The assessment of coping mechanisms in patients diagnosed with oncological diseases is essential for mental health specialists, who have to design the most appropriate case management strategy for comorbid mood disorders, anxiety disorders, adjustment disorders, and other psychiatric conditions that may be detected in this vulnerable population. The adequate treatment of these disorders is important for the preservation of mental health status, quality of life, and overall functionality in patients diagnosed with cancer. Coping mechanisms modulate the vulnerability toward psychiatric disorders, but they also have an impact on treatment adherence, which is an important factor correlating with prognosis. Appraisal-focused, problem-focused, emotion-focused, and occupation-focused coping represent the most well-defined strategies patients use when confronted with a stressful life situation, like a diagnosis with potentially vital consequences. Maladaptive coping strategies may also be identified in these patients, e.g., withdrawal from reality, including complete or partial denial of the disease, substance abuse, behavioral addictions, refusal of the recommended treatment, etc. The psychotherapeutic approach in patients with oncological diseases should include an initial evaluation of the coping strategies used either currently or in the past stressful conditions, an assessment of all the psychosocial resources the patients have (e.,g., support group, professional insertion, hobbies), and screening for mood and anxiety disorders that may have been triggered by the cancer diagnosis. Consequently, within the psychotherapeutic framework, a trained specialist can enhance the role of adaptive coping strategies and highlight the disadvantages of dysfunctional ones. This process may possess a favorable impact on treatment adherence, mental health status, and quality of life in patients with cancer.
Treatment resistant schizophrenia (TRS) is a severely disabling disorder, which decreases dramatically the quality of life and overall functionality, while it increases the rate of hospital admissions and overall healthcare costs. The main objective of this research was to evaluate the risk factors for TRS in a group of patients based on a retrospective analysis. The secondary objective was to design an algorithm for initial evaluation in patients with schizophrenia, in order to detect the candidates at risk for developing TRS. Medical charts and consultation records of all patients aged between 18 and 30, diagnosed with TRS, evaluated during 1-year in our department, were selected for analysis. The most significant risk factors for TRS found in univariate model were younger age at schizophrenia onset, male gender, living in rural areas, co-morbid drug dependence, lower therapeutic adherence, and premorbid personality disorder. Marginally significant were higher Positive and Negative Syndrome Scale (PANSS) scores at previous admissions, higher scores on PANSS negative symptoms sub-scale, and lower educational background. In the multivariate model, TRS was still significantly predicted (p<0.05) by younger age at the disease onset, addictive co-morbidity, and lower therapeutic adherence. An algorithm based on these risk factors is suggested, based on (a) structured PANSS evaluation using SCI-PANSS and Informant Questionnaire for PANSS, (b) a scale for the detection of co-morbid drug dependence (i.e. Inventory of Drug Taking Situations, IDTS), (c) an interview for detecting premorbid personality disorders (i.e. Structured Clinical Interview for DSM IV – Axis II Disorders, SCID-II), and (d) Treatment Satisfaction Questionnaire for Medication (TSQM) for therapeutic adherence monitoring. Also, the inclusion of several pharmacogenetic parameters (at least CYP450 2D6 panel for detection of poor/ultrarapid metabolizers) could be useful when establishing an adequate therapeutic management, and may help in decreasing the rate of non- response due to variations in antipsychotics plasma levels.
