READMIT: a clinical risk index to predict 30-day readmission after discharge from acute psychiatric units

Early and Frequent Psychiatric Readmissions in a Brazilian Cohort of Hospitalized Patients Between 2001 and 2013

OBJECTIVE

To characterize the profile of patients who were readmitted for mental and behavioral disorders, in the Brazilian Unified Health System, from 2001 to 2014, and the factors associated with early and frequent readmission.

METHOD

A retrospective, non-concurrent cohort study of patients admitted with a primary diagnosis of mental or behavioral disorders, from 2001 to 2014. This study selected demographic variables and clinical variables, as well as variables related to the characteristics of the hospitals. Poisson Regression methods with a robust variance estimator were used to estimate the incidence rate ratio (IRR) for each of the outcomes.

RESULTS

Early readmission occurred for 6.8% of the patients and frequent readmission for 8.3%. Characteristics such as being male, younger, with a diagnosis of a bipolar disorder, and admitted to a specialized hospital show a higher IRR for early readmission. The occurrence of early readmission was the most heavily associated characteristic with an increased rate of early readmission, and the magnitude of this increase depends on the patient's age.

CONCLUSION

Early and frequent readmissions are linked to patients' demographics, clinical information and health system's organization. Early readmission should be a priority in treatment planning to prevent frequent readmissions due to its strong association.

Reducing wait times and avoiding unnecessary use of high-cost mental health services through a Rapid Access and Stabilization Program: protocol for a program evaluation study

BACKGROUND

Emergency psychiatric care, unplanned hospital admissions, and inpatient health care are the costliest forms of mental health care. According to Statistics Canada (2018), almost 18% (5.3 million) of Canadians reported needing mental health support. However, just above half of this figure (56.2%) have reported their needs were fully met. In light of this evidence there is a pressing need to provide accessible mental health services in flexible yet cost-effective ways. To further expand capacity and access to mental health care in the province, Nova Scotia Health has launched a novel mental health initiative for people in need of mental health care without requiring emergency department visits or hospitalization. This new service is referred to as the Rapid Access and Stabilization Program (RASP). This study evaluates the effectiveness and impact of the RASP on high-cost health services utilization (e.g. ED visits, mobile crisis visits, and inpatient treatments) and related costs. It also assesses healthcare partners' (e.g. healthcare providers, policymakers, community leaders) perceptions and patient experiences and satisfaction with the program and identifies sociodemographic characteristics, psychological conditions, recovery, well-being, and risk measures in the assisted population.

METHOD

This is a hypothesis-driven program evaluation study that employs a mixed methods approach. A within-subject comparison (pre- and post-evaluation study) will examine health services utilization data from patients attending RASP, one year before and one year after their psychiatry assessment at the program. A controlled between-subject comparison (cohort study) will use historical data from a control population will examine whether possible changes in high-cost health services utilization are associated with the intervention (RASP). The primary analysis involves extracting secondary data from provincial information systems, electronic medical records, and regular self-reported clinical assessments. Additionally, a qualitative sub-study will examine patient experience and satisfaction, and health care partners' impressions.

DISCUSSION

We expect that RASP evaluation findings will demonstrate a minimum 10% reduction in high-cost health services utilization and corresponding 10% cost savings, and also a reduction in the wait times for patient consultations with psychiatrists to less than 30 calendar days, in both within-subject and between-subject comparisons. In addition, we anticipate that patients, healthcare providers and healthcare partners would express high levels of satisfaction with the new service.

CONCLUSION

This study will demonstrate the results of the Mental Health and Addictions Program (MHAP) efforts to provide stepped-care, particularly community-based support, to individuals with mental illnesses. Results will provide new insights into a novel community-based approach to mental health service delivery and contribute to knowledge on how to implement mental health programs across varying contexts.

Metabolic disturbances are risk factors for readmission to psychiatric hospitals in non-smokers but not in smokers: results from a Swiss psychiatric cohort and in first-episode psychosis patients

Background

Psychiatric patients are at high risk of readmission, and a high body mass index has previously been shown as a risk factor. We sought to replicate this finding and 1) to prospectively assess the association of metabolic syndrome and its five components with readmission in psychiatric hospitals and 2) to identify other clinical and sociodemographic predictors of readmission.

Methods

Between 2007 and 2019, data on 16727 admissions of 7786 adult and elderly patients admitted to the Department of Psychiatry of the Lausanne University Hospital, were collected. Metabolic syndrome was defined according to the International Diabetes Federation definition. Cox frailty models were used to investigate the associations between readmission and metabolic disturbances.

Results

A total of 2697 (35%) patients were readmitted to our psychiatric hospital. Novel risk factors for readmission in non-smokers were identified, including being overweight (HR=1.26; 95%CI=[1.05; 1.51]) or obese (HR=1.33; 95%CI=[1.08; 1.62]), displaying hypertriglyceridemia (HR=1.21; 95%CI=[1.04; 1.40]) and metabolic syndrome (HR=1.26; 95%CI=[1.02; 1.55]). Central obesity and hyperglycemia increased the risk of readmission when considering the Health of the Nation Outcome Scales variable. In first-episode psychosis patients, obesity (HR=2.23; 95%CI=[1.14; 4.30]) and high-density lipoprotein hypocholesterolemia (HR=1.90; 95%CI=[1.14; 3.20]) doubled the risk of readmission.

Conclusion

The observed interaction between smoking and metabolic variables are compatible with a ceiling effect; metabolic variables increase the risk of readmission in non-smokers but not in smokers who are already at higher risk. Future studies should determine whether better metabolic monitoring and treatment can reduce readmission risk.

Characterization of Psychiatric Inpatients: The Role of Gender Differences in Clinical and Pharmacological Patterns

BACKGROUND

Severe mental disorders that require hospitalization are disabling conditions that contribute to the burden of mental diseases. They pose increased clinical challenges and highlight the need to thoroughly explore variables emerging from daily clinical practice. In this study, we assessed to what extent gender differences may characterize a large population of psychiatric inpatients.

METHODS

We conducted a cross-sectional study in 2 Italian teaching medical centers, which included 2358 patients who were consecutively admitted to the psychiatric emergency units. We explored and characterized gender differences for variables such as prevalence of psychiatric diagnosis, presence of suicidal ideation, suicide attempts, age at onset of psychiatric illness, presence of substance or alcohol abuse, length of stay, number of hospitalizations, presence of involuntary admission, type of discharge from the hospital, and pharmacological treatment at discharge.

RESULTS

Female patients were primarily diagnosed with bipolar disorder or personality disorders. Female patients had a significantly higher prevalence of lifetime suicide attempts (23.1% vs. 16.5%, P<0.001) and a longer length of hospitalization (11.43±10.73 d vs. 10.52±10.37 d, t=-2.099, gl=2356, P=0.036) compared with male patients. Male patients had more involuntary admissions (25.1% vs. 19.7%, χ2=9.616, gl=1, P=0.002), more use of illicit substances (34.1% vs. 20.9%, χ2=51.084, gl=1, P<0.001), and higher rates of alcohol abuse (21.3% vs. 14.7%, χ2=17.182, gl=1, P<0.001) compared with female patients. Finally, antidepressants and lithium were prescribed more frequently to the female patients, whereas other mood stabilizers were more often prescribed to the male patients.

CONCLUSIONS

Our real-world results highlighted gender differences among patients with severe mental disorders admitted to psychiatric units, and suggest further investigations that may help in understanding trajectories accompanying disabling clinical conditions.

Independent Predictors of 30-Day Readmission to Acute Psychiatric Wards in Patients With Mental Disorders: A Systematic Review and Meta-Analysis

Psychiatric readmissions have long been considered significant indicators for healthcare planning. The aim of this study was to identify factors influencing early (30-day) readmissions to acute psychiatric wards. A meta-analysis and systematic review were conducted according to Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Comprehensive database searching was conducted using online databases, including PubMed and Google Scholar, to search for articles identifying factors associated with early (30-day) readmissions to acute psychiatric wards. Keywords used to search for relevant articles included "Mental illness," "readmission," and factors along with their synonyms and Medical Subject Headings (MeSH) terms. The search included studies published between 2011 and June 2023. A total of 13 studies were included in this meta-analysis. The pooled rate of the 30-day readmission was 16% (95% confidence interval: 13%-20%). A pooled analysis showed that factors significantly associated with an unplanned hospital readmission included gender, length of stay, and insurance status as predictors of the unplanned hospital readmission among individuals with psychiatric illness. Additionally, we also found that the rate of 30-day unplanned admissions was greater in patients with schizophrenia, followed by personality disorder, bipolar disorder, depression, and substance use. This study highlights the importance of providing targeted interventions and support for individuals with these conditions to reduce the risk of readmissions.

Risk of psychiatric readmission in the homeless population: A 10-year follow-up study

Homelessness continues to be a major social and clinical problem. The homeless population has a higher burden of disease that includes psychiatric disorders. In addition, they have a lower use of ambulatory health services and a higher use of acute care. Few investigations analyze the use of services of this population group in the long term. We analyzed the risk of psychiatric readmission of homeless individuals through survival analysis. All admissions to a mental health hospitalization unit in the city of Malaga, Spain, from 1999 to 2005, have been analyzed. Three analyses were carried out: two intermediate analyses at 30 days and 1 year after starting follow-up; and one final analysis at 10 years. In all cases, the event was readmission to the hospitalization unit. The adjusted Hazard Ratio at 30 days, 1-year, and 10-year follow-ups were 1.387 (p = 0.027), 1.015 (p = 0.890), and 0.826 (p = 0.043), respectively. We have found an increased risk of readmission for the homeless population at 30 days and a decreased risk of readmission at 10 years. We hypothesize that this lower risk of long-term readmission may be due to the high mobility of the homeless population, its low degree of adherence to long-term mental health services, and its high mortality rate. We suggest that time-critical intervention programs in the short term could decrease the high rate of early readmission of the homeless population, and long-term interventions could link them with services and avoid its dispersion and abandonment.

Exploring risk factors for re-hospitalization in a psychiatric inpatient setting: a retrospective naturalistic study

BACKGROUND

The reduction of multiple psychiatric hospitalizations is an important clinical challenge in mental health care. In fact, psychiatric re-hospitalization negatively affects the quality of life and the life expectancy of patients with psychiatric disorders. For these reasons, identifying predictors of re-hospitalization is important for better managing psychiatric patients. The first purpose of the present study was to examine the readmission rate in a large sample of inpatients with a psychiatric disorder. Second, we investigated the role of several demographical and clinical features impacting re-hospitalization.  METHOD: This retrospective study enrolled 1001 adult inpatients (510 men and 491 women) consecutively admitted to the University Psychiatric Clinic, Sant'Andrea Hospital, Sapienza University of Rome between January 2018 and January 2022. To identify risk factors for psychiatric re-hospitalization, we divided the sample into 3 subgroups: the Zero-Re group which had no readmission after the index hospitalization, the One-Re group with patients re-admitted only once, and the Two-Re with at least two re-admissions.  RESULTS: The groups differed according to previous hospitalizations, a history of suicide attempts, age at onset, and length of stay. Furthermore, the results of the regression model demonstrated that the Two-Re group was more likely to have a history of suicide attempts and previous hospitalizations.

DISCUSSION

These results indicate the importance of assessing risk factors in psychiatric hospitalized patients and implementing ad hoc prevention strategies for reducing subsequent re-hospitalizations.

Effective hospital readmission prediction models using machine-learned features

BACKGROUND

Hospital readmissions are one of the costliest challenges facing healthcare systems, but conventional models fail to predict readmissions well. Many existing models use exclusively manually-engineered features, which are labor intensive and dataset-specific. Our objective was to develop and evaluate models to predict hospital readmissions using derived features that are automatically generated from longitudinal data using machine learning techniques.

METHODS

We studied patients discharged from acute care facilities in 2015 and 2016 in Alberta, Canada, excluding those who were hospitalized to give birth or for a psychiatric condition. We used population-level linked administrative hospital data from 2011 to 2017 to train prediction models using both manually derived features and features generated automatically from observational data. The target value of interest was 30-day all-cause hospital readmissions, with the success of prediction measured using the area under the curve (AUC) statistic.

RESULTS

Data from 428,669 patients (62% female, 38% male, 27% 65 years or older) were used for training and evaluating models: 24,974 (5.83%) were readmitted within 30 days of discharge for any reason. Patients were more likely to be readmitted if they utilized hospital care more, had more physician office visits, had more prescriptions, had a chronic condition, or were 65 years old or older. The LACE readmission prediction model had an AUC of 0.66 ± 0.0064 while the machine learning model's test set AUC was 0.83 ± 0.0045, based on learning a gradient boosting machine on a combination of machine-learned and manually-derived features.

CONCLUSION

Applying a machine learning model to the computer-generated and manual features improved prediction accuracy over the LACE model and a model that used only manually-derived features. Our model can be used to identify high-risk patients, for whom targeted interventions may potentially prevent readmissions.

Postpartum depression: a developed and validated model predicting individual risk in new mothers

Postpartum depression (PPD) is a serious condition associated with potentially tragic outcomes, and in an ideal world PPDs should be prevented. Risk prediction models have been developed in psychiatry estimating an individual's probability of developing a specific condition, and recently a few models have also emerged within the field of PPD research, although none are implemented in clinical care. For the present study we aimed to develop and validate a prediction model to assess individualized risk of PPD and provide a tentative template for individualized risk calculation offering opportunities for additional external validation of this tool. Danish population registers served as our data sources and PPD was defined as recorded contact to a psychiatric treatment facility (ICD-10 code DF32-33) or redeemed antidepressant prescriptions (ATC code N06A), resulting in a sample of 6,402 PPD cases (development sample) and 2,379 (validation sample). Candidate predictors covered background information including cohabitating status, age, education, and previous psychiatric episodes in index mother (Core model), additional variables related to pregnancy and childbirth (Extended model), and further health information about the mother and her family (Extended+ model). Results indicated our recalibrated Extended model with 14 variables achieved highest performance with satisfying calibration and discrimination. Previous psychiatric history, maternal age, low education, and hyperemesis gravidarum were the most important predictors. Moving forward, external validation of the model represents the next step, while considering who will benefit from preventive PPD interventions, as well as considering potential consequences from false positive and negative test results, defined through different threshold values.

The prevalence and impact of adolescent hospitalization to adult psychiatric units

BACKGROUND

With increasing psychiatric hospitalizations among adolescents and constrained hospital resources, there are times when youth are hospitalized in adult inpatient psychiatry units. Evidence on the prevalence of this practice and associated impacts is lacking.

AIMS

We sought to explore the prevalence, determinants, and outcomes related to the hospitalization of adolescents aged 12-17 years on adult inpatient psychiatry units in Ontario.

METHODS

Using health administrative data, we constructed a cohort of adolescents with an inpatient psychiatric admission in Ontario (2007-2011). We classified adolescents as having an admission to an adult psychiatry unit or to other inpatient units. Multivariable regression models were used to estimate prevalence ratios (PR) for factors associated with adult admission, as well as risk ratios (RR) for the impact of adult admission on length of stay, discharge against medical advice, and 30-day readmission.

RESULTS

Over the study period, 22.6% of adolescents with a psychiatric hospitalization (n = 16 998) had an admission to an adult psychiatry unit. Older age (16 vs. 15 years: PR = 2.27, 95% CI = 2.07-2.48; 17 vs. 15 years: PR = 2.91, 95% CI = 2.66-3.18), rural residence (PR = 1.46, 95% CI = 1.38-1.55), psychotic (PR = 1.25, 95% CI = 1.15-1.36) or personality disorder (PR = 1.59, 95% CI = 1.41-1.80) diagnoses, and involuntary status (PR = 2.18, 95% CI = 2.05-2.31) were independently associated with adult admission. Adolescents admitted to adult units were more likely to be discharged against medical advice (RR = 1.77, 95% CI = 1.45-2.17).

CONCLUSIONS

Nearly one in four adolescent psychiatric admissions occurs on an adult psychiatric unit. These findings help to fill gaps in the prior literature, and highlight the need for further research to inform policy decisions and resource allocation for adolescent inpatient psychiatric care.

Personality predictors of 6-month readmission in adult psychiatric inpatients

Readmission of psychiatric inpatients is highly prevalent and places a significant financial burden on the healthcare system. Rehospitalisation is often used as a metric of quality of care in psychiatric settings, but little is known about how specific personality traits impact readmission in adult psychiatric inpatients. A convenience sample of 94 adults (mean age = 36.8 years; female = 54.3%; European American = 76.6%) at an inpatient psychiatric hospital completed the Personality Inventory for DSM-5-Brief Form (PID-5-BF; American Psychiatric Association, 2013); demographic and medical information and readmission data were extracted via chart review. Poisson regression was used to predict number of readmissions at 6 months after discharge from PID-5-BF domain scores of Negative Affectivity, Detachment, Antagonism, Disinhibition and Psychoticism. Twenty-three patients (24.5%) were readmitted at least once by 6-month follow-up. Higher PID-5-BF Negative Affectivity domain scores predicted greater number of readmissions at 6 months (incidence rate ratio (IRR) = 1.14, robust standard error (RSE) = 0.05, p < .01, 95% confidence interval [1.04, 1.25]). The other PID-5-BF domain scores were not significantly related to number of readmissions. Thus, greater negative affect, indicative of higher trait neuroticism, heightened experience of negative emotions and poor self-concept, was a significant personality predictor of readmission in the study. These results suggest that assessing this trait domain might help to identify psychiatric inpatients at greater risk for readmission and determine those most in need of enhanced services to reduce rehospitalisation.

Predicting 30-Day Readmissions: Evidence From a Small Rural Psychiatric Hospital

To improve quality of care and patient outcomes, and to reduce costs, hospitals in the United States are trying to mitigate readmissions that are potentially avoidable. By identifying high-risk patients, hospitals may be able to proactively adapt treatment and discharge planning to reduce the likelihood of readmission. Our objective in this study was to derive and validate a predictive model of 30-day readmissions for a small rural psychiatric hospital in the northeast. However, this model can be adapted by other rural psychiatric hospitals-a context that has been understudied in the literature. Our sample consisted of 1912 adult inpatients (1281 in the derivation cohort and 631 in the validation cohort), who were admitted between August 1, 2014, and July 31, 2016. We used deidentified data from the hospital's electronic medical record, including physician orders and discharge summaries. These data were merged with community-level variables that reflected the availability of care in the patients' zip codes. We first considered the correlates of 30-day readmission in a regression framework. We found that the probability of readmission increased with the number of previous admissions (vs. no readmissions). Moreover, the probability of readmission was much higher for patients with a depressive disorder (vs. no depressive disorder), with another mood disorder (vs. no other mood disorder), and/or with a psychotic disorder (vs. no psychotic disorder). We used these associations to derive a predictive model, in which we used the regression coefficients to construct a score for each patient. We then estimated the predicted probability of 30-day readmission on the basis of that score. After validating the model, we discuss the implications for clinical practice and the limitations of our approach.

Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers

Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model’s prediction of hospital readmission.

Published models that predict hospital readmission: a critical appraisal

INTRODUCTION

The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness.

OBJECTIVE

To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation.

METHODS

We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors.

RESULTS

We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%).

CONCLUSIONS

The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

Does physician compensation for declaration of involuntary status increase the likelihood of involuntary admission? A population-level cross-sectional linked administrative database study

BACKGROUND

There is substantial variability in involuntary psychiatric admission rates across countries and sub-regions within countries that are not fully explained by patient-level factors. We sought to examine whether in a government-funded health care system, physician payments for filling forms related to an involuntary psychiatric hospitalization were associated with the likelihood of an involuntary admission.

METHODS

This is a population-based, cross-sectional study in Ontario, Canada of all adult psychiatric inpatients in Ontario (2009-2015, n = 122 851). We examined the association between the proportion of standardized forms for involuntary admissions that were financially compensated and the odds of a patient being involuntarily admitted. We controlled for socio-demographic characteristics, clinical severity, past-health care system utilization and system resource factors.

RESULTS

Involuntary admission rates increased from the lowest (Q1, 70.8%) to the highest (Q5, 81.4%) emergency department (ED) quintiles of payment, with the odds of involuntary admission in Q5 being nearly significantly higher than the odds of involuntary admission in Q1 after adjustment (aOR 1.73, 95% CI 0.99-3.01). With payment proportion measured as a continuous variable, the odds of involuntary admission increased by 1.14 (95% CI 1.03-1.27) for each 10% absolute increase in the proportion of financially compensated forms at that ED.

CONCLUSIONS

We found that involuntary admission was more likely to occur at EDs with increasing likelihood of financial compensation for invoking involuntary status. This highlights the need to better understand how physician compensation relates to the ethical balance between the right to safety and autonomy for some of the world's most vulnerable patients.

Improving community care for patients discharged from hospital through zone-wide implementation of a seamless care transition policy

BACKGROUND

Several studies within the psychiatry literature have illustrated the importance of discharge planning and execution, as well as accessibility of outpatient follow-up post-discharge. We report the results of implementing a new seamless care transition policy to expedite post-discharge follow-up in the community Addiction and Mental Health (AMH) program in the Edmonton Zone, Alberta, Canada. The policy involved a distribution mechanism for assessment by a mental health therapist (MHT) within 7 days of discharge as well as a dedicated roster of community psychiatrists to accept newly discharged patients.

OBJECTIVE

Our aim was to assess the feasibility of this novel policy and to assess its effect on our outcome measures of wait time to first outpatient MHT assessment and re-admission rate to hospital.

METHODS

Our study involved a retrospective clinical audit with total sampling design and a comparison of data 1 year before (2015/2016 fiscal year) and 1 year after (2017/2018 fiscal year) the implementation of the seamless care policy within the Edmonton Zone. Extracted data were analyzed with simple descriptive statistics and presented as percentages, mean and median.

RESULTS

Overall, with the enactment of this policy, follow-up volumes ultimately increased, while wait times for initial assessment decreased on average for patients discharged from the hospital. In the 2015/2016 fiscal year, MHT completed 128 assessments of post-discharge patients who were new to the community AMH program compared to 298 completed new assessments for the 2017/2018 fiscal year. The corresponding wait times for the new MHT assessments were 12.7 days (median of 12 days) and 7.8 days (median of 6 days), respectively. Similarly, psychiatrists completed only 59 assessments of post-discharge patients who were new to AMH compared to 133 new psychiatric assessments for the 2017/2018 fiscal year. The corresponding wait times for the new psychiatric assessments were 15.3 days (median of 14 days) and 8.8 days (median of 7 days), respectively. We correspondingly found a slight decline in readmission rates after the implementation of our model in the subsequent fiscal year.

CONCLUSION

We envision that this policy will set a precedent with regard to streamlining post-discharge follow-up care for admitted inpatients, ultimately improving mental health outcomes for patients.

A practical overview and decision tool for analyzing recurrent events in mental illness: A review

Mental illnesses are chronic conditions in which an individual will often experience recurrent outcomes such as hospitalization, symptomatic relapse or self-harm behaviours. Most clinical research in psychiatry considers only the first event, and does not analyze subsequent recurrent events. Methods exist to analyze recurrent events; however, these methods are underused in the psychiatric research literature. This review identifies that recurrent events can be analyzed using a time homogenous or time-to-recurrent-event (TTRE) framework. The TTRE framework is underutilized in psychiatric research; however, this framework allows for longitudinal observations that are more congruent with the chronic nature of psychiatric illness than typical first event analyses. There are several readily available statistical models using the TTRE framework extending the standard Cox proportional hazards model. Our decision tool outlines four aspects of a research question to consider when selecting a TTRE model: (1) importance of event timing, (2) explanatory vs predictive, (3) common vs event-specific hazard, and (4) correlation of events within an individual. Analyzing recurrent events in psychiatric research provides an opportunity to address research questions aimed at understanding the longitudinal course of a chronic condition. These approaches may provide novel insights into risk factors or interventions for psychiatric illness, and ultimately improved outcomes for these chronic conditions.

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.

Structure and predictors of in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia in Japan: A cross-sectional study

Schizophrenia is a disorder characterized by psychotic relapses. Globally, about 15%-30% of patients with schizophrenia discharged from inpatient psychiatric admissions are readmitted within 90 days due to exacerbation of symptoms that leads to self-harm, harm to others, or self-neglect. The purpose of this study was to investigate the structure and predictors of in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia. A new questionnaire was developed to assess the extent to which respondents delivered in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia. This study adopted a cross-sectional research design. The survey was conducted with the new questionnaires. The participants were registered nurses working in psychiatric wards. Item analyses and exploratory factor analyses were performed using the new questionnaires to investigate the structure of in-hospital nursing care leading to reduction in early readmission. Stepwise regression analyses were conducted to examine the factors predicting in-hospital nursing care leading to reduction in early readmission. Data were collected from 724 registered nurses in Japan. In-hospital nursing care leading to reduction in early readmission was found to consist of five factors: promoting cognitive functioning and self-care, identifying reasons for readmission, establishing cooperative systems within the community, sharing goals about community life, and creating restful spaces. In-hospital nursing care leading to reduction in early readmission was predicted by the following variables: the score on the nursing excellence scale in clinical practice, the score on therapeutic hold, and the participation of community care providers in pre-discharge conferences. Japanese psychiatric nurses provide nursing care based on these five factors leading to reduction in early readmission. Such nursing care would be facilitated by not only nurses' excellence but also nurses' environmental factors, especially the therapeutic climate of the ward and the participation of community care providers in pre-discharge conferences.

Psychiatric readmission rates in a multi-level mental health care system - a descriptive population cohort study

BACKGROUND

Readmission rates are frequently used as a quality indicator for health care, yet their validity for evaluating quality is unclear. Published research on variables affecting readmission to psychiatric hospitals have been inconsistent. The Norwegian specialist mental health care system is characterized by a multi-level structure; hospitals providing specialized -largely unplanned care and district psychiatric centers (DPCs) providing generalized -more often planned care. In certain service systems, readmission may be an integral part of individual patients' treatment plan. The aim of the present study was to describe and examine the task division in a multi-level health care system. This we did through describing differences in patient population (age, sex, diagnosis, substance abuse comorbidity and length of stay) and admissions types (unplanned vs. planned) treated at different levels (hospital, DPC or both), and by examining whether readmission risk differ according to type and place of treatment of index-admission and travel-time to nearest hospital and DPC.

METHODS

In this population-based cohort study using administrative data we included all individuals aged 18 and older who were discharged from psychiatric inpatient care with an ICD-10 diagnosis F2-F6 ("functional mental disorders") in 2012. Selecting each individual's first discharge during 2012 as index gave N = 16,185 for analyses following exclusions. Analysis of readmission risk were done using Kaplan-Maier failure curves.

RESULTS

Overall, 15.1 and 47.7% of patients were readmitted within 30 and 365 days, respectively. Unplanned admission patients were more likely to be readmitted within 30 days than planned patients. Those transferred between hospital and DPC during index admission were more likely to be readmitted within 365 days, and to experience planned readmission. Patients with short travel time were more likely to have unplanned readmission, while patients with long travel time were more likely to have planned readmission.

CONCLUSIONS

DPCs and hospitals fill different purposes in the Norwegian health care system, which is reflected in different patient populations. Differences in short term readmission rates between hospitals and DPCs disappeared when type of admission (unplanned/planned) was considered. The results stress the importance of addressing differences in organisation and task distribution when comparing readmission rates between mental health systems.

Implementation of nurse navigation for behavioral health inpatient services to divert early readmissions: A pilot program

OBJECTIVE

Inpatient behavioral health units across the United States have high readmission rates. The Centers for Medicare and Medicaid Services (CMS) as well as healthcare organizations are focused on reducing readmission rates, especially those readmissions that occur within 30-90 days. Behavioral Health Nurse Navigation was implemented at St. Luke's University Health Network's Sacred Heart Campus to investigate, address, and divert early behavioral health readmissions through evidence-based discharge calls and problem-solving methods.

METHOD

Following approval for Behavioral Health Nurse Navigation, workflow and discharge call screens were developed for behavioral health discharge calls. Discharge call screens populated at specific intervals and calls were made at those times. Data was maintained on those patients who participated in calls. Increased collaboration and dissemination of information ensued.

RESULTS

613 patients participated in discharge calls from March 2020 to October 2020 as 98% of patients liked receiving calls. 56% of participants used substances; the most commonly used substance was THC. MDD was the most common diagnosis among participants and most patients utilized St. Luke's Psychiatric Associates for outpatient mental health care. Participants most often required assistance in the areas of medication, community coordination, and support/reassurance. The Behavioral Health Nurse Navigation diversion rate ranged from 81% to 96%.

CONCLUSION

Behavioral Health Nurse Navigation promotes patients' successful adherence to discharge plans through increased collaboration, communication, and skillful problem-solving in the areas of medication, community coordination, and therapeutic reassurance/support. The outcome of Behavioral Health Nurse Navigation is increased patient satisfaction, compliance with treatment, and diversion of patients at high risk for early readmission.

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.

Individualized prediction of 2-year risk of relapse as indexed by psychiatric hospitalization following psychosis onset: Model development in two first episode samples

BACKGROUND

Although most patients with psychotic disorders experience relapse, it is not possible to predict whether or when an individual patient is going to relapse. We aimed to develop a multifactorial risk prediction algorithm for predicting risk of relapse in first episode psychosis (FEP).

METHODS

Data from two prospectively collected cohorts of FEP patients (N = 1803) were used to develop three multiple logistic prediction models to predict risk of relapse (defined as hospitalization) within the first 2 years of onset of psychosis. Model 1 (M1_S1) used data obtained from clinical notes (Sample 1) while model 2 (M2_S2) applied the same set of predictors using data obtained from research interviews (Sample 2). The final model (Sample 2: M3_S2) used the same predictors plus additional detailed information on predictors. Model performance was evaluated employing measures of overall accuracy, calibration, discrimination and internal validation.

RESULTS

In both samples, the 2-year probability of psychiatric hospitalization was 37%. Of all the models, discrimination accuracy was lowest when limited information (such as socio-demographic and clinical parameters) was included in the prediction model. Model M3_S2 using additional information (descriptors of pattern of cannabis, nicotine, alcohol and other illicit drug use) obtained from research interview had the best discrimination accuracy (Harrell's C index 0.749).

CONCLUSIONS

The measures that contributed most to predicting hospitalization are readily accessible in routine clinical practice, suggesting that a risk prediction tool based on these models would be clinically practicable following validation in independent samples and permit a personalized approach to relapse prevention in psychosis.

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.

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.

Predicting postpartum psychiatric admission using a machine learning approach

AIMS

The accurate identification of mothers at risk of postpartum psychiatric admission would allow for preventive intervention or more timely admission. We developed a prediction model to identify women at risk of postpartum psychiatric admission.

METHODS

Data included administrative health data of all inpatient live births in the Australian state of Queensland between January 2009 and October 2014. Analyses were restricted to mothers with one or more indicator of mental health problems during pregnancy (n = 75,054 births). The predictors included all maternal data up to and including the delivery, and neonatal data recorded at delivery. We used multiple machine learning methods to predict hospital admission in the 12 months following delivery in which the primary diagnosis was recorded as an ICD-10 psychotic, bipolar or depressive disorders.

RESULTS

The boosted trees algorithm produced the best performing model, predicting postpartum psychiatric admission in the validation data with good discrimination [AUC = 0.80; 95% CI = (0.76, 0.83)] and achieving good calibration. This model outperformed benchmark logistic regression model and an elastic net model. In addition to indicators of maternal metal health history, maternal and neonatal anthropometric measures and social/lifestyle factors were strong predictors.

CONCLUSION

Our results indicate the potential of a big data approach when aiming to identify mothers at risk of postpartum psychiatric admission. Mothers at risk could be followed-up and supported after neonatal discharge to either remove the need for admission or facilitate more timely admission.

An economic evaluation of a mobile text messaging intervention to improve mental health care in resource-poor communities in China: a cost-effectiveness study

BACKGROUND

Severe mental disorders, a leading cause of disability has become a major public health problem. In order to promote mental health, a series of programs have been promulgated by the Chinese government. However, economic evaluations of such programs are lacking. The purpose of this study is to develop and validate an economic model to assess the cost and health outcomes of the LEAN (Lay health supporters, E-platform, Award, and iNtegration) program, and to perform an economic evaluation of LEAN versus the nationwide community-based mental health program that provides free antipsychotic medications.

METHODS

A cost-effectiveness and cost-utility analysis of the LEAN intervention will be performed. A Markov model will be developed, validated and used to assess and compare the costs and outcomes for the LEAN intervention versus nationwide community-based mental health program. The calculated sample size is 258 participants for the analysis. A societal perspective will be applied with the time horizon of 1-year after the termination of the LEAN program. The cost-utility will be measured primarily using Quality Adjusted Life Years and the cost-effectiveness will be measured using number of relapses and number of re-hospitalizations avoided 6-month after the intervention. Univariate and probabilistic sensitivity analysis will be conducted for the analysis of uncertainty.

DISCUSSION

If proven cost-effective, this study will contribute to the nationwide implementation of the program, not only for schizophrenia but for all kind of severe mental disorders. Markov model developed as part of the study will benefit potential researchers in analyzing cost-effectiveness of other programs. The Chinese context of the study may limit the generalizability of the study results to some extent.

TRIAL REGISTRATION

This study was registered in a Chinese Clinical Trial Registry ( ChiCTR2000034962 ) on 25 July 2020.

Hospitalization following eating disorder diagnosis: The buffering effect of marriage and childbearing events

Eating Disorders (ED) are defined as abnormal eating behaviors, stemming from an obsession with food, body weight, or body shape. EDs affect 10 million men and 20 million women in the US, with an estimated 15% lifetime prevalence among women. An ED diagnosis is often accompanied with a host of adverse physical and mental health outcomes, including a heightened risk for suicidality. Given the complex comorbidities associated with EDs, treatment occurs in inpatient and outpatient settings. This study used linked administrative and health records from the Utah Population Database to create a cohort of women n = 4183 and men n = 423 who had a known diagnosis of ED between 1995 and 2015. Cox proportional hazard regression was used to model ED-related hospitalization trajectories, including subsequent risk for suicidality/self-injurious behavior-related hospitalization. To better estimate the risk profiles associated with different health care utilization patterns, models explored how family-related life course events (childbirth, marriage transitions) and sociodemographic characteristics (race, sex, and median income at census-block) modify hospitalization trajectories following initial diagnosis. Results suggested that increased outpatient treatment was associated with reduced risk of initial ED-related hospitalization, but higher risk for subsequent ED-related hospital readmission. In addition, transition to marriage (i.e., getting married) was associated with reduced risk of ED-related and suicidality/self-injurious behavior-related hospitalizations (initial hospitalization and subsequent readmission). Increased number of children was only associated with reduced risk of initial ED-hospitalization, but not readmission. When assessing individuals' risk for ED-related hospitalizations, social and health services researchers should contextualize treatment trajectories within the individual's life experiences, particularly marital transitions, while simultaneously considering sociodemographic characteristics and utilization of outpatient care. Future research should further examine whether marriage represents an important turning point in the health trajectories of individuals with EDs.

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Childbearing reduces risk of initial eating disorder hospitalization.

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Marriage reduces risk of eating disorder hospitalization and readmission.

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Marriage reduces risk of suicidality hospitalization for persons with prior eating disorder.

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Higher outpatient treatment reduces risk of initial eating disorder hospitalization.

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Higher outpatient treatment increases risk of eating disorder hospital readmission.

Predicting Patients' Readmission: Do Clinicians Outperform a Statistical Model? An Exploratory Study on Clinical Risk Judgment in Mental Health

This study explores whether clinicians or a statistical model can better identify patients at risk of early readmission and investigates variables potentially associated with clinicians' risk judgment. We focus on a total of 142 patients discharged from acute psychiatric wards in the Verona Mental Health Department (Italy). Psychiatrists assessed patients' risk of readmission at 30 and 90 days postdischarge, predicted their postdischarge compliance, and assessed their Global Assessment of Functioning (GAF) score at admission and discharge. Clinicians' judgment outperformed the statistical model, with the difference reaching statistical significance for 30-day readmission. Clinicians' readmission risk judgment, both for 30 and 90 days, was found to be statistically associated with predicted compliance with community treatment and GAF score at discharge. Clinicians' superior performance might be explained by their risk judgment depending on nonmeasurable factors, such as experience and intuition. Patients with a poorer GAF score at discharge and poor assumed compliance were predicted to have a higher risk of readmission.

Factors Associated With Multiple Psychiatric Readmissions for Youth With Mood Disorders

OBJECTIVE

Inpatient psychiatric readmission rates are increasingly considered indicators of quality of care. This study builds upon prior research by examining patient-, hospital-, and community-level factors associated with single and multiple readmissions for youth.

METHOD

A retrospective cohort study was conducted using Medicaid claims data from four states supplemented with the American Hospital Association survey, the Area Resource File, and the National Survey of Mental Health Treatment Services. Multinomial logistic regression examined patient-, hospital-, and community-level factors that were associated with inpatient psychiatric readmission for 6,797 Medicaid-eligible youth with a primary diagnosis of mood disorder using a three-level nominal dependent variable coded as no readmission, one readmission, and two or more readmissions within 6 months after discharge.

RESULTS

Six months after initial discharge, 941 youth (13.8%) were readmitted once and 471 (6.9%) were readmitted two or more times. The odds of single or multiple readmissions were significantly higher (p < .05) for youth classified as disabled or in foster care, those with multiple psychiatric comorbidities, medical comorbidity, and prior psychiatric hospitalization. Treatment in hospitals with high percentage of Medicaid discharges and a high number of beds was associated with lower odds of readmission. There was a significant interaction between length of stay and outpatient mental health follow-up within 7 days of discharge.

CONCLUSION

Patient- and hospital-level factors are associated with likelihood of both single and multiple youth inpatient psychiatric readmissions, suggesting potential risk markers for psychiatric readmission.

Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach

OBJECTIVE

Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.

METHODS

We analyzed patients with continuous enrollment for three years and at least one episode of M/SUD as the primary reason for hospital admission. The outcome was readmission within 30-days from discharge. Model performance was evaluated using the Area under the Receiver Operating Characteristic (AUROC). We compared the AUROCs of an extreme gradient boosted tree (XGBoost) model to generalized linear model with elastic net regularization (GLMNet).

RESULTS

We analyzed 65,426 unique patients and 97,688 admissions. Patients with mental disorders accounted for 66 % (13.2 % readmission rate) and substance use disorders, 34 % (22.3 % readmission rate). Among all those who had readmissions, 70.7 %, 17.0 %, and 12.4 % had 1, 2, or 3+ readmissions, respectively. Previous hospitalizations, hospital utilization, discharge disposition, diagnosis category, and comorbidity were among the highest important features in the XGBoost model. The XGBoost model AUROC was 0.737 (95 % CI: 0.732 to 0.742) versus the GLMNet 0.697 (95 % CI: 0.690 to 0.703). The AUROC of the final XGBoost model on the testing set was 0.738 (95 % CI: 0.730 to 0.748), higher than published readmission models for mental health patients.

CONCLUSIONS

The XGBoost model has a better performance than GLMNet and previously published models in predicting readmissions in mental health patients. Our model may be further tested to aid targeted demographic initiatives to reduce M/SUDs readmissions and benchmarking.

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

BACKGROUND

Psychiatric readmissions negatively impact patients and their families while increasing healthcare costs. This study aimed at investigating factors associated with psychiatric readmissions within 30 days and 1 year of the index admissions and exploring the possibilities of monitoring and improving psychiatric care quality in China.

METHODS

Data on index admission, subsequent admission(s), clinical and hospital-related factors were extracted in the inpatient medical record database covering 10 secondary and tertiary psychiatric hospitals in Beijing, China. Logistic regressions were used to examine the associations between 30-day and 1-year readmissions plus frequent readmissions (≥3 times/year), and clinical variables as well as hospital characteristics.

RESULTS

The 30-day and 1-year psychiatric readmission rates were 16.69% (1289/7724) and 33.79% (2492/7374) respectively. 746/2492 patients (29.34%) were readmitted 3 times or more within a year (frequent readmissions). Factors significantly associated with the risk of both 30-day and 1-year readmission were residing in an urban area, having medical comorbidities, previous psychiatric admission(s), length of stay > 60 days in the index admission and being treated in tertiary hospitals (p < 0.001). Male patients were more likely to have frequent readmissions (OR 1.30, 95%CI 1.04-1.64). Receiving electroconvulsive therapy (ECT) was significantly associated with a lower risk of 30-day readmission (OR 0.72, 95%CI 0.56-0.91) and frequent readmissions (OR 0.60, 95%CI 0.40-0.91).

CONCLUSION

More than 30% of the psychiatric inpatients were readmitted within 1 year. Urban residents, those with medical comorbidities and previous psychiatric admission(s) or a longer length of stay were more likely to be readmitted, and men are more likely to be frequently readmitted. ECT treatment may reduce the likelihood of 30-day readmission and frequent admissions. Targeted interventions should be designed and piloted to effectively monitor and reduce psychiatric readmissions.

Short stay unit for patients in acute mental health crisis: A case-control study of readmission rates

INTRODUCTION

Past evaluations of psychiatric short stay units have shown positive outcomes for patients, yet very little is known about the factors related to readmissions.

METHODS

A Short Stay Pathway (SSP) has been introduced on the Gold Coast, Australia, for patients in acute mental health crisis with admissions of up to 3 days. Rates of readmissions within 28 days were compared for SSP patients (N = 678), and a diagnosis-matched control group of patients from acute mental health beds (N = 1356). Demographic and clinical factors were considered as predictors of subsequent readmissions.

RESULTS

Average length of stay for SSP patients was 3.4 days, compared to 7.6 days in the control group. 10.6% of SSP patients and 18.4% of the control group were readmitted within 28 days (P < .001). For both groups, a 7-day follow up significantly reduced readmissions (P < .05). Indigenous patients on SSP had higher odds of readmissions than non-Indigenous patients (P < .05), and a diagnosis of a personality disorder increased readmission in the control group but not the SSP group (P < .001).

DISCUSSION

SSP reduced repeated hospitalizations for patients in acute crisis by 42%. An identification of factors related to future admissions can inform future tailoring of this model of care to subgroups of patients.

Epidemiology of Interpersonal Trauma among Women and Men Psychiatric Inpatients: A Population-Based Study

OBJECTIVE

Small clinical samples suggest that psychiatric inpatients report a lifetime history of interpersonal trauma. Since past experiences of trauma may complicate prognosis and treatment trajectories, population-level knowledge is needed about its prevalence and correlates among inpatients.

METHODS

Using health-administrative databases comprising all adult psychiatric inpatients in Ontario, Canada (2009 to 2016, n = 160,436, 49% women), we identified those who reported experiencing physical, sexual, and/or emotional trauma in their lifetime, 1 year, and 30 days preceding admission. We described the prevalence of each type of trauma, comparing women and men using modified Poisson regression, and identified individual-level characteristics associated with lifetime trauma history using multivariable logistic regression.

RESULTS

31.7% of inpatients reported experiencing trauma prior to admission. Lifetime prevalence was higher in women (39.6% vs. 24.1%; age-adjusted prevalence ratio [aPR] = 1.68; 95% CI, 1.65 to 1.71), including sexual (22.7% vs. 8.4%; aPR = 2.81; 95% CI, 2.73 to 2.89), emotional (33.3% vs. 19.4%; aPR = 1.76; 95% CI, 1.72 to 1.79), and physical trauma (24.2% vs. 14.8%; aPR = 1.68; 95% CI, 1.65 to 1.72). Factors most prominently associated with lifetime trauma were witnessing parental substance use (adjusted odds ratio [aOR] = 8.68; 95% CI, 8.39 to 8.99), female sex (aOR = 2.29; 95% CI, 2.23 to 2.35), and number of recent stressful life events (aOR = 1.62; 95% CI, 1.59 to 1.65).

CONCLUSIONS

These results suggest that trauma-informed approaches are essential to consider in the design and delivery of inpatient psychiatric services for both women and men.

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.

Physical Comorbidities are Independently Associated with Higher Rates of Psychiatric Readmission in a Chinese Han Population

BACKGROUND

In people with psychosis, physical comorbidities are highly widespread and leading contributors to the untimely death encountered. Readmission rates in psychiatric patients are very high. Somatic comorbidities could be one of the considerable risk factors for psychiatric rehospitalization. Nevertheless, much less is known about the relation between physical comorbidities and psychiatric readmission. We aimed to investigate the association between physical comorbidities and psychiatric readmission in Han Chinese patients with psychiatric disorders.

METHODS

We used administrative data for January 1, 2009 to December 31, 2018 from the headquarters of the Affiliated Brain Hospital of Guangzhou Medical University to identify adults with schizophrenia, unipolar depression or bipolar disorder discharged from hospital. Data were extracted on sociodemographic and clinical characteristics. The Charlson comorbidity index (CCI) was used to assess the existence of significant physical comorbidity. Cox proportional hazards regression estimated rehospitalization risk after discharge.

RESULTS

A total of 15,620 individuals were included in this study, with the mean age of 35.1 years (SD = 12.8), and readmission occurred for 23.6% of participants. Survival analysis showed that physical comorbidities were statistically and significantly associated with psychiatric readmission, even after the adjustment for the number of psychiatric comorbidities, other sociodemographic and clinical variables.

CONCLUSION

Our results suggest that somatic comorbidities are related with higher rates of psychiatric readmission. Hence, to treat psychosis more effectively and to reduce rehospitalization, it is crucial to treat physical comorbidities promptly and adequately. It is absolutely necessary to bring somatic comorbidities to the forefront of psychiatric treatment and research.

Factors Associated With High Use of Hospital Psychiatric Services in Málaga, Spain: Analysis of First Admissions

The early prediction of patients at risk may facilitate the efficient use of interventions that have been demonstrated to reduce readmissions. The aim of the study was to analyze variables during first admissions associated with further high use of an inpatient hospitalization psychiatric unit in Málaga, Spain. The risk of having three or more psychiatric admissions was analyzed in a sample of 1535 first-time admissions with multivariate Cox regression. In the multivariate model, the variables associated with the risk of high use were age at admission (p < 0.001), length of stay (p < 0.001), place of residence (p < 0.001), and previous history with mental health services (p < 0.001). The results suggest that there are several easily accessible characteristics at first admission that are potentially useful in detecting patients at risk.

Associations between readmission and patient-reported measures in acute psychiatric inpatients: a study protocol for a multicenter prospective longitudinal study (the ePOP-J study)

Background

Several previous observational studies have reported the risk factors associated with readmission in people with mental illness. While patient-reported experiences and outcomes have become increasingly important in healthcare, only a few studies have examined these parameters in terms of their direct association with readmission in an acute psychiatric setting. This project will investigate multiple factors associated with readmission and community living in acute psychiatric patients in Japan. This study will primarily investigate whether patient-reported experiences at discharge, particularly quality of life (QoL), are associated with future readmission and whether readmission after the index hospitalization is associated with changes in patient-reported outcomes during the study period. Here, we describe the rationale and methods of this study.

Methods

This multicenter prospective cohort study is being conducted in 21 participating Japanese hospitals, with a target sample of approximately 600 participants admitted to the acute psychiatric ward. The study has four planned assessment points: time of index admission (T1), time of discharge (from the index admission) (T2), 6 months after discharge from the index admission (T3), and 12 months after discharge from the index admission (T4). Participants will complete self-reported measures including a QoL scale, a subjective disability scale, and an empowerment- and self-agency-related scale at each assessment point; additionally, service satisfaction, subjective view of need for services, and subjective relationships with family members will be assessed at T2 and T3. We will assess the participants’ hospitalization during the study period and evaluate several potential individual- and service-level factors associated with readmission and patient-reported experiences and outcomes. Multivariate analyses will be conducted to identify potential associations between readmission and patient-reported experiences and outcomes.

Discussion

The present study may produce evidence on how patient-reported experiences at discharge influence readmission and on the influence of readmission on the course of patient-reported outcomes from admission to community living after discharge. The study may contribute to improving care for both patients’ subjective views of their own health conditions and their community lives in an acute psychiatric setting.

Trial registration University Hospital Medical Information Network—Clinical Trials Registry (UMIN-CTR) UMIN000034220. Registered on September 20, 2018.

Electronic supplementary material

The online version of this article (10.1186/s13033-019-0298-3) contains supplementary material, which is available to authorized users.

Differences between psychiatric disorders in the clinical and functional effectiveness of an acute psychiatric day hospital, for acutely ill psychiatric patients

INTRODUCTION

Intensive treatment in acute day-care psychiatric units may represent an efficient alternative to inpatient care. However, there is evidence suggesting that this clinical resource may not be equally effective for every psychiatric disorder. The primary aim of this study was to explore differences between main psychiatric diagnostic groups, in the effectiveness of an acute partial hospitalization program. And, to identify predictors of treatment response.

MATERIAL AND METHODS

The study was conducted at an acute psychiatric day hospital. Clinical severity was assessed using BPRS, CGI, and the HoNOS scales. Main socio-demographic variables were also recorded. Patients were clustered into 4wide diagnostic groups (i.e.: non-affective psychosis; bipolar; depressive; and personality disorders) to facilitate statistical analyses.

RESULTS

A total of 331 participants were recruited, 115 of whom (34.7%) were diagnosed with non-affective psychosis, 97 (28.3%) with bipolar disorder, 92 (27.8%) with affective disorder, and 27 (8.2%) with personality disorder. Patients with a diagnosis of bipolar disorder showed greater improvement in BPRS (F=5.30; P=0.001) and CGI (F=8.78; P<0.001) than those suffering from psychosis or depressive disorder. Longer length of stay in the day-hospital, and greater baseline BPRS severity, were identified as predictors of good clinical response. Thirty-day readmission rate was 3%; at long-term (6 months after discharge) only 11.8% (N=39) of patients were re-admitted to a psychiatric hospitalization unit, and no differences were observed between diagnostic groups.

CONCLUSIONS

Intensive care in an acute psychiatric day hospital is feasible and effective for patients suffering from an acute mental disorder. However, this effectiveness differs between diagnostic groups.

High-Risk Phenotypes of Early Psychiatric Readmission in Bipolar Disorder With Comorbid Medical Illness

BACKGROUND

Individuals with co-existing serious mental illness and non-psychiatric medical illness are at high risk of acute care utilization. Mining of electronic health record data can help identify and categorize predictors of psychiatric hospital readmission in this population.

OBJECTIVE

This study aimed to identify modifiable predictors of psychiatric readmission among individuals with comorbid bipolar disorder and medical illness. This goal was accomplished by applying objective variable selection via machine learning techniques.

METHOD

This was a retrospective analysis of electronic health record data derived from 77,296 episodes of care from 2006 to 2016 within the University of California Health Care System. Data included 1,250 episodes of care involving patients with bipolar disorder and serious comorbid medical illnesses (defined by transfer between medicine and psychiatry services or concomitant primary medical and psychiatric admission diagnoses). Machine learning (classification trees) was used to identify potential predictors of 30-day psychiatric readmission across hospital encounters. Predictors included demographics, medical and psychiatric diagnoses, medication regimen, and disposition. The algorithm was internally validated using 10-fold cross-validation.

RESULTS

The model predicted 30-day readmission with high accuracy (98% unbalanced model, 88% balanced model). Modifiable predictors of readmission were length of stay, transfers between medical and psychiatric services, discharge disposition to home, and all-cause acute health service utilization in the year before the index hospitalization.

CONCLUSION

Among bipolar disorder patients with comorbid medical conditions, characteristics of the index hospitalization (e.g., duration, transfer, and disposition) emerged as more predictive than static properties of the patient (e.g., sociodemographic factors and psychiatric comorbidity burden). Findings identified phenotypes of patients at high risk for rehospitalization and suggest potential ways of modifying the risk of early readmission.

Changing characteristics of forensic psychiatric patients in Ontario: a population-based study from 1987 to 2012

PURPOSE

To quantify the demand for forensic psychiatric services in Ontario over the past 25 years and investigate whether the sociodemographic, clinical and offense-based characteristics of forensic patients have changed over time.

METHODS

We investigated all forensic admissions from 1987 to 2012 resulting in a disposition of Not Criminally Responsible on account of Mental Disorder (N = 2533). We present annual proportions of patients with specified sociodemographic, clinical and offense characteristics, and investigate whether the duration of forensic system tenure varies as a function of admission year, psychiatric diagnosis, or index offense.

RESULTS

There has been a steady increase in forensic admissions over this time period, particularly individuals with comorbid substance use disorders and individuals of non-Caucasian ethno-racial background. The proportion of persons committing severe violence has remained low and has decreased over time. Having a comorbid personality, neurological, or substance use disorder significantly increased forensic system tenure, as did committing a violent offense. Individuals who came into the system in earlier years had slower rates of discharge compared to more recent admissions.

CONCLUSIONS

Defining the trends characterizing the growth of the forensic population has important policy implications, as forensic services are costly and involve a significant loss of liberty. The current results indicate that young, substance abusing individuals of diverse ethno-racial backgrounds and who commit relatively low-level violence comprise an increasing proportion of Ontario's forensic population, and suggest that treatment must be optimized to best serve the needs of these individuals.

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.

Effectiveness of Transitional Interventions in Improving Patient Outcomes and Service Use After Discharge From Psychiatric Inpatient Care: A Systematic Review and Meta-Analysis

Background: The transition from psychiatric hospital to community is often hindered by challenges that influence community adjustment and continuity of care. Transitional interventions with bridging components are provided prior to discharge and continue beyond inpatient care. They provide continuity of care and may be effective in preventing readmission. We aimed to assess the effectiveness of transitional interventions with predischarge and postdischarge components in reducing readmissions and improving health-related or social outcomes of patients discharged from psychiatric hospitals. Methods: We conducted a systematic review by searching electronic databases (MEDLINE, Embase, Cochrane Library, CINAHL, PsycINFO, and Psyndex) and included randomized, nonrandomized, and one-group study designs. A random effects meta-analysis was conducted with randomized controlled trials (RCTs) reporting data on readmission rates. Other study designs were synthesized qualitatively. Results: After screening 2,673 publications, 16 studies (10 RCTs, three quasi-experimental, and three cohort studies) were included and nine RCTs were included in the meta-analysis. The tested interventions included components from case management, psychoeducation, cognitive behavioral therapy, and peer support. All studies with significant improvements in at least one outcome provided elements of case management, most frequently in combination with cognitive behavioral therapy and psychoeducation. Readmission rates during follow-up ranged between 13% and 63% in intervention groups and 19% and 69% in control groups. Overall, we found an odds ratio of 0.76 (95% confidence interval = 0.55-1.05) for readmission due to transitional interventions. Heterogeneity was low at only 31% (p = 0.17) and the funnel plot indicated no obvious publication biases. Conclusions: We observed that transitional interventions with bridging components were no more effective in reducing readmission than treatment as usual; however, these results are based on limited evidence. Therefore, additional high-quality research is required to conclude the effectiveness of transitional interventions. Nevertheless, transitional interventions with bridging components are preferred by service users and could be an alternative to strategies regularly employed.

Predictive models for hospital readmission risk: A systematic review of methods

OBJECTIVES

Hospital readmission risk prediction facilitates the identification of patients potentially at high risk so that resources can be used more efficiently in terms of cost-benefit. In this context, several models for readmission risk prediction have been proposed in recent years. The goal of this review is to give an overview of prediction models for hospital readmission, describe the data analysis methods and algorithms used for building the models, and synthesize their results.

METHODS

Studies that reported the predictive performance of a model for hospital readmission risk were included. We defined the scope of the review and accordingly built a search query to select the candidate papers. This query string was used as input for the chosen search engines, namely PubMed and Google Scholar. For each study, we recorded the population, feature selection method, classification algorithm, sample size, readmission threshold, readmission rate and predictive performance of the model.

RESULTS

We identified 77 studies that met the inclusion criteria, out of 265 citations. In 68% of the studies (n = 52) logistic regression or other regression techniques were utilized as the main method. Ten (13%) studies used survival analysis for model construction, while 14 (18%) used machine learning techniques for classification, of which decision tree-based methods and SVM were the most utilized algorithms. Among these, only four studies reported the use of any class imbalance addressing technique, of which resampling is the most frequent (75%). The performance of the models varied significantly among studies, with Area Under the ROC Curve (AUC) values in the ranges between 0.54 and 0.92.

CONCLUSION

Logistic regression and survival analysis have been traditionally the most widely used techniques for model building. Nevertheless, machine learning techniques are becoming increasingly popular in recent years. Recent comparative studies suggest that machine learning techniques can improve prediction ability over traditional statistical approaches. Regardless, the lack of an appropriate benchmark dataset of hospital readmissions makes a comparison of models' performance across different studies difficult.

Thirty-Day and 5-Year Readmissions following First Psychiatric Hospitalization: A System-Level Study of Ontario's Psychiatric Care

OBJECTIVE

Analyses of representative, system-level data to examine trends in short- and longer-term readmission rates for psychiatric illnesses are largely absent. The objective of this article is to examine key trends and variables with implications for inpatient care as indicated by 30-day readmission and outpatient care as reflected by readmission within 5 years.

METHODS

Using OMHRS data from 2005 to 2015, patients who had their first inpatient admission were followed for 5 years to examine their subsequent 30-day and overall admission rates stratified by discharge time and diagnosis.

RESULTS

The study cohort consisted of 42,280 patients. The 30-day and 5-year readmission rates for the entire cohort were 7.2% and 35.1%, respectively. Using a time course analysis of readmission for discharges in different years, both 30-day readmission and 5-year readmission rates decreased in a linear manner from 2005 to 2010, primarily because of readmission patterns for patients diagnosed with mood disorders and schizophrenia/other psychotic disorders. It was also evident that both demographic considerations such as age and gender and variables reflective of social determinants such as education level and employment were predictive of rehospitalization risk.

CONCLUSIONS

The trends of decreasing readmission rates may be reflective of improvements in the quality of hospital and community-based outpatient care. Such system-level indicators warrant tracking and may inform more effective tertiary prevention.

Association between quality domains and health care spending across physician networks

One of the more fundamental health policy questions is the relationship between health care quality and spending. A better understanding of these relationships is needed to inform health systems interventions aimed at increasing quality and efficiency of care. We measured 65 validated quality indicators (QI) across Ontario physician networks. QIs were aggregated into domains representing six dimensions of care: screening and prevention, evidence-based medications, hospital-community transitions (7-day post-discharge visit with a primary care physician; 30-day post-discharge visit with a primary care physician and specialist), potentially avoidable hospitalizations and emergency department (ED) visits, potentially avoidable readmissions and unplanned returns to the ED, and poor cancer end of life care. Each domain rate was computed as a weighted average of QI rates, weighting by network population at risk. We also measured overall and sector-specific per capita healthcare network spending. We evaluated the associations between domain rates, and between domain rates and spending using weighted correlations, weighting by network population at risk, using an ecological design. All indicators were measured using Ontario health administrative databases. Large variations were seen in timely hospital-community transitions and potentially avoidable hospitalizations. Networks with timely hospital-community transitions had lower rates of avoidable admissions and readmissions (r = -0.89, -0.58, respectively). Higher physician spending, especially outpatient primary care spending, was associated with lower rates of avoidable hospitalizations (r = -0.83) and higher rates of timely hospital-community transitions (r = 0.81) and moderately associated with lower readmission rates (r = -0.46). Investment in effective primary care services may help reduce burden on the acute care sector and associated expenditures.

Outcomes and feasibility of the short transitional intervention in psychiatry in improving the transition from inpatient treatment to the community: A pilot study

Discharge from psychiatric inpatient care is frequently described as chaotic, stressful, and emotionally charged. Following discharge, service users are vulnerable to becoming overwhelmed by the challenges involved in readapting to their home environments, which could result in serious problems and lead to readmission. The short transitional intervention in psychiatry (STeP) is a bridging intervention that includes pre- and post-discharge sections. It aims to prepare patients for specific situations in the period immediately following discharge from a psychiatric hospital. We conducted a quasi-experimental pilot study to determine the feasibility of the intervention, and gain insight into the effects of the STeP. Two inpatient wards at a Swiss psychiatric hospital participated in the study, and represented the intervention and control arms. Patient recruitment and baseline assessment were performed 2 weeks prior to discharge. Follow-up data were collected 1 week subsequent to discharge. Questionnaires measured coping, admission and health-care usage, self-efficacy, working alliance, experience of transition, and the number of difficulties experienced following discharge. Fourteen and 15 patients completed the follow-up assessment in the control and intervention groups, respectively. The STeP did not affect primary or secondary outcomes; however, it was shown to be feasible, and patients' feedback highlighted the importance of post-discharge contact sessions. Further research is required to improve understanding of the discharge experience, identify relevant patient outcomes, and assess the effectiveness of the intervention in an adequately-powered randomized, controlled trial.

Readmission in psychiatry inpatients within a year of discharge: The role of symptoms at discharge and post-discharge care in a Brazilian sample

INTRODUCTION

Readmission into inpatient psychiatric beds is a useful outcome for patients, care providers, and policymakers. This study aims to investigate the role of level of symptoms at discharge and type of post-discharge care in determining readmissions after a year before a psychiatric admission.

METHODS

We performed a prospective and observational study in a general hospital psychiatric facility. Patients were assessed at admission, discharge, and one year after discharge. We used a multivariable logistic regression to determine predictors of readmission.

RESULTS

In total, 488 patients were included at admission, and 401 (82,17%) were accessed in the follow-up period. Psychiatric readmissions occurred in 29.17% of the followed patients. The number of previous admissions represents a 38% higher chance of being readmitted (OR 1.38; CI 1.16-1.60). For patients admitted in a depressive episode, not being in remission at discharge increases 140% the chance to be readmitted (OR 2.40; CI 1.14-5.07) as well as the follow-up at primary (OR 5.27; CI 1.06-26.15). For those with Schizophrenia and related disorders, higher scores in BPRS at discharge increases the chance to be readmitted (OR 1.28, CI 1.11-1.48).

CONCLUSION

Level of symptoms at discharge was related to higher chance to be readmitted in patients admitted in a depressive episode and those with schizophrenia and related disorders. Findings of the type of care raise the need for further investigation. Also, this finding confirms the importance of the history of previous admissions in predicting future admissions.

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.