Psychiatric morbidity as a risk factor for hospital readmission for acute myocardial infarction: an 8-year follow-up study in Spain

The impact of comorbid severe mental illness and common chronic physical health conditions on hospitalisation: A systematic review and meta-analysis

BACKGROUND

People with severe mental illness (SMI) are at higher risk of physical health conditions compared to the general population, however, the impact of specific underlying health conditions on the use of secondary care by people with SMI is unknown. We investigated hospital use in people managed in the community with SMI and five common physical long-term conditions: cardiovascular diseases, COPD, cancers, diabetes and liver disease.

METHODS

We performed a systematic review and meta-analysis (Prospero: CRD42020176251) using terms for SMI, physical health conditions and hospitalisation. We included observational studies in adults under the age of 75 with a diagnosis of SMI who were managed in the community and had one of the physical conditions of interest. The primary outcomes were hospital use for all causes, physical health causes and related to the physical condition under study. We performed random-effects meta-analyses, stratified by physical condition.

RESULTS

We identified 5,129 studies, of which 50 were included: focusing on diabetes (n = 21), cardiovascular disease (n = 19), COPD (n = 4), cancer (n = 3), liver disease (n = 1), and multiple physical health conditions (n = 2). The pooled odds ratio (pOR) of any hospital use in patients with diabetes and SMI was 1.28 (95%CI:1.15-1.44) compared to patients with diabetes alone and pooled hazard ratio was 1.19 (95%CI:1.08-1.31). The risk of 30-day readmissions was raised in patients with SMI and diabetes (pOR: 1.18, 95%CI:1.08-1.29), SMI and cardiovascular disease (pOR: 1.27, 95%CI:1.06-1.53) and SMI and COPD (pOR:1.18, 95%CI: 1.14-1.22) compared to patients with those conditions but no SMI.

CONCLUSION

People with SMI and five physical conditions are at higher risk of hospitalisation compared to people with that physical condition alone. Further research is warranted into the combined effects of SMI and physical conditions on longer-term hospital use to better target interventions aimed at reducing inappropriate hospital use and improving disease management and outcomes.

Mental health disorders and readmissions following acute myocardial infarction in the United States

Hospital readmissions following an acute myocardial infarction (MI) are associated with increased mortality and morbidity. The aim of this study was to investigate if there is a significant association between specific mental health disorders (MHD) and risk of hospital readmission after an index hospitalization for acute MI. We analyzed the U.S. National Readmission Database for adult acute MI hospitalizations from 2016 to 2017. Co-morbid diagnoses of MHD were obtained using appropriate ICD-10-CM diagnostic codes. The primary outcome of interest was 30-day all-cause unplanned readmission. Cox-regression analysis was used to identify the association of various MHD and risk of 30-day readmission adjusted for demographics, medical and cardiac comorbidities, and coronary revascularization. We identified a total of 1,045,752 hospitalizations for acute MI; patients had mean age of 67 ± 13 years with 37.6% female. The prevalence of any MHD was 15.0 ± 0.9%. After adjusting for potential confounders, comorbid diagnosis of major depression [HR 1.11 (95% CI 1.07–1.15)], bipolar disorders [1.32 (1.19–1.45)], anxiety disorders [1.09 (1.05–1.13)] and schizophrenia/other psychotic disorders [1.56 (1.43–1.69)] were independently associated with higher risk of 30-day readmission compared to those with no comorbid MHD. We conclude that MHD are significantly associated with a higher independent risk of 30-day all-cause hospital readmissions among acute MI hospitalizations.

Medical Comorbidity in Elderly Schizophrenic Patients: A Preliminary Study in Iran

Background:

While comorbidity between mental disorders and physical illnesses is the rule rather than an exception, appraising the impact of comorbidity is challenging due to lack of consensus about how to define and measure the concept of comorbidity.

Objective:

The aim of the present evaluation was to appraise the prevalence and features of medical comorbidities among a group of native elderly schizophrenic patients.

Methods:

Geriatric unit of Razi psychiatric hospital was selected as the field of investigation and 168 elderly schizophrenic patients (≥65 years old), including 101 males and 67 females, who have been hospitalized there as chronic cases, were chosen as an accessible sample, and were surveyed with respect to existing comorbid medical disorders. Psychiatric diagnosis was based on ‘Diagnostic and Statistical Manual of Mental Disorders’, 5th edition (DSM-5), and the medical diagnosis was based on ‘International Classification of Diseases’, 10th edition.

Results:

As shown by the results, 89% (n=151) of elderly schizophrenic patients had some kind of registered physical co-morbidity, which was more significant than the frequency of medical comorbidities among native senior citizens. Amongst the listed co-morbidities, falls, hypertension and osteoarthritis were the most prevalent comorbidities with a frequency of around 48.8%, 44.6% and 39.2%, respectively. Hypertension, renal disease and malnutrition were significantly more prevalent among male patients (p<0.0000, p<0.0045 and p< 0.0018, respectively) and hyponatremia, aspiration/ asphyxiation and seizure were meaningfully more prevalent among female patients (p<0.0075, p<0.0000 and p<0.0009, respectively). As stated by the findings and in comparison with the native seniors, while diabetes, renal diseases and malnutrition were significantly more frequent, coronary artery disease, gastrointestinal disorder and osteoarthritis were significantly less frequent in the existing sample of elderly schizophrenic patients.

Conclusion:

In comparison with the native senior people, the rate of medical comorbidities, particularly diabetes, renal diseases and malnutrition, was significantly higher in elderly schizophrenic patients, a significant difference, was observed regarding physical comorbidities between male and female patients, which demands further methodical and gender-based studies for defining more appropriate care.

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.

Psychiatric readmissions and their association with physical comorbidity: a systematic literature review

BACKGROUND

Comorbidity between mental and physical disorder conditions is the rule rather than the exception. It is estimated that 25% of adult population have mental health condition and 68% of them suffer from comorbid medical condition. Readmission rates in psychiatric patients are high and we still lack understanding potential predictors of recidivism. Physical comorbidity could be one of important risk factors for psychiatric readmission. The aim of the present study was to review the impact of physical comorbidity variables on readmission after discharge from psychiatric or general inpatient care among patients with co-occurring psychiatric and medical conditions.

METHODS

A comprehensive database search from January 1990 to June 2014 was performed in the following bibliographic databases: Ovid Medline, PsycINFO, ProQuest Health Management, OpenGrey and Google Scholar. An integrative research review was conducted on 23 observational studies.

RESULTS

Six studies documented physical comorbidity variables only at admission/discharge and 17 also at readmission. The main body of studies supported the hypothesis that patients with mental disorders are at increased risk of readmission if they had co-occurring medical condition. The impact of physical comorbidity variables on psychiatric readmission was most frequently studied in in patients with affective and substance use disorders (SUD). Most common physical comorbidity variables with higher probability for psychiatric readmission were associated with certain category of psychiatric diagnoses. Chronic lung conditions, hepatitis C virus infection, hypertension and number of medical diagnoses were associated with increased risk of readmission in SUD; Charlson Comorbidity Index, somatic complaints, physical health problems with serious mental illnesses (schizophrenia, schizoaffective disorder, personality disorders); not specified medical illness, somatic complaints, number of medical diagnoses, hyperthyroidism with affective disorders (depression, bipolar disorder). Co-occurring physical and mental disorders can worsen patient's course of illness leading to hospital readmission also due to non-psychiatric reasons.

CONCLUSIONS

The association between physical comorbidity and psychiatric readmission is still poorly understood phenomenon. Nevertheless, that physical comorbid conditions are more common among readmitted patients than single admission patients, their association with readmission can vary according to the nature of mental disorders, characteristics of study population, applied concept of comorbidity, and study protocol.

Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data

Early rehospitalization after discharge for an acute coronary syndrome, including acute myocardial infarction (AMI), is generally considered undesirable. The Centers for Medicare and Medicaid Services (CMS) base hospital financial incentives on risk-adjusted readmission rates after AMI, using claims data in its adjustment models. Little is known about the contribution to readmission risk of factors not captured by claims. For 804 consecutive patients >65 years discharged in 2011 to 2013 from 6 hospitals in Massachusetts and Georgia after an acute coronary syndrome, we compared a CMS-like readmission prediction model with an enhanced model incorporating additional clinical, psychosocial, and sociodemographic characteristics, after principal components analysis. Mean age was 73 years, 38% were women, 25% college educated, and 32% had a previous AMI; all-cause rehospitalization occurred within 30 days for 13%. In the enhanced model, previous coronary intervention (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.34 to 3.16; chronic kidney disease OR 1.89, 95% CI 1.15 to 3.10; low health literacy OR 1.75, 95% CI 1.14 to 2.69), lower serum sodium levels, and current nonsmoker status were positively associated with readmission. The discriminative ability of the enhanced versus the claims-based model was higher without evidence of overfitting. For example, for patients in the highest deciles of readmission likelihood, observed readmissions occurred in 24% for the claims-based model and 33% for the enhanced model. In conclusion, readmission may be influenced by measurable factors not in CMS' claims-based models and not controllable by hospitals. Incorporating additional factors into risk-adjusted readmission models may improve their accuracy and validity for use as indicators of hospital quality.

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Objective Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations. Methods The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation. Results The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71–0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66–0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission. Conclusions Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions. What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.