Estimation of Urine Specific Gravity from Serum Chemistry, CBC, and Patient Age | Memorial Cat Hospital | March 2026
Urine Specific Gravity is a cornerstone of feline renal assessment. IRIS staging of Chronic Kidney Disease incorporates USG alongside creatinine and SDMA to differentiate stages and guide management. Loss of concentrating ability (USG <1.035 in cats) is frequently among the earliest detectable signs of tubular dysfunction, often preceding azotemia.
However, urine collection is not always achievable at the time of presentation. An empty bladder, patient temperament, contraindications to cystocentesis (coagulopathy, abdominal masses), or client constraints may preclude urinalysis.
This model estimates USG from serum chemistry, CBC, and patient age — values already being collected on the same blood draw — providing a screening estimate of concentrating ability at zero additional cost, zero additional procedure time, and zero additional client charge.
| Aspect | v1.4 | v1.5a |
|---|---|---|
| Input Features | 7 | 5 |
| Dropped Features | — | Amylase Cholesterol |
| Eligible Patients (in dataset) | 1,296 | 1,334 (+3%) |
| Sensitivity | 85.6% | 84.9% |
| Specificity | 73.7% | 75.5% |
| Accuracy | 79.8% | 80.3% |
| Prediction Explanations | No | New |
We also evaluated a variant that adds SDMA as a 6th feature. Adding SDMA yielded only +1% sensitivity (85.9% vs 84.9%) but reduced eligible patients from 1,334 to 1,083 due to SDMA not being reported on all panels. Given the marginal benefit and significant data loss, SDMA was not included in the production model. This decision may be revisited if SDMA reporting becomes more universal.
Machine learning classifier trained with clinically-weighted error costs (missing a sick cat is penalized more heavily than flagging a healthy one). All reported metrics are from held-out data that the model never trained on.
3 lab values from a standard chemistry + CBC panel, plus patient age. No Amylase, Cholesterol, T4, electrolyte panel, or SDMA required.
| BUN | mg/dL |
| Creatinine | mg/dL |
| Hemoglobin (HGB) | g/dL |
| Abs. Lymphocytes | /μL |
| Patient Age | years |
Binary screening classification — Adequate (≥1.035) or Impaired (<1.035), with probability score, risk tier, and per-feature explanations showing which bloodwork values are driving the prediction. This is a triage tool that identifies cats who may benefit from urinalysis, not a diagnostic test.
| Metric | v1.5a (5 feat) | v1.4 (7 feat) | Change | Clinical Meaning |
|---|---|---|---|---|
| Sensitivity (catches sick cats) | 84.9% | 85.6% | -0.7% | Within normal statistical variance; effectively unchanged |
| Specificity (clears healthy cats) | 75.5% | 73.7% | +1.8% | Fewer unnecessary urinalysis recommendations |
| Accuracy | 80.3% | 79.8% | +0.5% | Correct call 4 out of 5 times |
| Miss Rate | 15.2% | 14.4% | +0.8% | ~1 additional missed cat per 125 screened |
| False Flag Rate | 24.5% | 26.3% | -1.8% | Fewer healthy cats flagged |
| Metric | Training Evaluation | Validation Set | Gap |
|---|---|---|---|
| Sensitivity | 84.9% | 81.0% | -3.9% |
| Specificity | 75.5% | 73.9% | -1.6% |
| Accuracy | 80.3% | 77.5% | -2.8% |
A 3–4% generalization gap between CV and validation is typical and healthy. v1.4 did not have a held-out validation set, so its reported metrics may be slightly more optimistic than v1.5a’s.
Cross-validation metrics evaluated on 1,334 cases with paired bloodwork and urinalysis. Class balance: 685 Impaired / 649 Adequate.
| Predicted | |||
|---|---|---|---|
| Adequate | Impaired | ||
| Actual | Adequate | 392 | 127 |
| Impaired | 83 | 465 | |
The binary classification uses a clinically-optimized decision threshold, tuned to prioritize catching impaired cats while keeping the false flag rate manageable. Missing a sick cat is weighted more heavily than flagging a healthy one.
“Don’t let a sick cat walk out the door.”
84.9% of truly impaired cats are caught. The tradeoff: 24.5% of healthy cats are
flagged for a urinalysis they may not need. An unnecessary UA (~$35 add-on) is far
better than a missed kidney diagnosis.
A flag is not a diagnosis. It means: “this cat’s bloodwork pattern is consistent with cats that have impaired concentrating ability — consider collecting urine.”
If UA confirms impairment: early detection, earlier intervention.
If UA is normal: client gets peace of mind, cat gets a clean bill.
With only 5 features, the model concentrates its predictive power on the strongest renal and hematologic markers. BUN, Age, and Creatinine now account for over 70% of the model’s total importance:
| Analyte | Importance | Physiological Link to Urine Concentration |
|---|---|---|
| BUN | 30.0% | Primary marker of glomerular filtration rate. As GFR declines, BUN rises and concentrating ability diminishes. BUN also contributes to the medullary concentration gradient via urea recycling — elevated BUN paradoxically reflects the failing kidney’s inability to maintain this gradient. |
| Patient Age | 22.0% | CKD is progressive and age-dependent. In this dataset, 98% of cats over 18 years had impaired concentration vs 15% of cats aged 5–10. Age captures the cumulative renal decline that bloodwork alone may not fully reflect, including subclinical nephron loss. |
| Creatinine | 20.0% | Muscle-derived GFR marker. Co-regulated with BUN through renal excretion. Together with BUN, captures the primary renal axis. |
| Abs. Lymphocytes | 15.0% | Hematologic marker of immune status and systemic illness chronicity. CKD cats often develop lymphopenia as part of the chronic disease syndrome. Low lymphocyte counts correlate with disease severity and duration. |
| Hemoglobin | 13.0% | Reflects hydration status and erythropoietin production. Dehydrated cats have higher HGB and more concentrated urine. CKD cats develop non-regenerative anemia (low HGB) with concurrent loss of concentrating ability. |
Dropped features: Amylase (6.6% in v1.4) and Cholesterol (4.5%) were the two lowest-importance features. Together they contributed only 11.1% of v1.4’s predictive power. With their removal, the remaining features absorbed their contribution with negligible performance loss.
New in v1.5a: every prediction now includes a per-feature breakdown showing how each bloodwork value contributed to the result. This answers the question: “Why is this cat being flagged (or cleared)?”
This transparency helps veterinarians understand which bloodwork values are driving the recommendation, rather than treating the model as a black box. For example, a cat might be flagged primarily because of elevated BUN and advanced age, even though its creatinine is still within normal range — the explanation chart makes this reasoning visible.
| Version | Features | Sensitivity | Specificity | Key Change |
|---|---|---|---|---|
| v1.0 | 10 | — | — | Initial model — bloodwork only |
| v1.1 | 11 | — | — | Added patient age |
| v1.2 | 14 | — | — | Full feature set; regression + classification |
| v1.3 | 7 | 85.6% | 70.0% | Reduced to 7 fields; clinically-weighted error costs |
| v1.4 | 7 | 85.6% | 73.7% | Hyperparameter tuning; classification only; fewer false flags |
| v1.5a | 5 | 84.9% | 75.5% | Dropped Amylase & Cholesterol (pilot data); added per-prediction explanations; improved validation |
| Limitation | Clinical Impact | Mitigation Plan | Status |
|---|---|---|---|
| Resolved in v1.4. Previous versions predicted continuous USG values, which suffered from regression to the mean at extremes. v1.4+ uses binary classification only, eliminating this limitation. | Resolved | Complete | |
| Resolved in v1.5a. Pilot deployment revealed these two analytes are not on every panel, excluding otherwise eligible patients from screening. | Resolved | Complete | |
| Single-practice, single-species dataset | Trained on 3,642 feline cases from one hospital. External validation is required before broader deployment. | Pursuing collaboration with Texas A&M Veterinary Medical Teaching Hospital or external validation on a second, independent patient population. Additional validation sites will be recruited from professional networks and through conference contacts. Target: 2–3 external datasets. | Oct 2026 |
| No urinalysis replacement | USG is one component of urinalysis. Sediment analysis, urine protein, urine culture, and pH provide independent diagnostic information. | By design — not a limitation to resolve. This tool is a screening triage tool that identifies cats who should receive urinalysis. It is not intended to replace UA. | N/A |
| Pre-renal and post-renal effects | Dehydration elevates BUN disproportionately and concentrates urine simultaneously. The model may conflate pre-renal and intrinsic renal causes. | Add hydration status and recent fluid therapy as optional input fields in a future version. Explore adding BUN/Creatinine ratio as an engineered feature to help distinguish pre-renal azotemia. | Q1 2027 |
| Temporal confounders | Blood and urine may not be collected simultaneously. Hydration status and recent fluid therapy affect USG independently of serum values. | During prospective pilot at Memorial Cat Hospital, record time delta between blood draw and urine collection. Analyze whether prediction accuracy degrades with increasing time gap. | Jun 2026 |
| SDMA marginal benefit | Testing showed SDMA adds only +1% sensitivity but reduces eligible patients by ~19% due to inconsistent reporting. | Not included in production. Will revisit if SDMA reporting becomes more universal or if external validation on SDMA-rich datasets (e.g., TAMU teaching hospital) shows greater benefit. | Deferred |
| ~15% miss rate | Approximately 1 in 7 impaired cats will be classified as adequate (15.2% miss rate). | Pursue three paths: (1) additional training data from external sites, (2) explore advanced modeling techniques, (3) evaluate whether flagging of borderline cases can reduce clinically significant misses. | Q1 2027 |
| No prospective outcome data | No data yet showing that flagging cats with this tool leads to earlier diagnosis or improved clinical outcomes. | Memorial Cat Hospital pilot will track every flag: was UA performed, what was the USG result, what was the diagnosis at 6 and 12 months. This prospective dataset will be the basis for outcome claims in the publication. | Mar 2027 |
| Parameter | Value |
|---|---|
| Source | Memorial Cat Hospital, Houston, TX |
| Date Range | January 2022 – February 2026 |
| Species | 100% Feline |
| Total Lab Reports | 3,642 |
| Reports with Urinalysis | 1,506 (41%) |
| Cases Used for v1.5a | 1,334 (complete bloodwork + USG, no missing values in the 5 input features) |
| USG Range in Dataset | 1.005 – 1.086 |
| USG Mean / Median | 1.036 / 1.034 |
| Class Balance (≥1.035 / <1.035) | 649 Adequate / 685 Impaired (49% / 51%) |
| Validation Method | Cross-validation with independent held-out validation set |
| Age Group | n | Mean USG | % Impaired (<1.035) |
|---|---|---|---|
| Under 5 years | 5 | 1.049 | 20% |
| 5–10 years | 100 | 1.047 | 15% |
| 10–14 years | 556 | 1.043 | 29% |
| 14–18 years | 553 | 1.028 | 73% |
| Over 18 years | 81 | 1.019 | 98% |
Model v1.5a | March 2026 | Memorial Cat Hospital | For investigational and research use | Not validated for clinical deployment