Development and validation of a new chronic kidney disease risk equation: the role of pulse pressure
Bibliographic record
Abstract
Purpose: Chronic kidney disease (CKD) is a major cause of morbidity and, with a prevalence of about 10% in most developed countries, represents a major burden for healthcare systems. Interventions currently target known risk factors for CKD including diabetes, cardiac comorbidities, and hypertension (HTN). Recent evidence suggests that pulse pressure (PP), defined as the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), may be associated with CKD. This study aims to develop and validate a new CKD risk equation assessing PP as a potential risk factor. Methods: Electronic medical records (01/2004-05/2012), including laboratory data, of a random sample of 97,237 hypertensive patients from a large US integrated healthcare delivery system were analyzed. Patients were required to have ≥6 months of observation (baseline period) and no record of CKD prior to the first evidence of HTN (index date), defined either as 2 visits with a diagnosis of HTN (ICD-9: 401 - 405) or 1 visit for HTN following a high BP reading (SBP ≥140 mmHg or DBP ≥90 mmHg). CKD was defined using ICD-9 250.4, 403, 404, 585, 586, 588. The study population was randomly split into a development sample (2/3 of the sample) to identify the optimal CKD risk equation and a validation sample (1/3 of the sample) to validate its performance. Cox proportional hazards models were used to assess time to first CKD event based on baseline risk factors, including PP, age, gender, SBP, smoking status, BMI, diabetes, and cardiac comorbidities. Selection of the optimal model was based on the least absolute shrinkage and selection operator (LASSO). Performance of the risk equation was measured by the c-statistic. Results: Among the 34,915 eligible patients, 4,665 developed CKD (mean age 67.0; 47% male) and 30,250 did not (mean age 58.2, 48% male). Average observation was 3.90 years. PP was significantly higher among patients who developed CKD (mean [SD] PP, CKD: 62.5 [17.6] mmHg; non-CKD: 58.1 [16.4] mmHg, p<0.001). The best performing risk equation (c-statistic, development: 0.736; validation: 0.735) included PP (hazard ratio per mmHg increase: 1.0076, p<0.001) as a significant risk factor for CKD in addition to age, diabetes, and cardiac comorbidities among others. Conclusions: This study shows that greater pulse pressure is a significant predictive factor for increased CKD risk and is a better predictor than the traditional markers of SBP or DBP. Pulse pressure should be considered by practitioners along with other traditional risk factors when monitoring patients as well as in treatment strategies to prevent CKD.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".