Researchers from Ohio State University have developed a machine learning model that can estimate the optimal time to administer antibiotics to patients with suspected sepsis. The model was trained on publicly available critical care data and tested on different portions of the data, resulting in improved sepsis outcomes. However, the researchers emphasized the need for clinician input and the risks of administering antibiotics must be considered carefully by the care team. Further research is needed for clinical validation and human-AI collaboration for better patient care.
The Ohio State University (OSU) has developed a machine learning (ML) model that can estimate the optimal timing for sepsis treatment and support clinical decision-making. The study was published in Nature Machine Intelligence and highlights the challenge of diagnosing sepsis, a life-threatening condition that can rapidly lead to tissue damage, organ failure, and death without timely treatment. The symptoms of sepsis can resemble those of multiple other conditions, making diagnosing sepsis a challenge.
Federal guidelines for sepsis treatment mandate quick treatment using broad-spectrum antibiotics. However, this approach requires clinicians to take action on a sepsis diagnosis before results confirming infection can come back from the lab. To support clinical decision-making in this situation, the research team developed an ML model to estimate the optimal time to administer antibiotics to a patient with a suspected case of sepsis.
The research team used publicly available critical care data known as MIMIC-III to train the model. Using these data, the tool was tasked with making treatment timing estimates using indicators of illness severity and type of infection, such as lab results, vital signs, and risk-related demographic information. The model also incorporated the Sequential Organ Failure Assessment (SOFA) score, which helps assess how an ICU patient’s organ systems are performing at regular intervals based on results from six lab tests.
The tool was tested on different portions of MIMIC-III and an additional dataset from the Amsterdam University Medical Centers Database (AmsterdamUMCdb). The outcomes measured during model testing and validation were patient survival at 30 and 60 days following sepsis treatment.
Outcomes in patients whose actual treatment matched the model’s recommended treatment timeline were compared to outcomes for patients whose actual treatment had differed from what the model would have recommended based on illness severity and infection type indicators.
The researchers found that the model’s recommendations helped improve sepsis outcomes. The researchers emphasized the need for clinician input when making any treatment decision, especially with a condition as serious as sepsis. Because the symptoms of sepsis are shared with other conditions, not every patient who meets the criteria will have sepsis, so the risks of administering antibiotics must be considered carefully by the care team.
The researchers also highlighted the need for more research in this area. The research team indicated that more research in this area is needed, stating that “Any research like this needs clinical validation – this is phase one for retrospective data analysis, and phase two will involve human-AI collaboration for better patient care.”
Leave a Reply