Application of the Gradient Boosted method in randomised clinical trials: Participant variables that contribute to depression treatment efficacy of duloxetine, SSRIs or placebo

Role of age, gender and marital status in prognosis for adults with depression: An individual patient data meta-analysis

AIMS

To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care.

METHODS

Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from their general practitioners, that used the Revised Clinical Interview Schedule so that there was uniformity in the measurement of clinical prognostic factors, and that reported on age, gender and marital status. Individual participant data were gathered from all nine eligible RCTs (N = 4864). Two-stage random-effects meta-analyses were conducted to ascertain the independent association between: (i) age, (ii) gender and (iii) marital status, and depressive symptoms at 3-4, 6-8, and 9-12 months post-baseline and remission at 3-4 months. Risk of bias was evaluated using QUIPS and quality was assessed using GRADE. PROSPERO registration: CRD42019129512. Pre-registered protocol https://osf.io/e5zup/.

RESULTS

There was no evidence of an association between age and prognosis before or after adjusting for depressive 'disorder characteristics' that are associated with prognosis (symptom severity, durations of depression and anxiety, comorbid panic disorderand a history of antidepressant treatment). Difference in mean depressive symptom score at 3-4 months post-baseline per-5-year increase in age = 0(95% CI: -0.02 to 0.02). There was no evidence for a difference in prognoses for men and women at 3-4 months or 9-12 months post-baseline, but men had worse prognoses at 6-8 months (percentage difference in depressive symptoms for men compared to women: 15.08% (95% CI: 4.82 to 26.35)). However, this was largely driven by a single study that contributed data at 6-8 months and not the other time points. Further, there was little evidence for an association after adjusting for depressive 'disorder characteristics' and employment status (12.23% (-1.69 to 28.12)). Participants that were either single (percentage difference in depressive symptoms for single participants: 9.25% (95% CI: 2.78 to 16.13) or no longer married (8.02% (95% CI: 1.31 to 15.18)) had worse prognoses than those that were married, even after adjusting for depressive 'disorder characteristics' and all available confounders.

CONCLUSION

Clinicians and researchers will continue to routinely record age and gender, but despite their importance for incidence and prevalence of depression, they appear to offer little information regarding prognosis. Patients that are single or no longer married may be expected to have slightly worse prognoses than those that are married. Ensuring this is recorded routinely alongside depressive 'disorder characteristics' in clinic may be important.

The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis

BACKGROUND

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

METHODS

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

RESULTS

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3-4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3-4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

CONCLUSIONS

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

Major Depressive Disorder in Older Patients as an Inflammatory Disorder: Implications for the Pharmacological Management of Geriatric Depression

Depression is a common and highly disabling condition in older adults. It is a heterogenous disorder and there is emerging evidence of a link between inflammation and depression in older patients, with a possible inflammatory subtype of depression. Persistent low-level inflammation, from several sources including psychological distress and chronic disease, can disrupt monoaminergic and glutaminergic systems to create dysfunctional brain networks. Despite the evidence for the role of inflammation in depression, there is insufficient evidence to recommend use of any putative anti-inflammatory agent in the treatment of depression in older adults at this stage. Further characterisation of markers of inflammation and stratification of participants with elevated rates of inflammatory markers in treatment trials is needed.

Depression in the medically ill

BACKGROUND

Depressive disorders are significantly more common in the medically ill compared to the general population. Depression is associated with worsening of physical symptoms, greater healthcare utilization and poorer treatment adherence. The present paper provides a critical review on the assessment and management of depression in the medically ill.

METHODS

Relevant articles pertaining to depression in the medically ill were identified, reviewed and synthesized qualitatively. A systematic review was not performed due to the large breadth of this topic, making a meaningful summary of all published and unpublished studies not feasible. Notable studies were reviewed and synthesized by a diverse set of experts to provide a balanced summary.

RESULTS

Depression is frequently under-recognized in medical settings. Differential diagnoses include delirium, personality disorders and depressive disorders secondary to substances, medications or another medical condition. Depressive symptoms in the context of an adjustment disorder should be initially managed by supportive psychological approaches. Once a mild to moderate major depressive episode is identified, a stepped care approach should be implemented, starting with general psychoeducation, psychosocial interventions and ongoing monitoring. For moderate to severe symptoms, or mild symptoms that are not responding to low-intensity interventions, the use of antidepressants or higher intensity psychotherapeutic interventions should be considered. Psychotherapeutic interventions have demonstrated benefits with small to moderate effect sizes. Antidepressant medications have also demonstrated benefits with moderate effect sizes; however, special caution is needed in evaluating side effects, drug-drug interactions as well as dose adjustments due to impairment in hepatic metabolism and/or renal clearance. Novel interventions for the treatment of depression and other illness-related psychological symptoms (e.g. death anxiety, loss of dignity) are under investigation.

LIMITATIONS

Non-systematic review of the literature.

CONCLUSION

Replicated evidence has demonstrated a bidirectional interaction between depression and medical illness. Screening and stepped care using pharmacological and non-pharmacological interventions is merited.

Youth Depression Alleviation with Anti-inflammatory Agents (YoDA-A): a randomised clinical trial of rosuvastatin and aspirin

BACKGROUND

Inflammation contributes to the pathophysiology of major depressive disorder (MDD), and anti-inflammatory strategies might therefore have therapeutic potential. This trial aimed to determine whether adjunctive aspirin or rosuvastatin, compared with placebo, reduced depressive symptoms in young people (15-25 years).

METHODS

YoDA-A, Youth Depression Alleviation with Anti-inflammatory Agents, was a 12-week triple-blind, randomised, controlled trial. Participants were young people (aged 15-25 years) with moderate to severe MDD (MADRS mean at baseline 32.5 ± 6.0; N = 130; age 20.2 ± 2.6; 60% female), recruited between June 2013 and June 2017 across six sites in Victoria, Australia. In addition to treatment as usual, participants were randomised to receive aspirin (n = 40), rosuvastatin (n = 48), or placebo (n = 42), with assessments at baseline and weeks 4, 8, 12, and 26. The primary outcome was change in the Montgomery-Åsberg Depression Rating Scale (MADRS) from baseline to week 12.

RESULTS

At the a priori primary endpoint of MADRS differential change from baseline at week 12, there was no significant difference between aspirin and placebo (1.9, 95% CI (- 2.8, 6.6), p = 0.433), or rosuvastatin and placebo (- 4.2, 95% CI (- 9.1, 0.6), p = 0.089). For rosuvastatin, secondary outcomes on self-rated depression and global impression, quality of life, functioning, and mania were not significantly different from placebo. Aspirin was inferior to placebo on the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF) at week 12. Statins were superior to aspirin on the MADRS, the Clinical Global Impressions Severity Scale (CGI-S), and the Negative Problem Orientation Questionnaire scale (NPOQ) at week 12.

CONCLUSIONS

The addition of either aspirin or rosuvastatin did not to confer any beneficial effect over and above routine treatment for depression in young people. Exploratory comparisons of secondary outcomes provide limited support for a potential therapeutic role for adjunctive rosuvastatin, but not for aspirin, in youth depression.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry, ACTRN12613000112763. Registered on 30/01/2013.

The addition of fluoxetine to cognitive behavioural therapy for youth depression (YoDA-C): a randomised, double-blind, placebo-controlled, multicentre clinical trial

BACKGROUND

Medication is commonly used to treat youth depression, but whether medication should be added to cognitive behavioural therapy (CBT) as first-line treatment is unclear. We aimed to examine whether combined treatment with CBT and fluoxetine was more effective than CBT and placebo in youth with moderate-to-severe major depressive disorder.

METHODS

The Youth Depression Alleviation-Combined Treatment (YoDA-C) trial was a randomised, double-blind, placebo-controlled, multicentre clinical trial. Participants were aged 15-25 years with moderate-to-severe MDD and had sought care at one of four clinical centres in metropolitan Melbourne, Australia. Patients were randomly assigned (1:1) to receive CBT for 12 weeks, plus either fluoxetine or placebo. Participants began on one 20 mg capsule of fluoxetine or one placebo pill per day. All participants received CBT, delivered by therapists in weekly 50-minute sessions and attended interviews at baseline, and at weeks 4, 8, and 12, during which they completed assessments with research assistants. Participants saw a psychiatrist or psychiatry trainee to complete medical assessments at the same timepoints. The primary outcome was change in the interviewer-rated Montgomery-Åsberg Depression Rating Scale (MADRS) score at 12 weeks. The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12612001281886).

FINDINGS

153 participants (mean age 19·6 years [SD 2·7]) were enrolled from Feb 20, 2013, to Dec 13, 2016. 77 (50%) patients were allocated to CBT and placebo and 76 (50%) to CBT and fluoxetine. Participants had severe depression at baseline (mean MADRS score 33·6 [SD 5·1] in the CBT and placebo group and 32·2 [5·6] in the CBT and fluoxetine group), with high proportions of participants with anxiety disorder comorbidity (47 [61%] in the CBT and placebo group and 49 [64%] in the CBT and fluoxetine group) and past-month suicidal ideation (55 [71%] in the CBT and placebo group and 59 [78%] in the CBT and fluoxetine group). 59 (77%) participants in the CBT and placebo group and 64 (84%) in the CBT and fluoxetine group completed follow-up at week 12. After 12 weeks of treatment both groups showed a reduction in MADRS scores (-13·7, 95% CI -16·0 to -11·4, in the CBT and placebo group and -15·1, -17·4 to -12·9, in the CBT and fluoxetine group). There was no significant between-group difference in MADRS scores (-1·4, -4·7 to 1·8; p=0·39). There were five suicide attempts in the CBT and placebo group and one suicide attempt in the CBT and fluoxetine group (odds ratio 0·2, 0·0-1·8; p=0·21), and no significant between-group differences for other suicidal behaviours.

INTERPRETATION

We did not find evidence that the addition of fluoxetine (rather than placebo) to CBT further reduced depressive symptoms in young people with moderate-to-severe MDD. Exploratory analyses showed that the addition of medication might be helpful for patients with comorbid anxiety symptoms and for older youth.

FUNDING

Australian National Health and Medical Research Council.

Gender, Personality Traits and Experience With Psychiatric Patients as Predictors of Stigma in Italian Psychology Students

A sample of undergraduate Psychology students (n = 1005), prevalently females (82.4%), mean age 20.5 (sd 2.5), was examined regarding their attitudes toward people suffering from mental illness. The survey instrument included a brief form for demographic variables, the Attribution Questionnaire-9 (AQ-9), the Ten Items Personality Inventory (TIPI), and two questions exploring attitudes toward open-door and restraint-free policies in Psychiatry. Higher levels of stigmatizing attitudes were found in males (Pity, Blame, Help, and Avoidance) and in those (76.5%) who had never had any experience with psychiatric patients (Danger, Fear, Blame, Segregation, Help, Avoidance and Coercion). A similar trend was also found in those who don't share the policy of no seclusion/restraint, while subjects who are favorable to open-door policies reported higher Coercion scores. No correlations were found between dimensions of stigma and personality traits. A machine learning approach was then used to explore the role of demographic, academic and personality variables as predictors of stigmatizing attitudes. Agreeableness and Extraversion emerged as the most relevant predictors for blaming attitudes, while Emotional Stability and Openness appeared to be the most effective contributors to Anger. Our results confirmed that a training experience in Psychiatry might successfully reduce stigma in Psychology students. Further research, with increased generalizability of samples and more reliable instruments, should address the role of personality traits and gender on attitudes toward people suffering from mental illness.

Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data

RATIONALE

Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.

OBJECTIVES

We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.

METHODS

This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).

RESULTS

Eleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.

CONCLUSIONS

A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C₁₆ remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70–0.78 and 0.72–0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ² test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

Escitalopram oxalate inhibits proliferation and migration and induces apoptosis in non-small cell lung cancer cells

Population-based cohort studies have revealed that neuroleptic medications are associated with a reduced cancer risk. Recent studies have demonstrated that selective serotonin reuptake inhibitors (SSRIs) have an antiproliferative or cytotoxic effect on certain cancer types. Known as a superior SSRI, escitalopram oxalate exhibits favorable tolerability with generally mild and temporary adverse events. The present study aimed to examine the effects of escitalopram oxalate on non-small cell lung cancer (NSCLC) cells. The experimental results revealed that escitalopram oxalate significantly inhibited the proliferation and invasion of A549, and H460 cells compared with BEAS-2B cells. Additionally, escitalopram oxalate significantly increased the sub-G₁ population and caspase-3 activity of A549, and H460 cells. Furthermore, escitalopram oxalate significantly induced mitochondria-dependent apoptotic signaling cascades in A549 and H460 cells, which included increases in the protein expression levels of apoptosis regulator Bax, truncated BH3-interacting domain death agonist, cytochrome c, apoptotic protease-activating factor 1, and cleaved caspase-9. These findings suggest that escitalopram oxalate could serve a therapeutic agent for the treatment of NSCLC due to its antiproliferative and apoptotic effects.

Escitalopram oxalate induces apoptosis in U-87MG cells and autophagy in GBM8401 cells

Glioblastoma multiforme (GBM) is recognized as a most aggressive brain cancer with the worst prognosis and survival time. Owing to the anatomic location of gliomas, surgically removing the tumour is very difficult and avoiding damage to vital brain regions during radiotherapy is impossible. Therefore, therapeutic strategies for malignant glioma must urgently be improved. Recent studies have demonstrated that selective serotonin reuptake inhibitors (SSRIs) have cytotoxic effect on certain cancers. Considering as a more superior SSRI, escitalopram oxalate exhibits favourable tolerability and causes generally mild and temporary adverse events. However, limited information is revealed about the influence of escitalopram oxalate on GBM. Therefore, an attempt was made herein to explore the effects of escitalopram oxalate on GBM. The experimental results revealed that escitalopram oxalate significantly inhibits the proliferation and invasive ability of U‐87MG cells and significantly reduced the expressions of cell cycle inhibitors such as Skp2, P57, P21 and P27. Notably, escitalopram oxalate also induced significant apoptotic cascades in U‐87MG cells and autophagy in GBM8401 cells. An animal study indicated that escitalopram oxalate inhibits the proliferation of xenografted glioblastoma in BALB/c nude mice. These findings implied that escitalopram oxalate may have potential in treatment of glioblastomas.

A gradient-boosted model analysis of the impact of body mass index on the short-term outcomes of critically ill medical patients

Objective

To evaluate the impact of body mass index on the short-term prognosis of non-surgical critically ill patients while controlling for performance status and comorbidities.

Methods

We performed a retrospective analysis on a two-year single-center database including 1943 patients. We evaluated the impact of body mass index on hospital mortality using a gradient-boosted model that also included comorbidities and was assessed by Charlson’s comorbidity index, performance status and illness severity, which was measured by the SAPS3 score. The SAPS3 score was adjusted to avoid including the same variable twice in the model. We also assessed the impact of body mass index on the length of stay in the hospital after intensive care unit admission using multiple linear regressions.

Results

A low value (< 20kg/m²) was associated with a sharp increase in hospital mortality. Mortality tended to subsequently decrease as body mass index increased, but the impact of a high body mass index in defining mortality was low. Mortality increased as the burden of comorbidities increased and as the performance status decreased. Body mass index interacted with the impact of SAPS3 on patient outcome, but there was no significant interaction between body mass index, performance status and comorbidities. There was no apparent association between body mass index and the length of stay at the hospital after intensive care unit admission.

Conclusion

Body mass index does appear to influence the shortterm outcomes of critically ill medical patients, who are generally underweight. This association was independent of comorbidities and performance status.

Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting

BACKGROUND

There are no widely used models in clinical care to predict outcome in acute lower gastro-intestinal bleeding (ALGIB). If available these could help triage patients at presentation to appropriate levels of care/intervention and improve medical resource utilisation. We aimed to apply a state-of-the-art machine learning classifier, gradient boosting (GB), to predict outcome in ALGIB using non-endoscopic measurements as predictors.

METHODS

Non-endoscopic variables from patients with ALGIB attending the emergency departments of two teaching hospitals were analysed retrospectively for training/internal validation (n=170) and external validation (n=130) of the GB model. The performance of the GB algorithm in predicting recurrent bleeding, clinical intervention and severe bleeding was compared to a multiple logic regression (MLR) model and two published MLR-based prediction algorithms (BLEED and Strate prediction rule).

RESULTS

The GB algorithm had the best negative predictive values for the chosen outcomes (>88%). On internal validation the accuracy of the GB algorithm for predicting recurrent bleeding, therapeutic intervention and severe bleeding were (88%, 88% and 78% respectively) and superior to the BLEED classification (64%, 68% and 63%), Strate prediction rule (78%, 78%, 67%) and conventional MLR (74%, 74% 62%). On external validation the accuracy was similar to conventional MLR for recurrent bleeding (88% vs. 83%) and therapeutic intervention (91% vs. 87%) but superior for severe bleeding (83% vs. 71%).

CONCLUSION

The gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models. These may be useful for risk stratification of patients on presentation to the emergency department.