Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

BACKGROUND: Currently, the prediction values of models for the prognosis of acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT) were ordinary and establishing a better prediction model is necessary. Nursing notes are an important predictor of in-hospital mortality in intensive care unit (ICU) patients. This study established prognostic prediction models for AKI patients receiving CRRT especially using nursing notes. METHODS: Totally, 682 AKI patients undergoing CRRT were included. AKI was diagnosed based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Four hundred and twelve patients lacking nursing notes data were excluded. Finally, 270 patients were included and randomly divided into a training set (n=189) and a testing set (n=81) at a ratio of 7:3. Univariate analysis explored the possible predictors of mortality in AKI patients receiving CRRT. Random forest models and broad learning system (BLS) models (with or without sentiment scores) were respectively constructed in the training set and verified in the testing set. The performances of the models were assessed by the sensitivity, specificity, and area under the curve (AUC). RESULTS: For the random forest model including the sentiment scores, the AUC was 0.86 (95% CI: 0.81–0.91), the sensitivity was 0.72 (95% CI: 0.63–0.80), and the specificity was 0.87 (95% CI: 0.80–0.94) in the training set and the AUC was 0.78 (95% CI: 0.68–0.88), the sensitivity was 0.65 (95% CI: 0.49–0.80), and the specificity was 0.75 (95% CI: 0.62–0.88) in the testing set. For the BLS model including the sentiment scores, the AUC was 0.87 (95% CI: 0.82–0.92), the sensitivity was 0.95 (95% CI: 0.91–0.99) and the specificity was 0.48 (95% CI: 0.38–0.59) in the training set and the AUC was 0.82 (95% CI: 0.73–0.91), the sensitivity was 0.41 (95% CI: 0.25–0.56) and the specificity was 0.98 (95% CI: 0.93–1.00) in the testing set. CONCLUSIONS: The BLS models including the sentiment scores might offer a tool for quickly identifying patients AKI patients receiving CRRT with high risk of mortality and providing timely interventions to them for improving their prognosis.