Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. (June 2021) - Hassan, Nehal; Slight, Robert; Weiand, Daniel; Vellinga, Akke; Morgan, Graham; Aboushareb, Fathy; Slight, Sarah P.
Highlights: Sepsis definition and predictors type influenced the predictive power and predictive timeframe of the developed algorithms. We have identified the most prevalent 13 predictors for the development of sepsis prediction algorithms. Predicting the likelihood of sepsis through artificial intelligence can concentrate finite resources to patients at risk. Abstract: Background and objectives: Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis. Methods: This systematic review was registered in PROSPERO database (CRD42020158685). We conducted a systematic literature review across 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase. Quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adults in all care settings were eligible for inclusion. Results: Seventeen articles met our inclusion criteria. We identified 194 predictors that were used to train machine learning algorithms, with 13 predictors used on average across all included studies. The most prevalent predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30 %. All included studies used artificial intelligence techniques, with average sensitivity 75.7 ± 17.88, and average specificity 63.08 ± 22.01. Conclusion: The type of predictors influenced the predictive power and predictive timeframe of the developed machine learning algorithm. Predicting the likelihood of sepsis through artificial intelligence can help concentrate finite resources to those patients who are most at risk. Future studies should focus on developing more sensitive and specific algorithms.