Prognostic tools for elderly patients with sepsis: in search of new predictive models

• Published in: Internal and emergency medicine Vol. 16; no. 4; pp. 1027 - 1030
• Main Author: Gamboa-Antiñolo, Fernando-Miguel
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021; Springer Nature B.V
• Subjects: Decision making; EM - Commentary; Internal Medicine; Learning algorithms; Machine learning; Medical prognosis; Medical records; Medicine; Medicine & Public Health; Mortality; Patients; Prediction models; Sepsis; Shared decision making
• ISSN: 1828-0447; 1970-9366; 1970-9366
• DOI: 10.1007/s11739-021-02729-5

As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. But there are currently no MPMs for adults with sepsis who meet the criteria of "good." Clinicians are unlikely to use complex MPMs that require extensive or expensive data collection to impede workflow. Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.