European Psychiatry Is cognitive dysfunction involved in difficult-to- www.cambridge.org/epa treat depression? Characterizing resistance from a cognitive perspective Clara López-Solà1,2,3 , Marta Subirà1,3 , Maria Serra-Blasco1 , Research Article 1,4 1,5 1 Muriel Vicent-Gil , Guillem Navarra-Ventura , Eva Aguilar , Cite this article: López-Solà C, Subirà M, 1 1,3,5 1,3,5 Serra-Blasco M, Vicent-Gil M, Navarra- Siddarta Acebillo , Diego J. Palao and Narcís Cardoner Ventura G, Aguilar E, Acebillo S, Palao DJ, 1 Cardoner N (2020). Is cognitive dysfunction Mental Health Department, Parc Taulí Hospital Universitari, Neuroscience and Mental Health Research Area, Institut involved in difficult-to-treat depression? d’Investigació i Innovació Parc Taulí (I3PT), Sabadell, Spain; 2Department of Clinical and Health Psychology, Universitat Characterizing resistance from a cognitive Autònoma de Barcelona, Barcelona, Spain; 3CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental), perspective. European Psychiatry, 63(1), e74, Carlos III Health Institute, Madrid, Spain; 4Department of Psychiatry, Sant Pau Biomedical Research Institute (IIB-Sant 1–8 https://doi.org/10.1192/j.eurpsy.2020.65 Pau), Barcelona, Spain and 5Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Received: 13 February 2020 Barcelona, Spain Revised: 11 May 2020 Accepted: 08 June 2020 Abstract Key words: Background. This study aimed to identify clinical and cognitive factors associated with Cognition; difficult-to-treat depression; increased risk for difficult-to-treat depression (DTD) or treatment-resistant depression (TRD). treatment-resistant depression; verbal memory Methods. A total of 229 adult outpatients with major depression were recruited from the mental Author for correspondence: health unit at a public hospital. Participants were subdivided into resistant and nonresistant Narcís Cardoner, groups according to their Maudsley Staging Model score. Sociodemographic, clinical, and E-mail: ncardoner@tauli.cat cognitive (objective and subjective measures) variables were compared between groups, and a logistic regression model was used to identify the factors most associated with TRD risk. Clara López-Solà and Marta Subirà should be Results. TRD group patients present higher verbal memory impairment than the nonresistant considered joint first authors. group irrespective of pharmacological treatment or depressive symptom severity. Logistic regression analysis showed that low verbal memory scores (odds ratio [OR]: 2.02; 95% confi- dence interval [CI]: 1.38–2.95) together with high depressive symptom severity (OR: 1.29; CI95%: 1.01–1.65) were associated with TRD risk. Conclusions. Our findings align with neuroprogression models of depression, in which more severe patients, defined by greater verbal memory impairment and depressive symptoms, develop a more resistant profile as a result of increasingly detrimental neuronal changes. Moreover, our results support a more comprehensive approach in the evaluation and treatment of DTD in order to improve illness course. Longitudinal studies are warranted to confirm the predictive value of verbal memory and depression severity in the development of TRD. Introduction Major depressive disorder (MDD) is a prevalent and complex mental illness that severely impacts health and daily functioning. Despite the large number of pharmacological treatments available, around 30% of MDD patients are considered treatment-resistant. Recent conceptualizations of treatment response suggest difficult-to-treat depression (DTD) as an alternative label. This approach aims to optimize disease management by means of a more comprehensive patient evaluation. However, identification of individuals with DTD currently implies the absence of response after several treatments [1]. © The Author(s), 2020. Published by Cambridge University Press on behalf of the European Since the first reports in the literature in 1970s, treatment-resistant depression (TRD) has Psychiatric Association. This is an Open Access been described as an “insufficient response to adequate antidepressant therapy” [2,3]. However, article, distributed under the terms of the this definition is imprecise, and there is a lack of consensus regarding the operational criteria Creative Commons Attribution- for treatment resistance [4]. In order to overcome heterogeneity in the definition of an NonCommercial-ShareAlike licence (http:// creativecommons.org/licenses/by-nc-sa/4.0/), “adequate treatment for depression” (which includes adequate pharmacological doses, dura- which permits non-commercial re-use, tion of treatment, and a satisfactory demonstration of treatment adherence), and since the distribution, and reproduction in any medium, management of depression involves stepwise administration of treatments, several staging provided the same Creative Commons licence models have been developed for its treatment. These models establish several resistance levels is included and the original work is properly considering response to antidepressant treatments available based on their dose and duration cited. The written permission of Cambridge University Press must be obtained for (see a review in Ruhé et al. [5]). In this regard, the Maudsley Staging Model (MSM) provides a commercial re-use. comprehensive solution for establishing the treatment resistance level of a particular patient with depression, including other clinical issues such as the duration of the current depressive episode (acute, subacute, and chronic), symptom severity (subsyndromal to severe), and treatment failures with different antidepressants, augmentation drugs, and electroconvulsive therapy (ECT) [6]. 2 Clara López-Solà et al. Several sociodemographic, clinical, and neurobiological factors Health Unit of the Psychiatry Department at Parc Taulí Hospital have been associated with the presence of treatment resistance in over a 12-month period (February 2016 to February 2017). Patients depression. A recent review by Caraci et al. [7], which includes the had to meet the following criteria to be selected for the study: study performed by the European Group for the Study of Resistant fulfilling criteria for a past or present diagnosis of MDD according Depression, reported several clinical features significantly associ- to the Diagnostic and Statistical Manual of Mental Disorders– ated with treatment resistance. These variables were comorbid Fourth Edition, Text Revision (DSM-IV TR); an IQ >85 measured psychiatric disorders, suicide risk, severity and duration of current by means of the Vocabulary subtest from the Wechsler Adult episode, number of hospitalizations, depressive recurrences, mel- Intelligence Scale–Fourth Edition (WAIS-IV) [32]; and to be able ancholic and psychotic features, nonresponse to a first antidepres- to understand and sign the informed consent. Senior psychiatrists sant treatment, occurrence of side effects during treatment, lower established diagnoses. Exclusion criteria were the presence of neu- age at onset, high occupational level, and family psychiatric history. rological or any other somatic conditions that can affect cognitive Neurocognitive complaints have frequently been reported by functioning, as well as comorbidity with bipolar disorder, schizo- patients with MDD [8]. Interestingly, previous literature on subjective phrenia, past or present substance use disorders, or any personality and objective cognition posit that these different constructs of the disorders. The presence of dysfunctional personality traits was not disorder need to be addressed separately [9–11]. Whereas objective an exclusion criterion, being compiled according to the clinical neurocognitive impairment has been studied in greater depth and impression of the evaluator/research psychiatrist. The MDD sam- confirmed by well-validated assessments, less is known about subjec- ple was divided into two groups (resistant, n = 104, and nonresis- tive cognitive complaints. Although an association between depression tant, n = 125) according to their score obtained on the MSM scale severity and subjective cognition has recently been reported [12], the used to assess treatment resistance. This sample is part of a broader effects of subjective cognition in TRD have not been studied to date. study assessing cognition and depression [12]. Overall, since the first study reporting on the neurocognitive profile of TRD [13], several meta-analyses have found that patients Assessment procedure with MDD demonstrate poor performance on a range of cognitive domains, such as executive function, attention, verbal and visual Clinical and sociodemographic variables memory, and processing speed/reaction time [14–18]. Moreover, A set of prominent patients’ characteristics was collected during a these impairments in neurocognition have been shown to be present clinical semi-structured interview (1.5–2 h duration): age at first in the disorder regardless of certain demographic and clinical medi- MDD episode, number of previous MDD episodes (including the ators, such as gender, symptom severity, age at onset, and number present one), current psychopharmacological treatment doses mea- and duration of depressive episodes [15,19–22]. Previous literature sured following the system code proposed by Sackeim [33], comor- has reported that objective neurocognitive deficits may be present at bid conditions according to the DSM-IV TR (such as anxiety different stages of the disorder, suggesting a likely role for cognition disorders and dysthymia) or dysfunctional personality traits serving as a risk biomarker predating illness onset, as a marker of according to clinical impressions, and the current severity of greater severity illness, or even as a trait marker that is present in depressive symptomatology using the Hamilton Depressive Rating patients with remitted depressive symptoms. This last proposal Scale (HDRS-17) [34]. Medication used at the time of evaluation would support neuroprogression models [16], in which the presence was categorized as (a) monotherapy with antidepressants, of cognitive residual deficits in the absence of affective symptoms (b) antidepressants in combination with benzodiazepines or with would be considered an indirect sign of illness persistence. any other psychotropic medication (antipsychotics, lithium, and/or Notably, these neurocognitive alterations, present at various anticonvulsants), and (c) medication-free. Sociodemographic vari- stages of the disease, have been significantly associated with poorer ables were also collected: age, gender, years of education, marital response to treatment, reduced social functioning, and impaired status, and current employment status. quality of life [15,17,20,23–25]. Regarding treatment response in Treatment resistance was assessed by means of the MSM scale particular, lower verbal memory [26] and flexibility [27], together ranging from 3 to 15 [6]. The MSM includes information regarding with chronic depression and older age [22,28], have been described three items: (a) duration, (b) severity, and (c) treatment strategies of as predictors of poor response to both antidepressant medication the current episode. (a) Duration is scored 1–3 corresponding to and cognitive therapy [28–30]. Moreover, persistence of residual acute (<1 year), subacute (1 to <2 years), and chronic (>2 years); symptoms such as neurovegetative and neurocognitive symptoms, (b) severity is scored 1–5 corresponding to subsyndromal, mild, even in patients without depressive symptomatology, hinders full moderate, severe without psychosis, and severe with psychosis; and recovery and increases the probability of relapse [31]. (c) treatment strategies are scored 1–5 with regard to antidepres- Thus, neurocognitive deficits appear to be important therapeu- sants (level 1: 1–2 medications; level 2: 3–4 medications; level 3: 5–6 tic targets in order to improve treatment response, reduce the risk of medications; level 4: 7–10 medications; and level 5: >10 medica- a relapse, and lead to a full restoration of patients’ everyday func- tions), plus 0 or 1 with regard to pharmacological augmentation tioning. However, the extent to which these cognitive factors are strategy, and plus 0 or 1 with regard to ECT. Study participants were associated with treatment resistance in depression is unknown. categorized as resistant (7–15) or nonresistant (3–6) according to This study aims to identify the clinical factors, subjective cognitive their total MSM score. complaints, and neurocognitive markers associated with an increased risk for TRD or DTD. Self-reported cognitive measure—subjective cognitive functioning Methods The Perceived Deficit Questionnaire (PDQ-20) [35–37] is a 20-item self-administered questionnaire used to measure patients’ Participants perspective of their cognitive functioning. The PDQ-20 assesses A total of 229 major depressive outpatients (68% women),18–65 three cognitive domains by asking patients about their everyday years of age, were consecutively recruited from the adult Mental activities: attention, memory (retrospective and prospective), and European Psychiatry 3 executive functions (planning and organization). The combined nonresistant since they scored 3–6 at the MSM and 104 patients subscales yield a total score ranging from 0 to 80, with higher scores reported 7 or more at the MSM and were included in the resistant indicating greater perceived cognitive impairment. group. As expected, HDRS-17 scores were significantly higher in the TRD group. The percentage of patients receiving antipsy- Neuropsychological battery—objective cognitive functioning chotics, lithium, anticonvulsants (lamotrigine), or benzodiazepines Based on prevalent neuropsychological assessment manuals was also higher in the TRD group. No differences were observed [38,39], four neurocognitive domains were assessed to create com- between groups regarding other clinical and sociodemographic posite scores including the following validated tests: variables, or in the use of antidepressants and psychotherapy. More extended information with a description of the mean doses of all 1. Attention and working memory index: Digit Span Forward pharmacological treatments used by each study group is provided and Backward subtests from the WAIS-IV [32]. in Supplementary Table S1. 2. Verbal memory index: Rey Auditory Verbal Learning Test Patients in the TRD group reported significantly higher subjec- (RAVLT), RAVLT Total Score (trials 1–5) and 30-minute tive cognitive complaints according to the PDQ-20 total score, and delayed recall score [39]. specifically, on the memory (both retrospective and prospective) and 3. Executive functions index: Phonetic Verbal Fluency (PMR) executive functions subscale (Table 2), although these differences did [40,41] set-shifting (Wisconsin Card Sorting Test, WCST, and not remain significant after Bonferroni correction. In the objective Trail Making Test Part B, TMT-B) and abstraction cognitive domains, patients with TRD presented poorer results in (Similarities subtest, WAIS-IV). The computerized version verbal memory, processing speed, and executive functions than their of the WCST was administered from the Inquisit Test Library nonresistant counterparts (Table 2). Since psychopharmacological (http://www.millisecond.com/download/library/). treatments and the severity of depressive symptoms may have an 4. Processing speed index: Trail Making Test Part A (TMT-A) impact on neurocognition, we compared performance in objective [40] and Digit Symbol Substitution subtest (WAIS-IV). and subjective cognition further, controlling for these variables. After the exclusion of patients receiving antipsychotics, benzodiazepines, Statistical analyses lithium, or anticonvulsants and covarying for the severity of depres- To study differences in demographic, clinical, and neuropsycho- sive symptoms, between-group cognitive differences remained sig- logical variables between the two study groups (resistant and non- nificant for verbal memory. In contrast, differences in the other resistant), a two-tailed equal variances t-test for independent objective cognitive domains partially retained significance, depend- samples was applied. Furthermore, a logistic regression (using the ing on the excluded treatment. Differences in subjective cognition enter method) was carried out with treatment resistance as the did not remain significant (Supplementary Tables S2 and S3). dependent variable in order to find specific clinical and neurocog- After studying the clinical and cognitive profiles of resistant and nitive factors that might be associated with a higher risk of TRD. nonresistant groups, a logistic regression analysis was used to identify Sociodemographic, clinical, and neuropsychological variables in the variables that might be associated with a higher risk for TRD. In which the t-test reached statistical significance (p ≤ 0.05), as well the first regression model, we included treatment resistance as depen- as clinical variables previously related with a higher risk of treat- dent variable and scores on objective cognitive domains assessed, ment resistance described in the literature (age, gender, age at scores on PDQ-20, gender, age, age at MDD onset, and number of MMD onset, and number of episodes) were entered as independent MDD episodes as independent variables. This first model found variables in the regression model. In a first step, depression severity verbal memory to be the only independent variable significantly and pharmacological treatment were not entered in the statistical associated with higher TRD risk (Table 3). In the second step, the model as they are considered in the MSM definition of TRD. variables that showed significant differences between study groups However, since severity and pharmacological treatment may have (Table 1) and that were included in the MSM definition of TRD been related to the independent variables, they were included in a (depression severity, independently assessed by the HDRS-17 and second step of the logistic regression model in order to control for transformed to a 7-point interval, and pharmacological treatment) their effects on the results. Post-hoc analyses were conducted to were entered in the regression model, as they are relevant factors that further control for the influence of pharmacological treatment on could potentially affect TRD. Although severity of depression and the the results. The t-tests and regression analysis were reassessed use of antipsychotics and anticonvulsants emerged as factors that excluding patients under pharmacological treatments that showed were significantly associated with a higher risk for TRD, verbal significance in the analysis. memory remained significant in the model (Table 4). Data from each individual neuropsychological test were first The final logistic regression model including significant vari- standardized to remove the effects of age, gender, and education level. ables in the second step model (Table 4) showed that low scores on The resulting standardized scores were averaged to create objective verbal memory together with higher symptom severity were asso- neuropsychological composites that were normalized and trans- ciated with a higher risk of treatment resistance in depression. formed into z-scores (mean = 0 and standard deviation = 1). Scores Thus, for each extra point on the HDRS-17 scale, risk increased on the HDRS-17 were transformed using 7-point intervals to aid the by 1.29 (95% confidence interval [95% CI]: 1.01–1.65) and for each clinical interpretation of the results obtained from the logistic regres- z-score point decrease on the verbal memory score (worse mem- sion. All data were analyzed using the Statistical Package for the Social ory), risk increased by 2.02 (95% CI: 1.38–2.95) (Table 5). As Sciences software version 21.0 (SPSS Inc; Chicago, IL). expected, the use of antipsychotics and anticonvulsants was also related with treatment resistance in the statistical model. To further assess the influence of pharmacological treatment on the results, the Results final regression model was also reassessed after the exclusion of patients under antipsychotic or anticonvulsant treatment. Verbal Sociodemographic and clinical characteristics of the study sample memory remained significantly associated with a higher risk for are described in Table 1. A total of 125 patients were classified as TRD in this model (Supplementary Table S4). To address a possible 4 Clara López-Solà et al. Table 1. Sociodemographic and clinical variables of the two study groups. Variables Nonresistance (N = 125) Moderate–severe resistance (N = 104) t test/χ2 (df ) p value (two-tailed) Sexa (female) 85 (68.0) 70 (67.3) 0.01 (1) 1.000 Ageb 51.70 (9.88) 53.83 (7.32) 1.87 (224) 0.062 b Years of education 10.80 (3.81) 9.90 (3.33) 1.90 (227) 0.059 b Age of onset 41.44 (12.62) 40.85 (11.32) 0.37 (227) 0.711 Number of episodesb 2.15 (1.32) 2.40 (1.28) 1.46 (227) 0.147 b Severity (HDRS) 13.67 (8.65) 17.64 (8.57) 3.47 (227) 0.001 a,c Marital status Married 89 (71.2) 71 (68.9) 7.32 (3) 0.062 Divorced 21 (16.8) 25 (24.3) Single 15 (12) 5 (4.9) Widowed 0 (0) 2 (1.9) Employment statusa,c Employedd 33 (26.4) 13 (12.6) 20.14 (5) 0.001 Sick leave because of MDD 33 (26.4) 25 (24.3) Non-MDD-related leave 8 (6.4) 3 (2.9) Unemployment 5 (4) 1 (1) d Pensioner 24 (19.2) 45 (43.7) Looking for an employment 22 (17.2) 16 (15.5) Comorbiditya Dysthymia 24 (19.2) 26 (25.0) 1.12 (1) 0.336 Anxiety disorders 9 (7.2) 10 (9.6) 0.435 (1) 0.632 Personality traitse 18 (14.4) 16 (15.4) 0.044 (1) 0.854 a Medication Antidepressants 118 (94.4) 103 (99.0) 3.62 (1) 0.075 Antipsychotics 15 (12.0) 41 (39.4) 23.11 (1) <0.001 Lithium 2 (1.6) 8 (7.7) 5.05 (1) 0.046 Anticonvulsants 16 (12.8) 28 (26.9) 7.30 (1) 0.011 Benzodiazepines 53 (42.4) 65 (62.5) 9.18 (1) 0.003 Medication free 6 (4.8) 1 (1.0) 2.83 (1) 0.243 Nonpharmacological treatmenta,c Past psychotherapy 53 (44.9) 47 (54.7) 1.89 (1) 0.202 Ongoing psychotherapy 26 (22.6) 26 (29.9) 1.37 (1) 0.259 Abbreviations: HDRS, Hamilton Depressive Rating Scale; MDD, major depressive disorder; SD, standard deviation. a Variable reported as n (%). b Variables reported as mean (SD). c Past psychotherapy and ongoing psychotherapy have missing data in 25 and 27 subjects respectively. Marital status and employment status have missing data in one subject. d Significant differences were observed between both study groups in employed and pensioner (χ2 = 6.66, p = 0.012 and χ2 = 16.05, p < 0.001 respectively). e Presence of dysfunctional personality traits according to the clinical impression of the evaluator. effect of collinearity among cognitive domains in the results of the highest verbal memory performance). Both groups represented logistic regression, we repeated the analysis including only one quartiles 1 (lower risk) and 4 (higher risk) within the distribution cognitive domain at a time, and only verbal memory remained of each variable. Patients in quartile 1 presented HDRS-17 scores of significant. ≤7 (low severity) and a verbal memory score of ≥ 0.30 (z scores), Last, we explored the risk (odds ratio [OR]) for TRD when both while patients in quartile 4 scored ≥22 on the HDRS-17 (moderate significant variables were present at the same time. With this aim in severity) and ≤ 1.47 on verbal memory. As such, patients who mind, we defined patients with higher risk (defined as those with presented both risk factors presented an OR of 4.04 (CI 95%: 3.02– the highest HDRS-17 score and worst verbal memory performance) 8.43) for being treatment-resistant in comparison to MDD patients and lower risk (those with the lowest HDRS-17 score and the with low depressive symptoms and preserved verbal memory. European Psychiatry 5 Table 2. Subjective and objective cognitive functioning. Variables Nonresistance (N = 125) Moderate–severe resistance (N = 104) t test (df ) p Value (two-tailed) PDQ-20a Total 33.89 (17.45) 38.73 (16.91) 2.12 (227) 0.035 Attention 10.14 (4.96) 11.25 (4.60) 1.75 (227) 0.082 Memory Total 14.28 (8.61) 16.67 (8.77) 2.08 (227) 0.039 Retrospective Memory 7.47 (4.64) 10.81 (4.77) 1.98 (227) 0.049 Prospective Memory 6.81 (4.41) 8.13 (4.60) 2.21 (227) 0.028 Executive functions 9.47 (5.32) 8.55 (4.64) 1.75 (227) 0.028 b,c Verbal memory 0.61 (0.93) 1.19 (0.77) 5.02 (219) <0.001 Attention/working memoryb,c 0.62 (0.83) 0.93 (0.74) 2.96 (226) 0.003 b,c Processing speed 0.32 (0.97) 0.76 (0.93) 3.52 (225) 0.001 b,c Executive functions 0.59 (0.73) 0.96 (0.79) 3.64 (225) <0.001 Abbreviations: PDQ-20, Perceived Deficit Questionnaire; SD, standard deviation. a Variables reported as mean (SD). b Variables reported as Z values (SD). c Missing data exist for verbal memory, attention/working memory, processing speed, and executive function in 8, 1, 2, and 2 subjects respectively. Table 3. Results of the logistic regression analysis (excluding variables reported. The difference may be due to the heterogeneity in the considered in the definition of resistance according to the MSM). definition of TRD [42] and the potential bias caused by the recruit- Significance Exp ment at a specialized psychiatry service, where patients are often B SE (p-value) (B) attended after a first treatment trial in primary care. Patients in the resistant group showed a higher severity of Verbal memory 0.659 0.216 0.002 1.934a depressive symptoms and a greater use of antipsychotics, lithium, Attention/working memory 0.189 0.250 0.449 0.828 anticonvulsants, and benzodiazepines. These findings are in agree- ment with the previous literature and clinical practice. Although Executive functioning 0.133 0.337 0.693 0.875 studies have assessed clinical characteristics related to treatment Processing speed 0.008 0.257 0.975 0.992 resistance in depression, the specific role of these variables in PDQ-20 attention 0.060 0.063 0.344 0.942 increasing the risk of developing a TRD has received less attention. In this sense, our results showed a mild risk of TRD in patients with PDQ-20 memory 0.030 0.031 0.336 1.030 severe depressive symptoms, which decreases after excluding cases PDQ-20 executive functions 0.018 0.050 0.726 1.018 under antipsychotics or anticonvulsants, probably due to the reduc- Gender 0.242 0.323 0.453 0.785 tion of the number of resistant patients included in the analysis. In line with these findings, the ORs for clinical predictors in TRD have Age 0.041 0.023 0.075 1.042 previously been demonstrated to be low (around 1.5) [43]. More- Age of MDD onset 0.015 0.018 0.412 0.985 over, a recent study using machine learning algorithms for classi- Number of MDD episodes 0.121 0.138 0.383 1.128 fying treatment-resistant patients with clinical variables reported low accuracy for the final model (0.74), suggesting the importance Constant 2.508 1.221 0.040 0.081 of exploring other factors not considered so far in predictive Abbreviations: MDD, major depressive disorder; MSM, Maudsley Staging Model; PDQ, models, such as cognitive variables [44]. Perceived Deficit Questionnaire; SE, standard error. a This value represents 1/0.517. Bold values represent statistically significant results. Cognitive variables Our results found that verbal memory was significantly associated Discussion with an increased risk for TRD above and beyond demographic and To our knowledge, this is the first study to examine TRD risk clinical characteristics and pharmacological treatment. The role of associated with objective and subjective cognition. In our cognition in the prediction of response to pharmacological interven- sample, verbal memory was found to be associated with a higher tions (selective serotonin reuptake inhibitors (SSRI) and serotoninâ risk for TRD independently of pharmacological treatment or norepinephrine reuptake inhibitors (SNRI)) but also to other treat- symptom severity. Moreover, symptom severity was also associated ment strategies reserved for patients who did not respond to standard with a higher risk for TRD. Results showed a higher impairment in pharmacological treatments (ketamine, transcranial magnetic stim- the resistant group for other subjective and objective cognitive vari- ulation (TMS), or ECT) has received increased interest [45–48]. The ables, although they were not associated with a higher risk for TRD. first TRD study on neurocognition found that individuals with TRD displayed slightly reduced neurocognitive performance, both glob- ally and across all domains [13]. Notably, regarding non–first line Clinical variables treatment strategies, a better baseline performance in episodic mem- In our study sample, 45.4% of our patients presented treatment ory has been described as a good predictor of depression response resistance, which is somewhat higher than the rates previously and remission using TMS [47]. Moreover, another study performed 6 Clara López-Solà et al. Table 4. Results of the logistic regression analysis including depression severity and pharmacological treatment. B SE Significance (p-value) Exp (B) Verbal memory 0.699 0.233 0.003 2.012a Attention/working memory 0.295 0.276 0.286 0.745 Executive functioning 0.255 0.369 0.490 0.775 Processing speed 0.423 0.294 0.149 1.527 PDQ-20 attention 0.095 0.072 0.186 0.910 PDQ-20 memory 0.017 0.033 0.608 1.017 PDQ-20 executive functions 0.017 0.055 0.753 0.983 Gender 0.180 0.348 0.606 0.835 Age 0.029 0.025 0.255 1.029 Age at onset 0.017 0.020 0.403 0.983 Number of episodes 0.051 0.152 0.736 1.052 Severity 0.452 0.187 0.016 1.572 Antipsychotics 1.566 0.401 <0.001 4.787 Lithium 1.049 0.962 0.275 2.854 Anticonvulsants 0.970 0.437 0.026 2.637 Benzodiazepines 0.548 0.329 0.096 1.729 Constant 2.658 1.327 0.045 0.070 Abbreviations: MDD, major depressive disorder; MSM, Maudsley Staging Model; PDQ, Perceived Deficit Questionnaire; SE, standard error. a This value represents 1/0.497. Bold values represent statistically significant results. Table 5. Final statistical model including the variables associated with a higher risk of treatment-resistant depression. B SE Significance (p-value) Exp (B) 95% CI for Exp (B) Lower–upper Verbal memory 0.703 0.193 <0.001 2.020a 1.383–2.950a Severity 0.255 0.125 0.042 1.290 1.009–1.649 Antipsychotics 1.367 0.367 <0.001 3.924 1.912–8.055 Anticonvulsants 0.813 0.397 0.040 2.254 1.036–4.905 Constant 1.900 0.367 <0.001 0.150 Abbreviations: CI, confidence interval; SE, standard error. a Those values represent 1/0.495 and CI: 1/0.723–1/0.339. in unipolar and bipolar depression showed that overgeneral memory Our study is not without limitations. First, the heterogeneity of (a lack of memory for specific events) was inversely related with time the definition of TRD may make it difficult to integrate our to depression recovery and the risk of a new relapse after ECT findings to the rest of the literature. Second, since one of the [46]. These previous results coincide with our findings, in which predictive variables for TRD in our analysis was depression sever- patients with more altered verbal memory were most associated with ity, which is included in the definition of treatment resistance in TRD risk. the MSM, results regarding this variable should be carefully con- The association between verbal memory impairment and the sidered. In this regard, it is important to note that the observed increased TRD risk could be linked to alterations in specific brain association between verbal memory and TRD remained signifi- regions, such as the hippocampus, which has been previously cant independently of symptom severity. Finally, the design of this associated with MDD pathophysiology [49–51] and memory study makes it difficult to disentangle whether cognitive impair- [52,53]. Moreover, our findings in verbal memory suggest the ment could represent a predictor of TRD or a symptom of depres- existence of a specific subtype of MDD patients that is more sion itself. Longitudinal studies are needed to confirm the resistant to treatment [54], a clinical condition that would imply predictive value of verbal memory and depression severity in the longer disorder duration and, presumably, higher neurotoxicity. development of TRD. Interestingly, memory impairment is also associated with treatment In summary, our results highlight the potential role of verbal resistance in other psychiatric disorders such as schizophrenia memory and depression severity as risk factors for TRD. These [55,56] and post-traumatic stress disorder [57–59], suggesting that results support neuroprogression models, in which more severe memory could be considered a cognitive marker for treatment patients (in terms of memory and depressive symptoms) present an resistance across mental disorders. unfavorable clinical development and a more resistant profile. A European Psychiatry 7 more comprehensive evaluation of patients with depression would neurobiology of treatment-resistant depression: from antidepressant clas- aid in identifying potential cognitive and clinical risk factors sifications to novel pharmacological targets. Pharmacol Rev. 2018;70: involved in DTD. The chance to adapt more personalized treatment 475–504. approaches would have an impact on clinical course and on the [8] Schwert C, Stohrer M, Aschenbrenner S, Weisbrod M, Schröder A. Biased neurocognitive self-perception in depressive and in healthy persons. J functional recovery of patients with depression. 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Clinical characteristics associated subfield hippocampal volume, independent of mood change with the discrepancy between subjective and objective cognitive impair- [63]. Finally, it would be of interest to continue investigating ment in depression. J Affect Disord. 2019;246:763–74. specific depressive symptoms, including cognition, that are prone [12] Serra-Blasco M, Torres IJ, Vicent-Gil M, Goldberg X, Navarra-Ventura G, Aguilar E, et al. Discrepancy between objective and subjective cognition in to become treatment resistant in order to improve the definition of major depressive disorder. Eur Neuropsychopharmacol. 2019;29:46–56. a more homogeneous subgroup of difficult-to-treat patients. [13] Gupta M, Holshausen K, Best MW, Jokic R, Milev R, Bernard T, et al. Acknowledgments. The authors thank J. C. Oliva, MSc (Statistical Consultant Relationships among neurocognition, symptoms, and functioning in at Parc Taulí University Hospital), for insights into the methodology, and the treatment-resistant depression. Arch Clin Neuropsychol. 2013;28:272–81. staff of the Psychiatry Department at Parc Tauli University Hospital for their [14] Snyder HR. Major depressive disorder is associated with broad impair- assistance in patient recruitment, specifically to S. Crivillés, MD; I. Figuereo, ments on neuropsychological measures of executive function: a meta- MD; and R. Coronas, MD, PhD. We also thank the patients who participated in analysis and review. Psychol Bull. 2014;139:81–132. this study for their kind cooperation. [15] McClintock SM, Husain MM, Greer TL, Cullum CM. Association between depression severity and neurocognitive function in major depressive Financial Support. The study was partially funded by Instituto de Salud disorder: a review and synthesis. Neuropsychology. 2010;24:9–34. Carlos III (ISCIII) cofunded by FEDER funds/European Regional Development [16] Bortolato B, Miskowiak KW, Köhler CA, Maes M, Fernandes BS, Berk M, Fund—a way to build Europe (ISCIII-PI18/0036, PI17/01205, and PI15/01478). et al. Cognitive remission: a novel objective for the treatment of major ISCIII had no further role in study design; in the collection, analysis, and depression? BMC Med. 2016;14:9. interpretation of data; in the writing of the report; and in the decision to submit [17] Evans VC, Iverson GL, Yatham LN, Lam RW. The relationship between the paper for publication. neurocognitive and psychosocial functioning in major depressive disorder: Conflict of Interest. All authors confirm that there are no known conflicts of a systematic review. J Clin Psychiatry. 2014;75:1359–70. interest associated with this publication and there has been no significant [18] Goodall J, Fisher C, Hetrick S, Phillips L, Parrish EM, Allott K. Neuro- financial support for this work that could have influenced its outcome. All cognitive functioning in depressed young people: a systematic review and authors contributed to and have approved the final manuscript. meta-analysis. Neuropsychol Rev. 2018;28:216–31. [19] Keefe RSE, McClintock SM, Roth RM, Murali Doraiswamy P, Tiger S, Data Availability Statement. The data that support the findings of this study Madhoo M. Cognitive effects of pharmacotherapy for major depressive are available upon request from Dr. Narcís Cardoner at ncardoner@tauli.cat. disorder: a systematic review. J Clin Psychiatry. 2014;75:864–76. [20] Allott K, Fisher CA, Amminger GP, Goodall J, Hetrick S. Characterizing Supplementary Materials. To view supplementary material for this article, neurocognitive impairment in young people with major depression: state, please visit http://dx.doi.org/10.1192/j.eurpsy.2020.65. trait, or scar? Brain Behav. 2016;6:1–12. 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