The association between trajectories of risk factors and risk of cardiovascular disease or mortality among patients with diabetes or hypertension: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Cardiometabolic risk factors and renal function are monitored regularly for patients with diabetes mellitus (DM)/ hypertension (HT). In addition to risk factor levels at a single time point, their trajectory (changes over time) can also be differentially related to the risk of cardiovascular diseases (CVD) and mortality. This study aimed to systematically examine the evidence regarding the association between risk factor trajectories and risk of CVD/mortality in patients with DM/HT. METHOD: PubMed, MEDLINE, and Embase were searched for articles from January 1963 to April 2021. Inclusion criteria: studies that 1) analyzed trajectories of risk factors including haemoglobin A1c (HbA1c), blood pressure, estimated glomerular filtration rate (eGFR), body mass index (BMI), and blood lipids; 2) were performed in the DM/HT population and, 3) included risk of CVD/mortality as outcomes. Study quality was assessed using the Newcastle-Ottawa quality assessment scale. RESULTS: A total of 22,099 articles were identified. After screening by title and abstract, 22,027 articles were excluded by irrelevant outcomes, exposure, population, or type of articles. Following full-text screening, 11 articles investigating the trajectories of HbA1c (N = 7), systolic blood pressure (SBP) (N = 3), and eGFR (N = 1) were included for data extraction and analysis. No studies were identified examining the association of BMI or lipid trajectories with CVD/mortality. All included studies were of good quality based on the NOS criteria. In general, stable trajectories within optimal ranges of the risk factors (HbA1c: <7%, SBP: 120-139mmHg, eGFR: >60mL/min/1.73m2) had the lowest CVD/mortality risk compared to an increasing HbA1c trajectory (from 8% to 10%), an increasing SBP trajectory (from 120-139 to ≥140mmHg), or a decreasing eGFR trajectory (from 90 to 70mL/min/1.73m2). CONCLUSION: A relatively stable and well-controlled trajectory for cardiometabolic risk factors was associated with the lowest risk of CVD/mortality. Risk factor trajectories have important clinical implications in addition to single time point measurements. More attention should be given to patients with suboptimal control and those with unstable trends of cardiometabolic risk factors.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".