Myocardial Performance Index to assess cardiac function in autoimmune connective tissue disease: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate cardiac function using Myocardial Performance Index (MPI) in autoimmune connective tissue disease (ACTD) patients without cardiovascular abnormalities. METHODS: A systematic search of databases including Medline, Google Scholar, ProQuest, Scopus and Cochrane Library was conducted to identify relevant studies on ACTD and MPI from 1995 to 2023. ACTD included in the search were rheumatoid arthritis (RA), systemic sclerosis (SSc), systemic lupus erythematosus (SLE), Sjögren syndrome (SjD), polymyositis and dermatomyositis. Quality assessment was performed using the Newcastle-Ottawa Scale, followed by meta-analysis computation of mean differences (MDs) of MPI using Review Manager V.5.4. RESULTS: A total of 22 studies for qualitative and 19 for quantitative synthesis were included. We found six studies on RA, eight studies on SSc, five studies on SLE, two studies on SjD and one on mixed connective tissue disorder. Conventional echocardiography and tissue Doppler imaging (TDI) were used to assess the MPI. Both conventional MPI and tissue Doppler MPI values were elevated compared with healthy control (MD=0.11, 95% CI 0.08 to 0.14, p value<0.00001 and MD=0.06, 95% CI 0.03 to 0.10, p value=0.00001, respectively). CONCLUSIONS: We found elevated MPI values in patients with ACTD compared with healthy controls. MPI assessment has the potential for early detection and management of cardiac dysfunction in patients with ACTD, but further studies are required to corroborate these findings. PROSPERO REGISTRATION NUMBER: CRD42023490643.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".