The scientists from UK-headquartered AI precision medicine company, PrecisionLife, have used the platform to identify 13 high risk genes for COVID-19, and 59 repurposing drug candidates that could be used to develop new therapeutic strategies to increase the survival rate of patients who develop sepsis while suffering from severe COVID-19. Sepsis is observed in 60% of severe COVID-19 patients and is a life-threatening condition with a mortality rate of approximately 20%. The study, released on Biorxiv, sought to identify genetic risk factors for sepsis especially in the context of COVID-19, and to use these insights to identify existing drugs that might be used to treat life-threatening late-stage disease. The team identified mutations in 70 sepsis risk genes, 61% of which were also present specifically in severe COVID-19 patients. Several of the disease associated genetic signatures found in both sepsis and severe COVID-19 patients have previously been linked to cancer, immune response, endothelial and vascular inflammation, and neuronal signalling.
Geisinger and IBM this week announced this week that they've co-created a new predictive model to help clinicians flag sepsis risk using data from the integrated health system's electronic health record. WHY IT MATTERS The new algorithm created with help from IBM Data Science Elite will help Geisinger can create more personalized clinical care plans for at-risk sepsis patients, according to the health system, which can increase the chances of recovery by helping caregivers pay closer attention to key factors linked to sepsis deaths. Dr. Shravan Kethireddy led a team of scientists to create a new model based on EHR data. Partnering with the IBM Data Science and AI Elite teams, researchers assembled a six-person team to develop a model to predict sepsis mortality as well as a tool to keep the team on top of the latest sepsis research. The researchers used open source technology from IBM Watson to build a predictive model that would ingest clinical data from thousands of de-identified sepsis patients spanning a decade, then used that model to predict patient mortality during the hospitalization period or during the 90 days following their hospital stay, officials say.
We study multiple rule-based and machine learning (ML) models for sepsis detection. We report the first neural network detection and prediction results on three categories of sepsis. We have used the retrospective Medical Information Mart for Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients. Features for prediction were created from only common vital sign measurements. We show significant improvement of AUC score using neural network based ensemble model compared to single ML and rule-based models. For the detection of sepsis, severe sepsis, and septic shock, our model achieves an AUC of 0.94, 0.91 and 0.89, respectively. Four hours before the onset, it predicts the same three categories with an AUC of 0.80, 0.81 and 0.84 respectively. Further, we ranked the features and found that using six vital signs consistently provides higher detection and prediction AUC for all the models tested. Our novel ensemble model achieves highest AUC in detecting and predicting sepsis, severe sepsis, and septic shock in the MIMIC-III ICU patients, and is amenable to deployment in hospital settings.
Sepsis has been declared a medical emergency by the Centers for Disease Control and Prevention (CDC) in a report Tuesday that showed the preventability of the condition in over 70 percent of the cases through early action. Sepsis is a possibly life-threatening complication of an infection that can lead to tissue damage, organ failure, and death. People over the age of 65 years, children below one, those with weak immune systems and people suffering from chronic medical conditions like diabetes are most vulnerable to sepsis. "When sepsis occurs, it should be treated as a medical emergency," CDC Director Tom Frieden said in a press release. "Doctors and nurses can prevent sepsis and also the devastating effects of sepsis, and patients and families can watch for sepsis and ask, 'could this be sepsis?'"
This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accuracy of 93.45% and a utility score of 0.271. The utility score as defined by the organizers takes into account true positives, negatives and false alarms.