Prediction of medical admissions after psychiatric inpatient hospitalization in bipolar disorder: a retrospective cohort study

Alessandro Miola; Michele De Prisco; Marialaura Lussignoli; Nicola Meda; Elisa Dughiero; Riccardo Costa; Nicolas A Nunez; Michele Fornaro; Marin Veldic; Mark A Frye; Eduard Vieta; Marco Solmi; Joaquim Radua; Fabio Sambataro

doi:10.3389/fpsyt.2024.1435199

Prediction of medical admissions after psychiatric inpatient hospitalization in bipolar disorder: a retrospective cohort study

Front Psychiatry. 2024 Sep 3:15:1435199. doi: 10.3389/fpsyt.2024.1435199. eCollection 2024.

Authors

Alessandro Miola^{1

2}, Michele De Prisco^{3

4

5

6

7}, Marialaura Lussignoli¹, Nicola Meda¹, Elisa Dughiero¹, Riccardo Costa¹, Nicolas A Nunez^{2

8}, Michele Fornaro⁹, Marin Veldic², Mark A Frye², Eduard Vieta^{3

4

5

6

7}, Marco Solmi^{10

11

12

13

14}, Joaquim Radua^{4

5

7}, Fabio Sambataro¹

Affiliations

¹ Department of Neuroscience, University of Padova, Padua, Italy.
² Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States.
³ Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain.
⁴ Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain.
⁵ Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
⁶ Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Barcelona, Spain.
⁷ Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), Barcelona, Spain.
⁸ Department of Psychiatry, University of Utah, Salt Lake City, UT, United States.
⁹ Department of Psychiatry, Federico II University of Naples, Naples, Italy.
¹⁰ SCIENCES lab, Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada.
¹¹ Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada.
¹² Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ottawa, ON, Canada.
¹³ School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
¹⁴ Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany.

Abstract

Objective: Bipolar Disorder (BD) is a severe mental illness associated with high rates of general medical comorbidity, reduced life expectancy, and premature mortality. Although BD has been associated with high medical hospitalization, the factors that contribute to this risk remain largely unexplored. We used baseline medical and psychiatric records to develop a supervised machine learning model to predict general medical admissions after discharge from psychiatric hospitalization.

Methods: In this retrospective three-year cohort study of 71 patients diagnosed with BD (mean age=52.19 years, females=56.33%), lasso regression models combining medical and psychiatric records, as well as those using them separately, were fitted and their predictive power was estimated using a leave-one-out cross-validation procedure.

Results: The proportion of medical admissions in patients with BD was higher compared with age- and sex-matched hospitalizations in the same region (25.4% vs. 8.48%). The lasso model fairly accurately predicted the outcome (area under the curve [AUC]=69.5%, 95%C.I.=55-84.1; sensitivity=61.1%, specificity=75.5%, balanced accuracy=68.3%). Notably, pre-existing cardiovascular, neurological, or osteomuscular diseases collectively accounted for more than 90% of the influence on the model. The accuracy of the model based on medical records was slightly inferior (AUC=68.7%, 95%C.I. = 54.6-82.9), while that of the model based on psychiatric records only was below chance (AUC=61.8%, 95%C.I.=46.2-77.4).

Conclusion: Our findings support the need to monitor medical comorbidities during clinical decision-making to tailor and implement effective preventive measures in people with BD. Further research with larger sample sizes and prospective cohorts is warranted to replicate these findings and validate the predictive model.

Keywords: bipolar disorder; clinical decision-making; comorbidity; general medicine admission; machine learning; premature mortality.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Open Access funding provided by Università degli Studi di Padova | University of Padua, Open Science Committee.