The Dynamical Assessment of Inflammatory Biomarkers in Predicting the Outcome of Septic Patients and the Response to Antimicrobial Therapy

Fibrinogen reflects severity and predicts outcomes in patients with sepsis and septic shock

The study investigates the diagnostic and prognostic value of fibrinogen and the albumin-to-fibrinogen-ratio (AFR) in patients with sepsis and septic shock. Limited data regarding the prognostic value of fibrinogen and AFR during the course of sepsis or septic shock are available. Consecutive patients with sepsis and septic shock from 2019 to 2021 were included monocentrically. Blood samples were retrieved from the day of disease onset (day 1), as well as on day 2 and 3. Firstly, the diagnostic value of fibrinogen and the AFR for the diagnosis of a septic shock was tested. Secondly, the prognostic value of fibrinogen and AFR was tested with regard to the 30-day all-cause mortality. Statistical analyses included univariable t-tests, Spearman's correlations, C-statistics, Kaplan-Meier and multivariable Cox regression analyses. Ninety-one patients with sepsis and septic shock were included. With an area under the curve (AUC) of 0.653-0.801, fibrinogen discriminated patients with septic shock from those with sepsis. In the septic shock group, fibrinogen levels were shown to decrease from day 1 to 3 (median decrease 41%). In line, fibrinogen was a reliable predictor for 30-day all-cause mortality (AUC 0.661-0.744), whereas fibrinogen levels less than 3.6 g/l were associated with an increased risk of 30-day all-cause mortality (78 vs. 53%; log rank P  = 0.004; hazard ratio = 2.073; 95% confidence interval 1.233-3.486; P  = 0.006), which was still observed after multivariable adjustment. In contrast, the AFR was no longer associated with the risk of mortality after multivariable adjustment. Fibrinogen was a reliable diagnostic and prognostic tool for the diagnosis of septic shock as well as for 30-day all-cause mortality and superior compared with the AFR in patients admitted with sepsis or septic shock.

Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations

OBJECTIVE

The aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients.

DESIGN

This is a retrospective single-institution study. All consecutive adult patients' cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included. The cases were reviewed and labeled for clinical deterioration by a multidisciplinary expert consensus panel. A supervised learning approach was adopted based on a set of biomarkers and demographic data available in the patient's electronic medical record (EMR).

SETTING

The setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards.

PATIENTS

The study analyzed the cases of 514 patients for which the RRT was activated. Rapid response teams were extracted from the hospital telephone log data. Two hundred eighteen clinical deterioration cases were identified in these patients after expert chart review and complemented by 146 "nonevent" cases to build the training and validation data set.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The best performance was achieved with the random forests algorithm, with a maximal area under the receiver operating curve of 0.90 and F1 score of 0.85 obtained at prediction time T0-6h, slightly decreasing but still acceptable (area under the receiver operating curve, >0.8; F1 score, >0.75) at T0-42h. The system outperformed most classical track-and-trigger systems both in terms of prediction performance and prediction horizon.

CONCLUSIONS

In hospitals with a basic EMR, a biomarker-based ML model could be used to predict clinical deterioration in general wards patients earlier than classical track-and-trigger systems, thus enabling appropriate clinical interventions for patient safety and improved outcomes.