Evidence map›Paper›PMID 42414911›Full record

SynthesisBMC cardiovascular disorders2026

Lipid profile changes through nurse-led cardiac rehabilitation: evidence from a systematic review and meta-analysis.

Yi Huang, Amirhossein Alizadeh-Nodehi, Kasra Pakdaman

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yi HuangDepartment of Emergency, The Second People's Hospital of Neijiang, Neijiang, 641000, China.
Amirhossein Alizadeh-NodehiDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0009-0008-4062-1318
Kasra PakdamanSchool of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari, Iran. Kasrapakdaman1999@gmail.com.ORCID http://orcid.org/0009-0005-1933-3715

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular diseases (CVDs) continue to be the leading cause of death worldwide, with dyslipidemia as one of their main risk factors. Nurse-led cardiac rehabilitation (NLCR) has been shown to improve the quality of life of cardiac patients. We aim to measure its effect on lipid levels.

methodsWe conducted a systematic review and meta-analysis of clinical trials comparing NLCR with usual care. Major electronic databases and grey literature sources were searched through July 31, 2025. Eligible studies reported at least one lipid outcome, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Risk of bias was assessed using RoB 2.0 for randomized controlled trials (RCTs) and ROBINS-I for non-RCTs. Random-effects models were used to calculate pooled standardized mean differences (SMDs) and mean differences (MDs) with 95% confidence intervals (CIs).

resultsTen studies were included in the meta-analysis. NLCR was associated with a significant reduction in TG levels (SMD = - 0.19; 95% CI: -0.36 to - 0.02), corresponding to an absolute reduction of 8.38 mg/dL (MD = - 8.38; 95% CI: -13.82 to - 2.94). Changes in TC (MD = - 6.03 mg/dL; 95% CI: -14.57 to 2.51), LDL-C (MD = - 4.78 mg/dL; 95% CI: -9.89 to 0.33), and HDL-C (MD = 1.90 mg/dL; 95% CI: -2.30 to 6.10) did not reach statistical significance in the overall analysis. Subgroup analyses demonstrated a significant TG reduction among RCTs (MD = - 9.25 mg/dL; 95% CI: -14.84 to - 3.65). According to follow-up duration, studies with follow-up durations of ≤ 3 months showed a significant reduction in TC levels (MD = - 9.81 mg/dL; 95% CI: -14.13 to - 5.49), whereas studies with follow-up durations of > 3 months demonstrated a significant reduction in TG levels (MD = - 8.02 mg/dL; 95% CI: -13.95 to - 2.09). Meta-regression analyses identified study design as a significant predictor of TG and LDL changes.

conclusionNLCR was associated with a significant reduction in TG levels among patients with CVD, while changes in TC, LDL-C, and HDL-C were not statistically significant. Subgroup and meta-regression analyses suggested that study design and follow-up duration may influence lipid outcomes. Further high-quality RCTs with longer follow-up are needed to confirm these findings and evaluate their long-term clinical implications.

Indexed as

Cardiac RehabilitationDyslipidemiasHeart DiseasesLipidsNurse's RolePractice Patterns, Nurses'AdultAgedBiomarkersFemaleHumansMaleMiddle AgedTreatment OutcomeBiomarkersLipidsDyslipidemiaLipidsMeta-analysisNursesRehabilitations

Identifiers

PMID42414911
PMCPMC13628755

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.