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

BMC Med Inform Decis Mak 2018 09 14;18(Suppl 3):79. Epub 2018 Sep 14.

Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.

Background: Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR).

Methods: The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes.

Results: Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)).

Conclusions: Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

Download full-text PDF

Source Listing
September 2018
32 Reads
1.500 Impact Factor

Publication Analysis

Top Keywords

side effects
prescription data
prescriptome data
digital comorbidity
primary side
psychiatric cohort
effects drug-drug
readmission risk
secondary side
shared genetic
data mining
psychiatric illness
readmission psychiatric
medications side
data emrs
highest risk
risk readmission
30-day readmission

Altmetric Statistics


(Supplied by CrossRef)

V Donisi et al.
Gen Hosp Psychiatry 2016

J Moss et al.
Compr Psychiatry 2014

S Vasudeva et al.
Indian J Psychiatry 2009

T Oiesvold et al.
Acta Psychiatr Scand 2000

RY Pablo et al.
Gen Hosp Psychiatry 1986

A Potter et al.
Am J Community Psychol 1975

AL Leppin et al.
JAMA Intern Med 2014

KE Joynt et al.
N Engl J Med 2012

M Charlson et al.
J Clin Epidemiol 1994

ME Charlson et al.
J Chronic Dis 1987

Similar Publications