FC006: Sex Differences in the Recognition, Monitoring and Management of Chronic Kidney Disease in Health Care
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Sex-specific differences exist in the population prevalence of chronic kidney disease (CKD), progression rates and health outcomes. Recognition, diagnosis and monitoring are central processes in the management of patients with CKD, but it is unknown whether these processes differ between men and women. METHOD This is an observational cohort study of all adult individuals (n = 227 847) with at least one eGFR (from serum creatinine) <60 mL/min/1.73 m2 during outpatient routine care in Stockholm, Sweden, 2009–2017. Through logistic regression, we evaluated differences by sex in carrying an ICD-10 code for CKD and provision of recommended medications (renin–angiotensin system inhibitors [RASi] and statins) at inclusion. Through Cox regression, we evaluated potential sex differences in receiving a CKD diagnosis, referral to nephrology care and performance of laboratory monitoring (creatinine and albuminuria) in the next 18 months from inclusion. We also explored time-trends, comparing the above-mentioned nephrology care indicators among 70–80 000 unique eligible individuals per calendar year (in total 695 632 observations), thus allowing participants to contribute to different calendar year-cohorts. RESULTS We identified more women (55%) than men (45%) with probable CKD (eGFR < 60 mL/min/1.73 m2). Women were generally older than men (median 77 versus 74 years), but eGFR was similar between sexes (51 mL/min/1.73 m2). More men than women had diabetes or myocardial infarction, but there was similar prevalence of hypertension. At inclusion and compared with men, women had lower risk of carrying an ICD-10 code for CKD (odds ratio [OR]: 0.47, 95% CI: 0.45–0.49) and were less likely to receive RASi (OR: 0.55; 0.50–0.61, among patient with albuminuria A3) or statins (OR: 0.64; 0.63–0.66, among patients ≥ 50 years), despite the presence of clear indications. In subsequent time-to-event analyses, women were less likely to receive a CKD diagnosis (hazard ratio [HR]: 0.43; 0.42–0.45) and visit a nephrologist (HR: 0.46; 0.43–0.48) regardless of CKD severity, presence of albuminuria or criteria for referral. Women were also less likely to undergo monitoring of creatinine or albuminuria, even among participants with diabetes (HR: 0.75; 0.74–0.76) or hypertension (HR: 0.76; 0.75–0.78). Multivariable adjustment including comorbidities, eGFR and indication for referral showed some attenuation of the effect, but a sex difference remained. Although in absolute terms there has been a gradual improvement in all nephrology care indicators over time, the observed sex gap persisted throughout. CONCLUSION We found profound sex differences in the detection, recognition, monitoring and management (including referrals) of CKD in healthcare, also across high-risk groups and indications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".