Identification of some individual and social factors on relapse intervals in bipolar disorder:Bayesian analysis of log-skew-normal accelerated failure time model

authors:

avatar Azadeh Gholizadeh , avatar Soleiman Kheiri ORCID , * , avatar Morteza Sedehi , avatar Habibollah Esmaily , avatar Zahra Arab Borzou


how to cite: Gholizadeh A, Kheiri S, Sedehi M, Esmaily H, Arab Borzou Z. Identification of some individual and social factors on relapse intervals in bipolar disorder:Bayesian analysis of log-skew-normal accelerated failure time model. koomesh. 2018;20(2):e152969. 

Abstract

Introduction: Bipolar disorder is severe, chronic, pleomorphic and often recurrent, which can lead to severe abnormalities in lif;#39s function. The purpose of this study was to determine some factors related to the time interval between relapse in bipolar disorder. Materials and Methods: In a retrospective study, 606 patients with bipolar disorder at Avicenna hospital in Mashhad were collected. In this study, 329 patients with at least one relapse of bipolar disorder were included. Survival time was defined as elapsed time from discharge to readmission due to relapse of disease. These patients wereadmitted from the beginning of 2008 to the end of 2009 due to their illness and were include in the study and followed until the end of 2013. The log-skew-normal accelerated failure time model was fitted to identify the factors related to the time of relapse. Estimation of parameters was obtained based on the Bayesian approach using Markov chain Monte Carlo algorithm by Open BUGS. Results: The estimate of mean and median time between discharge and readmission were 25.6 and 15 months, respectively. Age, family history, drug use, and gender had a significant association with the time of relapse, such that, male, younger patients, drug users, and positive family history of disease experienced a recurrence of disease earlier. With the mentioned variables in the model, stress and education were not associated with the recurrence of disease. Conclusion: Given that the time interval from discharge to recurrence of type 1 bipolar disorder is low in men, young people, family history of positive and drug users, it is recommended to identify strategies to prevent relapse or delay in these groups.

References

  • 1.

    Salvatore P, Tohen M, Khalsa H-MK, Baethge C, Tondo L, Baldessarini RJ. Longitudinal Research on Bipolar Disorders. Epidemiol Psichiatr Soc 2007; 6: 109-117.

  • 2.

    Pallaskorpi S, Suominen K, Ketokivi M, Mantere O, Arvilommi P, Valtonen H, Leppmki S, Isomets E. Five year outcome of bipolar I and II disorders: findings of the Jorvi Bipolar Study. Bipolar Disord 2015; 17: 363-374.

  • 3.

    Ghiasian A. Legal Jurisprudential Rreview of Bbipolar Disorder in Psychiatry. International Conference on Humanities and Behavioral Studies. Tehran, Iran; 2014. (Persian).

  • 4.

    Sadock BJ, Sadock VA. Kaplan and Sadock'sSynopsis of Psychiatry: Behavioral Sciences/Clinical Psychiatry: Lippincott Williams & Wilkins; 2011.

  • 5.

    Goldstein BI, Birmaher B, Carlson GA, DelBello MP, Findling RL, Fristad M, et al. The international society for bipolar disorders task force report on pediatric bipolar disorder: Knowledge to date and directions for future research. Bipolar Disord 2017; 19: 524-543.

  • 6.

    Etain B, Henry C, Bellivier F, Mathieu F, Leboyer M. Beyond genetics: childhood affective trauma in bipolar disorder. Bipolar Disord 2008; 10: 867-876.

  • 7.

    Hirschfeld R, Calabrese JR, Weissman MM, Reed M, Davies MA, Frye MA, et al. Screening for bipolar disorder in the community. J Clin Psychiatry 2003; 64: 53-59.

  • 8.

    Morris CD, Miklowitz DJ, Wisniewski SR, Giese AA, Thomas MR, Allen MH. Care satisfaction, hope, and life functioning among adults with bipolar disorder: data from the First 1000 participants in the systematic treatment enhancement program. Compr Psychiatry 2005; 46: 98-104.

  • 9.

    Revicki DA, Hanlon J, Martin S, Gyulai L, Ghaemi SN, Lynch F, et al. Patient-Based utilities for bipolar ddisorder-related health states. J Affect Disord 2005; 87: 203-210.

  • 10.

    Sadock BJ, Sadock VA, Ruiz P. Comprehensive textbook of psychiatry 7th ed. Philadelphia: Williams and Wilkins; 2000.

  • 11.

    Murray CJ, Lopez AD, Organization WH. The Global Burden of Ddisease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: Summary. The Harvard School of Public Health. 1996.

  • 12.

    Altman S, Haeri S, Cohen LJ, Ten A, Barron E, Galynker II, et al. Predictors of relapse in bipolar disorder: a review. J Psychiatr Pract 2006; 12: 269-268.

  • 13.

    American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5). American Psychiatric Pub; 2013.

  • 14.

    McElroy SL, Altshuler LL, Suppes T, Keck Jr PE, Frye MA, Denicoff KD, et al. Axis I psychiatric comorbidity and Its relationship to historicalIllness variables in 288 patients with bipolar disorder. Am J Psychiatry 2001; 158: 420-426.

  • 15.

    Perlis RH, Ostacher MJ, Patel JK, Marangell LB, Zhang H, Wisniewski SR, et al. Predictors of recurrence in bipolar disorder: primary outcomes from the systematic treatment enhancement program for bipolar disorder (STEP-BD). Am J Psychiatry 2006; 163: 217-224.

  • 16.

    Azzalini A. A class of distributions which includes the normal ones. Scand J Stat 1985; 1: 171-178.

  • 17.

    Arab Borzou Z. Application of fraility in determining the risk factors for disorder returned bipolar with a bayesian approach [MSc dissertation]. Mashhad University of Medical Sciences, Mashhad, Iran; 2015. (Persian).

  • 18.

    Klein JP, Moeschberger ML. Survival analysis: Techniques for censored and truncated data. New York: Springer-Verlag; 1997.

  • 19.

    Callegaro A. Log-skew-normal accelerated failure time models: Working Paper Series.Italy: Padua Univ.; 2012.

  • 20.

    Kheiri S, Meshkani MR, Faghihzadeh S. A correlated frailty model for analysing risk factors in bilateral corneal graft rejection for Keratoconus: a Bayesian approach. Stat Med 2005; 24: 2681-2693.

  • 21.

    Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Series B 2002; 64: 583-639.

  • 22.

    akir S, zerdem A. Psychotherapeutic and psychosocial approaches in bipolar disorder: a systematic literature review. Turk Psikiyatri Derg 2010; 21: 1-12,.

  • 23.

    Baethge C, Schlattmann P. A survival analysis for recurrent events in psychiatric research. Bipolar Disord 2004; 6: 115-121.

  • 24.

    Taheri S, Khodaie Ardakani MR, Karimlou M, Rahgozar M. Identifying risk factors of time to releases in patients with bipolar disorder using penalized likelihood model with shared gamma frailty compared with with-out frailty model. RJMS 2016; 23: 42-49. (Persian).

  • 25.

    Rosca P, Bauer A, Grinshpoon A, Khawaled R. Rehospitalizations among Psychiatric Patients whose First Admission was Involuntary: a 10-year follow-up. Isr J Psychiatry Relat Sci 2006; 43: 57-64.

  • 26.

    Lin CH, Chen MC, Chou LS, Lin CH, Chen CC, Lane HY. Time to rehospitalization in patients with major depression vs. those with schizophrenia or bipolar I disorder in a public psychiatric hospital. Psychiatry Res 2010; 180: 74-79.

  • 27.

    Thompson EE, Neighbors HW, Munday C, Trierweiler S. Length of stay, referral to aftercare, and rehospitalization among psychiatric inpatients. Psychiatr Serv 2003; 54: 1271-1276.

  • 28.

    Angst J, Gamma A, Sellaro R, Lavori PW, Zhang H. Recurrence of bipolar disorders and major depression. Eur Arch Psychiatry Clin Neurosci 2003; 253: 236-240.

  • 29.

    Kleinbaum DG, Klein M. Survival analysis. New York 2010.

  • 30.

    Gholizadeh A. Bayesian analysis of log-skew-normal accelerated failure time frailty model and its application to identifying some of risk factors in time intervals between relapse in patients with bipolar disorder [MSc dissertation]. Shahrekord Univ Med Sci 2017. (Persian).##.