In patients with sepsis, the use of a machine learning algorithm identified six variables that were predictive of 7- and 30-day mortality.
Sepsis can be defined as is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Furthermore, sepsis is responsible for around 11 million deaths each year, which amounts to approximately 20% of all global deaths. Thus, it is crucial that clinicians have a comprehensive understanding of all the relevant clinical factors that can help with the early identification of those patients for whom a poor outcome is likely. This is particularly important since early use of crystalloid therapy reduces mortality, as does prompt administration of antibiotics. Though several scoring systems for sepsis are available, these are based on the assessment of vital signs but which can sometimes be normal upon admission to an emergency department. While machine learning has been shown to have some level of predictive power for mortality, none of the variables currently used in these models are reflective of the symptoms at first presentation. This led a team from the Department of Medical Sciences, Orebro University, Sweden, to use machine learning in an attempt to identify the variables which were predictive of 7- and 30-day mortality in sepsis patients, based on the clinical presentation at an emergency department. They employed a retrospective design and included patients 18 years and older, admitted to hospital with suspected sepsis. The team input previously identified variables, e.g., abnormal temperature, acute altered mental status, etc into the machine learning algorithm. The sensitivity and specificity of the predictive models generated by the machine learning model, were calculated from the area under the receiver operating curve (AUC).
A total of 445 patients with sepsis and a median age of 73 years (52.6% male) were included in the retrospective analysis. Overall, 234 (49.7%) had severe sepsis and 63 patients died within 7-days of admission and 98 within 30 days. The accuracy of the 7-day predictive model was maximal after the inclusion of only six variables; fever, abnormal verbal response, low oxygen saturation, arrival by emergency services, abnormal behaviour/level of consciousness and chills. Using these variables, the AUC sensitivity was 0.84 (95 CI 0.78–0.89) and the specificity 0.67 (95% CI 0.64 –0.70). For the prediction of 30-day mortality, again, only 6 variables were significant; abnormal verbal response, fever, chills, arrival by emergency services, low oxygen saturation and breathing difficulties. This model gave a sensitivity of 0.87 (95% CI 0.81–0.93) and a specificity of 0.64 (95% CI 0.61–0.67).
In discussing their findings, the authors highlighted how their results revealed the importance of the using a clinical symptom complex that was representative of what an emergency department clinician would be likely to encounter in practice. They also suggested that the 7-day model might be of more use in practice since it would be of assistance to emergency care staff for the likely short-term outcome for patients. They concluded that given how the clinical presentation of sepsis can often be non-specific, the use of a machine learning algorithm, based on symptoms and observations, would be most helpful to staff and that future work should focus on validating the method in other cohorts.
Karlsson A et al. Predicting mortality among septic patients presenting to the emergency department– a cross sectional analysis using machine learning. BMC Emerg Med 2021