- Early identification and treatment of sepsis is central for identification and successful treatment of sepsis.
- Widespread systematic sepsis screening (manual or automated) is the recommended approach to detecting early sepsis.
- To date, employing systematic screening with traditional sepsis scoring tools has been suboptimal.
- Machine learning algorithms for sepsis screening offer a promising improvement in early sepsis detection and identification.
- A recent systematic review and network meta-analysis compared machine learning techniques to standard sepsis screening tools.
- -- 73 articles with 457,932 patients were reviewed, published from 2017-2023
- -- Area Under the Curve of the Receiver Operating Characteristic (AUROC) was used as the performance metric, with 95% confidence interval.
- Standard screening tools assessed were:
- -- Sequential Organ Failure Assessment (SOFA)
- -- Quick Sequential Organ Failure Assessment (qSOFA)
- -- National Early Warning Score/National Early Warning Score 2 (NEWS/NEWS2)
- -- Modified early Warning Score (MEWS)
- -- Simplified Acute Physiology Score (SAPS II)
- -- Systemic Inflammatory Response Syndrome (SIRS)
- Machine Learning Models assessed were:
- -- Neural Network Models – 6 different models
- -- Decision Trees – 4 different models
- -- Regression Models – 3 models
- -- Support Vector Machine
- -- K-Nearest Neighbors
- -- Generalized Linear Model
- -- Na?ve Bayes
- -- All Machine Learning algorithms were consistently statistically (P<0.0001) superior to traditional screening tools.
- ----Neural Network and Decision Tree models had the highest AUROC metrics.
- Other factors including sepsis prevalence, laboratory indicators and number of predictors did not influence the models.
- An ongoing concern is the “Black Box Syndrome” associated with machine learning algorithms, limiting the clinicians ability to completely view and understand the logic behind the outputs.
- Although machine learning algorithms can be incorporated into clinical practice, it is not known how these predictions will impact:
- -- Timing between notification and clinical recognition of sepsis
- -- Timing, initiation of treatments
- -- Clinical outcomes
Erkan Hassan, Pharm.D., FCCM is a transformational healthcare leader with extensive experience developing innovative solutions to improve clinical outcomes, enhance provider experience and increase revenue.
You can reach Erkan at [email protected]
President at Datuit
2 周Is sepsis well enough understood to get to the optimal tool to detect it? And does detecting it lead you to the best way to treat it? Decades ago, the way to treat sepsis was to "control the source". Sepsis is such a complex phenomenon that it's hard to figure out what this detection will lead to.
I am interested in new opportunities.
2 周Greetings to the students at SHHS who are interested in STEM careers. Thanks to our networking capabilities, young people and experts can exchange ideas freely and learn from each other. We are sending warm regards to Michele Z. and David E. for all the guidance you provided both of my kids. Most importantly, I want to thank a pioneer woman and founding member of the Science Research Program, Ms. Abinanti-Longo.
Nurse Manager Emergency Care Centre, King Abdulaziz Medical City. Riyadh, Saudi Arabia.
2 周Thank you for sharing