Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

Risk factors associated with readmissions of patients with severe mental disorders under treatment with antipsychotics

BACKGROUND

The aim of this study was to assess the risk of readmission in patients with severe mental disorders, compare it between patients using different types of antipsychotics and determine risk factors for psychiatric readmission.

METHODS

Medical records of a non-concurrent cohort of 625 patients with severe mental disorders (such as psychoses and severe mood disorders) who were first discharged from January to December 2012 (entry into the cohort), with longitudinal follow-up until December 2017 constitute the sample. Descriptive statistical analysis of characteristics of study sample was performed. The risk factors for readmission were assessed using Cox regression.

RESULTS

Males represented 51.5% of the cohort, and 75.6% of the patients had no partner. Most patients (89.9%) lived with relatives, and 64.7% did not complete elementary school. Only 17.1% used more than one antipsychotic, 34.2% did not adhere to the treatment, and 13.9% discontinued the medication due to unavailability in public pharmacies. There was a need to change the antipsychotic due to the lack of therapeutic response (11.2% of the patients) and adverse reactions to the antipsychotic (5.3% of the patients). Cox regression showed that the risk of readmission was increased by 25.0% (RR, 1.25; 95% CI, 1.03-1.52) when used typical antipsychotics, compared to those who used atypical ones, and by 92.0% (RR, 1.92; 95% CI, 1.63-2.27) when patients did not adhere to maintenance treatment compared to those who adhered.

CONCLUSIONS

Use of atypical antipsychotics and adherence to treatment were associated with a lower risk of psychiatric readmissions.

Predictors of drug-drug interactions of medications prescribed to patients admitted due to suicidal behavior

INTRODUCTION

Drug-drug interactions among people with suicidal behavior is a challenging topic, considering the harm it poses for patients already vulnerable and the lack of literature on the thematic. This aspect must not be neglected in research and clinical practice, and thus requires thorough investigation.

OBJECTIVE

to investigate predictors of drug-drug interaction of prescribed drugs and the prescription of two or more drugs for people admitted due to suicidal behavior in a psychiatric emergency department (short-stay hospital ward).

METHOD

A cross-sectional study with retrospective approach, carried out in a Brazilian psychiatric emergency unit in 2015. Data about first and last medical prescriptions were collected from 127 patients' files. Descriptive statistics and the Zero Adjusted Logarithmic Distribution (ZALG) model were adopted, with the significance level α = 0.05.

RESULTS

Potential drug-drug interactions were found in most of the first and last prescriptions. The sample majority were female, with previous suicide attempts, being discharged from the hospital with three drugs (or more) prescribed, and without referral to any health service. Age and comorbidities were predictors of more drug prescriptions and the amount of prescribed drugs was the most important predictor of drug-drug interactions (quantity and severity).

CONCLUSIONS

the variables associated with drug-drug interactions and prescription of two or more drugs among people with suicidal behavior needs to be investigated in different contexts and addressed in interventions with the aim to promote patient safety.

Association of cardiovascular metabolic risk factor measurements with psychiatric readmission among in-hospital patients with severe mental illness: a retrospective study

BACKGROUND

Patients with severe mental illness (SMI) and comorbid physical conditions were often associated with higher risks of mortality and hospital readmission. In this study, we aim to examine the association of cardiovascular metabolic risk factor measurements with risks of psychiatric readmissions among in-hospital patients with severe mental illness (SMI).

METHODS

We collected the longitudinal information of laboratory investigations, blood pressure and body mass index (BMI) among in-hospital patients who had been diagnosed with schizophrenia, major depression disorder or bipolar disorder and with comorbid diagnosis of hypertension, hyperlipidemia or diabetes from Jan 2014 to Jan 2019. The primary outcome was time to first psychiatric readmission. Cox proportional hazard model was utilized to calculate the hazard risks (HR) of cardiovascular metabolic risk factors with psychiatric readmission.

RESULTS

A total of 5,256 patients were included in the analysis. Compared to patients with normal blood parameters, patients with aberrant tests of high-density dyslipidemia (HDL) and diastolic blood pressure (DBP) during in-hospitalization period were associated with higher risks to first psychiatric readmission [ HR (Hazard Ratio), 1.37 95% Confidence interval (CI), 1.03-1.83 for HDL and HR, 1.32 (95% CI, 1.04-1.67])for DBP]. Compared to patients with optimal monitoring, patients with suboptimal monitoring of blood lipids and blood pressure during in-hospitalization period or recommended window period of cardiovascular disease (CVD) risk management were associated with higher risks to first psychiatric readmission.

CONCLUSIONS

Aberrant cardiovascular metabolic blood test and blood pressure and missing measurements among in-hospital patients with SMI were associated with increased risks of psychiatric readmissions. This calls for more active screening and monitoring of CVD risk factors for those in-hospital patients in need.

Association Rules Mining for Hospital Readmission: A Case Study

As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission.

Epidemiological Characteristics of Sedative-Hypnotics and Opioid Painkillers at High-Frequency Exposure

Drug poisoning has a high incidence and serious consequences in medical institutions; its epidemiological characteristics also directly affect the changes in national laws and policies and the implementation of local management policies. Chinese statistics on drug-related abnormal death cases generally come from judicial appraisal centers and medical units. However, due to differences in work content and professional restrictions, there are differences in information management forms, which makes it difficult for appraisers to conduct a professional and systematic analysis of drug-related cases. This article focuses on the analysis of epidemiological characteristics of sedative-hypnotics and opioid painkillers and their exposure patterns in cases of poisoning death by analyzing the annual report of the American Association of Poison Control Center, combined with the characteristics of drug exposure in China.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.