A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study

Abstract

Background: Accurate and prompt clinical assessment of the severity and prognosis of patients with acute pancreatitis (AP) is critical, particularly during hospitalization. Natural language processing algorithms gain an opportunity from the growing number of free-text notes in electronic health records to mine this unstructured data, e.g., nursing notes, to detect and predict adverse outcomes. However, the predictive value of nursing notes for AP prognosis is unclear. In this study, a predictive model for in-hospital mortality in AP was developed using measured sentiment scores in nursing notes. Methods:The data of AP patients in the retrospective cohort study were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sentiments in nursing notes were assessed by sentiment analysis. For each individual clinical note, sentiment polarity and sentiment subjectivity scores were assigned. The in-hospital mortality of AP patients was the outcome. A predictive model was built based on clinical information and sentiment scores, and its performance and clinical value were evaluated using the area under curves (AUCs) and decision-making curves, respectively.Results: Of the 631 AP patients included, 88 cases (13.9%) cases were dead in hospital. When various confounding factors were adjusted, the mean sentiment polarity was associated with a reduced risk of inhospital mortality in AP [odds ratio (OR): 0.448; 95% confidence interval (CI): 0.233-0.833; P=0.014]. A predictive model was established in the training group via multivariate logistic regression analysis, including 12 independent variables. In the testing group, the model showed an AUC of 0.812, which was significantly greater than the sequential organ failure assessment (SOFA) of 0.732 and the simplified acute physiology score-II (SAPS-II) of 0.792 (P<0.05). When the same level of risk was considered, the clinical benefits of the predictive model were found to be the highest compared with SOFA and SAPS-II scores. Conclusions:The model combined sentiment scores in nursing notes showed well predictive performance and clinical value in in-hospital mortality of AP patients.

Development and Validation of a Dynamic Nomogram for Predicting in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study in the Intensive Care Unit

“…BUN, albumin, and TBIL were significant predictors for mortality of AP, which were already confirmed by numerous studies. 11 , 17 , 42–44 Current nomograms which were established for mortality of AP by other researchers usually included one or more of them as predictors, 15 , 16 , 45 which were consistent with our nomogram. AS for SBP, respiratory rate, and oxygen saturation as a reflection of patients’ vital signs, it can surely be used as a predictor of mortality.…”

“…Currently, several studies have developed nomograms for the prediction of in-hospital mortality in AP based on critical care database. Liu 15 and Li 16 constructed nomograms to predict in-hospital mortality of AP based on Medical Information Mart for IntensiveCare-III (MIMIV-III) database and eICU-CRD, respectively. However, both two traditional nomograms lack external validation.…”

“…In the era of "big data", medical databases are increasingly used for scientific research. Although several studies have developed nomograms to predict in-hospital mortality of AP based on critical care databases, [15][16][17] small sample size and lacking further external validation were their main limitation. Moreover, the clinical applicability of traditional nomogram may be restricted to time-consuming and lacking an exact calculation.…”

Construction and validation of a nomogram for predicting survival in elderly patients with severe acute pancreatitis: a retrospective study from a tertiary center

“…A nomogram is a useful mathematical tool used to predict certain outcomes, such as disease progression or mortality, based on various key parameters [ 10 ]. While some studies have created nomograms to predict in-hospital mortality of AP using critical care databases [ 11 , 12 ], their main limitations were small sample sizes and lack of external validation. These studies have identified age as an independent risk factor for death in AP patients.…”