RESUMO
Background: Sepsis is a common indication for intensive care unit (ICU) admission and is associated with significant mortality and morbidity. The aim of our study was to first assess the incidence, severity, short-term and long-term mortality of sepsis in a combined medical and surgical high dependency/ ICU in Singapore, and to identify factors associated with increasing short-term and long-term mortality. Methods: All admissions from July 1 to December 31, 2017 were retrospectively screened and clinical data were collected. Patients were followed up until 3 years post ICU admission. Results: Of a total 1526 admissions, 281 had infection at ICU admission, and 254 (16.6%) fulfilled sepsis-3 criteria for sepsis. A total of 141 (9.2%) had septic shock. The 30-day, 1-year, 2-year and 3-year mortality of sepsis patients were 19.3%, 25.2%, 30.3% and 32.3%, respectively. Lung was the most common site of infection. Compared with 30-day sepsis survivors, non-survivors were older (median age 70 vs 63, P <0.001), had higher percentage of lung infection (65.3% vs 36.1%, P <0.05), higher admission Sequential Organ Failure Assessment (SOFA) score (median 9 vs 5, P <0.001), and longer ICU stay (median days: 4 vs 3, P = 0.037). In stepwise Cox regression analysis, lung infection was an independent risk factor for both increasing 30-day and 3-year mortality. Immunocompromised host, increasing age and SOFA score were associated with higher 30-day mortality. Diabetes, admission quick Sequential Organ Failure Assessment (qSOFA) score >1 and unplanned ICU re-admission were associated with increasing 3-year mortality in 30-day survivors. Conclusion: Our retrospective cohort single center study first reported sepsis admission incidence of 16.6% in a combined medical and surgical high dependency/ICU in Singapore, with significant short-term and long-term mortality. Lung infection was an independent risk factor for both 30-day and 3-year mortality.
RESUMO
BACKGROUND: Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes. OBJECTIVE: We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk. METHODS: We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission. RESULTS: The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians. CONCLUSIONS: The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.
