Peer-reviewed veterinary case report
Predicting survival in cats with acute-on-chronic kidney disease
By Renard, Jade et al.·Published in Journal of Feline Medicine and Surgery·2021·Alliance Small Animal Clinic, Bordeaux, France, France·View original on Crossref →
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Original publication title: Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat
- Species:
- cat
Plain-English summary
A cat with chronic kidney disease was hospitalized due to sudden worsening symptoms and high levels of waste products in the blood. Researchers developed a machine-learning tool to help predict how long these cats might survive based on factors like blood test results, age, and whether the cat was eating after 48 hours. They found that a specific blood test result (serum creatinine) was a strong indicator of survival at various time points, with certain thresholds indicating better or worse outcomes. This tool could help veterinarians assess the prognosis for cats with acute-on-chronic kidney disease more accurately.
People also search for: cat kidney disease prognosis · acute kidney disease in cats · cat high creatinine levels treatment
Abstract
Objectives The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats. Methods The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48 h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48 h (Crea 48 ), spontaneous feeding at 48 h (SpF 48 ) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated. Results Crea 48 was the best predictor of survival at 7 days (threshold 1043 µmol/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566 µmol/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566 µmol/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea 48 was above these thresholds. A short decision tree, including age and Crea 48 , predicted the 180-day outcome best. When Crea 48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF 48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea 48 . Conclusions and relevance Crea 48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF 48 and identification of small kidneys.
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Search related cases →Original publication on Crossref: https://doi.org/10.1177/1098612x211001273