenia: odds ratio [95% CI]: 2.83 [2.50–3.21; psychiatric]; 2.58 [2.31–2.87; all-cause]; BD-I: 2.42 [2.23–2.63]; 2.09 [1.96–2.23]; MDD: 2.81 [2.49–3.16]; 2.21 [2.03–2.40]), previous antipsychotic treatment (schizophrenia: 1.71 [1.52–1.93]; 1.31 [1.18–1.46]; BD-I: 1.33 [1.23–1.44]; 1.22 [1.14–1.30]; MDD: 1.31 (1.11–1.54); 1.17 (1.04–1.32) and substance abuse (schizophrenia: 1.42 [1.27–1.60]; 1.37 [1.23–1.53]; BD-I: 1.72 [1.58–1.86]; 1.61 [1.50–1.72]; MDD: 1.90 [1.68–2.15] and 1.55 [1.41–1.71]). Conclusion: Prior hospitalization, previous antipsychotic treatment and substance abuse were associated with increased hospitalization risk in schizophrenia, BD-I or MDD.

Homelessness at discharge and its impact on psychiatric readmission and physician follow-up: a population-based cohort study

AIMS

A significant proportion of adults who are admitted to psychiatric hospitals are homeless, yet little is known about their outcomes after a psychiatric hospitalisation discharge. The aim of this study was to assess the impact of being homeless at the time of psychiatric hospitalisation discharge on psychiatric hospital readmission, mental health-related emergency department (ED) visits and physician-based outpatient care.

METHODS

This was a population-based cohort study using health administrative databases. All patients discharged from a psychiatric hospitalisation in Ontario, Canada, between 1 April 2011 and 31 March 2014 (N = 91 028) were included and categorised as homeless or non-homeless at the time of discharge. Psychiatric hospitalisation readmission rates, mental health-related ED visits and physician-based outpatient care were measured within 30 days following hospital discharge.

RESULTS

There were 2052 (2.3%) adults identified as homeless at discharge. Homeless individuals at discharge were significantly more likely to have a readmission within 30 days following discharge (17.1 v. 9.8%; aHR = 1.43 (95% CI 1.26-1.63)) and to have an ED visit (27.2 v. 11.6%; aHR = 1.87 (95% CI 1.68-2.0)). Homeless individuals were also over 50% less likely to have a psychiatrist visit (aHR = 0.46 (95% CI 0.40-0.53)).

CONCLUSION

Homeless adults are at higher risk of readmission and ED visits following discharge. They are also much less likely to receive post-discharge physician care. Efforts to improve access to services for this vulnerable population are required to reduce acute care service use and improve care continuity.

Comprehensive comparison of monotherapies for psychiatric hospitalization risk in bipolar disorders

OBJECTIVES

This study compared 29 drugs for risk of psychiatric hospitalization in bipolar disorders, addressing the evidence gap on the >50 drugs used by US patients for treatment.

METHODS

The Truven Health Analytics MarketScan^® database was used to identify 190 894 individuals with bipolar or schizoaffective disorder who filled a prescription for one of 29 drugs of interest: lithium, first- or second-generation antipsychotics, mood-stabilizing anticonvulsants, and antidepressants. Competing risks regression survival analysis was used to compare drugs for risk of psychiatric hospitalization, adjusting for patient age, sex, comorbidities, and pretreatment medications. Other competing risks were ending monotherapy and non-psychiatric hospitalization.

RESULTS

Three drugs were associated with significantly lower risk of psychiatric hospitalization than lithium: valproate (relative risk [RR] = 0.80, P = 3.20 × 10^-4 ), aripiprazole (RR = 0.80, P = 3.50 × 10^-4 ), and bupropion (RR = 0.80, P = 2.80 × 10^-4 ). Eight drugs were associated with significantly higher risk of psychiatric hospitalization: haloperidol (RR = 1.57, P = 9.40 × 10^-4 ), clozapine (RR = 1.52, P = .017), fluoxetine (RR = 1.17, P = 3.70 × 10^-3 ), sertraline (RR = 1.17, P = 3.20 × 10^-3 ), citalopram (RR = 1.14, P = .013), duloxetine (RR = 1.24, P = 5.10 × 10^-4 ), venlafaxine (RR = 1.33; P = 1.00 × 10^-6 ), and ziprasidone (RR = 1.25; P = 6.20 × 10^-3 ).

CONCLUSIONS

This largest reported retrospective observational study on bipolar disorders pharmacotherapy to date demonstrates that the majority of patients end monotherapy within 2 months after treatment start. The risk of psychiatric hospitalization varied almost two-fold across individual medications. The data add to the evidence favoring lithium and mood stabilizer use in short-term bipolar disorder management. The findings that the dopaminergic drugs aripiprazole and bupropion had better outcomes than other members of their respective classes and that antidepressant outcomes may vary by baseline mood polarity merit further investigation.

Treatment Retention Among Patients Participating in Coordinated Specialty Care for First-Episode Psychosis: a Mixed-Methods Analysis

Young adults experiencing first-episode psychosis have historically been difficult to retain in mental health treatment. Communities across the United States are implementing Coordinated Specialty Care to improve outcomes for individuals experiencing first-episode psychosis. This mixed-methods research study examined the relationship between program services and treatment retention, operationalized as the likelihood of remaining in the program for 9 months or more. In the adjusted analysis, male gender and participation in home-based cognitive behavioral therapy were associated with an increased likelihood of remaining in treatment. The key informant interview findings suggest the shared decision-making process and the breadth, flexibility, and focus on functional recovery of the home-based cognitive behavioral therapy intervention may have positively influenced treatment retention. These findings suggest the use of shared decision-making and improved access to home-based cognitive behavioral therapy for first-episode psychosis patients may improve outcomes for this vulnerable population.

Predictors of hospitalization length of stay among re-admitted treatment-resistant Bipolar Disorder inpatients

BACKGROUND

Hospitalization accounts for significant health care resource utilization for treatment-resistant Bipolar Disorder (BD), especially among frequent users of acute inpatient psychiatric units. Appraisal of the clinical features and predictive role of selected variables is therefore crucial in such population, representing the aim of the present research.

METHODS

A hundred and nineteen BD inpatients with an established history of pharmacological treatment resistance for either mania or bipolar depression were classified as long hospitalization cases (LOS⁺) and their controls and compared against each other for a number of demographic, clinical, and psychopathological features.

RESULTS

Overall, female sex, current second-generation atypical antipsychotic (SGA)/mood stabilizer other than lithium as well as antidepressant treatment at the admission occurred statistically more frequently among LOS⁺ cases, concordant with higher scores at the Hamilton scales for depression and anxiety. Lithium utilization at the time of hospitalization did not differ between cases and controls (LOS^-, n = 81/119), as predominant affective temperament and other psychopathological rating did not. Overall, the time of admission, use of SGA, anticonvulsant (other than lithium), antidepressant, lifetime alcohol dependence, and BD Type (-I or -II), but not current mood polarity at the time of hospitalization, correctly predicted LOS⁺ grouping 68.2% of the times: Exp(B) = 3.151, p042.

LIMITATIONS

Post-hoc, cross-sectional study, relatively small sample size, recall and selection bias on some diagnoses.

CONCLUSIONS

Overall, LOS⁺ treatment-resistant BD inpatients characterize for higher severity and greater pharmaco-utilization use, which warrants replication studies to include additional predictors to shed further light on the matter.

Age trends in 30 day hospital readmissions: US national retrospective analysis

OBJECTIVE

To assess trends in and risk factors for readmission to hospital across the age continuum.

DESIGN

Retrospective analysis.

SETTING AND PARTICIPANTS

31 729 762 index hospital admissions for all conditions in 2013 from the US Agency for Healthcare Research and Quality Nationwide Readmissions Database.

MAIN OUTCOME MEASURE

30 day, all cause, unplanned hospital readmissions. Odds of readmission were compared by patients' age in one year epochs with logistic regression, accounting for sex, payer, length of stay, discharge disposition, number of chronic conditions, reason for and severity of admission, and data clustering by hospital. The middle (45 years) of the age range (0-90+ years) was selected as the age reference group.

RESULTS

The 30 day unplanned readmission rate following all US index admissions was 11.6% (n=3 678 018). Referenced by patients aged 45 years, the adjusted odds ratio for readmission increased between ages 16 and 20 years (from 0.70 (95% confidence interval 0.68 to 0.71) to 1.04 (1.02 to 1.06)), remained elevated between ages 21 and 44 years (range 1.02 (1.00 to 1.03) to 1.12 (1.10 to 1.14)), steadily decreased between ages 46 and 64 years (range 1.02 (1.00 to 1.04) to 0.91 (0.90 to 0.93)), and decreased abruptly at age 65 years (0.78 (0.77 to 0.79)), after which the odds remained relatively constant with advancing age. Across all ages, multiple chronic conditions were associated with the highest adjusted odds of readmission (for example, 3.67 (3.64 to 3.69) for six or more versus no chronic conditions). Among children, young adults, and middle aged adults, mental health was one of the most common reasons for index admissions that had high adjusted readmission rates (≥75th centile).

CONCLUSIONS

The likelihood of readmission was elevated for children transitioning to adulthood, children and younger adults with mental health disorders, and patients of all ages with multiple chronic conditions. Further attention to the measurement and causes of readmission and opportunities for its reduction in these groups is warranted.

Rehospitalization and suicide following electroconvulsive therapy for bipolar depression-A population-based register study

BACKGROUND

Electroconvulsive therapy (ECT) is effective in bipolar depression, but relapse is common. The aim of the study was (i) to identify prognostic factors (ii) and to determine the impact of pharmacological approaches on the risk for rehospitalization or suicide.

METHODS

This register study analyzed data from individuals treated with inpatient ECT for bipolar depression. Subjects were identified using the Swedish National Patient Register between 2011 and 2014 and the Swedish National Quality Register for ECT. Other national registers provided data on psychopharmacotherapy, socio-demographic factors, and causes of death. The endpoint was the composite of rehospitalization for any psychiatric disorder, suicide attempt or completed suicide (RoS). Cox regression was used to calculate hazard ratios in univariate and multivariate models.

RESULTS

Data from 1255 patients were analyzed. The mean period of follow-up was 346 days. A total of 29%, 41%, and 52% of patients reached RoS at 3, 6, and 12 months post-discharge. A history of multiple psychiatric admissions, lower age, and post-discharge treatment with antipsychotics or benzodiazepines was associated with RoS.

LIMITATIONS

Indication bias may have affected the results.

CONCLUSIONS

A history of multiple hospital admissions and lower age are key predictors of the composite of rehospitalization or suicide in patients treated with ECT for bipolar depression. Lithium might be effective. By contrast, antipsychotics and benzodiazepines were associated with increased risk, but possibly this finding was influenced by indication bias.

Big data for bipolar disorder

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

Areas of controversy in neuroprogression in bipolar disorder

OBJECTIVE

We aimed to review clinical features and biological underpinnings related to neuroprogression in bipolar disorder (BD). Also, we discussed areas of controversy and future research in the field.

METHOD

We systematically reviewed the extant literature pertaining to neuroprogression and BD by searching PubMed and EMBASE for articles published up to March 2016.

RESULTS

A total of 114 studies were included. Neuroimaging and clinical evidence from cross-sectional and longitudinal studies show that a subset of patients with BD presents a neuroprogressive course with brain changes and unfavorable outcomes. Risk factors associated with these unfavorable outcomes are number of mood episodes, early trauma, and psychiatric and clinical comorbidity.

CONCLUSION

Illness trajectories are largely variable, and illness progression is not a general rule in BD. The number of manic episodes seems to be the clinical marker more robustly associated with neuroprogression in BD. However, the majority of the evidence came from cross-sectional studies that are prone to bias. Longitudinal studies may help to identify signatures of neuroprogression and integrate findings from the field of neuroimaging, neurocognition, and biomarkers.