Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining 11 Medical and Health Sciences 1117 Public Health and Health Services

Khader Shameer, M. Mercedes Perez-Rodriguez, Roy Bachar, Li Li, Amy Johnson, Kipp W. Johnson, Benjamin S. Glicksberg, Milo R. Smith, Benjamin Readhead, Joseph Scarpa, Jebakumar Jebakaran, Patricia Kovatch, Sabina Lim, Wayne Goodman, David L. Reich, Andrew Kasarskis, Nicholas P. Tatonetti, Joel T. Dudley

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number79
JournalBMC Medical Informatics and Decision Making
Volume18
DOIs
StatePublished - Sep 14 2018
Externally publishedYes

Fingerprint

Patient Readmission
Data Mining
Health Services
Psychiatry
Public Health
Pharmacology
Health
Prescriptions
Comorbidity
Cardiovascular Diseases
Electronic Health Records
Drug-Related Side Effects and Adverse Reactions
Drug Interactions
Logistic Models
Databases
Pravastatin
Systems Biology
Prescription Drugs
Gene Regulatory Networks
Mentally Ill Persons

Keywords

  • Big data
  • Biomedical informatics
  • Computational psychiatry
  • Digital health
  • Healthcare data science
  • Hospital readmission
  • Pharma informatics
  • Prescriptome

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

Cite this

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining 11 Medical and Health Sciences 1117 Public Health and Health Services. / Shameer, Khader; Perez-Rodriguez, M. Mercedes; Bachar, Roy; Li, Li; Johnson, Amy; Johnson, Kipp W.; Glicksberg, Benjamin S.; Smith, Milo R.; Readhead, Benjamin; Scarpa, Joseph; Jebakaran, Jebakumar; Kovatch, Patricia; Lim, Sabina; Goodman, Wayne; Reich, David L.; Kasarskis, Andrew; Tatonetti, Nicholas P.; Dudley, Joel T.

In: BMC Medical Informatics and Decision Making, Vol. 18, 79, 14.09.2018.

Research output: Contribution to journalArticle

Shameer, K, Perez-Rodriguez, MM, Bachar, R, Li, L, Johnson, A, Johnson, KW, Glicksberg, BS, Smith, MR, Readhead, B, Scarpa, J, Jebakaran, J, Kovatch, P, Lim, S, Goodman, W, Reich, DL, Kasarskis, A, Tatonetti, NP & Dudley, JT 2018, 'Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining 11 Medical and Health Sciences 1117 Public Health and Health Services', BMC Medical Informatics and Decision Making, vol. 18, 79. https://doi.org/10.1186/s12911-018-0653-3
Shameer, Khader ; Perez-Rodriguez, M. Mercedes ; Bachar, Roy ; Li, Li ; Johnson, Amy ; Johnson, Kipp W. ; Glicksberg, Benjamin S. ; Smith, Milo R. ; Readhead, Benjamin ; Scarpa, Joseph ; Jebakaran, Jebakumar ; Kovatch, Patricia ; Lim, Sabina ; Goodman, Wayne ; Reich, David L. ; Kasarskis, Andrew ; Tatonetti, Nicholas P. ; Dudley, Joel T. / Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining 11 Medical and Health Sciences 1117 Public Health and Health Services. In: BMC Medical Informatics and Decision Making. 2018 ; Vol. 18.
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abstract = "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.",
keywords = "Big data, Biomedical informatics, Computational psychiatry, Digital health, Healthcare data science, Hospital readmission, Pharma informatics, Prescriptome",
author = "Khader Shameer and Perez-Rodriguez, {M. Mercedes} and Roy Bachar and Li Li and Amy Johnson and Johnson, {Kipp W.} and Glicksberg, {Benjamin S.} and Smith, {Milo R.} and Benjamin Readhead and Joseph Scarpa and Jebakumar Jebakaran and Patricia Kovatch and Sabina Lim and Wayne Goodman and Reich, {David L.} and Andrew Kasarskis and Tatonetti, {Nicholas P.} and Dudley, {Joel T.}",
year = "2018",
month = "9",
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doi = "10.1186/s12911-018-0653-3",
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TY - JOUR

T1 - Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining 11 Medical and Health Sciences 1117 Public Health and Health Services

AU - Shameer, Khader

AU - Perez-Rodriguez, M. Mercedes

AU - Bachar, Roy

AU - Li, Li

AU - Johnson, Amy

AU - Johnson, Kipp W.

AU - Glicksberg, Benjamin S.

AU - Smith, Milo R.

AU - Readhead, Benjamin

AU - Scarpa, Joseph

AU - Jebakaran, Jebakumar

AU - Kovatch, Patricia

AU - Lim, Sabina

AU - Goodman, Wayne

AU - Reich, David L.

AU - Kasarskis, Andrew

AU - Tatonetti, Nicholas P.

AU - Dudley, Joel T.

PY - 2018/9/14

Y1 - 2018/9/14

N2 - 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.

AB - 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.

KW - Big data

KW - Biomedical informatics

KW - Computational psychiatry

KW - Digital health

KW - Healthcare data science

KW - Hospital readmission

KW - Pharma informatics

KW - Prescriptome

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