Introduction: The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD). Methods: To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment. Results: At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node. Conclusion: NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis / Guerrera, C. S.; Platania, G. A.; Boccaccio, F. M.; Sarti, P.; Varrasi, S.; Colliva, C.; Grasso, M.; De Vivo, S.; Cavallaro, D.; Tascedda, F.; Pirrone, C.; Drago, F.; Di Nuovo, S.; Blom, J. M. C.; Caraci, F.; Castellano, S.. - In: BMC PSYCHIATRY. - ISSN 1471-244X. - 23:1(2023), pp. N/A-N/A. [10.1186/s12888-023-05300-y]

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis

Tascedda F.;Drago F.;Blom J. M. C.;Castellano S.
2023

Abstract

Introduction: The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD). Methods: To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment. Results: At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node. Conclusion: NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.
2023
23
1
N/A
N/A
The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis / Guerrera, C. S.; Platania, G. A.; Boccaccio, F. M.; Sarti, P.; Varrasi, S.; Colliva, C.; Grasso, M.; De Vivo, S.; Cavallaro, D.; Tascedda, F.; Pirrone, C.; Drago, F.; Di Nuovo, S.; Blom, J. M. C.; Caraci, F.; Castellano, S.. - In: BMC PSYCHIATRY. - ISSN 1471-244X. - 23:1(2023), pp. N/A-N/A. [10.1186/s12888-023-05300-y]
Guerrera, C. S.; Platania, G. A.; Boccaccio, F. M.; Sarti, P.; Varrasi, S.; Colliva, C.; Grasso, M.; De Vivo, S.; Cavallaro, D.; Tascedda, F.; Pirrone, C.; Drago, F.; Di Nuovo, S.; Blom, J. M. C.; Caraci, F.; Castellano, S.
File in questo prodotto:
File Dimensione Formato  
s12888-023-05300-y.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 1.61 MB
Formato Adobe PDF
1.61 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1330472
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 4
social impact