We hypothesized that propofol, a unique general anesthetic that engages N-methyl-D-aspartate and gamma-aminobutyric acid receptors, has antidepressant properties. This open-label trial was designed to collect preliminary data regarding the feasibility, tolerability, and efficacy of deep propofol anesthesia for treatment-resistant depression.
Ten participants with moderate-to-severe medication-resistant depression (age 18–45 years and otherwise healthy) each received a series of 10 propofol infusions. Propofol was dosed to strongly suppress electroencephalographic activity for 15 minutes. The primary depression outcome was the 24-item Hamilton Depression Rating Scale. Self-rated depression scores were compared with a group of 20 patients who received electroconvulsive therapy.
Propofol treatments were well tolerated by all subjects. No serious adverse events occurred. Montreal Cognitive Assessment scores remained stable. Hamilton scores decreased by a mean of 20 points (range 0–45 points), corresponding to a mean 58% improvement from baseline (range 0–100%). Six of the 10 subjects met the criteria for response (>50% improvement). Self-rated depression improved similarly in the propofol group and electroconvulsive therapy group. Five of the 6 propofol responders remained well for at least 3 months. In posthoc analyses, electroencephalographic measures predicted clinical response to propofol.
These findings demonstrate that high-dose propofol treatment is feasible and well tolerated by individuals with treatment-resistant depression who are otherwise healthy. Propofol may trigger rapid, durable antidepressant effects similar to electroconvulsive therapy but with fewer side effects. Controlled studies are warranted to further evaluate propofol’s antidepressant efficacy and mechanisms of action.
Treatment-resistant depression afflicts tens of millions of individuals worldwide, causing enormous suffering, economic costs, and mortality. Novel interventions are needed. This study provides the first evidence suggesting that propofol, a widely available anesthetic agent, has rapid and long-lasting antidepressant effects. Future studies are warranted to further evaluate propofol’s antidepressant efficacy and mechanisms of action.
Depression is among the most common and debilitating of mental disorders. Although many patients respond to currently available treatments, about one-third have a form of the illness that is not responsive to optimized treatment with antidepressant medications (Rush et al., 2006). Many individuals with severe, treatment-resistant depression pursue electroconvulsive therapy (ECT)—still considered the most effective treatment for depression—but the cognitive side effects of ECT cause many patients to avoid this treatment (Lisanby, 2007). Consequently, each year millions of individuals in the United States alone are debilitated by treatment-resistant depression and left with limited treatment options, at enormous societal costs (Mrazek et al., 2014).
The urgency of this problem has encouraged investigations of novel antidepressant interventions. Agents that target N-methyl-D-aspartate (NMDA) glutamate receptors and gamma-aminobutyric acid (GABA) receptors have appeared particularly promising. Substantial clinical evidence now supports the efficacy of ketamine for treatment-resistant depression (Berman et al., 2000; Zarate et al., 2006; McGirr et al., 2015), and a recent randomized controlled trial demonstrated antidepressant effects of nitrous oxide (Nagele et al., 2015). Several studies have suggested efficacy of another inhaled anesthetic, isoflurane, at high doses in humans (Langer et al., 1985, 1995; Weeks et al., 2013) and rodent models (Antila et al., 2017; Brown et al., 2018). Furthermore, positive GABA-A receptor modulators have shown promising antidepressant effects (Kanes et al., 2017; McMurray et al., 2018). These agents may share pharmacodynamic mechanisms, including inhibition of NMDA receptors and activation of GABAergic neurotransmission, as reviewed recently (Zanos et al., 2018). Indeed, ECT has been reported to reduce NMDA receptor expression and function (Fumagalli et al., 2010; Park et al., 2014), alter glutamatergic synaptic function (Stewart and Reid, 2000; Li et al., 2012), and increase cortical GABA levels in humans (Sanacora et al., 2003). The convergent observations among these diverse interventions suggest a new class of antidepressant agents that rapidly trigger plasticity within glutamate and GABAergic circuitry to induce antidepressant effects (Tadler and Mickey, 2018).
Propofol is a unique, intravenous, general anesthetic that potentiates the function of GABA-A and glycine receptors (Hales and Lambert, 1991) and inhibits the function of NMDA receptors (Orser et al., 1995; Yamakura et al., 1995; Kingston et al., 2006). It has been widely used for over 25 years for procedural sedation and general anesthesia. Propofol is known for its rapid onset and offset of action, tolerability, and safety (Lamperti, 2015). Similar to isoflurane, at high doses propofol induces burst-suppression, a state of intrinsic cortical hyperexcitability that is quantifiable using electroencephalography (EEG) and that is disrupted by blockade of GABA, NMDA, or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (Steriade et al., 1994; Lukatch et al., 2005; Kroeger and Amzica, 2007; Ferron et al., 2009). Taken together, these properties of propofol led us to hypothesize that high-dose propofol would have antidepressant effects and a favorable side-effect profile. To collect preliminary evidence of feasibility, tolerability, and efficacy among individuals with treatment-resistant depression, we performed an open-label trial of deep propofol anesthesia.
Design and Participants
This open-label study was approved by the University of Utah Institutional Review Board and preregistered at ClinicalTrials.gov (NCT02935647). All participants provided written informed consent. We recruited outpatients who were seen for consultation in a referral clinic for treatment-resistant mood disorders. Assessment included a comprehensive psychiatric evaluation, full medical history, physical examination, screening blood tests (complete blood count, comprehensive metabolic panel, thyroid-stimulating hormone), 12-lead electrocardiogram, and urine pregnancy test as indicated. Inclusion and exclusion criteria (Table 1) were confirmed by a psychiatrist and an anesthesiologist. Importantly, we excluded many individuals with medications or conditions that increased risk of experiencing adverse effects during propofol treatments (e.g., advanced age, severe obesity, hypertension, heart disease). Bipolar depression was not excluded because previous studies of ECT and ketamine have shown similar response rates for bipolar and unipolar depression (Dierckx et al., 2012; Coyle and Laws, 2015; Haq et al., 2015). Of 249 patients screened, 36 met criteria for the study, 11 consented to participate in the study, and 10 received at least 1 treatment. Baseline assessments incorporated the Structured Interview Guide for the Hamilton Depression Rating Scale (HDRS) with Atypical Depression Supplement (Williams et al., 1988) and the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005; Srisurapanont et al., 2017). See supplementary Information for further details about participants.
Inclusion and Exclusion Criteria
Age 18–55 y, inclusive
Primary diagnosis of DSM-5 major depressive disorder or bipolar disorder
Current moderate-to-severe depressive episode
Minimum of 2 failed antidepressant medication trials of adequate dose and duration
(at least one trial within the current depressive episode)a
Quick Inventory of Depressive Symptomatology, Self-Rated, total score >10 at baseline
24-item Hamilton Depression Rating Scale total score >18b
Other current DSM-5 disorders, with the exception of anxiety disorders and attention deficit disorder
Electroconvulsive therapy within the past 6 months
Lifetime history of DSM-5 cognitive disorder
Body mass index >40
Daily use of angiotensin converting enzyme inhibitor or angiotensin receptor blocker **
Symptomatic coronary artery disease or congestive heart failureb
History of transient ischemic attack or neurologic signs during the past yearb
History of or susceptibility to malignant hyperthermiab
Any contraindication to propofolb
Diabetes requiring insulinb
Abnormal kidney functionb
Daily use of opioid medicationb
Daily use of benzodiazepine medication
Pregnant or breastfeeding
Psychiatric instability requiring a higher level of care
Incompetent to provide consent
Anesthesiologists administered a series of 10 propofol infusions at a frequency of 3 times per week (1 subject received only 9 treatments due to a holiday schedule conflict). The decision to deliver a series of treatments rather than a single treatment was based on prior experience with ECT, ketamine, and isoflurane, all of which appear to produce higher response rates with 6 to 12 administrations (Langer et al., 1995; Lisanby, 2007; Weeks et al., 2013; Coyle and Laws, 2015). Monitoring included continuous EKG, pulse oximetry, blood pressure by noninvasive cuff, respiratory rate, and end-tidal carbon dioxide. A BIS Monitor (BIS VISTA Monitoring System, Aspect Medical Systems) was applied with a 4-electrode sensor (BIS Quatro, Covidien) to measure the left frontal EEG throughout the procedure. After preoxygenation, the anesthesiologist administered an induction dose of propofol (2,6-diisopropylphenol; Diprivan injectable emulsion; Fresenius Kabi) i.v., started a continuous infusion, and gave additional small boluses as needed. (Propofol dosing is described below.) A laryngeal mask airway and mechanical ventilation were employed. Trendelenburg positioning, IV fluids, and small boluses of pressors were used as needed for hypotension. During the recovery phase, a nurse monitored the participant in a postanesthesia care unit until discharge criteria were met. Further details about treatments are provided in supplementary Information.
Because brain concentrations and pharmacodynamic effects of a given dose of propofol vary substantially between individuals (Ludbrook et al., 2002), propofol dosing was guided by real-time EEG feedback via the BIS Monitor. This approach enabled us to produce relatively consistent pharmacodynamic effects across participants and across treatment sessions. Propofol induction (200–600 mg) was followed by a continuous infusion (300–650 µg/kg/min) and augmented with repeated small boluses (50–100 mg) as needed. Lower induction doses of 200 to 400 mg were used during each subject’s initial treatment to assure hemodynamic stability, and higher induction doses were introduced during later treatments as tolerated. After induction, the infusion rate was adjusted, and additional boluses were given with the goal of maintaining a burst-suppression state with a suppression ratio (SR) of 80% to 100% for 15 minutes. The SR is a metric calculated by the BIS Monitor that indicates the fraction of time the EEG is completely suppressed (isoelectric) during each 1-minute epoch. The rationale for suppressing EEG activity for 15 minutes was that previous studies of burst suppression using isoflurane anesthesia reported antidepressant effects using a similar protocol (Langer et al., 1995; Weeks et al., 2013).
After the procedure, EEG parameters calculated by the BIS Monitor were exported for off-line analysis. As shown in Figure 1, we defined the burst-suppression period of each treatment session as the interval during which SR was >50%. The duration at SR target was defined as the time during which SR was ≥80%. SR intensity was defined as the median SR value during the burst-suppression period. The integral of the SR curve (sum of SR values across all 1-minute epochs during the session) represented the cumulative time spent in the isoelectric state. The average signal quality index calculated by the BIS Monitor exceeded 95% throughout the burst-suppression period of all recording sessions.
For readers of our newsletter, this post might feel “different” from the others. Usually we like to talk about what you can do to help yourself or your loved one when it comes to addressing substance issues and finding support or treatment that will help changes last. In this post we are going to do something different because we are angry and scared about the misinformation that consumers are faced with everyday as they look for treatment for themselves or someone they care about.
An estimated 2 million Americans are dependent on opioids. The Centers for Disease Control reports that 115 Americans died daily in 2016 from opiate overdoses (42,249 deaths, five times higher in 2016 than 1999), and that 40% of these deaths involved a prescription opiate. Despite this deepening crisis, there is hope that a number of FDA approved, evidence-based Medication Assisted Treatments (e.g., buprenorphine, naltrexone/Vivitrol, methadone) for opioid use disorders can help. Studies have found that these medications support long-term change (including abstinence from opiates) and significantly reduce overdose rates. Yet the traditional drug and alcohol treatment industry has been shockingly slow to support their use. Even more astounding is that many doctors in the treatment industry have not been educated about their effectiveness or trained to use them despite comprehensive efforts such as the Surgeon General’s Report last year reiterating the important role of these treatments.
Why the delay? There continues to be a strong belief in the public and among many treatment providers that if you utilize medication to recover from a drug problem, you are not “really sober”. This bias persists even though mountains of evidence from well-conducted research studies demonstrate that medication-assisted treatments (MATs, like buprenorphine and naltrexone) save lives. Despite their benefits, MATs have been adopted in less than half of private treatment programs. Even in programs that do offer MAT options, only about a third of patients receive them.
In an effort to understand this at the ground level, we decided to do a little experiment. We know that when a family reaches out looking for treatment for a loved one, they are usually vulnerable, scared, and desperate for someone to offer advice and some hope. And often, in their fear, they will take the advice of the first person they speak to, assuming reasonably that that person is educated in substance use and has a clear understanding of current effective treatments. So we did what many people do: an internet search! Just like many desperate families do when they are looking for help. We typed in “top rehabs in the US” and “best rehabs in the country”, created a list of 34 rehabs that came up on these searches, and called them.
We called looking for treatment for “our brother” who was “in trouble with heroin”. In these calls we asked each program the same series of questions about their use of MATs like Vivitrol and Naltrexone (opiate blocking medications), and Suboxone (an opiate maintenance medication), including their treatment policies and stance on these medications. (We asked them a bunch of other questions but that is for another post)! Our mission? To see if the treatment landscape was changing now that multiple studies (over many years!) have found these medications to be life-saving. What we found is disturbing.
Out of the 34 residential programs called, less than 40% (n = 13) of the programs would consider maintaining someone on Suboxone (buprenorphine) or discharging a patient on suboxone. The rest (n = 19) only used Suboxone during detox as a taper medication, while 2 rehabs “never use suboxone”. One of these programs said, “we want to get them off everything, otherwise he would be a hopeless opiate addict for life.”
Hopeless addict for life? Really? Buprenorphine, which is a partial opioid agonist, does activate opioid receptors in the brain but does not produce the maximal effects that full opioid agonists (e.g., heroin) do. It does not produce feelings of euphoria, but it will produce enough effect to quell cravings, provide relief from withdrawal, and satisfy the brain into thinking it is receiving a full agonist. It is protective in lowering overdose risk as it blocks the receptor sites in the brain that opiates would otherwise attach to. It allows many opiate users to build a stable life without fighting off cravings to use everyday and protects them from overdosing if they relapse to opiate use (both “street” opiates like heroin and prescription opiates) while on the medication.
In these 34 contacted programs (“best of the best”!), openness to using Vivitrol was a little higher, with 65% of the rehabs saying that they could consider discharging someone on it. Vivitrol is the intramuscular injection (like a flu shot, for example) of Naltrexone, a medication that blocks opioids from binding with the opioid receptors in the brain, thereby eliminating any sense of a high. It last approximately 4 weeks and multiple studies have found that it contributes to reduced overdose rates among opiate users. It is not an opioid itself (unlike suboxone or methadone), which may account for it’s slightly more positive reception in our surveyed rehabs, by countering some of the (unfounded and stigmatizing) beliefs that an opiate user wanting to be on opioid medication is “just a drug user looking for another drug”.
Unfortunately, of the 12 programs that did not discharge clients on Vivitrol, 4 of them did not even know what it was! One of the programs we called said “We really try and get patients off of everything and we would not want to discharge him on Vivitrol” and another said “I don’t know what that is, but if you spell it for me I can look it up and see if we will use it”. We ask you: does your diabetes doctor not know what insulin is? Does your cancer doctor not believe in chemotherapy?
Many of the inpatient/residential facilities we talked to made comments that showed they do not understand these life-saving medications, that they do not value clients’ experiences or wishes, and that they are using fear and scare tactics to pressure people into treatments that may not be appropriate for them. One intake coordinator said after a 5 minute description of the potential referral, “I don’t want to sound aggressive, but he could die today…” This kind of statement, made after a short phone call and aimed at a scared family, is preying on their fears in an effort to book a client. This is what you’d expect when you’re buying a used car, not what you’re hoping for when you’re looking for help for a life-threatening problem. Several of the programs used other tried and true sales techniques, like transferring callers to many different programs without explanation, or making suggestions that they close the deal and “get on a plane right now.” And others said the fictional brother clearly “needs a lot of treatment, possibly a year,” without offering to speak with him or gathering more information. And “he could die today”? The refusal to consider use of MATs will leave clients treated at these facilities more vulnerable to dying.
In fact, if you seek treatment for an opioid use problem, there is a significant risk that the treatment professional you speak with will either not offer these medications or have ideologically-based opinions about them that negatively influence how you feel about being prescribed them. As many opioid users will attest, there is a sense that you are “just an addict looking for another drug.”
We’ve had countless people come to us at CMC who have been in multiple rehabs and never once were encouraged to be on medicatIon assisted treatments. This is in spite of the data that suggests as many as 90 percent of people detoxed completely off opiates relapse within the first 1-2 months unless treated with these medications.
The takeaway? If you or someone you love is one of the two million Americans estimated to be dependent on opioids, please demand better. Our nation’s opioid struggle is an undeniable tragedy, and is one of the worst public health crises in the nation’s history. It is made all the more tragic by the fact that there are many viable, proven treatments for the problem yet treatment programs continue to refuse to look at the science. This must change if we are to stem the tide of opioid addiction in this nation and prevent more loss of life.
Anyone looking for treatment needs to really investigate the different programs and ask lots of questions. Many people do more research about which car to buy than which treatment program to attend. That needs to be reversed. And clients should enter this process expecting their questions to be answered and answered well! If you’re not sure about something you heard, then it’s probably not right (or the right place for you). Demand better service, and you will have a better experience.
Health and Human Services Secretary Alex Azar is touting medication-assisted treatment (MAT) as a crucial component of stemming the opioid crisis plaguing the nation.
In his first extensive remarks on the opioid epidemic, set to be delivered Saturday, Azar announces two measures aimed at increasing this form of treatment.
“Medication-assisted treatment works,” Azar says in prepared remarks for a session of the National Governors Association’s winter meeting that were shared with The Hill. “The evidence on this is voluminous and ever growing.”
Addiction experts have long touted medication-assisted treatment — which aims to couple medicine with therapy — as a gold standard of treatment for an opioid addiction.
Azar’s remarks Saturday point to the challenge in obtaining this form of treatment. He said that about one-third of speciality substance abuse treatment programs offer MAT, calling failing to do so “like trying to treat an infection without antibiotics.”
“Under this administration, we want to raise that one-third number—in fact, it will be nigh impossible to turn the tide on this epidemic without doing so,” Azar says.
The Trump official says that the Food and Drug Administration (FDA) will release two new draft guidances “soon.”
Buprenorphine is one form of medication-assisted treatment. In November, the FDA approved the first monthly injection of Buprenorphine, aimed at making it easier to adhere to the medication.
The FDA will draft guidance to clarify what kind of evidence manufacturers that are trying to develop new forms of the medication need in order obtain approval for monthly injectable forms of buprenorphine.
The agency will also draft guidance aimed at “encourag[ing] more flexible and creative designs of MAT studies.” Researchers will be tasked with developing new ways to evaluate the effects of MAT formulations.
The opioid epidemic has ravaged areas across the country, and has shown no sign of stopping. Deaths from opioid overdoses increased nearly 28 percent from 2015 to 2016, according to the latest data from the Centers for Disease Control and Prevention.
In late October, President Trump declared the epidemic a public health emergency. Months later, however, some advocates had expressed frustration that it hadn’t led to much concrete action. In mid-January, the administration extended the emergency declaration another 90 days.
Advocates had also been pushing for more funding, saying a robust infusion of federal dollars is needed to curb the crisis.
A budget deal passed earlier this month included $6 billion over two years for the opioid and mental health crises. Trump’s budget proposed $10 billion in funding to address the opioid epidemic for fiscal 2019.
Congress will also examine bills aimed at curbing the epidemic, as the House Energy and Commerce Committee will kick off its legislative push on Wednesday.
The anesthesia medication ketamine has shown promise in treating depression, but its exact effects on the brain are unclear. Now, researchers have discovered that the drug changes the firing patterns of cells in a pea-size structure hidden away in the center of the brain. This could explain why ketamine is able to relieve symptoms of depression so quickly—and may provide a fresh target for scientists developing new antidepressants.
“It’s a spectacular study,” says Roberto Malinow, a neuroscientist at the University of California, San Diego, who was not involved in the work. “It will make a lot of people think.”
In clinical trials, ketamine appears to act much faster than existing antidepressants, improving symptoms within hours rather than weeks. “People have tried really hard to figure out why it’s working so fast, because understanding this could perhaps lead us to the core mechanism of depression,” says Hailan Hu, a neuroscientist at Zhejiang University School of Medicine in Hangzhou, China, and a senior author on the new study.
Hu suspected the drug might target a tiny region in the middle of the brain called the lateral habenula, the so-called “anti–reward center.” This region inhibits nearby reward areas, which can be useful in learning; for example, if a monkey pulls a lever expecting a treat but never receives it, the lateral habenula will reduce the activity of reward areas, and the monkey will be less likely to pull the lever in the future. But research over the past decade has suggested that the area may be overactive in depression, dampening down those reward centers too much.
In a series of experiments using mouse and rat models of depression reported today in Nature, Hu and her colleagues found that ketamine did affect the lateral habenula—but it was the pattern of firing, rather than the overall amount of activity, that proved crucial. A small proportion of the neurons in the lateral habenula fire several times in quick bursts, rather than firing once at regular intervals; the team found that “depressed” rodents had a lot more of these quick burst cells. In brain slices from normal rats, only about 7% of cells were the bursting type, but in rats bred to display depressionlike behavior, the number was 23%.
Direct recordings from the neurons of live mice showed the same pattern: Animals that had gone through a stressful procedure had more bursting cells in the lateral habenula. And, importantly, this bursting behavior appeared to cause depressionlike states. When researchers used optogenetics—a technique that allows cells to be switched on and off with light—to increase the amount of bursting in the lateral habenula, mice behaved in a more “depressed” way, remaining motionless when forced to swim in a container of water, for example. This kind of despair is thought to be similar to the feelings of hopelessness experienced in depression.
When “depressed” mice and rats were given ketamine, the number of bursting cells was much lower, similar to the number in normal animals, Hu’s team found. And even when the researchers forced the neurons to fire in bursts, animals that had been given ketamine no longer showed depressionlike behaviors.
Hu says that neurons firing several times in quick succession produce a more powerful signal. This means that bursting cells may be sending particularly strong messages to dampen down activity in reward areas, which could lead to depression. “Bursting has a special kind of signaling power,” Malinow says. “You get more bang for your buck.”
The findings could also explain why ketamine acts so quickly. By immediately blocking bursts in the lateral habenula, the drug releases the reward areas from those strong signals. This suggests that other drugs that reduce burst firing could also alleviate depression, whether they act on the same receptors or different ones. “Anything that can block the bursting … should be a potential target based on our model,” Hu says. In an accompanying paper, her team reports that a protein found on astrocytes, another type of brain cell that interacts closely with neurons, could be one of these targets.
Panos Zanos, a neuropharmacologist at the University of Maryland in Baltimore, says the immediate effects of the drug in the lateral habenula were interesting. “I’m very excited … to see whether this [also] applies to the long-lasting antidepressant effects of ketamine,” he says. “This is a great study that adds to the literature on how ketamine might work.”
The British Journal of Psychiatry, Volume 2 February 2018, pp. 103-111
Depression contributes to persistent opioid analgesic use (OAU). Treating depression may increase opioid cessation.
To determine if adherence to antidepressant medications (ADMs) v. non-adherence was associated with opioid cessation in patients with a new depression episode after >90 days of OAU.
Patients with non-cancer, non-HIV pain (n = 2821), with a new episode of depression following >90 days of OAU, were eligible if they received ≥1 ADM prescription from 2002 to 2012. ADM adherence was defined as >80% of days covered. Opioid cessation was defined as ≥182 days without a prescription refill. Confounding was controlled by inverse probability of treatment weighting.
In weighted data, the incidence rate of opioid cessation was significantly (P = 0.007) greater in patients who adhered v. did not adhered to taking antidepressants (57.2/1000 v. 45.0/1000 person-years). ADM adherence was significantly associated with opioid cessation (odds ratio (OR) = 1.24, 95% CI 1.05–1.46).
ADM adherence, compared with non-adherence, is associated with opioid cessation in non-cancer pain. Opioid taper and cessation may be more successful when depression is treated to remission.
Long-term prescription opioid analgesic use (OAU) for chronic non-cancer pain is defined as ‘daily or near-daily’ use for >90 days.1,2 Between 1.4 and 10% of patients with a new opioid prescription develop chronic OAU,2,3 and a majority, 65–80%, of patients who have >90 days OAU, are still taking opioids 3–5 years later.4,5 These patients are more likely than those who take opioids short term to develop opioid use disorder and overdose. Chronic OAU is also associated with new depressive episodes (NDEs)3,6,7 and treatment-resistant depression.6 Because depression and OAU are mutually reinforcing,8 these patients may be in a cycle of persistent OAU, depression and pain. Research on treating depression to improve outcomes for chronic non-cancer pain is sparse. In Kroenke et al‘s9 Stepped Care for Affective Disorders and Musculoskeletal Pain (SCAMP) study, where patients were randomised to optimal depression treatment v. usual care, treating depression led to reduced pain severity and anxiety, improved functioning and better health-related quality of life.9 Although SCAMP was not designed to measure change in OAU, the findings raise the possibility that depression treatment could reduce use of opioids, possibly from reduced pain or improved functioning.9–11 Improved functioning can occur independent of pain11 and should follow depression treatment. Independent of changes in pain severity, the need for OAU to self-regulate mood should dissipate following reduction in depression.
We are not aware of any studies that report changes in OAU following adherence to antidepressant medication (ADM) treatment in patients with chronic non-cancer pain. Using a retrospective cohort design, it is possible to test the hypothesis that adherence to ADM treatment v. non-adherence is associated with OAU cessation without the ethical barrier of randomising to inadequate treatment. Adherence serves as an indicator of depression improvement because patients who are non-adherent are less likely to have decreasing depression symptoms.12 Among patients initiating ADMs, response to treatment by 24 weeks is much lower in non-adherent v. adherent patients (55.8 v. 82.5%).13 Although it would be ideal to have 9-item Patient Health Questionnaire (PHQ-9)14 scores for all patients at the time of ADM initiation and opioid cessation, such data was available from only a subset of patients. Therefore, we used adherence as a proxy for depression improvement. In a large cohort of Veterans Health Administration patients with NDE following >90 days of OAU, we tested the hypothesis that depression treatment adherence was associated with OAU cessation. Specifically, the objective of the current study was to determine whether patients who developed depression following chronic OAU were more likely to stop using opioids if they adhered to ADM treatment compared with patients who did not adhere to ADM treatment. In addition, exploratory analysis in a subset of patients with sufficient data was computed to assess the change in depression symptoms and pain scores over time in patients adherent to treatment with ADM compared with those who were non-adherent who did and did not stop OAU.
This retrospective cohort analysis used patient data extracted from the Veterans Health Administration electronic medical record for 1 Jan 2000 to 31 Dec 2012. Data included ICD-9-CM diagnostic codes,15 in-patient stays, out-patient visits, prescriptions dispensed records, vital signs and demographic information.
A random sample of 500 000 patients was taken from a cohort of 2 910 335 identified with at least one out-patient visit in both fiscal years 1999 and 2000, and aged 18–80 years. We excluded patients over 80 because they are more likely to receive prescription opioids for end-of-life pain management and cancer pain and the risk of misclassifying depression increases because of the greater prevalence of vascular depression and depression related to dementia. From this sample, we excluded 151 500 patients with a cancer and/or HIV diagnosis. Patients must have had at least one yearly visit in the 2-year ‘washout’ period (2000–2001) during which they must have been free of a medical record depression diagnosis (n = 266 901). We then selected patients with a NDE beginning in 2002–2011 and not occurring on the last out-patient visit date (n = 31 224). Because our previous reports indicate >90-day OAU is associated with up to twice the risk of NDEs,3,7 we limited the cohort to patients with >90 days of OAU or by the date of the NDE (n = 3075).
NDE was defined by the presence of a primary diagnosis (ICD-9-CM: 296.2, 296.3, 311) of depression in at least one in-patient stay or two out-patient visits within the same 12-month period. This algorithm has been shown to be a valid measure of depression when compared with self-report or written medical record information.16,17 Patients without ADM treatment on or after the NDE were excluded (n = 138). Patients must have had >3 months follow-up after NDE diagnosis to allow for the possibility of the occurrence of least one acute-phase depression treatment period (≥84 days)18 (n = 2843). The final sample included patients with complete demographic data (n = 2821). The cohort selection process is shown in Fig. 1.
Outcome – opioid cessation
Opioids included codeine, fentanyl, hydrocodone, hydromorphone, levorphanol, meperidine, oxycodone, oxymorphone, morphine and pentazocine. Both short-acting and long-acting formulations were included. Opioid prescription information included days supplied, quantity (e.g. pills or liquid volume) and unit dose (mg). OAU cessation was defined as a gap of at least 182 days from the end date of the last prescription.5 OAU cessation date was the first day of this gap.
Exposure – ADM adherence
ADMs included monoamine oxidase inhibitors (MAOIs), selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), tricyclics (TCAs) and non-classified ADMs. ADM adherence was defined using proportion of days covered (PDC) from NDE to opioid cessation or censor date.19,20 ADM prescriptions dispensed were used to create time arrays to identify days of follow-up that an ADM was available. If multiple ADMs were available in a day, that particular day was only counted once as a covered day. The PDC was calculated by taking the total number of covered days in follow-up by the total number of days in follow-up. PDC was dichotomised to standard thresholds for adherence (≥80%) and non-adherence (<80%).19–21 To determine if patient adherence was correlated with duration of ADM treatment, we computed the number of continuous weeks of treatment. ADM use was considered continuous if there was no gap of >30 days between prescriptions dispensed and duration for all periods of continuous use in follow-up were assessed to categorise duration as ever ≥24 weeks, 12 to <23 weeks or <12 weeks.
We included an OAU duration variable to control for duration of use at the date of NDE (3–6 months, >6 to 12 months, >12 to 24 months, >24 months). Duration was computed from the months of continuous OAU (no gap >30 days between prescriptions dispensed). The opioid morphine equivalent dose (MED) was calculated using standard conversion tables. Days supplied and quantity variables were used to calculate daily MED in follow-up. We modelled the maximum daily MED before the end of follow-up (1–50 mg, 51–100 mg, >100 mg). We controlled for comedication with benzodiazepines, which are associated with long-term opioid use,22 and muscle relaxants, which could improve pain and functioning. Benzodiazepines included alprazolam, clonazepam, diazepam, lorazepam, chlordiazepoxide and clorazepate. Muscle relaxants included carisoprodol, cyclobenzaprine, baclofen, dantrolene, metaxalone, methocarbamol, chlorzoxazone, tizanidine and orphenadrine. Demographic variables included age, gender, ethnicity (white v. other), marital status (married v. other) and insurance coverage (Veterans Health Administration only v. other sources).
To control for detection bias related to more healthcare encounters, we created a healthcare utilisation variable defined as average number of out-patient clinic visits per month in follow-up. The distribution of the mean was then dichotomised into high utiliser, >75th percentile, v. low utiliser, ≤75th percentile. We controlled for psychiatric and physical comorbidities associated with depression23 and/or OAU.24–26 Comorbidities were defined using ICD-9-CM diagnostic codes. Psychiatric comorbidities included post-traumatic stress disorder and any other anxiety disorder, a composite of panic disorder, generalised anxiety disorder, social phobia, obsessive–compulsive disorder and anxiety disorder not otherwise specified. We controlled for alcohol misuse or dependence; illicit drug misuse or dependence, including opioids; and nicotine dependence. Chronic physical conditions included type 2 diabetes mellitus, hypertension, cerebrovascular disease, obesity, low testosterone, sleep apnoea and cardiovascular disease. Cardiovascular disease was a composite of hyperlipidaemia, ischaemic heart disease, disease of pulmonary circulation, other heart disease, hypertensive heart disease and myocardial infarction.
Five separate pain condition variables were created based on over 900 ICD-9-CM codes.7,24 These conditions were arthritis, back pain, musculoskeletal pain, headaches and neuropathic pain. Pain scores, collected during routine care in the Veterans Health Administration, were on a numerical rating scale ranging from 0 to 10, with higher scores indicating greater pain intensity. In propensity score models, we adjusted for a time invariant maximum pain score before the end of follow-up to control for the highest pain level. As variability in pain scores in the Veterans Health Administration have been previously reported,27 a time-varying pain score for each month of follow-up was used in final survival models. For the time-varying pain score assessment, the pain score was assumed to be consistent across subsequent months until a new monthly assessment was available.
Propensity scores and inverse probability of treatment weighting (IPTW) were used to balance potential confounders listed in Table 1 between ADM adherent and non-adherent treatment groups to reduce the effect of bias by indication and other sources of confounding. The propensity score is the probability of ADM adherence, given covariates and was calculated using a binary logistic regression model. Propensity scores were used to apply IPTW approaches using stabilised weights.28–33 A stabilised weight is the marginal probability of ADM adherence divided by the propensity score for the adherent group, and (1 – marginal probability of ADM adherence) divided by (1 – propensity score) for the non-adherent group. It helps reduce bias associated with extreme weights of either individuals in the ADM adherent group with low propensity scores or those in the non-adherent group with high propensity scores. Extreme weights are associated with increased variability of the exposure effect, thus, stabilising weights helps reduce type II error rate.34 Stabilised IPTW also preserves original sample size (i.e. does not inflate sample size in pseudo-data) in analysis thereby also preserving the type I error rate.33 Stabilised weights were trimmed if they were ≥10, as well-behaved weights have a mean close to 1 and a maximum <10.30,35 The mean of stabilised weights should be close to 1, extreme values indicate the propensity-score model poorly specified predictors of treatment exposure. IPTW resulted in pseudo-populations for ADM adherence groups such that covariates balanced across the two groups. Covariate balance was assessed by comparing covariate distributions between ADM adherence groups. Balance is indicated with no significant differences in the distribution of covariates between groups and by standard mean differences <10%.34,36
Table 1 Characteristics of patients with long-term opioid use with a new depression episode (NDE) by antidepressant medication (ADM) adherence status in unweighted data, 2002–2012 (n = 2821)
Results in bold are significant.
SMD %, standardised mean difference per cent.
a.Other anxiety disorders: panic disorder, obsessive–compulsive disorder, social phobia, generalised anxiety disorder, anxiety not otherwise specified.
b.Cardiovascular disease, hyperlipidaemia, ischaemic heart disease, diseases of pulmonary circulation, other heart disease, hypertensive heart disease, myocardial infarction.
Analyses were performed with SAS v9.4 at an alpha of 0.05. Bivariate analyses, using independent samples t-tests for continuous variables and chi-square tests for categorical variables, assessed the relationship of covariates with ADM adherence in unweighted and weighted data. A Poisson regression model was used in unweighted and weighted data to compare opioid cessation incidence rate (person-years) between ADM adherence groups. Unweighted and weighted Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals for the relationship of ADM adherence and time to opioid cessation. Weighted analyses used stabilised weights in probability weighting. Confidence intervals and P-values in weighted analyses were calculated using robust, sandwich-type variance estimators. Follow-up time was defined as months from date of NDE to date of OAU cessation or censor date, which was the last available Veterans Health Administration encounter.
The final model included variables to control for pain diagnoses and changing pain score after the initiation of ADM treatment. All pain variables and ADM adherence were modelled as time dependent. This allows ascertainment of exposure status over the multiyear observation period and permits new diagnoses and change in pain scores to contribute to the outcome. Initial evaluation of each interaction term of each covariate and follow-up time confirmed that the proportional hazards assumption was met for ADM adherence (P = 0.11) and pain covariates (P > 0.05).
This project was approved by the institutional review boards of participating institutions.
In unweighted data, the average monthly change in PHQ-9 score and pain score across follow-up was computed using random intercept longitudinal mixed models (Proc Mixed, SAS v9.4) for four groups; (a) ADM adherent with OAU cessation (n = 5 PHQ-9; n = 213 pain scores), (b) ADM adherent without OAU cessation (n = 96 PHQ-9; n = 864 pain scores), (c) ADM non-adherent with OAU cessation (n = 14 PHQ-9; n = 354) and (d) ADM non-adherent without OAU cessation(n = 147 PHQ-9; n = 1390 pain scores). Because of the lack of PHQ-9 data prior to 2008, monthly changes in PHQ-9 scores in follow-up were computed for a subset of 262 patients with NDE occurring in 2008–2012 and with at least one PHQ-9 score before end of follow-up. Time was modelled as months since NDE and models included all available pain and PHQ-9 data in follow-up.
In unweighted data, a Fisher’s exact test of independence revealed ADM adherence and duration were highly related (P < 0.0001, results not shown). Among 1077 patients who were adherent, 0.2% received continuous ADM for less than 12 weeks, 4.4% received an ADM for 12 to <24 weeks and 95.5% for at least 24 weeks. Among 1744 patients who were non-adherent, 15.0% received ADM for less than 12 weeks, 19.0% for 12 to <24 weeks and 66.1% for at least 24 weeks.
Figure 2 shows that the overall unweighted incidence rate for OAU cessation was 48.4 per 1000 person-years, with no significant differences between ADM adherent (50.2/1000 person-years) and non-adherent (47.4/1000 person-years) groups (P = 0.496). However, after weighting data using IPTW techniques, the incidence rate of OAU cessation was higher for ADM adherent (57.2/1000 person-years) compared with non-adherent (45.0/1000 person-years ) groups (P = 0.007).
ADM, antidepressant medication.
Fig. 2 Antidepressant adherence, non-adherence and prescription incidence of opioids.
Unweighted distributions of covariates by ADM adherence are shown in Table 1. Among those individuals taking opioids for >90 day with a NDE receiving ADM treatment, almost half (46.9%) had taken opioids for more than 2 years (>24 months) at the time of the NDE. Almost two-thirds (63.0%) reached a maximum MED of >100 mg. Maximum dose achieved was similar (P = 0.704) in ADM adherent and non-adherent groups. Benzodiazepine comedication with ADM was significantly more prevalent among ADM adherent (59.0%) v. non-adherent (53.6%) groups. Comorbidities that were significantly more prevalent among ADM adherent compared with non-adherent groups were post-traumatic stress disorder, type 2 diabetes, hypertension, cardiovascular disease, low testosterone and sleep apnoea. Alcohol and illicit drug misuse/dependence were more prevalent among the non-adherent group. Patients in the ADM adherent compared with the non-adherent group were significantly older, White, married, more likely to have other insurance in addition to Veterans Health Administration insurance and have higher healthcare utilisation.
After applying IPTW, all covariates balanced and were not significantly different between the ADM adherent and non-adherent groups (Table 2). IPTW stabilised weights ranged from 0.54 to 3.15, with a mean of 1.00 (s.d. = 0.25) and median of 0.95 (interquartile range (IQR) = 0.83–1.11). The standardised mean differences (SMDs) after weighting were all <10%. Good balance was achieved given differences between treatment groups were all non-significant and all SMDs were <10%.
Table 2 Weighted association of covariates with antidepressant medication (ADM) treatment adherence, weighted by inverse probability of ADM treatment adherence, in patients with chronic opioid use (>90 days) at time of new depression episode (NDE, 2002–2012; n = 2821)
SMD %, standardised mean difference per cent.
a.Other anxiety disorders: panic disorder, obsessive–compulsive disorder, social phobia, generalised anxiety disorder, anxiety not otherwise specified.
b.Cardiovascular disease, hyperlipidaemia, ischaemic heart disease, diseases of pulmonary circulation, other heart disease, hypertensive heart disease, myocardial infarction.
Results of unweighted and weighted Cox proportional hazards models estimating the association between ADM adherence and time to OAU cessation are shown in Table 3. In unweighted data, there was no relationship of ADM adherence and time to OAU cessation (hazard ratio (HR) = 1.05, 95% CI = 0.88–1.24). However, after weighting and adjusting for changes in pain scores and pain diagnoses that could occur after ADM initiation, adherence was associated with a 24% increased likelihood of OAU cessation compared with non-adherence (HR = 1.24, 95% CI = 1.05–1.46)
Table 3 Results from Cox proportional hazards models estimating the association between antidepressant medication (ADM) adherence and opioid cessation among patients with chronic opioid use ( (>90 days) with a new depression episode (NDE, 2002–2012) (n = 2821).
Results in bold are significant.
b.Inverse probability of adherence weighted data to control for confounding factors shown in Table 1.
c.Additional adjustment for painful conditions and pain scores after date of ADM initiation.
Longitudinal linear growth curves for PHQ-9 score among a subset of 262 patients by ADM adherent and OAU cessation groups are shown in Fig. 3(a). At time of NDE, mean PHQ-9 scores were not significantly different between groups (P = 0.995). Overall, there was a trend for a monthly decrease in PHQ-9 score (P = 0.08). Average monthly decrease in PHQ-9 scores was largest for the ADM adherent group with OAU cessation (β = −0.60, 95% CI −1.33 to 0.12) followed by the ADM non-adherent group with OAU cessation (β = −0.21, 95% CI −0.53 to 0.10), ADM adherent group without OAU cessation (β = −0.13, 95% CI = −0.21 to −0.04), and ADM non-adherent group without OAU cessation (β = −0.06, 95% CI −0.12 to 0.01). However, time trend slopes were not statistically different (P = 0.23).
The four study groups were antidepressant medication (ADM) adherent, opioid continuation; ADM adherent, opioid cessation; ADM non-adherent, opioid continuation; and ADM non-adherent, opioid cessation.
Fig. 3 Change in (a) 9-item Patient Health Questionnaire (PHQ-9) scores and (b) pain scores over time in the study groups.
Longitudinal linear growth curves for pain scores in follow-up among the entire sample of 2821 patients are shown in Fig. 3b. The ADM non-adherent group with OAU cessation had significantly higher pain scores than the other three groups at time of NDE (P = 0.0003). Results indicated there was an overall significant monthly decrease in pain score across follow-up (P = 0.002) and that these monthly changes were different between groups (P = 0.01), however, these changes were relatively flat (β range −0.0004 to 0.009).
In a cohort of 2821 Veterans Health Administration patients who developed NDE after >90 days of prescription OAU and who received at least one ADM prescription, we observed adherence v. non-adherence to ADM treatment was associated with a 24% greater likelihood of opioid cessation. The ADM adherent v. non-adherent group had a significantly greater incidence rate of OAU cessation (57.2/1000 v. 45.0/1000 person years; P = 0.007). These results were observed after balancing factors associated with ADM. We also controlled for confounding by pain that could persist during ADM treatment.
Exploratory analysis indicates that the ADM adherent group who stopped taking opioids experienced a rapid and greater decline in depression symptoms compared with patients who did not stop taking opioids, regardless of adherence; however, these results are preliminary because cell sizes were very small for OAU cessation groups. Monthly pain scores were significantly higher among the ADM non-adherent group with OAU cessation compared with the other ADM adherent, non-adherent/opioid cessation-no cessation groups, but the size of the difference was not clinically meaningful. Adjusting for maximum pain scores after ADM initiation in the full Cox proportional hazard models did not change the association between ADM adherence and opioid cessation. Together, these results provide preliminary evidence that a reduction in depression may lead to OAU cessation. Stronger evidence indicates change in pain scores does not explain the association between ADM adherence and opioid cessation.
Interestingly, ADM adherent and non-adherent groups who stopped OAU had the steepest reductions in depression symptoms across follow-up. Because response to antidepressant treatment is markedly greater in those individuals who are adherent v. non-adherent,12,13 we speculate people who are ADM non-adherent may have decreased PHQ-9 scores because of OAU cessation. This would be consistent with the evidence for a bidirectional relationship between OAU and depression.8 Prospective studies are warranted to verify this finding.
Patients with comorbid pain and depression may remain on opioids in an attempt to self-medicate mood37 and to avoid depression during opioid withdrawal.38 Patients with depression are more likely to drop-out of opioid taper, and withdrawal symptoms are exacerbated in patients with current depression.38 Thus, another explanation for our findings may be related to improved depression leading to decreasing attempts to self-medicate mood and greater probability of completing opioid taper.
ADM adherence may be a proxy for overall adherence to medical treatment, the ‘healthy adherer’ effect.39 These patients may adhere to physician instructions to end OAU, adhere to other forms of pain management or begin opioid substitution treatment.
It is possible that unmeasured confounders were not included in the propensity score and we violated the exchangeability assumption.34 For instance we do not have personality measures or indicators of an orientation toward health that might predict both adherence to antidepressants and contribute to opioid cessation. Thus, unmeasured confounding is a limitation. The cohort was a majority male, Veterans Health Administration patient population, which could limit generalizability to non-Veterans Health Administration patients. However, we have previously found that the association between duration of OAU and NDEs in Veterans Health Administration patients was replicated in two private-sector cohorts,3 and the association between opioid use v. no use and risk of depression recurrence in Veterans Health Administration patients was replicated in a private-sector cohort.40 This suggests our findings could be replicated in private-sector cohorts. OAU was based on prescriptions dispensed and we were unable to determine whether patients took their medication as prescribed. Some OAU could be misclassified if patients transitioned to non-Veterans Health Administration or illicit sources for opioids.
Some antidepressants such as TCAs and duloxetine are used in pain management,41,42 but ADM management for analgesia is not designed to treat depression. Our conclusions were consistent in post hoc analysis comparing adherence to TCAs/duloxetine only v. adherence to other ADMs, indicating our findings were not as a result of adherence to only ADMs commonly used in pain management.
ADM adherence was associated with increased likelihood of OAU cessation in individuals with chronic use of opioids and this association was independent of duration of opioid use, maximum MED, pain and numerous comorbid conditions. Several studies have reported that the majority of people who take opioids for >90 days remain on opioids for 3–5 years.4,5 In our cohort, 47% took them for >2 years. Thus, OAU cessation following adherent ADM treatment occurred in a patient cohort with a low probability of OAU cessation. We computed the number-needed-to-treat and found for every 20 patients adherent to ADMs, one patient will stop OAU who would not have stopped if they were non-adherent.
Treatment of opioid dependence in patients with comorbid depression may be successful following effective depression treatment. Preliminary evidence suggests OAU cessation may also contribute to improvement in depression. Therefore, opioid taper paired with ADM could result in a faster reduction of depression symptoms and increase likelihood of successful OAU cessation. Prospective data collection to obtain detailed depression and functioning measures, change in OAU and treatment trials are needed to confirm our findings.
This study was supported by the National Institute of Mental Health, Prescription Opioid Analgesics and Risk of Depression, R21MH101389. The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The views expressed in this paper do not necessarily reflect those of the Veterans Health Administration.
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APA Releases New Practice Guideline on AUD Pharmacotherapy
APA today released a new practice guideline for the pharmacological treatment of alcohol use disorder (AUD). Despite the high prevalence of AUD and its significant public health consequences, patients with this disorder remain undertreated.
“This new guideline is an important step in bringing effective, evidence-based treatments for alcohol use disorder to many more people and in helping address the public health burden of alcohol use,” APA President Anita Everett, M.D., said in a press release.
The guideline aims to increase physician and public knowledge on the effectiveness and risks of the five medications that may be used for the treatment of AUD: acamprosate, disulfiram, gabapentin, naltrexone, and topiramate.
Of these five, naltrexone and acamprosate have the best available evidence related to their benefits, and both have minimal side effects. As such, they should be considered the preferred pharmacological options for patients with moderate to severe AUD who want to reduce drinking or achieve abstinence. However, acamprosate should be avoided in patients with significant renal impairment, and naltrexone should be avoided in patients with acute hepatitis or liver failure, or in patients currently taking opioids or who may be expected to take opioids.
Disulfiram, gabapentin and topiramate are also options for treatment of AUD but should typically be considered after trying naltrexone and acamprosate, unless the patient has a strong preference for one of these medications. Disulfiram is a special case as it does cause a series of adverse reactions if alcohol is consumed within 12 to 24 hours of taking the medication; the reactions include elevated heart rate, flushed skin, headache, nausea, and vomiting. Therefore, disulfiram is suggested only to patients who wish to achieve abstinence from drinking. Patients taking topiramate are at an increased risk of cognitive dysfunction, dizziness, and loss of appetite, whereas patients taking gabapentin may experience fatigue, insomnia, and headache.
While the guideline focuses specifically on evidence-based pharmacological treatments for AUD, it also includes recommendations and suggestions related to psychiatric evaluation of patients with AUD and developing a person-centered treatment plan. Evidence-based psychotherapeutic treatments for alcohol use disorder also play a major role in treatment and peer support groups such as Alcoholics Anonymous, and other 12-step programs can be helpful for many patients. However, specific recommendations related to these treatments are outside the scope of this guideline.
LONDON—An ingredient in cannabis called cannabidiol or CBD has shown promise in a clinical trial as a potential new treatment for psychosis, scientists said on Friday.
Scientists conducted a small trial of people with psychosis and found patients treated with CBD had lower levels of psychotic symptoms than those who received a placebo. Psychosis is characterized by paranoia and hallucinations.
The study found that they were also more likely to be rated as “improved” by their psychiatrist and there were signs of better cognitive performance and functioning.
The most common forms of psychosis are part of mental illnesses such as schizophrenia – which affects more than 21 million people worldwide – and bipolar disorder, but psychotic symptoms can also occur in conditions like Parkinson’s disease and alcohol or drug abuse.
The main psychoactive ingredient in cannabis is delta-9-tetrahydrocannabinol, or THC. It can induce paranoia and anxiety and hallucinations and has been found in studies to increase the risk psychotic illness in people who regularly use potent forms of cannabis such as skunk.
But its second major constituent, CBD, has the opposite effects to THC – leading scientists to think it might one day be useful as a treatment in mental health.
Scientists at King’s College London’s Institute of Psychiatry, Psychology & Neuroscience conducted a placebo-controlled trial of CBD in patients with psychosis and published their findings online December 15 in the American Journal of Psychiatry.
In the trial, 88 patients with psychosis received either CBD or placebo for six weeks, alongside their existing antipsychotic medication. Beforehand and afterwards, the scientists assessed symptoms, functioning and cognitive performance, and the patients’ psychiatrists rated their overall condition overall.
“The study indicated that CBD may be effective in psychosis: patients treated with CBD showed a significant reduction in symptoms, and their treating psychiatrists rated them as having improved overall,” said Philip McGuire, who co-led the trial.
He noted that trial patients also reported few adverse side effects, and added: “Although it is still unclear exactly how CBD works, it acts in a different way to antipsychotic medication, and … could represent a new class of treatment.”
A single adjunctive infusion of ketamine appears to reduce suicidal thoughts in depressed patients within 24 hours, according to a study published yesterday in AJP in Advance. This improvement was maintained for six weeks with standard, optimized pharmacotherapy.
While previous studies have suggested ketamine rapidly reduces suicidal ideation in some patients, whether similar effects would be seen in patients with major depression and high levels of suicidal ideation was less clear.
Researchers from Columbia University Medical Center and the New York State Psychiatric Institute randomly assigned 80 adults with major depressive disorder and suicidal ideation to receive ketamine or midazolam infusion. At baseline, 54% of the sample was taking antidepressant medication.
The researchers assessed the study participants’ suicidal ideation at the start of the trial using the clinician-rated Scale for Suicidal Ideation (SSI). The SSI consists of 19 items, including severity of wish to die, passive and active suicide attempts, and duration and frequency of ideation, which can be used to monitor a patient’s response to interventions. This assessment was repeated 24 hours before infusion with ketamine or midazolam, 230 minutes after infusion, 24 hours after infusion, and at weeks one to six after infusion. Patients were also asked about symptoms of depression and anxiety before and after the infusion, as well as adverse effects following the infusion and again at six-week follow-up.
Within 24 hours of patients’ having received intravenous ketamine (0.5 mg/kg in 100 mL saline) or midazolam (0.02 mg/kg in 100 mL saline) infused over 40 minutes, patients in the ketamine group experienced a greater reduction in SSI score than that of the midazolam group. The proportion of patients who experienced a reduction ≥50% in their SSI score 24 hours after receiving an infusion was 55% for the ketamine group and 30% for the midazolam group. The ketamine group also experienced greater reductions in overall mood disturbance, depression, and fatigue, as measured by the Profile of Mood States, within 24 hours compared with the midazolam group.
“Longitudinal analysis of the uncontrolled six-week follow-up showed that clinical improvement after randomized and open ketamine treatment was generally maintained through six weeks of open, optimized clinical follow-up treatment with respect to SSI score and depression ratings,” Michael F. Grunebaum, M.D., and colleagues wrote.
Patients in the ketamine group experienced an increase in blood pressure and dissociative symptoms compared with patients in the midazolam group, but these adverse effects typically resolved within minutes to hours following the infusion.
“Given concerns about ketamine’s one- to two-week antidepressant effect in previous studies, it is notable that the improvement in suicidal ideation in this trial was largely maintained through the six-week follow-up ratings,” the researchers wrote. “This may be partly explained by the fact that patients continued prior psychotropic medication, which was optimized after completion of day 1 postinfusion ratings.”
Reuters Health – Long-term consumption of tiny amounts of lithium may reduce the risk of Alzheimer’s disease and other forms of dementia, but only if the dose isn’t too small, according to a study that looked at levels of the element in drinking water throughout Denmark.
In fact, the wrong amount may actually increase dementia risk, researchers report in JAMA Psychiatry.
Lithium, used for years in drugs to treat depression and bipolar disorder, is a naturally-occurring element in drinking water in Denmark but levels vary by location. The team compared the estimated amount found in the water supplies of 275 municipalities to the rates of dementia, including Alzheimer‘s, in those areas.
Residents who had been diagnosed with dementia from 1995 through 2013 tended to consume lower levels of lithium in their water than residents whose water had higher levels. However, there was a middle level of consumption where dementia risk was elevated or unchanged.
Compared to those whose water contained lithium concentrations of 2 to 5 micrograms of lithium per liter, which served as the baseline, the rate of dementia was 22 percent higher when the drinking water concentration was 5 to 10 micrograms.
Levels of 10.1 to 15 micrograms had no effect on the risk.
But at levels of 15.1 to 27 micrograms per liter, the dementia risk seemed to drop by 17 percent compared to places where the levels were 2 to 5 micrograms.
“It’s a really interesting study but you have to be careful about inferring cause and effect,” said Dr. Brent Forester, head of the geriatric division of McLean Hospital, a psychiatric hospital in Belmont, Massachusetts affiliated with Harvard Medical School, who wasn’t involved in the research.
In a telephone interview with Reuters Health, he called the fact that the effect didn’t strictly increase or decrease based on the lithium dose “a worrisome sign” and said the non-linear trend “could be a warning that there’s another confounding variable. It may not be the lithium itself but something about the mechanism of action might be protecting against developing dementia. The biology of lithium is worth exploring, for sure.”
The chief author of the study, Dr. Lars Vedel Kessing of the University of Copenhagen, did not respond to requests for an interview. But the researchers cautioned in their paper that “it cannot be excluded that other, unobserved environmental or social care factors related to individuals’ municipality of residence might have confounded the association between lithium exposure and dementia rate.”
The idea of a link between lithium and Alzheimer’s has been around for years, Forester said.
“Over a decade ago, a pathology study that looked at the brains of patients who had a lifelong history of bipolar disorder and were treated with lithium versus those with a lifelong history of bipolar disorder who were not on lithium showed that those exposed to lithium had about one sixth the rate of Alzheimer’s pathology,” he said. “It raised a lot of interesting questions and eyebrows about the mechanism.”
Other research has shown that low doses – but doses far higher than found in the drinking water in the Denmark trial – may reduce the odds of Alzheimer‘s. But research has been limited because drug companies don’t stand to make a lot of money from lithium therapy.
Forester said he would not advise people to try using lithium to ward off Alzheimer’s because the drug can harm the kidneys, although the levels seen in the drinking water in Denmark are too low to pose a serious risk.
The antidepressants paroxetine and citalopram distinctly outperformed placebo among patients who experienced no adverse effects from the drugs in US Food and Drug Administration (FDA)-registered, placebo-controlled trials, according to a new mega-analysis published online in Molecular Psychiatry.
The findings reject a widely disseminated theory, reported on in media outlets including Newsweek and 60 Minutes, that such medications exert no actual antidepressant effect.
“It has been suggested that the superiority of antidepressants over placebo in controlled trials is merely a consequence of side effects enhancing the expectation of improvement by making the patient realize that he/she is not on placebo,” wrote researchers from the University of Gothenburg, Sweden. “We explored this hypothesis in a patient-level post hoc analysis.”
Brain Scans Found to Predict Antidepressant Response
The analysis included 3344 adults with major depression from 15 trials of citalopram or paroxetine. When researchers compared the effects of the drugs among participants without any adverse events with results from participants on placebo, they found those with active treatment demonstrated larger symptom reduction. The effect size was 0.48 for citalopram and 0.33 for paroxetine.
Furthermore, among participants who did experience side effects from antidepressants, the severity of the adverse events failed to predict response, according to the study.
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“The finding that both paroxetine and citalopram are clearly superior to placebo also when not producing adverse events, as well as the lack of association between adverse event severity and response, argue against the theory that antidepressants outperform placebo solely or largely because of their side effects,” the study concluded.
Frequent questioning of antidepressants in the media is unjustified, the researchers suggested, and may actually deter people with depression from pursuing treatment.