Abstract
In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begins to be extracted from large-scale unstructured data like nursing notes. This study extracted sentiment information in nursing notes and explored its association with in-hospital 28-day mortality in sepsis patients. The data of patients and nursing notes were extracted from the MIMIC-III database. A COX proportional hazard model was used to analyze the relationship between sentiment scores in nursing notes and in-hospital 28-day mortality. Based on the COX model, the individual prognostic index (PI) was calculated, and then, survival was analyzed. Among eligible 1851 sepsis patients, 580 cases suffered from in-hospital 28-day mortality (dead group), while 1271 survived (survived group). Significant differences were shown between two groups in sentiment polarity, Simplified Acute Physiology Score II (SAPS-II) score, age, and intensive care unit (ICU) type (all P < 0.001). Multivariate COX analysis exhibited that sentiment polarity (HR: 0.499, 95% CI: 0.409-0.610, P < 0.001) and sentiment subjectivity (HR: 0.710, 95% CI: 0.559-0.902, P = 0.005) were inversely associated with in-hospital 28-day mortality, while the SAPS-II score (HR: 1.034, 95% CI: 1.029-1.040, P < 0.001) was positively correlated with in-hospital 28-day mortality. The median death time of patients with PI ≥ 0.561 was significantly earlier than that of patients with PI < 0.561 (13.5 vs. 49.8 days, P < 0.001). In conclusion, sentiments in nursing notes are associated with the in-hospital 28-day mortality and survival of sepsis patients.
Highlights
Sepsis, a syndrome of life-threatening physiologic, pathologic, and biochemical dysfunction due to uncontrolled responses to infection, is one of the leading causes of deaths in intensive care units (ICUs) [1]
We investigated the association of sentiments in nursing notes with the in-hospital 28-day mortality of sepsis patients based on the Medical Information Mart for Intensive Care (MIMIC-III) database, a freely accessible critical care database, aimed at providing some evidence for the improvement of patients’ outcomes in ICUs
The results showed that the power values of the sentiment polarity score and sentiment subjectivity score were all 1.000
Summary
A syndrome of life-threatening physiologic, pathologic, and biochemical dysfunction due to uncontrolled responses to infection, is one of the leading causes of deaths in intensive care units (ICUs) [1]. Sepsis remains among the costliest diseases, approximately accounting for over 20 billion (5.2%) of total United States (US) hospital costs [2]. The prevalence of sepsis is up to 535 cases per 100 100,000 person-years and on the rise [4]. Population-level epidemiological data show that there are 31.5 million cases of sepsis and 19.4 million cases of severe sepsis worldwide, with 5.3 million potential deaths each year [5], and the in-hospital mortality reaches up to 25%-30% [6].
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