Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning.
Ekanath Srihari RanganRahul Krishnan PathinarupothiKanwaljeet J S AnandMichael Paul SnyderPublished in: JAMIA open (2022)
It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.
Keyphrases
- intensive care unit
- septic shock
- acute kidney injury
- systematic review
- healthcare
- randomized controlled trial
- mechanical ventilation
- cardiovascular disease
- acute care
- type diabetes
- coronary artery disease
- high throughput
- machine learning
- big data
- adipose tissue
- artificial intelligence
- acute respiratory distress syndrome