Introduction
A few countries in the world permit euthanasia and/or assisted suicide based primarily on a psychiatric disorder (psychiatric EAS), including Belgium, the Netherlands, Luxembourg, Switzerland, and Canada as of March 2023 (CCA, 2018; Griffith, Weyers, & Adams, Reference Griffith, Weyers and Adams2008; Rukavina, Reference Rukavina2019). One of the key requirements for psychiatric EAS in the Netherlands and Belgium is irremediability, or the lack of reasonable treatment options (Box 1). For example, the Dutch law states that a physician must ‘come to the conclusion, together with the patient, that there is no reasonable alternative in the patient's situation’ (Dutch Act, 2002). Existing Dutch and Belgian guidelines for clinicians state that the requirement ‘must be assessed in light of the diagnosis and prognosis’ (Euthanasia Code, 2018), from an ‘objective medical-psychiatric perspective’ and ‘according to current medical understanding’ (NVVP, 2018; VVP et al., Reference Vandenberghe, Titeca, Matthys, Van den Broeck, Detombe and Van Buggenhout2017). In contrast, the Canadian law explicitly relies on a subjective judgment of irremediability, where remediable is defined by what a patient considers acceptable (CCA, 2018).
Box 1. Background information
Psychiatric EAS in the Netherlands and Belgium
Legal requirements for EAS
According to the Dutch Termination of Life on Request and Assisted Suicide Act (2002), the substantive requirements are that the attending physician must: be satisfied that the patient's request is voluntary and well-considered; be satisfied that the patient's suffering is unbearable and without prospect of improvement; have come to the conclusion, together with the patient, that there is no reasonable alternative in the patient's situation; have consulted at least one other, independent physician and have exercised due medical care in terminating the patient's life (Euthanasia Code, 2018; Onwuteaka-Philipsen et al., Reference Onwuteaka-Philipsen, Legemaate, van Der Heide, van Delden, Evenblij, El Hammoud and Willems2017). According to the Belgian 2002 Act Concerning Euthanasia, the physician must: come to the conviction, together with the patient, that there is no reasonable alternative in his/her condition and the request is voluntary; ascertain the continued physical or mental suffering of the patient and consult another physician about the serious and incurable nature of the disorder. If the patient is not expected to die in the near future, the following requirements apply in the Belgian Act: a second physician, a psychiatrist or a specialist in the disorder in question, needs to be consulted, and there should be at least one month between the patient's written request and the performance of euthanasia (Jones, Gastmans, and MacKellar, Reference Jones, Gastmans and MacKellar2017).
Process and oversight systems for EAS
The Belgian Act requires that the physician consult a second physician – a psychiatrist in cases of psychiatric EAS – and requires a waiting time of at least one month for all non-terminally ill cases. While the Dutch law requires that the physician consults at least one other, independent physician, it does not specify that this be a psychiatrist for psychiatric EAS cases. However, in these cases, a psychiatric consultation is required by the Dutch Euthanasia Review Committees. Both countries have established services providing such consultants: Support and Consultation for Euthanasia in the Netherlands (SCEN) and Life End Information Forum (LEIF) in Belgium (Van Wesemael, Cohen, Onwuteaka-Philipsen, Bilsen, and Deliens, Reference Van Wesemael, Cohen, Onwuteaka-Philipsen, Bilsen and Deliens2009). All EAS cases need to be reported post-hoc to the Regional Euthanasia Review Committees and the Federal Control and Evaluation Committee on Euthanasia, respectively in the Netherlands and Belgium. These committees review the EAS reports to assess whether the physician who performed EAS conformed to the legal due care criteria (Euthanasia Code, 2018; Jones, Gastmans, and MacKellar, Reference Jones, Gastmans and MacKellar2017).
Evolving situation in Canada
The Canadian Medical Assistance in Dying (MAID) law enacted in 2016 stated that, to receive MAID, a person must be capable of making health decisions, have a grievous and irremediable medical condition, have made a voluntary request that was not the result of extremal pressure. To meet the ‘grievous and irremediability medical condition’ requirement, a person needs to: (a) have a serious and incurable illness, disease or disability; (b) be in an advance state of irreversible decline in capability, (c) the illness, the disease or disability or that state of decline causes them enduring physical or psychological suffering that is intolerable to them and that cannot be relieved under conditions that they consider acceptable; (d) their natural death has become reasonably foreseeable, taking into account all of their medical circumstances, without a prognosis necessarily having been made as to the specific length of time that they have remaining (CCA, 2018). Current Parliamentary discussions are ongoing, after a Quebec Superior Court stated the ‘reasonably foreseeable death’ requirement is unconstitutional, which led to the new Bill C7 (Bill C-7, 2021; Rukavina, Reference Rukavina2019). As of March 2021, Canada has a commitment under its new law to legalize MAID based on a sole mental disorder (with a sunset provision of 2 years, going into effect March 2023) (Bryden, Reference Bryden2021). As of March 2022, a Parliamentary Review Committee is tasked with a comprehensive review of the provision of the Criminal Code relating to MAID and its applications, including MAID based on a mental disorder (Bill C-7, 2021; Gallant, Reference Gallant2022).
There is considerable conceptual debate about how irremediability should be defined in the context of psychiatric EAS, and whether an objective or subjective standard should prevail (Gaind, Reference Gaind2020; Nicolini, Kim, Churchill, & Gastmans, Reference Nicolini, Kim, Churchill and Gastmans2020a; Schuklenk, Reference Schuklenk2019; Sinyor & Schaffer, Reference Sinyor and Schaffer2020; Smith, Reference Smith2020; van Veen, Ruissen, & Widdershoven, Reference van Veen, Ruissen and Widdershoven2020). The question of whether clinicians can, on an objective standard, accurately determine irremediability and prognosis in psychiatry is the single most contested claim in the professional debate about the practice (Nicolini et al., Reference Nicolini, Kim, Churchill and Gastmans2020a). Given pressing policy discussions about psychiatric EAS in Canada and elsewhere, clarifying whether the objective standard for irremediability is relevant is of crucial importance for solid policymaking and implementation of psychiatric EAS.
Discussions have repeatedly invoked ‘the person with treatment-resistant depression’ as the paradigm case of an irremediable psychiatric condition (Blikshavn, Husum, & Magelssen, Reference Blikshavn, Husum and Magelssen2017; Broome & de Cates, Reference Broome and de Cates2015; Miller, Reference Miller2015; Schuklenk & van de Vathorst, Reference Schuklenk and van de Vathorst2015; Steinbock, Reference Steinbock2017), often assuming that treatment-resistant depression (TRD) is, by definition, irremediable. Yet what does it mean for a clinician to assess prognosis and irremediability in a particular case, ‘according to current medical understanding’? Rooney et al., have rightly argued that assessing irremediability is to ‘perform a cost-benefit analysis of given treatments on a case-by-case basis, making medical decisions based on the statistically likely outcome’ (Rooney, Schuklenk, & van de Vathorst, Reference Rooney, Schuklenk and van de Vathorst2018). For these medical decisions, they go on to argue, ‘evidence-based metrics for staging TRD, like the Maudsley Staging Method […] can be effective tools to help single out irremediable cases’. A thorough examination of whether clinicians can indeed single out irremediable cases in psychiatry –based on clinical judgment and/or on available tools– is lacking. This paper aims to address the glaring empirical gap in the debate over psychiatric EAS.
Focusing on TRD as a test case, we examine three claims relevant to the clinical assessment of prognosis and long-term outcome in a particular patient requesting psychiatric EAS, by asking in a stepwise approach: (1) What is the range of existing definitions of TRD? (2) What is known about the long-term outcomes of persons with TRD? and (3) What is the state-of-the-art regarding individual outcome prediction for a person with TRD? We then discuss how these findings inform the debate about irremediability in the context of psychiatric EAS.
Methods
We reviewed the state-of-the-art evidence for the claim that clinicians can or cannot predict long-term chances of recovery in a patient with TRD through a scoping review, by asking the following three questions (Box 2): (1) Is there a uniform definition of TRD, i.e., a shared understanding of what clinicians mean by the term, (2) Can clinicians predict group-level long-term outcomes of TRD, i.e., what do we know about population-level long-term outcomes and their predictors, and (3) Can clinicians make accurate individual outcome predictions in a person with TRD, i.e., can they accurately determine who will and who will not achieve recovery in practice.
Box 2. Search strategy and selection criteria.
We performed a scoping review focusing on three research questions, namely, what is the current state-of-the-art evidence about (1) definitions of TRD (2) long-term outcomes of TRD (3) individual prediction of TRD (Fig. 1). For the first research question about definitions of TRD, one author (M.N) performed a broad search in PubMed with no date restriction (Oct 6, 2020): (‘Depressive Disorder, Treatment-Resistant’ [Mesh]) yielded 1525 results. The question of how to define TRD has been extensively discussed in the literature. The aim was to examine the evidence for the (narrow) question of whether there is – or is not- a single definition of TRD. Hence, we further specified the search strategy to (systematic) reviews on the subject, by using PubMed filters ‘Reviews’ and ‘Systematic reviews’, yielding 242 results. Reviews focusing on definitions and concepts of TRD were included; reviews about specific or novel therapeutic strategies for TRD (pharmacology, psychotherapy, neuromodulation, basic research) were excluded. Reviews focusing on children and adolescents were excluded. 11 references were included, and another 3 included through hand search, for a total of 14 references.
For the second research question about long-term outcomes of TRD, M.N. used the following string: (‘Depressive Disorder, Treatment-Resistant’ [Mesh]) AND ‘Follow-up’), yielding 150 references. Inclusion criteria were publications focusing on (1) unipolar TRD, and (2) medium to long-term outcome at follow-up. The latter focused on naturalistic studies, excluding clinical trials where participants received adjunctive and/or experimental treatment. Medium to longer-term was defined as a period going beyond the usual period of several weeks or months or more as part of a clinical trial. Three publications were included, two additional references were yielded through hand search of the references, one of which was not indexed as ‘treatment-resistant’ as it was published before the specific MeSH term was introduced in PubMed in 2012.
For the third research question about individual prediction of TRD, one author (E.J.) performed a search with a broad and inclusive MeSH term and no date restriction: ‘(“Depressive Disorder, Treatment-Resistant”[Mesh] OR (“Depressive Disorder, Major”[Mesh] AND “Drug Resistance”[Mesh])) AND (“Algorithms”[Mesh] OR “Sensitivity and Specificity”[Mesh])’. Algorithms is a broad term including subcategories such as AI, Machine Learning, Natural Language Processing, and Neural Networks, while Sensitivity and Specificity includes subcategories such as Predictive Value of Tests, Roc Curve, And Signal-to-Noise Ratio (online Supplementary Materials 1). Taken together, these terms narrowed the search onto papers which focused on prediction. Fifty-seven references were returned and additional references were hand-searched. Papers which did not report metrics on the accuracy of predictions or did not focus on TRD were excluded, leaving 17 studies for review, with an additional 5 identified through hand search, for a total of 22 studies.
Search strategy and selection process.
Results
Is there a uniform definition of treatment-resistant depression?
The first search yielded a total of fourteen review studies focusing specifically on the topic of how the concept of TRD is defined and operationalized. They either focused on definitions of TRD and staging models (Brown et al., Reference Brown, Rittenbach, Cheung, McKean, Macmaster and Clement2019; Demyttenaere & Van Duppen, Reference Demyttenaere and Van Duppen2019; Gaynes et al., Reference Gaynes, Lux, Gartlehner, Asher, Forman-Hoffman, Green and Lohr2020; Malhi & Byrow, Reference Malhi and Byrow2016; McIntyre et al., Reference McIntyre, Filteau, Martin, Patry, Carvalho, Cha and Miguelez2014; Ng et al., Reference Ng, Kato, Han, Wang, Trivedi, Ramesh and Kasper2019; Pandarakalam, Reference Pandarakalam2108; Ruhé, Van Rooijen, Spijker, Peeters, & Schene, Reference Ruhé, Van Rooijen, Spijker, Peeters and Schene2012; Sackeim et al., Reference Sackeim, Aaronson, Bunker, Conway, Demitrack, George and Rush2019; Trevino, McClintock, McDonald Fischer, Vora, & Husain, Reference Trevino, McClintock, McDonald Fischer, Vora and Husain2014), or on the emerging shift away from the concept of TRD, in favor of the alternative notion of ‘difficult to treat’ depression (Cosgrove, Naudet, Hiogberg, Shaughnessy, & Cristea, Reference Cosgrove, Naudet, Hiogberg, Shaughnessy and Cristea2020; Demyttenaere, Reference Demyttenaere2019; McAllister-Williams et al., Reference McAllister-Williams, Arango, Blier, Demyttenaere, Falkai, Gorwood and Rush2020; Rush, Aaronson, & Demyttenaere, Reference Rush, Aaronson and Demyttenaere2019). All were published after 2012, the year in which PubMed started using the MeSH index term ‘Treatment-Resistant Depression’. Ten of fourteen review studies were published after 2018, indicating that this topic has been subject to recent discussions.
The reviews about definitions of TRD reported on the wide range of current definitions of TRD, and the associated challenges for TRD research and treatment. One systematic review found 155 definitions for TRD among the 150 studies included, with about half (50.3%) requiring at least 2 treatment failures and only a minority (11%) including neuromodulation (Brown et al., Reference Brown, Rittenbach, Cheung, McKean, Macmaster and Clement2019). Another review found that only 20% of studies used the most common definition of TRD of at least 2 failed treatments and confirmation of prior adequate dose and duration (Gaynes et al., Reference Gaynes, Lux, Gartlehner, Asher, Forman-Hoffman, Green and Lohr2020). Importantly, patient-oriented outcome measures focusing on functional impairment or quality-of-life were rarely used.
Reviews about alternative conceptualizations of TRD focused on ways to address the problem of heterogeneity in TRD definitions and concepts. Proponents of the shift to ‘difficult-to-treat’ depression call for a more holistic dimensional focus that includes psychosocial functioning and quality-of-life (McAllister-Williams et al., Reference McAllister-Williams, Arango, Blier, Demyttenaere, Falkai, Gorwood and Rush2020; Rush et al., Reference Rush, Aaronson and Demyttenaere2019). At the same time, others were skeptical about creating a possibly over-inclusive label (Cosgrove et al., Reference Cosgrove, Naudet, Hiogberg, Shaughnessy and Cristea2020). However, proponents and skeptics alike agree that current concepts of TRD have important limitations, notably their biological heterogeneity and their focus on psychopharmacological treatments, with limited data on psychotherapy or neuromodulation.
Hence, although there is no agreed upon definition of TRD, there is agreement that current definitions are limited (primarily designed for psychopharmacological research), and discussions about conceptualization of TRD in research and clinical practice are ongoing.
Can we predict group-level long-term outcomes of treatment-resistant depression?
We found a total of 5 studies focusing on long-term outcomes of TRD specifically defined as such (Table 1). The focus on TRD and its long-term outcomes in naturalistic settings is relatively recent: a first systematic review was published in 2009 (Fekadu et al., Reference Fekadu, Wooderson, Markopoulo, Donaldson, Papadopoulos and Cleare2009), after which a total of four medium to long-term observational studies were published, all from the same research group (Fekadu et al., Reference Fekadu, Wooderson, Rane, Markopoulou, Poon and Cleare2011, Reference Fekadu, Rane, Wooderson, Markopoulou, Poon and Cleare2012; Vergunst et al., Reference Vergunst, Fekadu, Wooderson, Tunnard, Rane, Markopoulou and Cleare2013; Wooderson et al., Reference Wooderson, Fekadu, Markopoulou, Rane, Poon, Juruena and Cleare2014).
Overview of medium- to longer-term outcome of TRD
The systematic review by Fekadu et al. (Table 1) is the first comprehensive review to incorporate follow-up studies of TRD, including studies which: (1) defined treatment-resistance as a failure to respond to at least one antidepressant or where treatment-resistance could be inferred from the overall description, (2) were longitudinal (3) had a minimum duration of 6 months (i.e. going beyond the usual short-term follow-up as part of an acute treatment trial) (4) used defined dimensional or categorical outcomes. The authors reviewed 9 studies (including a subsample of the well-known STAR-D study) for a total of 1279 participants. In all but one study, patients were recruited from secondary and tertiary services, but most patients had a chronic history of severe illness. Of the two largest studies, patients included had either chronic major depression of at least 4 previous episodes (Dunner et al., Reference Dunner, Rush, Russell, Burke, Woodard, Wingard and Allen2006), or a history of recurrent depression in 74.7%, with mean duration of illness of 15.3 years and mean age at first episode of 25.5 (Rush et al., Reference Rush, Trivedi, Wisniewski, Nierenberg, Stewart, Warden and Fava2006).
The largest study showed a cumulative remission rate of 70% at one-year follow-up (Rush et al., Reference Rush, Trivedi, Wisniewski, Nierenberg, Stewart, Warden and Fava2006). Other studies found a ‘good outcome’ (i.e. recovery or the absence of relapse) in 38–48% (3 studies) and a ‘poor outcome’ (i.e. relapse or premature death) varying between 28–68% (3 studies). Overall, the review found that TRD is a highly relapsing condition, with substantial disability and mortality. However, duration of follow-up was short in most studies. In fact, the two largest studies had a follow-up period of 1 and 2 years, respectively, and both studies used a very short duration to define relapse (1 week) (Dunner et al., Reference Dunner, Rush, Russell, Burke, Woodard, Wingard and Allen2006; Rush et al., Reference Rush, Trivedi, Wisniewski, Nierenberg, Stewart, Warden and Fava2006). The review leaves open the possibility that, based on longitudinal studies of affective disorders, outcomes might have been better if longer duration of follow-up had been used, as seen in a 12-year follow-up safety study (Nugent, Iadarola, Miller, Luckenbaugh, & Zarate, Reference Nugent, Iadarola, Miller, Luckenbaugh and Zarate2016). Finally, only two studies reported on social outcomes like quality-of-life or functioning.
Since the above systematic review, four studies have been published (2011–2014); these were the first follow-up studies to recruit participants explicitly defined as having TRD (Table 1). Although treatment-resistance was defined as a failed response to at least 1 antidepressant, the patients' severity of illness at entry was significant with a moderately severe to severe TRD (per the Maudsley Staging Method) (Fekadu et al., Reference Fekadu, Rane, Wooderson, Markopoulou, Poon and Cleare2012), a mean duration of illness of 16–22.2 years (Fekadu et al., Reference Fekadu, Wooderson, Rane, Markopoulou, Poon and Cleare2011), and treatment history of ECT in 69% (Vergunst et al., Reference Vergunst, Fekadu, Wooderson, Tunnard, Rane, Markopoulou and Cleare2013) or prolonged intensive multidisciplinary inpatient therapy with a minimum score of 16 on the 21-item Hamilton Depression Rating Scale (Wooderson et al., Reference Wooderson, Fekadu, Markopoulou, Rane, Poon, Juruena and Cleare2014). Overall, sample sizes were relatively small, ranging from 71 to 118, and two of the four studies involved the same set of participants (Fekadu et al., Reference Fekadu, Wooderson, Rane, Markopoulou, Poon and Cleare2011, Reference Fekadu, Rane, Wooderson, Markopoulou, Poon and Cleare2012).
These four studies reported on longer-term outcomes (mean of 3 years) in patients with TRD. The first study found that 69% achieved remission or partial remission, with outcomes at follow-up (median of 3 years) varying according to the status at discharge (Fekadu et al., Reference Fekadu, Wooderson, Rane, Markopoulou, Poon and Cleare2011). The second study found that at follow-up (mean of 39 months), 60.2% reached full remission, with 39.8% showing persistent depressive symptoms (Fekadu et al., Reference Fekadu, Rane, Wooderson, Markopoulou, Poon and Cleare2012). This study reported on predictors of longer-term outcome in TRD patients. Higher educational achievement (hazard ratio (HR) = 1.17, 95% CI 1.01–1.35; p = 0.03) and strong level of social support (HR = 1.76, 95% CI 1.07–2.89; p = 0.03) were found to be predictors of remission during follow-up. The third study showed similar outcomes at follow-up: 60.3% were asymptomatic or at subthreshold level and 39.7% had chronic symptoms (Vergunst et al., Reference Vergunst, Fekadu, Wooderson, Tunnard, Rane, Markopoulou and Cleare2013). Of the tested predictors of mean symptom severity (e.g. social support, number of prior of depressive episodes, duration of admission), only social support was found to be a significant predictor (beta −0.356, p = 0.001). The fourth study found that, with intensive multidisciplinary treatment, 66% had a good outcome and 18–34% had poor to intermediate outcome at follow-up (median of 34 months) (Wooderson et al., Reference Wooderson, Fekadu, Markopoulou, Rane, Poon, Juruena and Cleare2014). The study showed that patients can maintain clinical improvement 3 years (mean) post-discharge following intensive multidisciplinary TRD treatment.
These four longitudinal studies build on emerging evidence about long-term outcomes of TRD (Fekadu et al., Reference Fekadu, Wooderson, Markopoulo, Donaldson, Papadopoulos and Cleare2009). Despite the significant severity of depression and chronicity of treatment-resistance upon study entry (mean duration of 16 to 22 years), a majority achieved remission, while a substantial minority had persistent depressive symptoms. This raises a separate question, namely whether physicians can reliably distinguish those who will recover from those who will not, on an individual, rather than group-level, basis.
Can we make individual predictions of treatment-resistance in depression?
We found 22 studies investigating individual prediction of treatment-resistance in depression. Thirteen studies (Table 2.A) focused on whether an individual patient who has failed to respond to multiple past treatments will respond to the next treatment. These studies were relatively recent, with small sample sizes. The remaining nine studies (Table 2.B) focused on the question of which patients with major depression will develop treatment-resistance (defined in a variety of ways).
Individual prediction of treatment-resistance in depression
*Note: Sensitivity and specificity and PPV and NPV were recalculated for some studies so that all metrics reflect prediction of TRD as the positive class.
Of the 13 studies focusing on patients with demonstrated treatment-resistance (Table 2.A), all but two had under fifty participants and several involved machine learning (Bailey et al., Reference Bailey, Hoy, Rogasch, Thomson, McQueen, Elliot and Fitzgerald2018, Reference Bailey, Hoy, Rogasch, Thomson, McQueen, Elliot and Fitzgerald2019; Bares, Novak, Brunovsky, Kopecek, & Höschl, Reference Bares, Novak, Brunovsky, Kopecek and Höschl2017; Bares et al., Reference Bares, Novak, Kopecek, Brunovsky, Stopkova and Höschl2015; Carrillo et al., Reference Carrillo, Sigman, Fernández Slezak, Ashton, Fitzgerald, Stroud and Carhart-Harris2018; Ge et al., Reference Ge, Blumberger, Downar, Daskalakis, Dipinto, Tham and Vila-Rodriguez2017; Kautzky et al., Reference Kautzky, Baldinger, Souery, Montgomery, Mendlewicz, Zohar and Kasper2015; Khodayari-Rostamabad, Reilly, Hasey, de Bruin, & Maccrimmon, Reference Khodayari-Rostamabad, Reilly, Hasey, de Bruin and Maccrimmon2013; Micoulaud-Franchi et al., Reference Micoulaud-Franchi, Richieri, Cermolacce, Loundou, Lancon and Vion-Dury2012; Minelli et al., Reference Minelli, Abate, Zampieri, Gainelli, Trabucchi, Segala and Bortolomasi2016; Richieri et al., Reference Richieri, Boyer, Farisse, Colavolpe, Mundler, Lancon and Guedj2011; Sun et al., Reference Sun, Farzan, Mulsant, Rajji, Fitzgerald, Barr and Daskalakis2016; van Waarde et al., Reference van Waarde, Scholte, van Oudheusden, Verwey, Denys and van Wingen2015). Most investigated whether patients would respond to one specific intervention (e.g. TMS, psilocybin). When predictive values were reported, predictions that a patient would not respond to the specific intervention tested varied, with accurate predictions ranging from 61.5% (total N = 45) to 100% (total N = 21) (Micoulaud-Franchi et al., Reference Micoulaud-Franchi, Richieri, Cermolacce, Loundou, Lancon and Vion-Dury2012; Minelli et al., Reference Minelli, Abate, Zampieri, Gainelli, Trabucchi, Segala and Bortolomasi2016). The nine studies focusing on which patients with major depression might develop TRD (broadly defined) are more extensive and include large multi-site trials of hundreds or thousands of patients, with a wide variety of predictors (Table 2.B). These studies vary by design and study size, and include: (1) pragmatic trials, i.e., reflecting real-world conditions, (2) large sampled, regimented trials involving large datasets like the STAR*D dataset, and (3) studies using medical records.
Firstly, the two pragmatic trials involved available treatment for depression (Chang et al., Reference Chang, Choi, Jeon, Lee, Han, Kim and Kang2019; Dinga et al., Reference Dinga, Marquand, Veltman, Beekman, Schoevers, Van Hemert and Schmaal2018). One study followed 804 MDD or dysthymia patients receiving any combination of pharmacological, psychotherapeutic, or no treatments (Dinga et al., Reference Dinga, Marquand, Veltman, Beekman, Schoevers, Van Hemert and Schmaal2018). This study, based on the Netherlands Study of Depression and Anxiety dataset, covered a wide range of illness severity. The model predicted who would develop TRD (defined as chronic depression with no improvement after two years of any or no treatments), found that about half (47%) of the TRD patients were correctly predicted to be so. This is the only prediction model built on naturalistic study data that we found, and the study with the longest prediction endpoint. However, it lacks external validation. The second study involved a network approach to antidepressant resistance with 121 patients (Chang et al., Reference Chang, Choi, Jeon, Lee, Han, Kim and Kang2019). In a small testing dataset (N = 13) of patients with MDD, 80% of the patients who were predicted to respond to treatment did in fact respond to their prescribed antidepressants. A network was designed to output modeling about the predicted effectiveness of antidepressants for every patient, and outperformed baseline models both for prediction of response and of remission.
A second set of five studies, involving large STAR*D or GRSD (Group for the Study of Resistant Depression) datasets, identified which depressed patients would not respond to their second (Kautzky et al., Reference Kautzky, Baldinger-Melich, Kranz, Vanicek, Souery, Montgomery and Kasper2017, Reference Kautzky, Dold, Bartova, Spies, Vanicek, Souery and Kasper2018, Reference Kautzky, Dold, Bartova, Spies, Kranz, Souery and Kasper2019; Perlis, Reference Perlis2013) or subsequent (Nie, Vairavan, Narayan, Ye, & Li, Reference Nie, Vairavan, Narayan, Ye and Li2018) antidepressant trial. Samples ranged from 400 to 2454 patients, with large external validation samples. The models' predictive accuracies during validation were variable: for predictions that a patient would respond to a subsequent antidepressant (i.e. symptom reduction), the models were correct from 39% (N = 225) to 81.9% (N = 314) of the time. For predictions that a patient would not respond to a subsequent antidepressant, the models' accuracy ranged from 66.5% (N = 80) to 92% (N = 225).
A third set of two studies used patient records to predict treatment-resistance (Cepeda et al., Reference Cepeda, Reps, Fife, Blacketer, Stang and Ryan2018; Perlis et al., Reference Perlis, Iosifescu, Castro, Murphy, Gainer, Minnier and Smoller2012). One study used insurance claims of 22 057 patients to predict which patients would receive neuromodulation after trying an antidepressant in the past year (Cepeda et al., Reference Cepeda, Reps, Fife, Blacketer, Stang and Ryan2018). The authors found that their algorithmically-derived decision-tree rule performed more accurately in internal validation than any of the five decision rules defined by expert psychiatrists (F-1 = 0.44 compared to F-1′s = 0.39–0.42) and held up in external validation samples totaling 14 845 patients from alternate insurance databases (AUCs = 0.78–0.79). A second study used natural language processing of 5198 patient records to develop a model predicting whether patients were depressed on every given visit following an antidepressant prescription (Perlis et al., Reference Perlis, Iosifescu, Castro, Murphy, Gainer, Minnier and Smoller2012). The authors classified individual visits as depressed (v. well) with a positive predictive value of 78%. Next, patients were classified as treatment-resistant if they had a majority of predicted-depressed visits despite 2 antidepressant trials in the past year. Agreement between the model's predictions of treatment-resistance and the opinion of a board of expert clinicians was 76.4%. However, these studies used somewhat unusual endpoints for treatment resistance.
In sum, there is a growing body of evidence assessing the accuracy of predictions about treatment-resistance. Most of the studies are limited by unconventional definitions of treatment-resistance or the use of limited interventions. Studies predicting whether a patient who failed to respond to multiple past treatments will respond to the next treatment are relatively new, underpowered, and lack external validation. Studies investigating whether a patient will develop treatment-resistance are more developed, with larger sample sizes, more comprehensive sets of predictors, and larger external validation datasets. Predictive accuracy across a range of metrics varies widely, with the largest and best validated studies showing lower predictive abilities.
Discussion
Irremediability is a key eligibility requirement for psychiatric EAS and is defined as the lack of reasonable alternatives, which must be ‘assessed in light of the diagnosis and prognosis’ (Euthanasia Code, 2018). While current frameworks allow for a person to refuse a treatment option, guidelines emphasize a default objective standard for irremediability (NVVP, 2018; VVP et al., Reference Vandenberghe, Titeca, Matthys, Van den Broeck, Detombe and Van Buggenhout2017). Whether clinicians can accurately determine, as per ‘current medical understanding’, prognosis and irremediability in the context of psychiatric EAS is a key question in the debate about irremediability – and the central question we examined here. Given that debates about irremediability hinge on the key issue of ‘objective v. subjective’ standard of irremediability, whether the objective standard of irremediability in psychiatry is relevant is of crucial importance for policymaking around the world, for ongoing and future discussions about extending EAS laws to include psychiatric EAS.
Discussion of main findings
Although the term TRD has gained wide use, it is used primarily for research purposes and relatively recently: over 150 definitions exist, and active discussions about appropriate outcome measures for TRD are ongoing. Unlike what it seems to suggest, ‘treatment-resistance’ does not mean that there are no remaining options, and definitions evolve with the introduction of new treatments (e.g. esketamine, NNT of 5) (Kasper, Reference Kasper2022). At the same time, scientific knowledge about group-level long-term outcomes of TRD is limited. Four naturalistic studies focused on medium to long-term outcomes in patients who were explicitly defined as having TRD at beginning of follow-up (Fekadu et al., Reference Fekadu, Wooderson, Rane, Markopoulou, Poon and Cleare2011, Reference Fekadu, Rane, Wooderson, Markopoulou, Poon and Cleare2012; Vergunst et al., Reference Vergunst, Fekadu, Wooderson, Tunnard, Rane, Markopoulou and Cleare2013; Wooderson et al., Reference Wooderson, Fekadu, Markopoulou, Rane, Poon, Juruena and Cleare2014). These studies showed that included patients, despite being well-characterized as treatment-resistant at the onset of the studies and after having received years of community treatment – i.e., persons with extensive psychiatric histories, comparable those requesting and receiving psychiatric EAS currently (Kim, De Vries, & Peteet, Reference Kim, De Vries and Peteet2016; Nicolini, Peteet, Donovan, & Kim, Reference Nicolini, Peteet, Donovan and Kim2020b; Thienpont et al., Reference Thienpont, Verhofstadt, Van Loon, Distelmans, Audenaert and De Deyn2015) – a majority significantly improved – i.e., reached remission. Furthermore, they found a role for non-biological predictors such as education level or social support in TRD outcomes. The limitations of these studies included: (a) their small number overall, (b) their small sample sizes with internal overlap in terms of participants, (c) their focus on TRD defined primarily as failed pharmacological treatments, (d) the absence of newer agents with proven efficacy for TRD, and (e) their overall limited usefulness for individual outcome prediction.
Individual prediction studies were found to have overall modest predictive ability, were often not tested in prospective studies, and limited applicability in practice. Studies focusing specifically on response prediction in patients with TRD were relatively limited in size and scope, involving only specific treatments (e.g. TMS, psilocybin), and focusing on experimentally relevant predictors (e.g. ECT seizure quality). Among the larger and more rigorous studies of patients with major depression, the models' predictive ability is unlikely to be sufficient for clinical use. Overall, the individual prediction studies had the following limitations: (a) most models only predicted whether patients will respond to a particular treatment rather than all available treatments (and if so, to which of available treatments), (b) only one study involved long-term follow-up of sustained remission (Dinga et al., Reference Dinga, Marquand, Veltman, Beekman, Schoevers, Van Hemert and Schmaal2018), (c) potential wrongful inflation of accuracy estimates (e.g. related to small sample sizes, absence of testing model performance in an external sample, and problematic validation methods), precluding reliable immediate implementation in clinical practice (Hosseini et al., Reference Hosseini, Powell, Collins, Callahan-Flintoft, Jones, Bowman and Wyble2020; Jacobucci, Littlefield, Millner, Kleiman, & Steinley, Reference Jacobucci, Littlefield, Millner, Kleiman and Steinley2020; Poldrack, Huckins, & Varoquaux, Reference Poldrack, Huckins and Varoquaux2020). Finally, the model which came closest to reflecting real-life conditions (Dinga et al., Reference Dinga, Marquand, Veltman, Beekman, Schoevers, Van Hemert and Schmaal2018), accurately predicted outcomes (i.e. who would continue to have chronic depression after two years of any or no treatments) in only 47% of cases – that is, at chance level.
Implications for the debate about irremediability in psychiatric EAS
The findings of this scoping review raise several implications for the debate about irremediability in psychiatric EAS. First, our findings show that the objective standard for irremediability will be difficult to meet, at least in the paradigm case of depression, because a clinician cannot accurately determine irremediability, as argued by many (Appelbaum, Reference Appelbaum2017; Blikshavn et al., Reference Blikshavn, Husum and Magelssen2017; Broome & de Cates, Reference Broome and de Cates2015; Cowley, Reference Cowley2013, Reference Cowley2015; Jansen, Wall, & Miller, Reference Jansen, Wall and Miller2019; Kelly, Reference Kelly2017; Kelly & McLoughlin, Reference Kelly and McLoughlin2002; Kim & Lemmens, Reference Kim and Lemmens2016; Kissane & Kelly, Reference Kissane and Kelly2000; Miller, Reference Miller2015; Naudts et al., Reference Naudts, Ducatelle, Kovacs, Laurens, Van Den Eynde and Van Heeringen2006; Olié & Courtet, Reference Olié and Courtet2016; Schoevers, Asmus, & Van Tilburg, Reference Schoevers, Asmus and Van Tilburg1998; Simpson, Reference Simpson2018; Steinbock, Reference Steinbock2017; Vandenberghe, Reference Vandenberghe2011, Reference Vandenberghe2018). Our findings point to the fact that in psychiatric disorders, unlike in somatic disorders, lack of treatment-response does not necessarily entail lack of long-term recovery. This further shows that, in professional debates about irremediability, invoking the construct of TRD is not ‘a good starting point for identifying an irremediable psychiatric condition’ (Rooney et al., Reference Rooney, Schuklenk and van de Vathorst2018). Given that a diagnosis of TRD is clearly not sufficient to establish irremediability, the concepts of ‘treatment-resistance’ and ‘irremediability’ should not be conflated.
Second, our findings do not support the claim, made by some, that clinicians can rely on existing statistical and staging tools like the Maudsley Staging Method to predict chances of recovery in a person requesting psychiatric EAS (Provencher-Renaud, Larivée, & Sénéchal, Reference Provencher-Renaud, Larivée and Sénéchal2019; Rooney et al., Reference Rooney, Schuklenk and van de Vathorst2018; Tanner, Reference Tanner2018). Unlike in somatic medicine, staging methods used for depression do not correlate with prognosis. The fact that a majority of patients with severe chronic depressive illness and high scores on the Maudsley Staging Method – i.e., patients with history similar to those who currently request and receive psychiatric EAS – will enter remission, shows that high disease severity or chronicity does not correlate with long-term symptom persistence or a lack of recovery. Furthermore, potentially promising statistical tools, like machine learning models for individual prediction, although promising, cannot yet be reliably implemented in clinical practice. The best proxy model shows a prediction accuracy at chance level, suggesting that, as things stand, precision psychiatry cannot yet resolve the problem of prognosis prediction in psychiatry.
Finally, our findings provide preliminary evidence for the claim that non-biological social factors, e.g. social support, can affect chances of recovery in psychiatry (Blikshavn et al., Reference Blikshavn, Husum and Magelssen2017; Cowley, Reference Cowley2013; Jansen et al., Reference Jansen, Wall and Miller2019; Kelly, Reference Kelly2017; Kissane & Kelly, Reference Kissane and Kelly2000; Miller & Appelbaum, Reference Miller and Appelbaum2018; Pearce, Reference Pearce2017; Schoevers et al., Reference Schoevers, Asmus and Van Tilburg1998). The role of social support is especially relevant for psychiatric EAS, as loneliness and social isolation are reported in over half of Dutch psychiatric EAS cases (Kim et al., Reference Kim, De Vries and Peteet2016), and described explicitly as one of the reasons for requesting psychiatric EAS in a Belgian qualitative study (Verhofstadt, Thienpont, & Peters, Reference Verhofstadt, Thienpont and Peters2017). The role of social factors points to the key issue of explanatory pluralism in psychiatry (Gardner & Kleinman, Reference Gardner and Kleinman2019; Kendler, Reference Kendler2019) – a foundational question of clear ethical relevance for the debate about psychiatric EAS.
Future research
The debate about irremediability in psychiatric EAS needs clarity about whether it adheres to an objective or a subjective standard for irremediability. Our findings show that for the paradigm case of TRD, as things stand, the objective standard for irremediability in psychiatric EAS fails, and points to several avenues for future research.
On the objective standard for irremediability, there is an open empirical question of how reliable prediction psychiatry will be regarding long-term outcomes and responses to (a list of) available evidence-based treatments. In addition, there is an open policy question of what an acceptable threshold for reliability might be. Our findings point to avenues that inform the former. First, we need more large-sampled naturalistic and prediction psychiatry studies looking at long-term outcomes, both at the group-level and individual-level. Second, given that persons requesting psychiatric EAS often have psychiatric comorbidities, notably personality disorders (Kim et al., Reference Kim, De Vries and Peteet2016; Nicolini et al., Reference Nicolini, Peteet, Donovan and Kim2020b; Thienpont et al., Reference Thienpont, Verhofstadt, Van Loon, Distelmans, Audenaert and De Deyn2015), trials that include the effect of comorbidity on long-term outcomes are crucial. Third, predictors of outcomes need to include a range of clinical (biological and psychological) and social predictors, in a way that aligns with the recognized explanatory pluralism in psychiatry.
While this empirical research might further our conception of the objective standard for irremediability, which standard should prevail – objective or subjective– is a separate question, one that cannot be settled by empirical evidence. Further normative debate is needed to determine whether a subjective standard should prevail and if so, how it should be conceived of – issues beyond the purview of this paper.
Strengths and limitations
This paper is the first to comprehensively examine the scientific evidence about what we mean by ‘treatment-resistant’ – using depression as a test case – and the implications for debates about irremediability in psychiatric EAS. The fact that long-term follow-up studies included TRD patients with chronic and severe illness makes it especially relevant for the context of psychiatric EAS. The paper has several limitations. First, we chose TRD as a focus as this has been the paradigm case within the debate on irremediability. The results remain thus limited to TRD. However, this type of review can be applied to other psychiatric disorders such as schizophrenia or bipolar, e.g. using available evidence for prediction algorithms (Alonso et al., Reference Alonso, de la Torre-Díez, Hamrioui, López-Coronado, Barreno, Nozaleda and Franco2018). Second, our scoping review involved only one database. Finally, our findings clarify what we mean by irremediability when this includes a medical judgment, as emphasized by prevailing guidelines for psychiatric EAS evaluations. For those who emphasize a subjective interpretation of irremediability – i.e., that it is what the patient defines as irremediable – our findings provide a rigorous evidence-based picture of the objective standard for irremediability, that can be juxtaposed against the subjective standard.
Conclusion
Irremediability remains at the center of debates about the practice of EAS for psychiatric disorders, with main disagreement about whether clinicians can reliably assess irremediability in psychiatry. Using TRD as a test case, we find that current evidence does not support the view that clinicians can accurately predict long-term chances of recovery in a particular person with TRD, nor that statistical and staging tools can be used for reliable assessments of irremediability. Our findings suggest that the objective standard for irremediability in psychiatric EAS cannot be met, raising implications for policy and practice around the world.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291722002951.
Acknowledgements
The authors acknowledge Talia Bernhard for her assistance during an earlier version of this manuscript.
Author contributions
Substantial contributions to the conception or design of the work: All authors; Acquisition, analysis, or interpretation of data for the work, and drafting the work: M.N., E.J.; Revising critically for important intellectual content and final approval of the version to be published: All authors.
Financial support
Funded in part by the Intramural Research Program of the National Institutes of Health, USA (M. N., E. J., and S. K.).
Conflict of interest
M. N., E. J., C. G. and S. K. report no competing interests. C. Z. is listed as a co-inventor on a patent for the use of ketamine in major depression and suicidal ideation. C. Z. is listed as co-inventor on a patent for the use of (2R,6R)-hydroxynorketamine, (S)-dehydronorketamine, and other stereo- isomeric dehydro and hydroxylated metabolites of (R,S)-ketamine metabolites in the treatment of depression and neuropathic pain; and as co-inventor on a patent application for the use of (2R,6R)-hydroxynorketamine and (2S,6S)-hydroxynorketamine in the treatment of depression, anxiety, anhedonia, suicidal ideation, and posttraumatic stress disorders. He has assigned his patent rights to the US government but will share a percentage of any royalties that may be received by the government.
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