Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat.
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 (Crea48), spontaneous feeding at 48 h (SpF48) 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
Crea48 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 Crea48 was above these thresholds. A short decision tree, including age and Crea48, predicted the 180-day outcome best. When Crea48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea48.


CONCLUSIONS AND RELEVANCE
Crea48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF48 and identification of small kidneys.
['Renard|Jade|J|', 'Faucher|Mathieu R|MR|https://orcid.org/0000-0002-3136-737X', 'Combes|Anaïs|A|', 'Concordet|Didier|D|', 'Reynolds|Brice S|BS|']
Survival machine learning creatinine uremic crisis