Using survival analysis to predict septic shock onset in ICU patients

Forecasting algorithms in the ICU

Despite significant advances in modeling methods and access to large datasets, there are very few real-time forecasting systems deployed in highly monitored environment such as the intensive care unit. Forecasting models may be developed as classification, regression or time-to-event tasks; each could be using a variety of machine learning algorithms. An accurate and useful forecasting systems include several components beyond a forecasting model, and its performance is assessed using end-user-centered metrics. Several barriers to implementation and acceptance persist and clinicians will play an active role in the successful deployment of this promising technology.

Prediction of sepsis onset in hospital admissions using survival analysis

To determine the efficacy of modern survival analysis methods for predicting sepsis onset in ICU, emergency, medical/surgical, and TCU departments. We performed a retrospective analysis on ICU, med/surg, ED, and TCU cases from multiple Mercy Health hospitals from August 2018 to March 2020. Patients in these departments were monitored by the Mercy Virtual vSepsis team and sepsis cases were determined and documented in the Mercy EHR via a rule-based engine utilizing clinical data. We used survival-based modeling methods to predict sepsis onset in these cases. The three survival methods that were used to predict the onset of severe sepsis and septic shock produced AUC values > 0.85 and each provided a median lead time of > 20 h prior to disease onset. This methodology improves upon previous work by demonstrating excellent model performance when generalizing survival-based prediction methods to both severe sepsis and septic shock as well as non-ICU departments.IRB InformationTrial Registration ID: 1,532,327-1.Trial Effective Date: 12/02/2019.

Forecasting medical state transition using machine learning methods

Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transformed. The performance of the proposed method is compared with the original method under three models, including logistic regression, AdaBoost and XGBoost. The results show that the model XGBoost generally has the best performance measured by AUC, F1 and Sensitivity with values around 0.93, 0.91 and 0.90, at the prediction gaps 5, 10 and 20 separately. Under the model XGBoost, the method with transformed response variable has significantly better performance than that with the original response variable, with the performance metrics being around 1% to 4% higher, and the t values are all significant under the level 0.01. In order to explore the model performance under different baseline information, a subgroup analysis is conducted under sex, age, weight and height. The results demonstrate that sex and age have more significant influence on the model performance especially at the higher gaps than weight and height.

Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review

BACKGROUND

Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and electronic health records allow for new opportunities to compute predictions based on machine learning techniques. There has been a lack of consensus identifying best practices for model development and validation towards early identification of sepsis.

OBJECTIVE

To evaluate the modeling approach and statistical methodology of machine learning prediction models for sepsis in the adult hospital population.

METHODS

PubMed, CINAHL, and Cochrane databases were searched with the Preferred Reporting Items for Systematic Reviews guided protocol development. We evaluated studies that developed or validated physiologic sepsis prediction models or implemented a model in the hospital environment.

RESULTS

Fourteen studies met the inclusion criteria, and the AUROC of the prediction models ranged from 0.61 to 0.96. We found a variety of sepsis definitions, methods used for event adjudication, model parameters used, and modeling methods. Two studies tested models in clinical settings; the results suggested that patient outcomes were improved with implementation of machine learning models.

CONCLUSION

Nurses have a unique perspective to offer in the development and implementation of machine learning models detecting patients at risk for sepsis. More work is needed in developing model harmonization standards and testing in clinical settings.

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm's potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm's accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.