Identifying Unmet Needs in Major Depressive Disorder Using a Computer-Assisted Alternative to Conventional Thematic Analysis: Qualitative Interview Study With Psychiatrists.

BACKGROUND: The development of digital health tools that are clinically relevant requires a deep understanding of the unmet needs of stakeholders, such as clinicians and patients. One way to reveal unforeseen stakeholder needs is through qualitative research, including stakeholder interviews. However, conventional qualitative data analytical approaches are time-consuming and resource-intensive, rendering them untenable in many industry settings where digital tools are conceived of and developed. Thus, a more time-efficient process for identifying clinically relevant target needs for digital tool development is needed. OBJECTIVE: The objective of this study was to address the need for an accessible, simple, and time-efficient alternative to conventional thematic analysis of qualitative research data through text analysis of semistructured interview transcripts. In addition, we sought to identify important themes across expert psychiatrist advisor interview transcripts to efficiently reveal areas for the development of digital tools that target unmet clinical needs. METHODS: We conducted 10 (1-hour-long) semistructured interviews with US-based psychiatrists treating major depressive disorder. The interviews were conducted using an interview guide that comprised open-ended questions predesigned to (1) understand the clinicians' experience of the care management process and (2) understand the clinicians' perceptions of the patients' experience of the care management process. We then implemented a hybrid analytical approach that combines computer-assisted text analyses with deductive analyses as an alternative to conventional qualitative thematic analysis to identify word combination frequencies, content categories, and broad themes characterizing unmet needs in the care management process. RESULTS: Using this hybrid computer-assisted analytical approach, we were able to identify several key areas that are of interest to clinicians in the context of major depressive disorder and would be appropriate targets for digital tool development. CONCLUSIONS: A hybrid approach to qualitative research combining computer-assisted techniques with deductive techniques provides a time-efficient approach to identifying unmet needs, targets, and relevant themes to inform digital tool development. This can increase the likelihood that useful and practical tools are built and implemented to ultimately improve health outcomes for patients.

Improving prediction of psychosis in youth at clinical high-risk: pre-baseline symptom duration and cortical thinning as moderators of the NAPLS2 risk calculator.

BACKGROUND: Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models. METHODS: Participants from both the NAPLS2 and NAPLS3 samples were classified as either 'long' or 'short' symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis. RESULTS: The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78). CONCLUSIONS: These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management.

Introduction: Digital health technologies (DHTs) driven by artificial intelligence applications, particularly those including predictive models derived with machine learning methods, have garnered substantial attention and financial investment in recent years. Yet, there is little evidence of widespread adoption and scant proof of gains in patient health outcomes. One factor of this paradox is the disconnect between DHT developers and digital health ecosystem stakeholders, which can result in developing technologies that are highly sophisticated but clinically irrelevant. Here, we aimed to uncover challenges faced by psychiatrists treating patients with major depressive disorder (MDD). Specifically, we focused on challenges psychiatrists raised about bipolar disorder (BD) misdiagnosis. Methods: We conducted semi-structured interviews with 10 United States-based psychiatrists. We applied text and thematic analysis to the resulting interview transcripts. Results: Three main themes emerged: (1) BD is often misdiagnosed, (2) information crucial to evaluating BD is often occluded from clinical observation, and (3) BD misdiagnosis has important treatment implications. Discussion: Using upstream stakeholder engagement methods, we were able to identify a narrow, unforeseen, and clinically relevant problem. We propose an organizing framework for development of digital tools based upon clinician-identified unmet need.

Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach.

Importance: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. Objective: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. Design, Setting, and Participants: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. Main Outcomes and Measures: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. Results: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = -0.92; P < .001) compared with baseline models (r = -0.50; P < .001). Conclusions and Relevance: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.

Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis.

The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied with the goal of understanding the development of psychosis; however, less attention has been paid to the 75%-80% of CHR-P individuals who do not transition to psychosis. It is an open question whether multivariable models could be developed to predict remission outcomes at the same level of performance and generalizability as those that predict conversion to psychosis. Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS3). An empirically derived set of clinical and demographic predictor variables were selected with elastic net regularization and were included in a gradient boosting machine algorithm to predict prodromal symptom remission. The predictive model was tested in a comparably sized independent sample (NAPLS2). The classification algorithm developed in NAPLS3 achieved an area under the curve of 0.66 (0.60-0.72) with a sensitivity of 0.68 and specificity of 0.53 when tested in an independent external sample (NAPLS2). Overall, future remitters had lower baseline prodromal symptoms than nonremitters. This study is the first to use a data-driven machine-learning approach to assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The predictive power of the models in this study suggest that remission represents a unique clinical phenomenon. Further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P state.

Prediction and Prevention in the Clinical High-Risk for Psychosis Paradigm: A Review of the Current Status and Recommendations for Future Directions of Inquiry.

Prediction and prevention of negative clinical and functional outcomes represent the two primary objectives of research conducted within the clinical high-risk for psychosis (CHR-P) paradigm. Several multivariable "risk calculator" models have been developed to predict the likelihood of developing psychosis, although these models have not been translated to clinical use. Overall, less progress has been made in developing effective interventions. In this paper, we review the existing literature on both prediction and prevention in the CHR-P paradigm and, primarily, outline ways in which expanding and combining these paths of inquiry could lead to a greater improvement in individual outcomes for those most at risk.

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort.

BACKGROUND: Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. METHODS: We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. RESULTS: After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. CONCLUSIONS: Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Association of Aripiprazole With Reduced Hippocampal Atrophy During Maintenance Treatment of First-Episode Schizophrenia.

PURPOSE/BACKGROUND: Hippocampal volume loss in early schizophrenia has been linked with markers of inflammation and oxidative stress, and with less response of negative symptoms. Aripiprazole has been reported to preserve hippocampal volume and to reduce inflammation. METHODS/PROCEDURES: Study 1 was a 12-month multicenter randomized placebo-controlled trial of citalopram added to clinician-determined second-generation antipsychotic medication in 95 patients with first-episode schizophrenia (FES), 19 of whom received aripiprazole. We compared participants taking aripiprazole with those on other antipsychotics to determine whether those on aripiprazole had less hippocampal volume loss. We also examined peripheral biomarker data from medication-naive patients with schizophrenia receiving 8 weeks of antipsychotic treatment (n = 24) to see whether markers of inflammation and oxidative stress that previously predicted hippocampal volume differed between aripiprazole (n = 9) and other antipsychotics (study 2). FINDINGS/RESULTS: Aripiprazole was associated with a mean increase in hippocampal volume of 0.35% (SD, 0.80%) compared with a 0.53% decrease (SD, 1.2%) with other antipsychotics during the first year of maintenance treatment in patients with FES. This difference was significant after adjusting for age, sex, citalopram treatment, and baseline Brief Psychiatric Rating Scale score (B = 0.0079, P = 0.03). Aripiprazole was also associated with reduced concentrations of the inflammatory cytokines interleukin-8 and tumor necrosis factor (P < 0.01) during the first 8 weeks of treatment in medication-naive patients with FES. IMPLICATIONS/CONCLUSIONS: These results suggest that aripiprazole may protect against hippocampal atrophy via an anti-inflammatory mechanism, but these results require replication in larger, randomized trials, and the clinical relevance of hippocampal volume loss is not established.

Effect of citalopram on hippocampal volume in first-episode schizophrenia: Structural MRI results from the DECIFER trial.

Hippocampal volume loss is prominent in first episode schizophrenia (FES) and has been associated with poor clinical outcomes and with BDNF genotype; antidepressants are believed to reverse hippocampal volume loss via release of BDNF. In a 12-month, placebo-controlled add-on trial of the antidepressant, citalopram, during the maintenance phase of FES, negative symptoms were improved with citalopram. We now report results of structural brain imaging at baseline and 6 months in 63 FES patients (34 in citalopram group) from the trial to assess whether protection against hippocampal volume loss contributed to improved negative symptoms with citalopram. Hippocampal volumetric integrity (HVI) did not change significantly in the citalopram or placebo group and did not differ between treatment groups, whereas citalopram was associated with greater volume loss of the right CA1 subfield. Change in cortical thickness was associated with SANS change in 4 regions (left rostral anterior cingulate, right frontal pole, right cuneus, and right transverse temporal) but none differed between treatment groups. Our findings suggest that minimal hippocampal volume loss occurs after stabilization on antipsychotic treatment and that citalopram's potential benefit for negative symptoms is unlikely to result from protection against hippocampal volume loss or cortical thinning.

Selection for psychosocial treatment for youth at clinical high risk for psychosis based on the North American Prodrome Longitudinal Study individualized risk calculator.

AIM: Recent findings suggest that family-focused therapy (FFT) is effective for individuals at clinical high-risk for psychosis (CHR-P). As outcomes of CHR-P individuals are quite varied, certain psychosocial interventions may be differentially effective in subgroups. The present study examined change in positive symptoms for CHR-P individuals at different levels of predicted risk for conversion to psychosis who received either FFT, a brief form of family education termed enhanced care (EC) or treatment as usual. METHODS: Participants were drawn from the North American Prodromal Longitudinal Study (NAPLS2). A subset of NAPLS2 participants completed a randomized study involving FFT or EC. The present study includes participants from the FFT-CHR sub-study and non-randomized NAPLS2 participants. Predicted risk of conversion was calculated using the Individualized Risk Calculator for Psychosis. Robust linear regressions evaluated whether the association between predicted risk of conversion and positive symptom change differed across intervention groups. RESULTS: A total of 94 participants from the FFT-CHR sub-study (FFT-CHR n = 50, EC n = 44) and 401 non-randomized NAPLS2 participants were included in this study. There was a treatment group by predicted risk of conversion interaction that predicted positive symptom improvement: higher risk individuals improved more with FFT-CHR than EC or the non-randomized NAPLS group, whereas lower-risk individuals did not differ in positive symptom improvement across treatment groups (FFT-CHR vs EC: P = .03, ß = 20.27; FFT-CHR vs NAPLS2: P < .001, ß = 28.40). CONCLUSIONS: Intensive treatments such as FFT-CHR may be most appropriate for individuals at the highest levels of clinical risk for psychosis.

Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis.

BACKGROUND: Risk calculators are useful tools that can help clinicians and researchers better understand an individual's risk of conversion to psychosis. The North American Prodrome Longitudinal Study (NAPLS2) Individualized Risk Calculator has good predictive accuracy but could be potentially improved by the inclusion of a biomarker. Baseline cortisol, a measure of hypothalamic-pituitary-adrenal (HPA) axis functioning that is impacted by biological vulnerability to stress and exposure to environmental stressors, has been shown to be higher among individuals at clinical high-risk for psychosis (CHRP) who eventually convert to psychosis than those who do not. We sought to determine whether the addition of baseline cortisol to the NAPLS2 risk calculator improved the performance of the risk calculator. METHODS: Participants were drawn from the NAPLS2 study. A subset of NAPLS2 participants provided salivary cortisol samples. A multivariate Cox proportional hazards regression evaluated the likelihood of an individual's eventual conversion to psychosis based on demographic and clinical variables in addition to baseline cortisol levels. RESULTS: A total of 417 NAPLS2 participants provided salivary cortisol and were included in the analysis. Higher levels of cortisol were predictive of conversion to psychosis in a univariate model (C-index = 0.59, HR = 21.5, p-value = 0.004). The inclusion of cortisol in the risk calculator model resulted in a statistically significant improvement in performance from the original risk calculator model (C-index = 0.78, SE = 0.028). CONCLUSIONS: Salivary cortisol is an inexpensive and non-invasive biomarker that could improve individual predictions about conversion to psychosis and treatment decisions for CHR-P individuals.

Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning.

BACKGROUND: Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable. METHODS: Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2. RESULTS: Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure). CONCLUSIONS: Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

Air pollution and hippocampal atrophy in first episode schizophrenia.

Air pollution has recently been linked to central nervous system (CNS) diseases, possibly mediated by inflammation and oxidative stress. Hippocampal atrophy in individuals with first episode schizophrenia (FES) has also been associated with biomarkers of inflammation and oxidative stress, whereas hippocampal atrophy was not observed in matched healthy controls with similar biomarker levels of inflammation and oxidative stress. Fine particulate matter (PM2.5), one component of air pollution, is most strongly implicated in CNS disease. The present study examined the association between PM2.5 and hippocampal volume in individuals with FES who participated in a 52-week placebo-controlled clinical trial of citalopram added to clinician-determined antipsychotic treatment at four sites in the US and China. Left hippocampal volumetric integrity (LHVI; inversely related to atrophy) was measured at baseline and week 52 using an automated highly-reliable algorithm. Mean annual PM2.5 concentrations were obtained from records compiled by the World Health Organization. The relationships between baseline LHVI and PM2.5 and change in LHVI and PM2.5 were evaluated using regression analyses. 89 participants completed imaging at baseline and 46 participants completed imaging at week 52. Mean annual PM2.5 was significantly associated with both baseline LHVI and change in LHVI after controlling for age, sex, baseline LHVI, duration of untreated psychosis and baseline antipsychotic medication dose. Air pollution may contribute to the progression of hippocampal atrophy after a first episode of illness, but these findings should be considered preliminary since other unmeasured factors may have differed between cities and contributed to the observed effect.

Optical coherence tomography of the retina in schizophrenia: Inter-device agreement and relations with perceptual function.

BACKGROUND: Optical coherence tomography (OCT) studies have demonstrated differences between people with schizophrenia and controls. Many questions remain including the agreement between scanners. The current study seeks to determine inter-device agreement of OCT data in schizophrenia compared to controls and to explore the relations between OCT and visual function measures. METHODS: Participants in this pilot study were 12 individuals with schizophrenia spectrum disorders and 12 age- and sex-matched controls. Spectralis and Cirrus OCT machines were used to obtain retinal nerve fiber layer (RNFL) thickness and macular volume. Cirrus was used to obtain ganglion cell layer + inner plexiform layer (GCL + IPL) thickness. Visual function was assessed with low-contrast visual acuity and the King-Devick test of rapid number naming. RESULTS: There was excellent relative agreement in OCT measurements between the two machines, but poor absolute agreement, for both patients and controls. On both machines, people with schizophrenia showed decreased macular volume but no difference in RNFL thickness compared to controls. No between-group difference in GCL + IPL thickness was found on Cirrus. Controls showed significant associations between King-Devick performance and RNFL thickness and macular volume, and between low-contrast visual acuity and GCL + IPL thickness. Patients did not show significant associations between OCT measurements and visual function. CONCLUSIONS: Good relative agreement suggests that the offset between machines remains constant and should not affect comparisons between groups. Decreased macular volume in individuals with schizophrenia on both machines supports findings of prior studies and provides further evidence that similar results may be found irrespective of OCT device.

Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk.

In the past 2 to 3 decades, clinicians have used the clinical high risk for psychosis (CHR-P) paradigm to better understand factors that contribute to the onset of psychotic disorders. While this paradigm is useful to identify individuals at risk, the CHR-P criteria are not sufficient to predict outcomes from the CHR-P population. Because approximately 25% of the CHR-P population will ultimately convert to psychosis, more precise methods of prediction are needed to account for heterogeneity in both risk factors and outcomes in the CHR-P population. To this end, several groups in recent years have used data-driven approaches to refine predictive algorithms to predict both conversion to psychosis and functional outcomes. These models have generally used either clinical and behavioral data, including demographics and measures of symptom severity, neurocognitive functioning, and social functioning, or neuroimaging data, including structural and functional measures, to predict conversion to psychosis in CHR-P samples. This review focuses on the empirical models that have been derived within each of these lines of research and evaluates the performance and methodology of these models. This review also serves to inform best practices for data-driven approaches and directions moving forward to improve our prediction of psychotic disorders and associated outcomes. Because sample size is still the most critical consideration in the current models, we urge that algorithms to predict conversion be conducted using multisite data in order to obtain the power necessary to conclusively determine predictive accuracy without overfitting.

Citalopram in first episode schizophrenia: The DECIFER trial.

Antidepressants are frequently prescribed in first episode schizophrenia (FES) patients for negative symptoms or for subsyndromal depressive symptoms, but therapeutic benefit has not been established, despite evidence of efficacy in later-stage schizophrenia. We conducted a 52 week, placebo-controlled add-on trial of citalopram in patients with FES who did not meet criteria for major depression to determine whether maintenance therapy with citalopram would improve outcomes by preventing or improving negative and depressive symptoms. Primary outcomes were negative symptoms measured by the Scale for Assessment of Negative Symptoms and depressive symptoms measured by the Calgary Depression Scale for Schizophrenia; both were analyzed by an intent-to-treat, mixed effects, area-under-the-curve analysis to assess the cumulative effects of symptom improvement and symptom prevention over a one-year period. Ninety-five patients were randomized and 52 (54%) completed the trial. Negative symptoms were reduced with citalopram compared to placebo (p = .04); the effect size of citalopram versus placebo was 0.32 for participants with a duration of untreated psychosis (DUP) of <18 weeks (median split) and 0.52 with a DUP >18 weeks. Rates of new-onset depression did not differ between groups; improvement in depressive symptoms was greater with placebo than citalopram (p = .02). Sexual side effects were more common with citalopram, but overall treatment-emergent side effects were not increased compared to placebo. In conclusion, citalopram may reduce levels of negative symptoms, particularly in patients with longer DUP, but we found no evidence of benefit for subsyndromal depressive symptoms.

Immune-modulating formulas: who wins the meta-analysis race?

Most agree that enteral nutrition is the ideal way to feed critically ill patients who have a functional gastrointestinal tract, but selecting the appropriate enteral formula can be difficult. Specifically, the use of immune-modulating diets has brought much excitement as well as debate. Literature to date presents both positive and potential adverse effects. To aid the clinician in the decision-making process, this article reviews the current research and recommendations regarding the use of immune-modulating diets.