Statistical analysis
organ failure, Sepsis, organ dysfunction
REGRESSION, SEPSIS
To analyse the difference in HRV features between the POD, SOD and NOD groups the data was summarized in three hour windows. The windows of all patients were aligned to the start of admission to the ED. This way, patients were all in the same stage of diagnosis and treatment while on the ED. The mean and slope of each feature were calculated per three-hour time windows. A Kruskal–Wallis test was used to calculate differences between the different groups. Significantly different features were then selected for a univariate regression model of each feature to investigate the predictive ability for progressive organ dysfunction. Both the SOD and the NOD groups were considered controls in the model, while progressive organ failure was considered as cases. For this model only the features in the first three hours after admission were used, as they were expected to be useful in prediction at the ED. Next, a stepwise forward multivariate logistic regression model was developed to associate HRV features with the POD group. Features with a p-value < 0.20 in the univariate analysis were used for the multivariate model. For the stepwise regression Akaike’s Information Criterion (AIC) was used to find the optimal model. The resulting model was then compared to models using the qSOFA and Sepsis Severity Score. Statistical analysis was performed and figures were made using Matlab R2018b (Matlab, the Mathworks, Natick, USA) and R for Windows workstations (version 4.0.5) was used for the model, a 