Relationships Among Inflammation, Physical and Mental Health in Subjects With Chronic Inflammatory Physical Diseases.
NCT ID: NCT05125458
Last Updated: 2023-11-22
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
Recruitment Status
COMPLETED
Total Enrollment
93 participants
Study Classification
OBSERVATIONAL
Study Start Date
2021-04-01
Study Completion Date
2022-11-30
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
The prevalence of common mental disorders is high in patients with chronic inflammatory physical diseases(e.g., autoimmune or infectious diseases). The traditional explanatory causation model in which physical symptoms and related disability drive mental health problems is now called into question, and evidence has accumulated supporting more complex interactions whereby psychiatric disorders can both result from and contribute to the progression of physical diseases. In the present project, the investigators will focus on comorbidity of depression and anxiety symptoms or syndromes with chronic inflammatory skin diseases (psoriasis, hidradenitis suppurativa and atopic dermatitis) or chronic infectious diseases (chronic HBV and HIV infection).
The study is aimed to clarify the mechanisms underlying the high frequency of those comorbidities. It will overcome the main limitations of previous investigations and use innovative statistical tools to model complex interrelationships and causal links among the assessed variables.
The identification of key variables driving the causal chain of determinants of poor global health and quality of life may impact treatment outcome and models of care.
The study is aimed to clarify the mechanisms underlying the high frequency of those comorbidities. It will overcome the main limitations of previous investigations and use innovative statistical tools to model complex interrelationships and causal links among the assessed variables.
The identification of key variables driving the causal chain of determinants of poor global health and quality of life may impact treatment outcome and models of care.
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
STATE OF ART:
Healthcare professionals often underestimate the effects of chronic inflammatory processes on patients' quality of life (QoL). The prevalence of common mental disorders, such as depression and anxiety, is high in patients with physical diseases entailing chronic inflammatory processes (e.g., autoimmune or infectious diseases), adding to the psychosocial burden of these disorders. In fact, mental symptoms may harm response to treatment and may have a negative impact on the physical diseases, which, in their turn, may worsen mental symptoms, and contribute, together with them, to undermine persons' QoL.
Associations of depression and anxiety with dermatological and infectious diseases (entailing chronic inflammatory processes) have been the subject of an extensive literature. Depression is reported in up to 42% and 60% of people with hidradenitis suppurativa (HS) and psoriasis (PS), respectively. Patients with atopic dermatitis (AD) and HS seem to have a high suicidal risk. High comorbidity with depression and anxiety is also reported in chronic infectious diseases such as chronic hepatitis C or B (HCV, HBV), and human immunodeficiency virus (HIV) infection. Prevalence of anxiety symptoms is as high as 80% in people living with HIV, with 20% meeting criteria for generalized anxiety disorder. HBV and HCV are associated with clinically significant depression in up to 30% of cases.
Research, so far, has not succeeded at fully clarifying mechanisms underlying the high frequency of comorbidity between common mental disorders and chronic inflammatory diseases.
The presence of mental disorders adds to the psychosocial burden of chronic inflammatory skin diseases or chronic infectious diseases, may harm response to treatment and may have a negative impact on the physical diseases themselves, which, in their turn, may worsen mental symptoms, and contribute, together with them, to undermine persons' QoL.
The project's ambitions are manifold and are listed below.
1. Shedding light on the interrelationships between mental and physical symptoms in chronic inflammatory diseases.
2. Clarifying mechanisms underlying the high frequency of comorbidity between common mental disorders and chronic inflammatory diseases.
3. Identifying mechanisms responsible for the vicious circle in which mental symptoms can be promoted by proinflammatory cytokines and immune deficiency; in their turn can stimulate the production of proinflammatory cytokines and down-regulate the cellular immune response, and can contribute to prolonged infection and delayed healing, as well as to functional decline.
4. Improving treatment planning by identifying key variables driving the causal chain of determinants of poor QoL.
5. Uncovering the possible influence of unmeasured ("latent") variables not included in the model.
6. Overcoming the main limitations of the current state of the art, through modelling the above-mentioned interrelationships in a large sample of patients by: a) performing a comprehensive psychopathological evaluation carried out by both self- and clinician-rated instruments; b) assessing cognitive functioning, stigma, coping styles and QoL; c) evaluating, besides plasma levels of inflammatory markers, tissue markers of subclinical inflammatory damage.
In addition, the investigators will use network analyses to model the complex interrelationships and causal links among the assessed variables. So far, no study modelled the comorbidity between mental and physical symptoms in chronic inflammatory diseases by using this approach. The network analysis does not rely on an a priori model of cause-effect relationships among variables, as current alternative statistical models (e.g., structural equation models) do; it is largely used in physical and communication sciences to study how complex interactions among sets of variables maintain complex systems. It has been used to improve knowledge on the complex phenomena of psychiatric comorbidity, and identify high value targets for interventions. More recently, network modeling algorithms have been used to study causal relationships among sets of variables and identify those variables that in the causal chain hold precedence and represent "activators,", "mediators,", and/or "products" within a syndromic condition. The identification of key variables in the network, and in particular of those that represent causal antecedents in the chain of variables leading to poor QoL, is crucial to design treatment programs that target those variables, increasing the likelihood to improve patients' QoL. In addition, as the analysis enables the possibility to identify the influence of unmeasured ("latent") variables not included in the model, the project's findings may guide further research in the field.
OBJECTIVES:
The project aims to clarify the mechanisms underlying the high frequency of comorbidity of physical diseases entailing chronic inflammatory processes (e.g., autoimmune or infectious diseases) with common mental disorders.
The traditional explanatory causation model in which physical symptoms and related disability drive mental health problems is called into question by the relevant literature, and evidence has accumulated supporting more complex interactions whereby psychiatric disorders can both result from and contribute to the progression of physical diseases. In this frame, recognizing the co-existence of physical and mental disease symptoms pertains not only to the need to treat both conditions, but also to the need to avoid a vicious circle in which mental symptoms may harm response to treatment and may have a negative impact on the physical ones that, in their turn, may worsen mental symptoms, and contribute, together with them, to undermine person's disability and quality of life.
To these aims, the present project will pursue the following objectives.
1. To investigate the frequency of depressive and anxiety symptoms and disorders in 500 clinically stable, outpatients affected by one of the following dermatological diseases: psoriasis, hidradenitis suppurativa or atopic dermatitis, who are being treated for the disorder; as well as in 500 clinically stable outpatients with documented HIV or HBV infection, on treatment with antiviral therapy.
2. To demonstrate that in both samples the presence of clinically significant depressive or anxiety symptoms is associated with worse global health and QoL.
3. To identify mechanisms responsible for the vicious circle in which mental symptoms can be promoted by proinflammatory cytokines and immune deficiency; in their turn can stimulate the production of proinflammatory cytokines and down-regulate the cellular immune response, and can contribute to prolonged infection and delayed healing, as well as to functional decline.
4. To test the hypotheses listed below by the network analysis, using a machine-learning algorithm:
1. chronic inflammation causes both physical and mental symptoms;
2. mental symptoms contribute to inflammatory processes, which cause physical signs and symptoms;
3. inflammatory processes cause physical symptoms, which in their turn cause the mental ones;
4. physical symptoms contribute to both inflammatory processes and to mental symptoms;
5. physical symptoms are associated with inflammatory processes which cause mental symptoms;
6. physical symptoms cause mental symptoms and both are associated with an increase of proinflammatory cytokines and other indices of inflammatory processes.
5. To identify key determinants of disability and poor quality of life, and improve treatment planning by addressing possible unmet treatment targets.
6. To uncover the possible influence of unmeasured variables not included in the model and to be investigated in future studies.
STATISTICAL ANALYSIS
Data will be checked for missing values, inconsistencies, and outliers. Continuous variables will be summarized by the following descriptive statistics: number of observations, mean, standard deviation, median, minimum, and maximum; while categorical ones by counts of patients and percentages.
The normality assumption will be tested with Shapiro-Wilk test. Pairwise correlations between continuous study measures will be calculated using Pearson correlation coefficients or Spearman rank correlation coefficients if needed.
The statistical testing will be conducted at the two-sided a = 0.05 and 95% confidence interval (CI) will be computed, unless otherwise specified.
For continuous variables, differences among groups will be tested by means of t-test or one-way ANOVA and Mann-Whitney or Kruskal-Wallis tests when appropriate. For categorical variables, differences among groups will be tested by means of chi-square or Fisher's exact test when needed.
The study hypotheses will be tested in the overall sample and separately in the two subgroups of patients with dermatological diseases or infectious diseases using network analysis, an exploratory technique that is increasingly used to identify causal relationships within and between manifestations of psychiatric disorders.
A causal network is represented graphically as a set of nodes (variables) connected through edges (directed arrows), that may be interpreted as causal relations. Specifically, the complex relationships among variables will be investigated by means of partial ancestral graphs (PAGs), which use an enriched set of edge types to convey both edge orientation and the possible influence of unmeasured variables not represented in the model. Analyses will be conducted using the machine-learning Greedy Fast Causal Inference (GFCI) algorithm, which uses a 2-step process to make causal inferences that can be represented in graphical format as PAGs. In the first step, all possible non-recursive causal relationships among the variables will be searched automatically to establish which variable pairs are potentially causally related using a method called Fast Greedy Equivalence Search. In the second step, the preliminary assessment will be refined by performing a series of conditional independence tests to iteratively rule out causal models that imply conditional independence statements not found to be true of the data. At the end of this process, it will be possible to interpret the links between pairs of variables as direct causal relationships, indirect causal relationships, possible causal relationship confounded by unmeasured variables, or correlations, in which the direction of causality cannot be defined. Thus, the GFCI algorithm will help understand which of the study hypotheses is/are more plausible and compatible with the data.
All statistical analyses and data processing will be performed using R version 3.3.3 (R Foundation for Statistical Computing).
Sample size considerations:
Among constraint-based causal search algorithms, GFCI showed better results in terms of precision and good performance in the simulations with a sample size of 200. Our sample size of 1000 patients (500 patients for each subsample) is adequate to detect causal relationships among the study variables, with a ratio patients/variables of about 30/1 in each subgroup.
IMPLEMENTATION: PERFORMANCE OF THE INTERMEDIATE AND FINAL OBJECTIVES
During the first 3 months of the project, the following procedures will be completed: enrollment of five research assistants (one biologist or one psychiatrist with experience in procedures for assessment of plasma biomarkers of inflammation, two dermatologists, one infectious disease specialist and one statistician for data quality control); acquisition of equipment and consumables for the study (7.5 Mhz probe for color-doppler instrument, bone quantitative ultrasound device, ELISA kits for dosage of blood markers; computerized neurocognitive test battery CANTAB; 5 portable computers, 1 personal computer, 3 iPAD, 3 STATA/SE version 15; stationery); implementation and testing of the study database by a computer engineer under the supervision of the coordinating unit; training of the researchers to the use of the study instruments and to the correct study procedures, carried out by each research unit for the respective assessments.
At the beginning of month 4, the recruitment of the subjects will start and will be carried out during the following 18 months.
Confirmation of patients' eligibility will be done by associate professors and researchers of Psychiatry, Dermatology and Infectious diseases research units, who will review the inclusion/exclusion criteria relevant to their discipline, such as the diagnosis of skin diseases or infectious diseases listed in the inclusion criteria, as well as lifetime psychiatric diagnoses. Associate professors and researchers from the Dermatology and Infectious diseases research unit will also obtain informed consent from eligible patients.
Trainees in Psychiatry from the Psychiatry research unit will collect data relevant to socio-demographic characteristics, psychopathology, disability, quality of life, cognition, coping skills and stigma.
Assessment of dermatological indices will be performed by the dermatologists enrolled as research assistants in the project, as well as by the researcher and trainee of the Dermatology research unit.
Assessment of markers of subclinical inflammatory damage will be done by the associate professor and research assistant of the Infectious Diseases research unit. Measurement of plasma levels of inflammatory markers will be done by a research assistant enrolled in the project (a biologist or a psychiatrist with experience in procedures for assessment of plasma biomarkers) under the supervision of a researcher of the Psychiatry research unit.
Trainees will fill in data collected for their respective research unit in the electronic database.
To control for missing data or insufficient sample size, every three months, recruitment strategies and subject enrollment will be reviewed by an ad hoc appointed internal audit committee. Incomplete data for any recruited subject will be allowed in the measure of 5%. Subjects missing all data in one of the main assessment areas (biomarkers, mental symptoms, physical and primary disease assessments) will be replaced.
Intermediate objectives of the study are the followings: 1) recruitment of at least 400 patients (200 from the Dermatological unit and 200 from the Infection disease unit) by the end of month 12; 2) Study registration and publication of the study protocol and its dissemination in national and international congresses by the end of month 9; 3) publication of two systematic reviews on the comorbidity of common mental disorders with chronic inflammatory skin and infectious diseases by month 12.
Final objectives of the project are: 1) the recruitment of at least 700 subjects with complete data by month 21; 2) consultation with a statistician with specific expertise in network analysis and completion of final statistical analyses by the end of month 22; 3) publication of the results in high-impact international journals and their dissemination in national and international congresses.
Healthcare professionals often underestimate the effects of chronic inflammatory processes on patients' quality of life (QoL). The prevalence of common mental disorders, such as depression and anxiety, is high in patients with physical diseases entailing chronic inflammatory processes (e.g., autoimmune or infectious diseases), adding to the psychosocial burden of these disorders. In fact, mental symptoms may harm response to treatment and may have a negative impact on the physical diseases, which, in their turn, may worsen mental symptoms, and contribute, together with them, to undermine persons' QoL.
Associations of depression and anxiety with dermatological and infectious diseases (entailing chronic inflammatory processes) have been the subject of an extensive literature. Depression is reported in up to 42% and 60% of people with hidradenitis suppurativa (HS) and psoriasis (PS), respectively. Patients with atopic dermatitis (AD) and HS seem to have a high suicidal risk. High comorbidity with depression and anxiety is also reported in chronic infectious diseases such as chronic hepatitis C or B (HCV, HBV), and human immunodeficiency virus (HIV) infection. Prevalence of anxiety symptoms is as high as 80% in people living with HIV, with 20% meeting criteria for generalized anxiety disorder. HBV and HCV are associated with clinically significant depression in up to 30% of cases.
Research, so far, has not succeeded at fully clarifying mechanisms underlying the high frequency of comorbidity between common mental disorders and chronic inflammatory diseases.
The presence of mental disorders adds to the psychosocial burden of chronic inflammatory skin diseases or chronic infectious diseases, may harm response to treatment and may have a negative impact on the physical diseases themselves, which, in their turn, may worsen mental symptoms, and contribute, together with them, to undermine persons' QoL.
The project's ambitions are manifold and are listed below.
1. Shedding light on the interrelationships between mental and physical symptoms in chronic inflammatory diseases.
2. Clarifying mechanisms underlying the high frequency of comorbidity between common mental disorders and chronic inflammatory diseases.
3. Identifying mechanisms responsible for the vicious circle in which mental symptoms can be promoted by proinflammatory cytokines and immune deficiency; in their turn can stimulate the production of proinflammatory cytokines and down-regulate the cellular immune response, and can contribute to prolonged infection and delayed healing, as well as to functional decline.
4. Improving treatment planning by identifying key variables driving the causal chain of determinants of poor QoL.
5. Uncovering the possible influence of unmeasured ("latent") variables not included in the model.
6. Overcoming the main limitations of the current state of the art, through modelling the above-mentioned interrelationships in a large sample of patients by: a) performing a comprehensive psychopathological evaluation carried out by both self- and clinician-rated instruments; b) assessing cognitive functioning, stigma, coping styles and QoL; c) evaluating, besides plasma levels of inflammatory markers, tissue markers of subclinical inflammatory damage.
In addition, the investigators will use network analyses to model the complex interrelationships and causal links among the assessed variables. So far, no study modelled the comorbidity between mental and physical symptoms in chronic inflammatory diseases by using this approach. The network analysis does not rely on an a priori model of cause-effect relationships among variables, as current alternative statistical models (e.g., structural equation models) do; it is largely used in physical and communication sciences to study how complex interactions among sets of variables maintain complex systems. It has been used to improve knowledge on the complex phenomena of psychiatric comorbidity, and identify high value targets for interventions. More recently, network modeling algorithms have been used to study causal relationships among sets of variables and identify those variables that in the causal chain hold precedence and represent "activators,", "mediators,", and/or "products" within a syndromic condition. The identification of key variables in the network, and in particular of those that represent causal antecedents in the chain of variables leading to poor QoL, is crucial to design treatment programs that target those variables, increasing the likelihood to improve patients' QoL. In addition, as the analysis enables the possibility to identify the influence of unmeasured ("latent") variables not included in the model, the project's findings may guide further research in the field.
OBJECTIVES:
The project aims to clarify the mechanisms underlying the high frequency of comorbidity of physical diseases entailing chronic inflammatory processes (e.g., autoimmune or infectious diseases) with common mental disorders.
The traditional explanatory causation model in which physical symptoms and related disability drive mental health problems is called into question by the relevant literature, and evidence has accumulated supporting more complex interactions whereby psychiatric disorders can both result from and contribute to the progression of physical diseases. In this frame, recognizing the co-existence of physical and mental disease symptoms pertains not only to the need to treat both conditions, but also to the need to avoid a vicious circle in which mental symptoms may harm response to treatment and may have a negative impact on the physical ones that, in their turn, may worsen mental symptoms, and contribute, together with them, to undermine person's disability and quality of life.
To these aims, the present project will pursue the following objectives.
1. To investigate the frequency of depressive and anxiety symptoms and disorders in 500 clinically stable, outpatients affected by one of the following dermatological diseases: psoriasis, hidradenitis suppurativa or atopic dermatitis, who are being treated for the disorder; as well as in 500 clinically stable outpatients with documented HIV or HBV infection, on treatment with antiviral therapy.
2. To demonstrate that in both samples the presence of clinically significant depressive or anxiety symptoms is associated with worse global health and QoL.
3. To identify mechanisms responsible for the vicious circle in which mental symptoms can be promoted by proinflammatory cytokines and immune deficiency; in their turn can stimulate the production of proinflammatory cytokines and down-regulate the cellular immune response, and can contribute to prolonged infection and delayed healing, as well as to functional decline.
4. To test the hypotheses listed below by the network analysis, using a machine-learning algorithm:
1. chronic inflammation causes both physical and mental symptoms;
2. mental symptoms contribute to inflammatory processes, which cause physical signs and symptoms;
3. inflammatory processes cause physical symptoms, which in their turn cause the mental ones;
4. physical symptoms contribute to both inflammatory processes and to mental symptoms;
5. physical symptoms are associated with inflammatory processes which cause mental symptoms;
6. physical symptoms cause mental symptoms and both are associated with an increase of proinflammatory cytokines and other indices of inflammatory processes.
5. To identify key determinants of disability and poor quality of life, and improve treatment planning by addressing possible unmet treatment targets.
6. To uncover the possible influence of unmeasured variables not included in the model and to be investigated in future studies.
STATISTICAL ANALYSIS
Data will be checked for missing values, inconsistencies, and outliers. Continuous variables will be summarized by the following descriptive statistics: number of observations, mean, standard deviation, median, minimum, and maximum; while categorical ones by counts of patients and percentages.
The normality assumption will be tested with Shapiro-Wilk test. Pairwise correlations between continuous study measures will be calculated using Pearson correlation coefficients or Spearman rank correlation coefficients if needed.
The statistical testing will be conducted at the two-sided a = 0.05 and 95% confidence interval (CI) will be computed, unless otherwise specified.
For continuous variables, differences among groups will be tested by means of t-test or one-way ANOVA and Mann-Whitney or Kruskal-Wallis tests when appropriate. For categorical variables, differences among groups will be tested by means of chi-square or Fisher's exact test when needed.
The study hypotheses will be tested in the overall sample and separately in the two subgroups of patients with dermatological diseases or infectious diseases using network analysis, an exploratory technique that is increasingly used to identify causal relationships within and between manifestations of psychiatric disorders.
A causal network is represented graphically as a set of nodes (variables) connected through edges (directed arrows), that may be interpreted as causal relations. Specifically, the complex relationships among variables will be investigated by means of partial ancestral graphs (PAGs), which use an enriched set of edge types to convey both edge orientation and the possible influence of unmeasured variables not represented in the model. Analyses will be conducted using the machine-learning Greedy Fast Causal Inference (GFCI) algorithm, which uses a 2-step process to make causal inferences that can be represented in graphical format as PAGs. In the first step, all possible non-recursive causal relationships among the variables will be searched automatically to establish which variable pairs are potentially causally related using a method called Fast Greedy Equivalence Search. In the second step, the preliminary assessment will be refined by performing a series of conditional independence tests to iteratively rule out causal models that imply conditional independence statements not found to be true of the data. At the end of this process, it will be possible to interpret the links between pairs of variables as direct causal relationships, indirect causal relationships, possible causal relationship confounded by unmeasured variables, or correlations, in which the direction of causality cannot be defined. Thus, the GFCI algorithm will help understand which of the study hypotheses is/are more plausible and compatible with the data.
All statistical analyses and data processing will be performed using R version 3.3.3 (R Foundation for Statistical Computing).
Sample size considerations:
Among constraint-based causal search algorithms, GFCI showed better results in terms of precision and good performance in the simulations with a sample size of 200. Our sample size of 1000 patients (500 patients for each subsample) is adequate to detect causal relationships among the study variables, with a ratio patients/variables of about 30/1 in each subgroup.
IMPLEMENTATION: PERFORMANCE OF THE INTERMEDIATE AND FINAL OBJECTIVES
During the first 3 months of the project, the following procedures will be completed: enrollment of five research assistants (one biologist or one psychiatrist with experience in procedures for assessment of plasma biomarkers of inflammation, two dermatologists, one infectious disease specialist and one statistician for data quality control); acquisition of equipment and consumables for the study (7.5 Mhz probe for color-doppler instrument, bone quantitative ultrasound device, ELISA kits for dosage of blood markers; computerized neurocognitive test battery CANTAB; 5 portable computers, 1 personal computer, 3 iPAD, 3 STATA/SE version 15; stationery); implementation and testing of the study database by a computer engineer under the supervision of the coordinating unit; training of the researchers to the use of the study instruments and to the correct study procedures, carried out by each research unit for the respective assessments.
At the beginning of month 4, the recruitment of the subjects will start and will be carried out during the following 18 months.
Confirmation of patients' eligibility will be done by associate professors and researchers of Psychiatry, Dermatology and Infectious diseases research units, who will review the inclusion/exclusion criteria relevant to their discipline, such as the diagnosis of skin diseases or infectious diseases listed in the inclusion criteria, as well as lifetime psychiatric diagnoses. Associate professors and researchers from the Dermatology and Infectious diseases research unit will also obtain informed consent from eligible patients.
Trainees in Psychiatry from the Psychiatry research unit will collect data relevant to socio-demographic characteristics, psychopathology, disability, quality of life, cognition, coping skills and stigma.
Assessment of dermatological indices will be performed by the dermatologists enrolled as research assistants in the project, as well as by the researcher and trainee of the Dermatology research unit.
Assessment of markers of subclinical inflammatory damage will be done by the associate professor and research assistant of the Infectious Diseases research unit. Measurement of plasma levels of inflammatory markers will be done by a research assistant enrolled in the project (a biologist or a psychiatrist with experience in procedures for assessment of plasma biomarkers) under the supervision of a researcher of the Psychiatry research unit.
Trainees will fill in data collected for their respective research unit in the electronic database.
To control for missing data or insufficient sample size, every three months, recruitment strategies and subject enrollment will be reviewed by an ad hoc appointed internal audit committee. Incomplete data for any recruited subject will be allowed in the measure of 5%. Subjects missing all data in one of the main assessment areas (biomarkers, mental symptoms, physical and primary disease assessments) will be replaced.
Intermediate objectives of the study are the followings: 1) recruitment of at least 400 patients (200 from the Dermatological unit and 200 from the Infection disease unit) by the end of month 12; 2) Study registration and publication of the study protocol and its dissemination in national and international congresses by the end of month 9; 3) publication of two systematic reviews on the comorbidity of common mental disorders with chronic inflammatory skin and infectious diseases by month 12.
Final objectives of the project are: 1) the recruitment of at least 700 subjects with complete data by month 21; 2) consultation with a statistician with specific expertise in network analysis and completion of final statistical analyses by the end of month 22; 3) publication of the results in high-impact international journals and their dissemination in national and international congresses.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Chronic Infectious Disease
Chronic Skin Disease
Mental Disorders
Psoriasis
Hidradenitis Suppurativa
Atopic Dermatitis
Hiv
HBV
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
Observational Model Type
COHORT
Study Time Perspective
CROSS_SECTIONAL
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Subjects with dermatological diseases
500 Subjects attending the Dermatology outpatient clinic of the Vanvitelli University hospital with a diagnosis of psoriasis or hidradenitis suppurativa or atopic dermatitis of any grade of severity.
No interventions assigned to this group
Subjects with HIV or HBV
500 Subjects attending the Infectious diseases outpatient clinic of the Vanvitelli University hospital and with HIV or HBV infection.
No interventions assigned to this group
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* diagnosis of psoriasis, hidradenitis suppurativa, or atopic dermatitis, any grade of severity;
* stability of the cutaneous disease;
* ongoing treatment.
* documented HIV or HBV infection;
* treatment with antiviral therapy;
* viral suppression (HIV-RNA or HBV-DNA) for at least 6 months.
* age ≥ 18 years
* willingness to provide informed consent.
* stability of the cutaneous disease;
* ongoing treatment.
* documented HIV or HBV infection;
* treatment with antiviral therapy;
* viral suppression (HIV-RNA or HBV-DNA) for at least 6 months.
* age ≥ 18 years
* willingness to provide informed consent.
Exclusion Criteria
\- a history of intellectual disability, bipolar disorder, psychosis or schizophrenia, or current high suicide risk.
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
University of Campania Luigi Vanvitelli
OTHER
Sponsor Role
lead
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Prof. Silvana Galderisi
Prof.
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Silvana Galderisi
Role: PRINCIPAL_INVESTIGATOR
University of Campania Luigi Vanvitelli
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Department of Psychiatry - University of Campania "Luigi Vanvitelli"
Napoli, , Italy
Site Status
Countries
Review the countries where the study has at least one active or historical site.
Italy
References
Explore related publications, articles, or registry entries linked to this study.
Dalgard FJ, Gieler U, Tomas-Aragones L, Lien L, Poot F, Jemec GBE, Misery L, Szabo C, Linder D, Sampogna F, Evers AWM, Halvorsen JA, Balieva F, Szepietowski J, Romanov D, Marron SE, Altunay IK, Finlay AY, Salek SS, Kupfer J. The psychological burden of skin diseases: a cross-sectional multicenter study among dermatological out-patients in 13 European countries. J Invest Dermatol. 2015 Apr;135(4):984-991. doi: 10.1038/jid.2014.530. Epub 2014 Dec 18.
Reference Type
BACKGROUND
Gupta MA, Gupta AK. Psychiatric and psychological co-morbidity in patients with dermatologic disorders: epidemiology and management. Am J Clin Dermatol. 2003;4(12):833-42. doi: 10.2165/00128071-200304120-00003.
Reference Type
BACKGROUND
Vitiello B, Burnam MA, Bing EG, Beckman R, Shapiro MF. Use of psychotropic medications among HIV-infected patients in the United States. Am J Psychiatry. 2003 Mar;160(3):547-54. doi: 10.1176/appi.ajp.160.3.547.
Reference Type
BACKGROUND
Evans DL, Charney DS, Lewis L, Golden RN, Gorman JM, Krishnan KR, Nemeroff CB, Bremner JD, Carney RM, Coyne JC, Delong MR, Frasure-Smith N, Glassman AH, Gold PW, Grant I, Gwyther L, Ironson G, Johnson RL, Kanner AM, Katon WJ, Kaufmann PG, Keefe FJ, Ketter T, Laughren TP, Leserman J, Lyketsos CG, McDonald WM, McEwen BS, Miller AH, Musselman D, O'Connor C, Petitto JM, Pollock BG, Robinson RG, Roose SP, Rowland J, Sheline Y, Sheps DS, Simon G, Spiegel D, Stunkard A, Sunderland T, Tibbits P Jr, Valvo WJ. Mood disorders in the medically ill: scientific review and recommendations. Biol Psychiatry. 2005 Aug 1;58(3):175-89. doi: 10.1016/j.biopsych.2005.05.001.
Reference Type
BACKGROUND
Shavit E, Dreiher J, Freud T, Halevy S, Vinker S, Cohen AD. Psychiatric comorbidities in 3207 patients with hidradenitis suppurativa. J Eur Acad Dermatol Venereol. 2015 Feb;29(2):371-376. doi: 10.1111/jdv.12567. Epub 2014 Jun 9.
Reference Type
BACKGROUND
Weigle N, McBane S. Psoriasis. Am Fam Physician. 2013 May 1;87(9):626-33.
Reference Type
BACKGROUND
Nicholas MN, Gooderham MJ. Atopic Dermatitis, Depression, and Suicidality. J Cutan Med Surg. 2017 May/Jun;21(3):237-242. doi: 10.1177/1203475416685078. Epub 2017 Jan 9.
Reference Type
BACKGROUND
Thorlacius L, Cohen AD, Gislason GH, Jemec GBE, Egeberg A. Increased Suicide Risk in Patients with Hidradenitis Suppurativa. J Invest Dermatol. 2018 Jan;138(1):52-57. doi: 10.1016/j.jid.2017.09.008. Epub 2017 Sep 20.
Reference Type
BACKGROUND
Adinolfi LE, Nevola R, Lus G, Restivo L, Guerrera B, Romano C, Zampino R, Rinaldi L, Sellitto A, Giordano M, Marrone A. Chronic hepatitis C virus infection and neurological and psychiatric disorders: an overview. World J Gastroenterol. 2015 Feb 28;21(8):2269-80. doi: 10.3748/wjg.v21.i8.2269.
Reference Type
BACKGROUND
Modabbernia A, Ashrafi M, Malekzadeh R, Poustchi H. A review of psychosocial issues in patients with chronic hepatitis B. Arch Iran Med. 2013 Feb;16(2):114-22.
Reference Type
BACKGROUND
Mannes ZL, Hearn LE, Zhou Z, Janelle JW, Cook RL, Ennis N. The association between symptoms of generalized anxiety disorder and appointment adherence, overnight hospitalization, and emergency department/urgent care visits among adults living with HIV enrolled in care. J Behav Med. 2019 Apr;42(2):330-341. doi: 10.1007/s10865-018-9988-6. Epub 2018 Nov 1.
Reference Type
BACKGROUND
Farzanfar D, Dowlati Y, French LE, Lowes MA, Alavi A. Inflammation: A Contributor to Depressive Comorbidity in Inflammatory Skin Disease. Skin Pharmacol Physiol. 2018;31(5):246-251. doi: 10.1159/000490002. Epub 2018 Jun 28.
Reference Type
BACKGROUND
Martin DA, Towne JE, Kricorian G, Klekotka P, Gudjonsson JE, Krueger JG, Russell CB. The emerging role of IL-17 in the pathogenesis of psoriasis: preclinical and clinical findings. J Invest Dermatol. 2013 Jan;133(1):17-26. doi: 10.1038/jid.2012.194. Epub 2012 Jun 7.
Reference Type
BACKGROUND
Buske-Kirschbaum A, Geiben A, Hollig H, Morschhauser E, Hellhammer D. Altered responsiveness of the hypothalamus-pituitary-adrenal axis and the sympathetic adrenomedullary system to stress in patients with atopic dermatitis. J Clin Endocrinol Metab. 2002 Sep;87(9):4245-51. doi: 10.1210/jc.2001-010872.
Reference Type
BACKGROUND
Matsunaga MC, Yamauchi PS. IL-4 and IL-13 Inhibition in Atopic Dermatitis. J Drugs Dermatol. 2016 Aug 1;15(8):925-9.
Reference Type
BACKGROUND
Hoffman LK, Ghias MH, Garg A, Hamzavi IH, Alavi A, Lowes MA. Major gaps in understanding and treatment of hidradenitis suppurativa. Semin Cutan Med Surg. 2017 Jun;36(2):86-92. doi: 10.12788/j.sder.2017.024.
Reference Type
BACKGROUND
Paiardini M, Muller-Trutwin M. HIV-associated chronic immune activation. Immunol Rev. 2013 Jul;254(1):78-101. doi: 10.1111/imr.12079.
Reference Type
BACKGROUND
Maggi P, Bellacosa C, Leone A, Volpe A, Ricci ED, Ladisa N, Cicalini S, Grilli E, Viglietti R, Chirianni A, Bellazzi LI, Maserati R, Martinelli C, Corsi P, Celesia BM, Sozio F, Angarano G. Cardiovascular risk in advanced naive HIV-infected patients starting antiretroviral therapy: Comparison of three different regimens - PREVALEAT II cohort. Atherosclerosis. 2017 Aug;263:398-404. doi: 10.1016/j.atherosclerosis.2017.05.004. Epub 2017 May 5.
Reference Type
BACKGROUND
D'Abramo A, Zingaropoli MA, Oliva A, D'Agostino C, Al Moghazi S, De Luca G, Iannetta M, d'Ettorre G, Ciardi MR, Mastroianni CM, Vullo V. Higher Levels of Osteoprotegerin and Immune Activation/Immunosenescence Markers Are Correlated with Concomitant Bone and Endovascular Damage in HIV-Suppressed Patients. PLoS One. 2016 Feb 25;11(2):e0149601. doi: 10.1371/journal.pone.0149601. eCollection 2016.
Reference Type
BACKGROUND
Hart BL. Biological basis of the behavior of sick animals. Neurosci Biobehav Rev. 1988 Summer;12(2):123-37. doi: 10.1016/s0149-7634(88)80004-6.
Reference Type
BACKGROUND
Dantzer R, O'Connor JC, Freund GG, Johnson RW, Kelley KW. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat Rev Neurosci. 2008 Jan;9(1):46-56. doi: 10.1038/nrn2297.
Reference Type
BACKGROUND
Dantzer R. Cytokine-induced sickness behavior: where do we stand? Brain Behav Immun. 2001 Mar;15(1):7-24. doi: 10.1006/brbi.2000.0613.
Reference Type
BACKGROUND
Capuron L, Gumnick JF, Musselman DL, Lawson DH, Reemsnyder A, Nemeroff CB, Miller AH. Neurobehavioral effects of interferon-alpha in cancer patients: phenomenology and paroxetine responsiveness of symptom dimensions. Neuropsychopharmacology. 2002 May;26(5):643-52. doi: 10.1016/S0893-133X(01)00407-9.
Reference Type
BACKGROUND
Capuron L, Ravaud A, Dantzer R. Early depressive symptoms in cancer patients receiving interleukin 2 and/or interferon alfa-2b therapy. J Clin Oncol. 2000 May;18(10):2143-51. doi: 10.1200/JCO.2000.18.10.2143.
Reference Type
BACKGROUND
Constant A, Castera L, Dantzer R, Couzigou P, de Ledinghen V, Demotes-Mainard J, Henry C. Mood alterations during interferon-alfa therapy in patients with chronic hepatitis C: evidence for an overlap between manic/hypomanic and depressive symptoms. J Clin Psychiatry. 2005 Aug;66(8):1050-7. doi: 10.4088/jcp.v66n0814.
Reference Type
BACKGROUND
Capuron L, Ravaud A. Prediction of the depressive effects of interferon alfa therapy by the patient's initial affective state. N Engl J Med. 1999 Apr 29;340(17):1370. doi: 10.1056/NEJM199904293401716. No abstract available.
Reference Type
BACKGROUND
Capuron L, Raison CL, Musselman DL, Lawson DH, Nemeroff CB, Miller AH. Association of exaggerated HPA axis response to the initial injection of interferon-alpha with development of depression during interferon-alpha therapy. Am J Psychiatry. 2003 Jul;160(7):1342-5. doi: 10.1176/appi.ajp.160.7.1342.
Reference Type
BACKGROUND
Swaab DF, Bao AM, Lucassen PJ. The stress system in the human brain in depression and neurodegeneration. Ageing Res Rev. 2005 May;4(2):141-94. doi: 10.1016/j.arr.2005.03.003.
Reference Type
BACKGROUND
Holsboer F. Corticotropin-releasing hormone modulators and depression. Curr Opin Investig Drugs. 2003 Jan;4(1):46-50.
Reference Type
BACKGROUND
Irwin MR, Miller AH. Depressive disorders and immunity: 20 years of progress and discovery. Brain Behav Immun. 2007 May;21(4):374-83. doi: 10.1016/j.bbi.2007.01.010. Epub 2007 Mar 13.
Reference Type
BACKGROUND
Strogatz SH. Exploring complex networks. Nature. 2001 Mar 8;410(6825):268-76. doi: 10.1038/35065725.
Reference Type
BACKGROUND
Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91-121. doi: 10.1146/annurev-clinpsy-050212-185608.
Reference Type
BACKGROUND
Boschloo L, van Borkulo CD, Rhemtulla M, Keyes KM, Borsboom D, Schoevers RA. The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders. PLoS One. 2015 Sep 14;10(9):e0137621. doi: 10.1371/journal.pone.0137621. eCollection 2015.
Reference Type
BACKGROUND
Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci. 2010 Jun;33(2-3):137-50; discussion 150-93. doi: 10.1017/S0140525X09991567.
Reference Type
BACKGROUND
Jones PJ, Mair P, Riemann BC, Mugno BL, McNally RJ. A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. J Anxiety Disord. 2018 Jan;53:1-8. doi: 10.1016/j.janxdis.2017.09.008. Epub 2017 Nov 2.
Reference Type
BACKGROUND
Borsboom D. A network theory of mental disorders. World Psychiatry. 2017 Feb;16(1):5-13. doi: 10.1002/wps.20375.
Reference Type
BACKGROUND
Bryant RA, Creamer M, O'Donnell M, Forbes D, McFarlane AC, Silove D, Hadzi-Pavlovic D. Acute and Chronic Posttraumatic Stress Symptoms in the Emergence of Posttraumatic Stress Disorder: A Network Analysis. JAMA Psychiatry. 2017 Feb 1;74(2):135-142. doi: 10.1001/jamapsychiatry.2016.3470.
Reference Type
BACKGROUND
Galderisi S, Rucci P, Kirkpatrick B, Mucci A, Gibertoni D, Rocca P, Rossi A, Bertolino A, Strauss GP, Aguglia E, Bellomo A, Murri MB, Bucci P, Carpiniello B, Comparelli A, Cuomo A, De Berardis D, Dell'Osso L, Di Fabio F, Gelao B, Marchesi C, Monteleone P, Montemagni C, Orsenigo G, Pacitti F, Roncone R, Santonastaso P, Siracusano A, Vignapiano A, Vita A, Zeppegno P, Maj M; Italian Network for Research on Psychoses. Interplay Among Psychopathologic Variables, Personal Resources, Context-Related Factors, and Real-life Functioning in Individuals With Schizophrenia: A Network Analysis. JAMA Psychiatry. 2018 Apr 1;75(4):396-404. doi: 10.1001/jamapsychiatry.2017.4607.
Reference Type
BACKGROUND
Haag C, Robinaugh DJ, Ehlers A, Kleim B. Understanding the Emergence of Chronic Posttraumatic Stress Disorder Through Acute Stress Symptom Networks. JAMA Psychiatry. 2017 Jun 1;74(6):649-650. doi: 10.1001/jamapsychiatry.2017.0788. No abstract available.
Reference Type
BACKGROUND
McNally RJ. Networks and Nosology in Posttraumatic Stress Disorder. JAMA Psychiatry. 2017 Feb 1;74(2):124-125. doi: 10.1001/jamapsychiatry.2016.3344. No abstract available.
Reference Type
BACKGROUND
Anker JJ, Kummerfeld E, Rix A, Burwell SJ, Kushner MG. Causal Network Modeling of the Determinants of Drinking Behavior in Comorbid Alcohol Use and Anxiety Disorder. Alcohol Clin Exp Res. 2019 Jan;43(1):91-97. doi: 10.1111/acer.13914. Epub 2018 Nov 25.
Reference Type
BACKGROUND
Fredriksson T, Pettersson U. Severe psoriasis--oral therapy with a new retinoid. Dermatologica. 1978;157(4):238-44. doi: 10.1159/000250839.
Reference Type
BACKGROUND
Louden BA, Pearce DJ, Lang W, Feldman SR. A Simplified Psoriasis Area Severity Index (SPASI) for rating psoriasis severity in clinic patients. Dermatol Online J. 2004 Oct 15;10(2):7.
Reference Type
BACKGROUND
Ardigo M, Prow T, Agozzino M, Soyer P, Berardesca E. Reflectance confocal microscopy for inflammatory skin diseases. G Ital Dermatol Venereol. 2015 Oct;150(5):565-73.
Reference Type
BACKGROUND
Swindells K, Burnett N, Rius-Diaz F, Gonzalez E, Mihm MC, Gonzalez S. Reflectance confocal microscopy may differentiate acute allergic and irritant contact dermatitis in vivo. J Am Acad Dermatol. 2004 Feb;50(2):220-8. doi: 10.1016/j.jaad.2003.08.005.
Reference Type
BACKGROUND
Ricci G, Dondi A, Patrizi A. Useful tools for the management of atopic dermatitis. Am J Clin Dermatol. 2009;10(5):287-300. doi: 10.2165/11310760-000000000-00000.
Reference Type
BACKGROUND
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59 Suppl 20:22-33;quiz 34-57.
Reference Type
BACKGROUND
Costantini M, Musso M, Viterbori P, Bonci F, Del Mastro L, Garrone O, Venturini M, Morasso G. Detecting psychological distress in cancer patients: validity of the Italian version of the Hospital Anxiety and Depression Scale. Support Care Cancer. 1999 May;7(3):121-7. doi: 10.1007/s005200050241.
Reference Type
BACKGROUND
HAMILTON M. The assessment of anxiety states by rating. Br J Med Psychol. 1959;32(1):50-5. doi: 10.1111/j.2044-8341.1959.tb00467.x. No abstract available.
Reference Type
BACKGROUND
HAMILTON M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960 Feb;23(1):56-62. doi: 10.1136/jnnp.23.1.56. No abstract available.
Reference Type
BACKGROUND
Scott PJ, Huskisson EC. Measurement of functional capacity with visual analogue scales. Rheumatol Rehabil. 1977 Nov;16(4):257-9. doi: 10.1093/rheumatology/16.4.257.
Reference Type
BACKGROUND
Seldrup J. Comparison between physician and patient completed visual analogue scales. J Int Med Res. 1977;5(1 Suppl):55-60.
Reference Type
BACKGROUND
Miller MD, Paradis CF, Houck PR, Mazumdar S, Stack JA, Rifai AH, Mulsant B, Reynolds CF 3rd. Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992 Mar;41(3):237-48. doi: 10.1016/0165-1781(92)90005-n.
Reference Type
BACKGROUND
Garin O, Ayuso-Mateos JL, Almansa J, Nieto M, Chatterji S, Vilagut G, Alonso J, Cieza A, Svetskova O, Burger H, Racca V, Francescutti C, Vieta E, Kostanjsek N, Raggi A, Leonardi M, Ferrer M; MHADIE consortium. Validation of the "World Health Organization Disability Assessment Schedule, WHODAS-2" in patients with chronic diseases. Health Qual Life Outcomes. 2010 May 19;8:51. doi: 10.1186/1477-7525-8-51.
Reference Type
BACKGROUND
Federici S, Meloni F, Mancini A, Lauriola M, Olivetti Belardinelli M. World Health Organisation Disability Assessment Schedule II: contribution to the Italian validation. Disabil Rehabil. 2009;31(7):553-64. doi: 10.1080/09638280802240498.
Reference Type
BACKGROUND
Kodraliu G, Mosconi P, Groth N, Carmosino G, Perilli A, Gianicolo EA, Rossi C, Apolone G. Subjective health status assessment: evaluation of the Italian version of the SF-12 Health Survey. Results from the MiOS Project. J Epidemiol Biostat. 2001;6(3):305-16. doi: 10.1080/135952201317080715.
Reference Type
BACKGROUND
Sahakian BJ, Morris RG, Evenden JL, Heald A, Levy R, Philpot M, Robbins TW. A comparative study of visuospatial memory and learning in Alzheimer-type dementia and Parkinson's disease. Brain. 1988 Jun;111 ( Pt 3):695-718. doi: 10.1093/brain/111.3.695.
Reference Type
BACKGROUND
Carver CS. You want to measure coping but your protocol's too long: consider the brief COPE. Int J Behav Med. 1997;4(1):92-100. doi: 10.1207/s15327558ijbm0401_6.
Reference Type
BACKGROUND
Rao D, Choi SW, Victorson D, Bode R, Peterman A, Heinemann A, Cella D. Measuring stigma across neurological conditions: the development of the stigma scale for chronic illness (SSCI). Qual Life Res. 2009 Jun;18(5):585-95. doi: 10.1007/s11136-009-9475-1. Epub 2009 Apr 25.
Reference Type
BACKGROUND
Molina Y, Choi SW, Cella D, Rao D. The stigma scale for chronic illnesses 8-item version (SSCI-8): development, validation and use across neurological conditions. Int J Behav Med. 2013 Sep;20(3):450-60. doi: 10.1007/s12529-012-9243-4.
Reference Type
BACKGROUND
Vitiello P, Taramasso L, Ricci E, Maggi P, Martinelli C, Gabrielli C, Vittorio De Socio G, Di Cristo V, Rusconi S, Falasca K, Menzaghi B, Tebini A, Di Biagio A. Use of quantitative ultrasound as bone mineral density evaluation in an Italian female population living with HIV: A real-life experience. J Women Aging. 2019 Mar-Apr;31(2):176-188. doi: 10.1080/08952841.2018.1428100. Epub 2018 Jan 25.
Reference Type
BACKGROUND
Pinzone MR, Castronuovo D, Di Gregorio A, Celesia BM, Gussio M, Borderi M, Maggi P, Santoro CR, Madeddu G, Cacopardo B, Nunnari G. Heel quantitative ultrasound in HIV-infected patients: a cross-sectional study. Infection. 2016 Apr;44(2):197-203. doi: 10.1007/s15010-015-0842-2. Epub 2015 Sep 9.
Reference Type
BACKGROUND
Argentiero A, Neglia C, Peluso A, di Rosa S, Ferrarese A, Di Tanna G, Caiaffa V, Benvenuto M, Cozma A, Chitano G, Agnello N, Paladini D, Baldi N, Distante A, Piscitelli P. The ability of lumbar spine DXA and phalanx QUS to detect previous fractures in young thalassemic patients with hypogonadism, hypothyroidism, diabetes, and hepatitis-B: A 2-year subgroup analysis from the Taranto Area of Apulia Region. J Pediatr Hematol Oncol. 2013 Aug;35(6):e260-4. doi: 10.1097/MPH.0b013e31828e6cab.
Reference Type
BACKGROUND
Ogarrio JM, Spirtes P, Ramsey J. A Hybrid Causal Search Algorithm for Latent Variable Models. JMLR Workshop Conf Proc. 2016 Aug;52:368-379.
Reference Type
BACKGROUND
Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, Rahman A. No health without mental health. Lancet. 2007 Sep 8;370(9590):859-77. doi: 10.1016/S0140-6736(07)61238-0.
Reference Type
BACKGROUND
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
417
Identifier Type: -
Identifier Source: org_study_id