Background Despite decades of research, sepsis remains a leading cause of mortality and morbidity in intensive care units worldwide. The key to effective management and patient outcome is early detection, for which no prospectively validated machine learning prediction algorithm is currently available for clinical use in Europe. Objective We aimed to develop a high-performance machine learning sepsis prediction algorithm based on routinely collected intensive care unit data, designed to be implemented in European intensive care units. Methods The machine learning algorithm was developed using convolutional neural networks, based on Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III clinical data from intensive care unit patients aged 18 years or older. The model uses 20 variables to produce hourly predictions of onset of sepsis, defined by international Sepsis-3 criteria. Predictive performance was externally validated using hold-out test data. Results The algorithm—NAVOY Sepsis—uses 4 hours of input and can identify patients with high risk of developing sepsis, with high performance (area under the receiver operating characteristics curve 0.90; area under the precision-recall curve 0.62) for predictions up to 3 hours before sepsis onset. Conclusions The prediction performance of NAVOY Sepsis was superior to that of existing sepsis early warning scoring systems and comparable with those of other prediction algorithms designed to predict sepsis onset. The algorithm has excellent predictive properties and uses variables that are routinely collected in intensive care units.
Objectives: (1) to determine the adherence and persistence rates of adalimumab therapy among Swedish patients with Crohn’s disease (CD), and (2) to compare self-administration devices to predict the medication adherence and persistence. Methods: We conducted a retrospective analysis of the Swedish National Board of Health and Welfare database during a unique time period, when both the pen and the syringe were available. The pen was proposed to indicate a larger extent of internal control, according to health locus of control. Medication adherence was defined as a medication possession ratio (MPR) ≥ 0.8. A patient was considered nonpersistent if the time between any two dispensing records, minus the days of supply dispensed exceeded 180 days. The predictors of adherence were evaluated using a logistic regression, and the predictors of persistence were evaluated using a Cox proportional hazards model. Results: Among the 1083 patients studied, 89% were adherent and 77% were persistent. The patients using the pen and the patients treated in gastroenterology centers were more likely to be adherent and less likely to be nonpersistent. Conclusions: The adherence rate to adalimumab therapy was 89% and the one-year persistence rate was 70%. The pen and treatment in a gastroenterology center had a positive impact on the adherence and persistence among Swedish patients with CD.
BACKGROUND Despite decades of research, sepsis remains a leading cause of mortality and morbidity in ICUs worldwide. The key to effective management and patient outcome is early detection, where no prospectively validated machine learning prediction algorithm is available for clinical use in Europe today. OBJECTIVE To develop a high-performance machine learning sepsis prediction algorithm based on routinely collected ICU data, designed to be implemented in Europe. METHODS The machine learning algorithm is developed using Convolutional Neural Network, based on the Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III Clinical Database, focusing on ICU patients aged 18 years or older. Twenty variables are used for prediction, on an hourly basis. Onset of sepsis is defined in accordance with the international Sepsis-3 criteria. RESULTS The developed algorithm NAVOY Sepsis uses 4 hours of input and can with high accuracy predict patients with high risk of developing sepsis in the coming hours. The prediction performance is superior to that of existing sepsis early warning scoring systems, and competes well with previously published prediction algorithms designed to predict sepsis onset in accordance with the Sepsis-3 criteria, as measured by the area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC). NAVOY Sepsis yields AUROC = 0.90 and AUPRC = 0.62 for predictions up to 3 hours before sepsis onset. The predictive performance is externally validated on hold-out test data, where NAVOY Sepsis is confirmed to predict sepsis with high accuracy. CONCLUSIONS An algorithm with excellent predictive properties has been developed, based on variables routinely collected at ICUs. This algorithm is to be further validated in an ongoing prospective randomized clinical trial and will be CE marked as Software as a Medical Device, designed for commercial use in European ICUs.
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