Module 3 Assignment: Critical Appraisal of Evidence
Three main questions
When conducting a review, these are the 3 main questions that need to be answered when reviewing literature. What level of evidence? How well was the study conducted? How useful is the study for practice? (These are the questions that will be answered as you fill out this form; you do not need to provide an answer here.)
Part I
Literature review
| Literature Focus/Title | Identify underlying theory, framework, or model | Intervention | Sample # of subjects | Variables independent/dependent | Data collection Quantitative/Qualitative | Ethical considerations/Vulnerable populations | Impact to practice |
| 1. Metabolomics signatures of SSRI/SNRI and CBT in adult MDD | Beck’s cognitive theory (CBT); monoamine/biopsychosocial model (pharmacotherapy); and precision-psychiatry biomarker framework | Randomized to 12 weeks: escitalopram vs duloxetine vs manualized CBT; targeted LC-electrochemistry metabolomics baseline & week 12; HRSD-17/HRSA-14 symptoms (Bhattacharyya et al., 2025) | N = 163 treatment-naïve adult outpatients; 3 arms | IV: treatment arm. DVs: metabolite changes; HRSD-17/HRSA-14 changes; metabolite–symptom associations
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Quantitative RCT analysis with biomarker profiling + validated clinician-rated scales (Bhattacharyya et al., 2025). | Adult outpatients; RCT ethics/consent in parent trial; no vulnerable populations specifically targeted
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These findings support precision matching between CBT and pharmacotherapy early in care and should be applied through the EBP steps of asking, acquiring, appraising, and applying the best evidence with patient values and context (Melnyk & Fineout-Overholt, 2023), while weighing validity and applicability to your local setting before translation to practice (Flanagan et al., 2024) |
| 2. Clinical response to SSRIs relative to CBT: symptom-specific approach | Symptom-network & patient-level modeling (HDRS-17 item trajectories)
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Secondary IPD of six SSRI-vs-CBT RCTs (Boschloo et al., 2022); modeled direct vs indirect effects on item-level symptoms (≈8–16 weeks) | N = 599 adults (65% SSRI; 35% CBT)(Boschloo et al., 2022)
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IV: treatment (SSRI vs CBT). DV: HDRS-17 item changes; moderators: baseline symptom profiles | Quantitative secondary IPD/network modeling | Secondary analysis of previously consented RCT datasets; no new human subjects. | Symptom-specific patterns (like mood/psychic anxiety vs. somatic agitation) can guide shared decision-making about CBT vs SSRI during the first month; integrate this evidence using a stepwise EBP approach (Melnyk & Fineout-Overholt, 2023) and confirm clinical significance and external validity for your population before adopting it (Flanagan et al., 2024). |
| 3. Prediction of individual patient outcomes to psychotherapy vs medication for major depression | Beck’s cognitive theory (for CBT); monoamine/biopsychosocial models (for SSRIs/SNRIs); precision-psychiatry/personalized medicine framework using partial least squares regression (PLSR) for treatment selection (LoParo et al., 2025) | Random assignment to 16 one-hour CBT sessions vs escitalopram 10–20 mg/day vs duloxetine 30–60 mg/day for 12 weeks; HAM-D administered frequently; ML models predicted end-of-treatment severity/remission and simulated treatment recommendations (matched vs mismatched) | CBT N=72, escitalopram N=92, duloxetine N=84 (adult outpatients); inclusion required HAM-D ≥18 at screening and ≥15 at baseline (LoParo et al., 2025) | treatment arm (CBT vs escitalopram vs duloxetine). DVs: end-of-treatment HAM-D (severity), remission; model predictors were baseline clinical/demographic items. | Quantitative—secondary predictive modeling on RCT clinical data with repeated HAM-D at weeks 1–6, 8, 10, 12; external validation in an independent sample (LoParo et al., 2025) | Conducted within an RCT framework with standard consent/IRB; adult outpatients; no specific vulnerable populations targeted (LoParo et al., 2025) | Model-based recommendations that differentiate CBT, escitalopram, and duloxetine can be used as decision support when selecting initial treatment, consistent with implementing evidence alongside patient preferences and resources (Melnyk & Fineout-Overholt, 2023) and with best practices for translating research into practice after appraising bias, precision, and applicability (Flanagan et al., 2024). |
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Part II
Rapid Appraisal Overview
Complete this section
| Date: |
| Reviewer: |
| Article citation: Bhattacharyya, S., MahmoudianDehkordi, S., Sniatynski, M. J., Belenky, M., Marur, V. R., Rush, A. J., Craighead, W. E., Mayberg, H. S., Dunlop, B. W., Kristal, B. S., & Kaddurah-Daouk, R. (2025). Metabolomics signatures of serotonin reuptake inhibitor (escitalopram), serotonin norepinephrine reuptake inhibitor (duloxetine), and cognitive-behavioral therapy on key neurotransmitter pathways in major depressive disorder. Journal of Affective Disorders. https://doi.org/10.1016/j.jad.2025.01.064 |
| Article year 2025 |
| Picot Question/Problem: In adults suffering from Major depressive disorder, does a non-pharmacological approach, such as cognitive behavioral therapy, give a better outcome in reducing depression symptoms as compared to a selective serotonin reuptake inhibitor within 30 days? |
| Purpose of the Study
· The study was done to characterize metabolomic signatures associated with escitalopram, duloxetine, and CBT, and relate those changes to clinical symptom change. · There was a clear explanation of the purpose of the study–random assignment to three treatments; targeted metabolomics at baseline and week 12; outcomes via HRSD-17/HRSA-14 (Bhattacharyya et al., 2025). |
| Sampling Technique, Sample Size & Characteristics
· What is the sample size? 163 treatment-naïve adult outpatients with MDD (Bhattacharyya et al., 2025). · Were there enough people in the study to establish that the findings did not occur by chance? Reasonable for a three-arm biomarker RCT; robust within-arm symptom reductions were observed, supporting that the findings are unlikely due to chance. |
| Variables, Reliability, Validity
· Variables & definitions: IV: treatment arm (CBT, duloxetine, escitalopram). DVs: targeted metabolite changes and HRSD-17/HRSA-14 score changes; metabolite–symptom associations (Bhattacharyya et al., 2025). · Instrument validity/reliability: HRSD-17 and HRSA-14 are standard, validated clinician-rated scales; targeted LC-electrochemistry is a validated platform for specified metabolites. |
| Ethical Considerations
· Were there any untoward events during the study? none · Were there ethical considerations? Conducted within a randomized-trial framework (parent PReDICT) with consent/IRB; adult outpatients. · Did people leave the study? Week-12 data available for a subset due to lab/sample availability; symptom follow-up reported. · Any vulnerable populations? None |
| Clinical Practice
· What does the research mean for clinical practice? Distinct biochemical signatures for CBT vs SSRIs suggest different mechanisms for improvement; supports precision matching and counseling on early expectations (especially during the first month). · Is the study’s purpose an important clinical issue? Highly relevant to PMHNP decision-making when choosing CBT vs SSRI and arranging early (≤30-day) follow-up. In practice, these data favor individualized selection between CBT and antidepressants during the first month of treatment: symptom profiles and patient preference can be matched to the modality most likely to yield early benefit, while monitoring and adjusting care at follow-up. Implementation should follow a systematic EBP process—ask a focused question, acquire and appraise the best available evidence, apply it with patient values and context, and evaluate outcomes (Melnyk & Fineout-Overholt, 2023). Before adopting results locally, appraise internal and external validity and the clinical significance of effects to ensure findings are applicable to your setting (Flanagan et al., 2024). |
Adapted with permission from Melnyk BM, Fineout-Overholt editors Evidence based practice in nursing and healthcare: a guide to best practice2nd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams, and Wilkins – Evidence-Based Practice Step by Step: Critical Appraisal of the Evidence: Part I. AJN The American Journal of Nursing110(7):47-52, July 2010.
References
Bhattacharyya, S., MahmoudianDehkordi, S., Sniatynski, M. J., Belenky, M., Marur, V. R., Rush, A. J., Craighead, W. E., Mayberg, H. S., Dunlop, B. W., Kristal, B. S., & Kaddurah-Daouk, R. (2025). Metabolomics signatures of serotonin reuptake inhibitor (escitalopram), serotonin norepinephrine reuptake inhibitor (duloxetine) and cognitive-behavioral therapy on key neurotransmitter pathways in major depressive disorder. Journal of Affective Disorders. https://doi.org/10.1016/j.jad.2025.01.064
Boschloo, L., Hieronymus, F., Cuijpers, P., Lisinski, A., Weitz, E. S., DeRubeis, R. J., Dimidjian, S., Dunner, D. L., Dunlop, B. W., Hegerl, U., Hollon, S. D., Jarrett, R. B., Kennedy, S. H., Miranda, J., Mohr, D. C., Simons, A. D., Parker, G., Petrak, F., Herpertz, S., & Quilty, L. C. (2022). Clinical response to ssris relative to cognitive behavioral therapy in depression: A symptom‐specific approach. World Psychiatry, 21(1), 152–153. https://doi.org/10.1002/wps.20944
Flanagan, Tatano, C., & Polit, D. F. (2024). Polit & beck’s nursing research: Generating and assessing evidence for nursing practice. Lippincott Williams & Wilkins.
LoParo, D., Dunlop, B. W., Nemeroff, C. B., Mayberg, H. S., & Craighead, W. E. (2025). Prediction of individual patient outcomes to psychotherapy vs medication for major depression. Npj Mental Health Research, 4(1). https://doi.org/10.1038/s44184-025-00119-9
Melnyk, B. M., & Fineout-Overholt, E. (2023). Evidence-based practice in nursing & healthcare (5th ed.). Lippincott Williams & Wilkins.