http://orcid.org/0000-0003-1349-9042
Figures
Abstract
Introduction
Bipolar disorder (BD) is a heterogeneous disorder needing personalized and shared decisions. We aimed to empirically develop a cluster-based classification that allocates patients according to their severity for helping clinicians in these processes.
Methods
Naturalistic, cross-sectional, multicenter study. We included 224 subjects with BD (DSM-IV-TR) under outpatient treatment from 4 sites in Spain. We obtained information on socio-demography, clinical course, psychopathology, cognition, functioning, vital signs, anthropometry and lab analysis. Statistical analysis: k-means clustering, comparisons of between-group variables, and expert criteria.
Results and discussion
We obtained 12 profilers from 5 life domains that classified patients in five clusters. The profilers were: Number of hospitalizations and of suicide attempts, comorbid personality disorder, body mass index, metabolic syndrome, the number of comorbid physical illnesses, cognitive functioning, being permanently disabled due to BD, global and leisure time functioning, and patients’ perception of their functioning and mental health. We obtained preliminary evidence on the construct validity of the classification: (1) all the profilers behaved correctly, significantly increasing in severity as the severity of the clusters increased, and (2) more severe clusters needed more complex pharmacological treatment.
Citation: Fuente-Tomas Ldl, Arranz B, Safont G, Sierra P, Sanchez-Autet M, Garcia-Blanco A, et al. (2019) Classification of patients with bipolar disorder using k-means clustering. PLoS ONE 14(1): e0210314. https://doi.org/10.1371/journal.pone.0210314
Editor: Sinan Guloksuz, Department of Psychiatry and Neuropsychology, Maastricht University Medical Center, NETHERLANDS
Received: February 25, 2018; Accepted: November 20, 2018; Published: January 23, 2019
Copyright: © 2019 Fuente-Tomas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This research was supported by grant FIS PI11/02493 (PI: Maria Paz Garcia-Portilla) of the Instituto de Salud Carlos III, Spanish Ministry of Economy and Competitiveness and Fondos Europeos de Desarrollo Regional (FEDER). Lorena de la Fuente-Tomás has received a Severo Ochoa grant supported by Principado de Asturias Goberment. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Bipolar disorder (BD) is an episodic, often chronic, affective disorder frequently associated with functional impairment [1], excess morbidity [2], and premature mortality [3]. BD has been defined as a pleomorphic condition with a diversity of patterns and trajectories throughout its natural course [4], thus needing personalized decisions shared with patients [5] regarding treatment. Despite the aforementioned, current classifications are based mainly on psychopathological and functional information, neglecting the impact of this disorder on other dimensions of patients' lives and their subjective preferences, which limits their usefulness. In the last decade, staging models have been proposed [6–9] as an alternative that is better adapted to the processes of personalized medicine and shared decision-making. These models provide an alternative framework where mental disorders are placed on a probabilistic continuum from an at-risk or latent stage to late or end-stage disease [10].Thus, they would help guide clinicians in the selection of intervention approaches based on the possibility of disorder progression, as opposed to focusing on diagnostic category alone [11].
Several scientific literature on BD notes that is a progressive condition that would benefit from a staging model [12–13], particularly based on the concept of allostasis and allostatic load [12, 14], although there is no conclusive evidence. In addition, several studies have demonstrated the neurotoxicity [12, 15] of the disorder, leading to greater frequency and severity of episodes, greater sensitivity to adverse life events, and less time in euthymia. The raising interest in this subject is demonstrated by the increasing number of papers, among them some recent systematic reviews of great interest [16–18].
The first staging model of BD was proposed by Berk et al. [4] in 2006. They proposed 5 clinical stages (from 0—increased risk to 4—persistent disorder) based on psychopathology. In 2009, Kapczinski et al. [19–20] suggested a similar model, with the addition of functioning, cognitive performance, and blood biomarkers. Subsequently, Cosci and Fava [21] proposed an integrative model that emphasized the lack of evidence supporting a stage 0 (at-risk). Duffy et al. [22] later suggested a staging model that took into account the natural history of BD and the heterogeneity of subtypes.
While the models proposed are interesting, they only partially address the clinical reality of BD since they do not include other life domains that are relevant and may be affected in the course of the disorder, such as physical health status [23–24] and health-related quality of life [25–26], and all of them were theoretically developed. However, recently, an increasing number of authors are testing the behavior of different parameters in these theoretically proposed models. Thus, concerning peripheral blood inflammatory biomarkers, a decrease in IL-6, BDNF, and SIL-2R along with an increase in TNF-α, sTNFR80, MMP9 and sICAM-1 have been described in late stages by different authors [27–29] while an increase of IL-10 has been described in early stages [30]. Although promising, these results are preliminary and in some cases contradictory as demonstrated by the fact that Grande et al [31] found increased levels of IL-6 in late stage. Also, neuroanatomical anomalies and a reduction of the hippocampal volume have been described in late stages [32–33]. Cognition and functioning were also employed to differentiate among the different stages proposed. Contrary to biomarkers, the results found showed great concordance and indicated a progressive decline in cognition and functioning in late stages [31, 34–36], thus supporting the construct of staging in BD.
Despite the high interest on the area, Alda and Kapczinski [37] point out that most of the proposed models have not been tested, and the ISBD Task Force Report specifies that more efforts are needed to determine the details of the stages and how many are optimal [13]. In the meanwhile, we aim to develop a cluster-based classification, which reflects the different severity degrees of the disorder, using, in addition to the psychopathology of BD, cognition, physical health status, real-world functioning, and subjective health-related quality of life as relevant information for assigning each patient to a specific cluster.
Methods
Study design
Naturalistic, cross-sectional, multicenter study of patients with BD in ambulatory treatment. A total of 200 patients will allow us to classify subjects in different clusters based on a minimum of 8 variables.
The study was conducted at 4 sites in Spain [Oviedo (1 center), Barcelona (2 centers), and Valencia (1 center)] between January 2012 and December 2014. The Clinical Research Ethics Committee of Hospital Universitario Central de Asturias in Oviedo approved the study protocol (Ref. 36/2012). Written informed consent was obtained from all subjects prior to enrolment.
Participants
Subjects were patients with a diagnosis of BD. Diagnoses were confirmed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) [38]. Inclusion criteria were (1) outpatients with a confirmed DSM-IV-TRN [39] diagnosis of BD in treatment at any of the 4 participating centers; (2) age ≥18 years; and (3) written informed consent to participate in the study.
Exclusion criteria were designed to be minimal in order to obtain a heterogeneous and representative sample of all phases of the disorder. Thus, we excluded only individuals who refused to participate in the study.
All patients who consecutively attended their regular appointments at any of the 4 study sites were offered to participate in the study if they met all the inclusion criteria and none of the exclusion criteria. A total of 224 patients fulfilled this requirement and agreed to participate giving their written informed consent. No patient was legally incapacitated, and their ability to give informed consent was determined by their psychiatrists.
Assessments
We collected demographic and clinical data for all subjects and made comprehensive assessments at baseline. In addition to the classical variables, sociodemographic data included some pragmatic variables, such as number of school grades repeated, number of children and their custody, driver’s license, legal problems, officially recognized disability, etc. The clinical course and characteristics of BD, psychiatric and physical comorbidities, and treatments were also collected (Table 1).
Psychometric assessments were done using the Spanish versions of all the following instruments: (1) Psychopathology: Hamilton Depression Rating Scale (HDRS) [40], Young Mania Rating Scale (YMRS) [41], Hamilton Anxiety Scale (HARS) [42], Oviedo Sleep Questionnaire (OSQ) [43], Changes in Sexual Functioning Questionnaire Short-Form (CSFQ-14) [44], (2) Cognition: Scale for Cognitive Impairment in Psychiatry (SCIP) [45], (3) Real-world functioning: Functioning Assessment Short Test (FAST) [46] and the Global Assessment of Functioning (GAF) [47], (4) Health-related quality of life: The MOS 36-item Short-Form Health-Survey (SF-36) [48], and (5) Global severity: Clinical Global Impression, Severity (CGI-S) [49].
Clinical examinations included: anthropometry (height, weight, and waist circumference), vital signs (heart rate and blood pressure), and laboratory tests including hematology and biochemistry, hormones, and biomarkers of inflammation and oxidative stress (for complete information, see Table 1). Venous blood samples (10 mL) were collected between 8:00 and 10:00 am after fasting overnight. Body Mass Index (BMI) was calculated as body weight (kg) divided by height squared (m2). The presence of metabolic syndrome was determined according to the NHANES 1999–2000 criteria [50].
Statistical analyses
The statistical analyses were done using SPSS 17.0. A dimensional reduction was done using k-means clustering. This technique aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Comparisons of between-group variables were then performed by Chi-square and univariate ANOVA followed by Tukey’s honestly significant difference post-hoc testing. Those variables in which statistically significant differences between groups were found were selected to be part of the model along with other variables added by expert criteria. We used all these variables, hereafter called profilers, to calculate a global severity formula. We then calculated the 5th, 25th, 50th, 75th, and 95th percentiles of the score in this formula to propose our severity clusters.
To determine the construct validity of the proposed clusters, we employed the scores on the 12 profilers and the patient pharmacological treatment patterns (number of drugs, numbers of patients on stabilizers, antipsychotics, antidepressants, and benzodiazepines) as validator variables. The hypothesis is that, in the more severe clusters, patients will obtain more severe scores on all the profilers and will require highly complex pharmacological treatments. We used the ANOVA (Tukey’s honestly post hoc) and Chi-square tests for that purpose.
Results
In all, 224 patients were enrolled: 101 (45.1%) from Mental Health Centers I and II in Oviedo, 23 (10.3%) from Parc Sanitari Sant Joan de Deu in Barcelona, 50 (22.3%) from Hospital Universitari Mútua Terrasa in Barcelona, and 50 (22.3%) from Hospital Universitario La Fe in Valencia.
Demographic and clinical characteristics
The demographic and clinical characteristics of the sample are shown in Table 1. Mean age was 47.1 years, 65.2% were female, 46.9% were married, and 21.9% were working. Seventy-two percent of the patients had from type 1 BD and the mean time interval between illness onset and diagnoses was 6.5 (SD = 11.3) years. Comorbidity with personality and substance use disorders was found in 18.3% and 11.6% of patients, respectively. Regarding use of substances, tobacco was the most commonly used (44.6%), followed by alcohol (22.8%). Family history of affective disorders was positive in 49.6% of the sample (for further details, see Table 1).
The mean CGI-S score was 3.4 (SD = 3.8) (see Table 1). Of the 224 subjects at the time of assessment, 39.7% had a score consistent with depression according to the HDRS (≥7), 12.1% with a mixed episode (YMRS ≥ 7–20), and only one subject with a manic episode according to the YMRS (>20). Concerning cognition, 23.2% had mild, 25.9% moderate, and 15.5% severe impairment. On average, patients were receiving 2.9 (SD = 1.1) drugs for treatment of their BD at enrolment in the study. Of the 224 patients included, 192 were receiving mood stabilizers [155 (69.2%) were taking one and 37 (16.5%) two], 149 patients (66.5%) antipsychotics [125 (55.8%) one and 24 (10.7%) two], 88 patients (39.3%) antidepressants, and 122 (54.5%) benzodiazepines.
Concerning physical health, the mean BMI was 28.5 (SD = 5.3) kg/m2, and 36.2% patients were obese (BMI ≥ 30). In addition, 58 (26.7%) patients had metabolic syndrome. Table 1 shows the laboratory results.
Development of the global severity formula and clusters
Using k-means clustering, comparisons of between-group variables, and expert criteria, 12 profilers from 5 life domains were selected to form part of the global severity formula. The 12 profilers (all with the same weight) are: (1) Clinical characteristics of the BD: 3 profilers: Number of hospitalizations (HospN), Number of suicide attempts (SuicAttN), and Comorbid personality disorder (ComPD); (2) Physical health: 3 profilers: Body Mass Index (BMI), Metabolic Syndrome (MetS), and Number of comorbid physical illnesses (IllnessN); (3) Cognition: 1 profiler: Screen for Cognitive Impairment in Psychiatry score (SCIP-Tr4); (4) Real-world functioning: 3 profilers: Permanently disabled due to BD (PDxBD), Functioning Assessment Short Test total score (FAST-T), and Functioning Assessment Short Test leisure time subscale score (FAST-leisure); and (5) Health-related quality of life: 2 profilers: SF-36 physical functioning scale score (SF-PF), and SF-36 mental health scale score (SF-MH). Profilers in the formula can take values from 0 to 1 (Table 2).
The sum of all profilers is multiplied by 10/12 in order to obtain a global severity score that ranks severity from 0 (low) to 10 (high). The formula is:
The mean global severity score was 3.6 (SD = 1.4), with a minimum value of 0.8 and maximum of 8.0. As can be seen, data follows a normal univariate distribution with Skewness and Kurtosis values of 0.440 and -0.158, respectively.
In Table 3, we show the global severity score ranges corresponding to the 5th, 25th, 50th, 75th, and 95th percentiles. Thus, a global severity score ranging from 0 to 1.70 falls between the 0 and 5th percentiles, corresponding to the first cluster, called “Not widespread, effect limited to symptoms of BD.” Similarly, global severity scores greater than 6.10 belong to cluster 5, percentiles equal to or greater than 95, called “Globally widespread, all five life domains are affected.” Following this, 13 patients (5.8%) were classified as cluster 1, 43 (19.2%) as cluster 2, 108 (48.2) as cluster 3, 47 (21.0) as cluster 4, and 13 (5.8%) as cluster 5.
Construct validity of the proposed clusters
As can be seen in Table 4, all 12 profilers behave correctly, significantly increasing in severity as the severity of the clusters increases, thus providing proof of construct validity.
A second strategy to test the construct validity of our cluster-based classification was to study the pharmacological treatment patterns across clusters. We found that, while clusters 1 and 2 were associated with monotherapy or use of two or three drug combinations, clusters 3, 4, and 5 were highly associated with combinations of four or more drugs (see Table 5). Furthermore, late clusters were associated with the use of two mood stabilizers, antipsychotics, antidepressants, and benzodiazepines more frequently than early clusters (Table 5).
Discussion
Our study aimed to provide a cluster-based severity classification of patients with bipolar disorder using in addition to psychopathology and cognition other life domains such as physical health, functioning and quality of life. As previously hypothesized, our classification includes other relevant life areas that may be affected in patients with bipolar disorder, such as physical health and quality of life. Therefore, we propose a five-cluster classification that ranges from the least severity (only the psychopathological dimension is affected) to the most severe cluster in which other dimensions of life such as physical health, cognition, functioning and quality of life are also altered. Furthermore, the proposed classification successfully passes the preliminary test for construct validity, as all profilers significantly get worse scores from cluster 1 to 5 and patients needed more complex pharmacological treatments in the more severe clusters.
It is necessary to point out, that some of our life dimensions have also been proposed by other authors, with cognition [20–21, 34–35] and functioning [20–21, 32, 36–37] the most consistently proposed. However, we are unique in deconstructing life dimensions in profilers and, most importantly, in providing a way to measure them, as can be seen in Table 3. Thus, our three functioning profilers include a pragmatic variable, “being permanently disabled due to bipolar disorder,” and two psychometric scores, the total FAST and its leisure time subscale. It may be seen as a weakness that the model does not include the working subscale. However, from a psychometric point of view, the permanently disabled profiler gathers this type of information more accurately than the FAST working subscale. One unique profiler represents the cognitive dimension and classifies patients according to their SCIP score as having no cognitive impairment or mild, moderate, or severe cognitive impairment. Regarding clinical characteristics, number of previous episodes [36, 51–52], illness severity [35], and treatment response [53–55] have been proposed by different authors. Our formula includes three profilers in this dimension that better reflect illness severity: lifetime number of hospitalizations and suicide attempts, and presence of comorbid personality disorders. Finally, there are the two other dimensions included in our classification formula: physical health and quality of life. The physical health dimension involves three profilers: BMI, presence of metabolic syndrome, and number of comorbid physical illnesses. It should be noted that, although we included several blood biomarkers in the study, none of them were included in the model. This is especially surprising in the case of PCR, an inflammatory biomarker, since some authors have suggested a dysregulation of the immune response and a proinflammatory state in these patients [56]. We think this may be related to the classification formula’s inclusion of the metabolic syndrome, a pro-inflammatory state that could be redundant with inflammatory blood biomarkers, thus prevailing over them in the model. Lastly, the SF-36 scales of physical functioning and mental health are the two profilers of the quality of life dimension. The formula’s inclusion of the physical functioning scale is in keeping with the inclusion of the physical health dimension and reflects the great importance that patients themselves place on their physical condition.
We preliminarily tested the construct validity of our cluster-based severity classification by using the 12 profiler scores and the patient pharmacological treatment patterns. As hypothesized, we were able to demonstrate that patients belonging to the more severe clusters, reflecting a greater effect of the illness on their lives, received significantly worse scores in all the profilers than those allocated to the mildest clusters. The classification fits best in three life domains: physical health, functioning, and cognition. Regarding physical health, patients in more severe clusters had higher BMI, more physical comorbidities, and a greater proportion had metabolic syndrome than those in mildest clusters. Regarding functioning and cognition, from cluster 1 to cluster 5, we found a significant decline in total and leisure subscale FAST scores and in the percentage of patients who showed cognitive impairment. These results support the theoretical models proposed by Kapczinski et al. [20–21] and Grande et al. [32] and confirm the previous findings by Rosa et al. [37] which suggested functioning and cognition as markers that might help classify patients into different clinical stages of BD.
Further evidence of the construct validity of our cluster-based classification was the fact that the pattern of pharmacological treatment becomes more complex in the more severe clusters, with greater numbers and more types of drugs per patient compared to the mildest clusters. These results are in line with the theoretical hypothesis [57] and a recently published study [54].
To finalize, we would like to highlight that, since Passos and Kapczinsky [58] noticed that staging models currently available are not ready for clinical use, our cluster-based severity classification could be a first step that would help clinicians to classify, monitor and provide structured health care to their patients as a way to guarantee the quality of services [57]. Furthermore, as the profilers needed for doing so are very easy to obtain in daily clinical practice it could be implemented in the majority of the facilities dealing with patients with bipolar disorder.
Our results should be interpreted in light of one main limitation. We failed to include extremely severe patients according to our classification. Thus, the global severity score obtained, although normally distributed, was slightly left-skewed. The fact that one of the inclusion criteria was being an outpatient may have contributed to this failure. Furthermore, as subjects in the study had to have a diagnosis of bipolar disorder, prodromal phases were not included either. Also, further research should be conducted regarding two issues: first, to confirm the construct validity of the cluster-based classification using a completely different sample of subjects, and secondly, to determine if our assumption that all 12 profilers contribute with equal weight to global severity score is correct.
The main strength of our study is its empirical approach, that is, severity clusters were derived mathematically from clinical data rather than theoretically defined. Furthermore, our proposed classification includes relevant information from multiple life dimensions and precisely describes how to obtain and measure its 12 profilers. Moreover, these profilers are easy for clinicians to obtain in their daily clinical practice as they do not involved sophisticated techniques or additional costs. Another important point is the sample size and the exhaustive psychometric and clinical evaluations performed.
In conclusion, we propose a new, cluster-based severity classification that may improve current classifications in helping clinicians in the processes of personalized medicine and shared decision-making. Besides, it may be used easily in both clinical daily practice and research in patients with bipolar disorder.
Acknowledgments
We thank Sharon Grevet, Bachelor's Degree in languages from the University of Pennsylvania, for her English assistance. She was sponsored by research grant. She has no financial or other relationship relevant to the subject of this article.
References
- 1. Samalin L, de Chazeron I, Vieta E, Bellivier F, Llorca PM. Residual symptoms and specific functional impairments in euthymic patients with bipolar disorder. Bipolar Disord. 2016;18(2):164–73. pmid:26946486
- 2. Weber NS, Fisher JA, Cowan DN, Niebuhr DW. Psychiatric and general medical conditions comorbid with bipolar disorder in the National Hospital Discharge Survey. Psychiatr Serv. 2011;62(10):1152–8. pmid:21969641
- 3. Vancampfort D, Firth J, Schuch F, Rosenbaum S, De Hert M, Mugisha J, et al. Physical activity and sedentary behavior in people with bipolar disorder: A systematic review and meta-analysis. Journal of affective disorders. 2016;201:145–52. pmid:27235817
- 4. Berk M, Conus P, Lucas N, Hallam K, Malhi GS, Dodd S, et al. Setting the stage: from prodrome to treatment resistance in bipolar disorder. Bipolar Disord. 2007;9(7):671–8. pmid:17988356
- 5. Ramos Pozon S. Shared decision making in mental health: Myths, barriers, and benefits. Revista de psiquiatria y salud mental. 2016;9(3):175–6. pmid:26905819
- 6. McGorry P, Keshavan M, Goldstone S, Amminger P, Allott K, Berk M, et al. Biomarkers and clinical staging in psychiatry. World Psychiatry. 2014;13(3):211–23. pmid:25273285
- 7. Verduijn J, Milaneschi Y, van Hemert AM, Schoevers RA, Hickie IB, Penninx BW, et al. Clinical staging of major depressive disorder: an empirical exploration. J Clin Psychiatry. 2015;76(9):1200–8. pmid:26455670
- 8. Ferensztajn E, Remlinger-Molenda A, Rybakowski J. Staging of unipolar affective illness. Psychiatr Pol. 2014;48(6):1127–41. pmid:25717483
- 9. Guidi J, Tomba E, Cosci F, Park SK, Fava GA. The Role of Staging in Planning Psychotherapeutic Interventions in Depression. J Clin Psychiatry. 2017;78(4):456–63. pmid:28297594
- 10. Berk M, Berk L, Dodd S, Cotton S, Macneil C, Daglas R, et al. Stage managing bipolar disorder. Bipolar Disord. 2014;16(5):471–7. pmid:23782499
- 11. Fernandes BS, Berk M. Staging in bipolar disorder: one step closer to precision psychiatry. Revista brasileira de psiquiatria (Sao Paulo, Brazil: 1999). 2017;39(2):88–9.
- 12. Grande I, Magalhaes PV, Kunz M, Vieta E, Kapczinski F. Mediators of allostasis and systemic toxicity in bipolar disorder. Physiol Behav. 2012;106(1):46–50. pmid:22079584
- 13. Kapczinski F, Magalhaes PV, Balanza-Martinez V, Dias VV, Frangou S, Gama CS, et al. Staging systems in bipolar disorder: an International Society for Bipolar Disorders Task Force Report. Acta Psychiatr Scand. 2014;130(5):354–63. pmid:24961757
- 14. Vieta E, Popovic D, Rosa AR, Sole B, Grande I, Frey BN, et al. The clinical implications of cognitive impairment and allostatic load in bipolar disorder. Eur Psychiatry. 2013;28(1):21–9. pmid:22534552
- 15. Birner A, Platzer M, Bengesser SA, Dalkner N, Fellendorf FT, Queissner R, et al. Increased breakdown of kynurenine towards its neurotoxic branch in bipolar disorder. PloS one. 2017;12(2):e0172699. pmid:28241062
- 16. Berk M, Post R, Ratheesh A, Gliddon E, Singh A, Vieta E, et al. Staging in bipolar disorder: from theoretical framework to clinical utility. World Psychiatry. 2017;16:236–244. pmid:28941093
- 17. Muneer A. Staging models in bipolar disorder: A systematic review of the literature. Clin Psychopharmacol Neurosci. 2016;14:117–130. pmid:27121423
- 18. Castaño-Ramirez OM, Sepúlveda-Arias JC, Duica K, Díaz Zuluaga AM, Vargas C, López-Jaramillo C. Inflammatory markers in the staging of bipolar disorder: A systematic review of the literature. Rev Colombiana Psiquiatr 2018;47:119–128.
- 19. Kapczinski F, Dias VV, Kauer-Sant'Anna M, Frey BN, Grassi-Oliveira R, Colom F, et al. Clinical implications of a staging model for bipolar disorders. Expert Rev Neurother. 2009;9(7):957–66. pmid:19589046
- 20. Kapczinski F, Dias VV, Kauer-Sant'Anna M, Brietzke E, Vazquez GH, Vieta E, et al. The potential use of biomarkers as an adjunctive tool for staging bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2009;33(8):1366–71. pmid:19666076
- 21. Cosci F, Fava GA. Staging of mental disorders: systematic review. Psychother Psychosom. 2013;82(1):20–34. pmid:23147126
- 22. Duffy A. Toward a comprehensive clinical staging model for bipolar disorder: integrating the evidence. Can J Psychiatry. 2014;59(12):659–66. pmid:25702367
- 23. Garcia-Portilla MP, Saiz PA, Benabarre A, Sierra P, Perez J, Rodriguez A, et al. The prevalence of metabolic syndrome in patients with bipolar disorder. J Affect Disord. 2008;106(1–2):197–201. pmid:17631970
- 24. Garcia-Portilla MP, Saiz PA, Bascaran MT, Martinez AS, Benabarre A, Sierra P, et al. Cardiovascular risk in patients with bipolar disorder. J Affect Disord. 2009;115(3):302–8. pmid:18954911
- 25. Sierra P, Livianos L, Rojo L. Quality of life for patients with bipolar disorder: relationship with clinical and demographic variables. Bipolar Disord. 2005;7(2):159–65. pmid:15762857
- 26. Anaya C, Torrent C, Caballero FF, Vieta E, Bonnin Cdel M, Ayuso-Mateos JL. Cognitive reserve in bipolar disorder: relation to cognition, psychosocial functioning and quality of life. Acta Psychiatr Scand. 2016;133(5):386–98. pmid:26719018
- 27. Kauer-Sant'Anna M, Kapczinski F, Andreazza AC, Bond DJ, Lam RW, Young LT, et al. Brain-derived neurotrophic factor and inflammatory markers in patients with early- vs. late-stage bipolar disorder. Int J Neuropsychopharmacol. 2009;12(4):447–58. pmid:18771602
- 28. Siweck M, Sowa-Kucma M, Styczen K, Misztak P, Nowak RJ, Szewczyk B, et al. Associations of serum cytokine receptor levels with melancholia, staging of illness, depressive and manic phases, and severity of depression in bipolar disorder. Mol Neurobiol. 2017;54:5883–93. pmid:27660275
- 29. Reininghaus EZ, Lackner N, Birner A, Bengesser S, Fellendorf FT, Platzer M, et al. Extracellular matrix protein matrix metallopeptidase 9 (MMP9) and soluble intercellular adhesion molecule 1 (slCAM-1) and correlations with clinical staging in euthymic bipolar disorder. Bipolar Disord. 2016;18:155–163. pmid:27016286
- 30. Tatay-Manteiga A, Balanza-Martinez V, Bristot G, Tabares-Seisdedos R, Kapczinski F, Cauli O. Clinical staging and serum cytokines in bipolar patients during euthymia. Prog Neuropsychopharmacol Biol Psychiatry. 2017;77:194–201. pmid:28445689
- 31. Grande I, Magalhaes PV, Chendo I, Stertz L, Panizutti B, Colpo GD, et al. Staging bipolar disorder: clinical, biochemical, and functional correlates. Acta Psychiatr Scand. 2014;129(6):437–44. pmid:24628576
- 32. Mwangi B, Wu MJ, Cao B, Passos IC, Lavagnino L, Keser Z, et al. Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(2):186–94. pmid:27047994
- 33. Cao B, Passos IC, Mwangi B, Bauer IE, Zunta-Soares GB, Kapczinski F, et al. Hippocampal volume and verbal memory performance in late-stage bipolar disorder. J Psychiatr Res. 2016;73:102–7. pmid:26714201
- 34. Reinares M, Papachristou E, Harvey P, Mar Bonnin C, Sanchez-Moreno J, Torrent C, et al. Towards a clinical staging for bipolar disorder: defining patient subtypes based on functional outcome. J Affect Disord. 2013;144(1–2):65–71. pmid:22862890
- 35. Rosa AR, Gonzalez-Ortega I, Gonzalez-Pinto A, Echeburua E, Comes M, Martinez-Aran A, et al. One-year psychosocial functioning in patients in the early vs. late stage of bipolar disorder. Acta Psychiatr Scand. 2012;125(4):335–41. pmid:22283440
- 36. Rosa AR, Magalhaes PV, Czepielewski L, Sulzbach MV, Goi PD, Vieta E, et al. Clinical staging in bipolar disorder: focus on cognition and functioning. J Clin Psychiatry. 2014;75(5):e450–6. pmid:24922497
- 37. Alda M, Kapczinski F. Staging model raises fundamental questions about the nature of bipolar disorder. J Psychiatry Neurosci. 2016;41(5):291–3. pmid:27575857
- 38.
First MB, Spitzer , Robert L, Gibbon Miriam, and Williams , Janet B.W. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). New York: Biometrics Research. 2002.
- 39.
Association AP. Diagnostic and statistical manual of mental disorders. 4th edition, text rev. Washington: APA; 2000.
- 40. Bobes J, Bulbena A, Luque A, Dal-Re R, Ballesteros J, Ibarra N. A comparative psychometric study of the Spanish versions with 6, 17, and 21 items of the Hamilton Depression Rating Scale. Med Clin (Barc). 2003;120(18):693–700.
- 41. Colom F, Vieta E, Martinez-Aran A, Garcia-Garcia M, Reinares M, Torrent C, et al. Spanish version of a scale for the assessment of mania: validity and reliability of the Young Mania Rating Scale. Med Clin (Barc). 2002;119(10):366–71.
- 42. Lobo A, Chamorro L, Luque A, Dal-Re R, Badia X, Baro E. Validation of the Spanish versions of the Montgomery-Asberg depression and Hamilton anxiety rating scales. Med Clin (Barc). 2002;118(13):493–9.
- 43. Bobes J, González MP, Sáiz PA, Bascaran MT, Iglesias C, Fernández JM. Propiedades psciométricas del cuestionario de Oviedo de sueño. Psicothema. 2000;12:107–12.
- 44. Garcia-Portilla MP, Saiz PA, Fonseca E, Al-Halabi S, Bobes-Bascaran MT, Arrojo M, et al. Psychometric properties of the Spanish version of the Changes in Sexual Functioning Questionnaire Short-Form (CSFQ-14) in patients with severe mental disorders. J Sex Med. 2011;8(5):1371–82. pmid:20946156
- 45. Gomez-Benito J, Guilera G, Pino O, Rojo E, Tabares-Seisdedos R, Safont G, et al. The screen for cognitive impairment in psychiatry: diagnostic-specific standardization in psychiatric ill patients. BMC Psychiatry. 2013;13:127. pmid:23648193
- 46. Rosa AR, Sanchez-Moreno J, Martinez-Aran A, Salamero M, Torrent C, Reinares M, et al. Validity and reliability of the Functioning Assessment Short Test (FAST) in bipolar disorder. Clin Pract Epidemiol Ment Health. 2007;3:5. pmid:17555558
- 47.
American Psychiatric Association. DSM-IIIR. Diagnostic and Statistical Manual Disorders. Washington: APA; 1987.
- 48. McHorney CA, Ware JE Jr., Lu JF, Sherbourne CD. The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care. 1994;32(1):40–66. pmid:8277801
- 49.
Guy W. Early Clinical Drug Evaluation (ECDEU) Assessment Manual. Rockville: National Institute Mental Health; 1976.
- 50. Ford ES, Giles WH, Mokdad AH. Increasing prevalence of the metabolic syndrome among U.S. Adults. Diabetes Care. 2004;27(10):2444–9. pmid:15451914
- 51. Magalhaes PV, Dodd S, Nierenberg AA, Berk M. Cumulative morbidity and prognostic staging of illness in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Aust N Z J Psychiatry. 2012;46(11):1058–67. pmid:23015748
- 52. Passos IC, Jansen K, Kapczinski F. Developmental staging models in bipolar disorder. Int J Bipolar Disord. 2015;3(1):33. pmid:26228989
- 53. Berk M, Brnabic A, Dodd S, Kelin K, Tohen M, Malhi GS, et al. Does stage of illness impact treatment response in bipolar disorder? Empirical treatment data and their implication for the staging model and early intervention. Bipolar Disord. 2011;13(1):87–98. pmid:21320256
- 54. Goi PD, Bucker J, Vianna-Sulzbach M, Rosa AR, Grande I, Chendo I, et al. Pharmacological treatment and staging in bipolar disorder: evidence from clinical practice. Revista brasileira de psiquiatria (Sao Paulo, Brazil: 1999). 2015;37(2):121–5.
- 55. Joyce K, Thompson A, Marwaha S. Is treatment for bipolar disorder more effective earlier in illness course? A comprehensive literature review. Int J Bipolar Disord. 2016;4(1):19. pmid:27613276
- 56. Soria V, Uribe J, Salvat-Pujol N, Palao D, Menchon JM, Labad J. Psychoneuroimmunology of mental disorders. Rev Psiquiatr Salud Ment (Barc.). 2018;11(2);115–24.
- 57. Bernardo M, de Dios C, Pérez V, Ignacio E, Serrano M, Vieta E, et al. Quality indicators in the treatment of patients with depression, bipolar disorder or schizophrenia. Consensus study. Rev Psiquiatr Salud Ment (Barc.). 2018;11(2);66–75.
- 58. Passos IC, Kapczinski F. Should bipolar disorder treatment be modified depending on staging? Expert Rev Neurother. 2017;17(2):93–5. pmid:27915494