Biomarkers Associated with Thrombosis in Patients with Peripherally Inserted Central Catheter: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The measurement and identification of plasma biomarkers can support the estimation of risk and diagnosis of deep vein thrombosis (DVT) associated with the use of a peripherally inserted central catheter (PICC). OBJECTIVES: This systematic review and meta-analysis aimed to identify the association between the levels of potential biomarkers that reflect the activation of the blood system, long-term vascular complications, inflammatory system, and the occurrence of PICC-related DVT. METHODS: Seven electronic databases (Embase, Web of Science, Medline, Scopus, Cinahl, Cochrane Central Register of Controlled Trials, and ERIC) were searched to identify literature published until December 2022. Studies were required to report: (I) adult and pediatric patients, outpatient or admitted to clinical, surgical, or ICU with PICC; (II) patients with PICC-related DVT and patients without PICC-related DVT as a comparator; and (III) at least one biomarker available. The Newcastle-Ottawa Scale was used to evaluate the quality of the studies. Study precision was evaluated by using a funnel plot for platelets level. We provided a narrative synthesis and meta-analysis of the findings on the biomarkers' outcomes of the studies. We pooled the results using random effects meta-analysis. The meta-analysis was conducted using Review Manager software v5.4. This systematic review is registered in PROSPERO (CRD42018108871). RESULTS: for heterogeneity = 0.06). CONCLUSIONS: High levels of D-dimer and platelet were associated with DVT in patients with PICC. However, biomarkers such as APTT, fibrinogen, FDP, glucose, hemoglobin, glycated hemoglobin, INR, prothrombin time, prothrombin fragment 1.2, the thrombin-antithrombin complex, and WBC were not related to the development of DVT associated with PICC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".