The JJ Carrero group at @ki.se is hiring postdocs in cardio-renal epidemiology. This is a great opportunity to work with large-scale data, advanced causal methods, and clinically meaningful kidney research. If you’re interested, take a look at the position and apply here ki.varbi.com/en/what:job/...
9/9
👏 Huge congratulations to lead author Will Russel, the SCREAM and EKFC team @delanaye.bsky.social
🧾 Full open access article in @ndt-era.bsky.social shorturl.at/T5Dui
8/9
🧠Why this matters
Changing eGFR equations reshapes disease prevalence, referral patterns, and treatment thresholds — if we don't update those thresholds too. Implications for clinicians, health systems, and policy makers are substantial.
7/9
Clinical impact if EKFC adopted
· +22% nephrology referrals
· +39% eligible for SGLT2 inhibitors
· +26% more heart failure patients with spironolactone contraindication
· More dose adjustments for anticoagulants & diabetes medications
6/9
Counter-intuitively, people reclassified to a lower eGFR by EKFC had lower KFRT risk — likely because EKFC lowers eGFR more in older adults with stable, age-related decline rather than progressive disease, plus differences in equation modeling and calibration.
5/9
Risk associations:
Both equations were similarly predictive for:
· KFRT
· All-cause mortality
· MACE
After adjusting for age:
· Death and MACE risks stayed similar across categories
4/9
Main finding – eGFR shift
Median eGFR was 4.9 mL/min/1.73m² lower with EKFC.
➡️ CKD G3–G5 prevalence rose from 4.5% → 6.2% (+38%).
11% of people moved to a lower CKD category; <1% moved to a higher category.
3/9
📍 Setting: Stockholm, Sweden (2006–21)
👥 1,784,831 adults with serum creatinine in routine care
🔍 Aim: Compare CKD-EPI vs EKFC for:
· Reclassification across GFR categories
· Risk prediction for health outcomes
· Potential changes in referrals & drug eligibility
2/9 Background:
· eGFR guides diagnosis & staging of CKD.
· CKD-EPI 2009 is the most widely used equation in Europe.
· EKFC 2021 was designed for broader accuracy, especially in European populations.
· But… what would a real-world switch actually do?
1/9🚨 Changing one formula could make 38% more people “have CKD” overnight.
That’s what happens if we replace the CKD-EPI equation with EKFC in a European health system.
👉 Our @ndt-era.bsky.social paper shorturl.at/T5Dui
@delanaye.bsky.social
#NephSky #nephrology #EKFC #eGFR
8/9
🎯 Take-home
Switching from traditional eGFR/ACR-based to KFRE-based referral:
✅ Sharpens referral precision
✅ Cuts unnecessary specialist visits by ~25%
✅ Preserves safety with few missed cases
✅ Aligns with KDIGO 2024 — but suggests higher, locally optimized thresholds
7/9
🚨 Important nuance:
Even with higher thresholds, the models missed very few patients who progressed to kidney failure.
False negatives remained under 0.4%.
So we’re not sacrificing safety — just optimizing how we use specialist care.
6/9
❗️But what about the risk thresholds?
The widely cited 3–5% risk cutoffs might be too low for most primary care patients.
🔹 We found that 15% (original KFRE) and 9% (Sweden-recalibrated KFRE) gave the best balance of sensitivity and specificity.
5/9
💡 Key finding:
KFRE-based referral models had better sensitivity and specificity than traditional criteria.
They also improved:
✅ Net Reclassification Index
✅ Decision curve analysis
✅ Positive predictive value (PPV)
And they reduced unnecessary referrals by ~25%.
4/9
We compared 3 referral strategies:
1️⃣ Swedish criteria (age, eGFR, albuminuria)
2️⃣ KDIGO 2012 criteria
3️⃣ KFRE-based model, using the 4-variable equation (age, sex, eGFR, ACR)
KFRE was tested both in its original (non-North American) and recalibrated version for Sweden.
3/9
📊 Data:
192,964 adults with eGFR <60 ml/min/1.73m² and an albuminuria test, from 2006–2021.
🔁 887,388 repeated observations
👴 Median age: 76
📉 Median eGFR: 54
🎯 Outcome: initiation of kidney replacement therapy (KRT) within 5 years
2/9
The 2024 @kdigo.org guidelines recommend using the KFRE risk equation to guide referrals when the 5-year risk of kidney failure exceeds 3–5%.
But:
✅ Is that threshold right?
✅ Does KFRE actually outperform traditional rules?
We tested this in >190,000 patients from Stockholm.
🧵1/9
Could we improve kidney care by rethinking who gets referred to a nephrologist?
Our latest study in @ndt-era.bsky.social suggests: yes — if we base it on predicted risk rather than fixed eGFR/ACR cutoffs.
📄 t.co/xVcR8LEnhn
#nephrology #KDIGO #KFRE #NephJC #NephSky #Nephpearls
6/6
👏 Huge credit to first author Will Russel for completing this work, and to the dream team led by JJ Carrero
Proud to share this effort!
🔗 tinyurl.com/yx3m67yk
#Nephrology #eGFR #CKD #ClinicalPharmacology #BMI
5/6
📣 Take-home:
✅ Use eGFRcr-cys when estimating GFR in patients with underweight or obesity
✅ Consider non-indexed eGFR for precise dosing decisions
✅ Rethink reliance on creatinine alone in extremes of BMI
4/6
💊 Clinical impact:
Using eGFRcr-cys improved correct dosing and eligibility for drugs like carboplatin, apixaban, and SGLT2 inhibitors—especially in underweight and obese groups.
Non-indexed values offered small added gains, particularly for chemo dosing.
3/6
🔍 What we found:
– eGFR based on creatinine alone overestimated kidney function at both BMI extremes
– eGFR based on cystatin C alone underestimated GFR in obesity
– The combo (eGFRcr-cys) had least bias and highest accuracy across all BMIs
2/6
⚙️ What we did:
We analyzed 7,503 measured GFR (mGFR) tests from over 4,700 adults in Sweden. We compared multiple eGFR equations (creatinine, cystatin C, both) across BMI from <18 to >40 kg/m².
Both indexed and non-indexed formulas were evaluated.
1/6
💡 Did you know?
Many individuals today are obese or underweight, yet the original cohorts used to develop eGFR equations barely included them.
In our new JASN @asnpublications.bsky.social paper, we ask:
How accurate is eGFR in people at BMI extremes—and does it affect treatment?
👇
CAUSALab Summer Courses group photos (KTCI, ACA, CICI, TTE)
That's a wrap!
Last week, the 2025 CAUSALab Summer Courses on Causal Inference came to a close @hsph.harvard.edu.
Thank you to our 400+ attendees, who represent:
🏢 200+ organizations
🌎 38 countries
📍 35 U.S. states
Thank you to our wonderful instructors and fellows who make the courses possible.
Creatinine ≠ Cystatin C.
Both estimate GFR—but what drives their discordance?
Our @ajkd.bsky.social editorial explores what we do and don’t know about the non-GFR factors behind it.
📖 Read: shorturl.at/hK5qu
🔍 McCoy et al (AJKD, 2025): shorturl.at/NIuAL
#eGFR #CKD
Causal inference in observational studies can be tricky. We discuss key methodological considerations from a recent study in JASN @asnpublications.bsky.social. Read it here: tinyurl.com/rwzu37xu
Editorial by Jung-Im Shin, Antoine Créon, and Juan-Jesus Carrero:
Metformin in People With Diabetes and Advanced CKD: Should We Dare?
https://bit.ly/3P4BPzy (FREE)