Clotting inhibitors and fibronectin as potential markers in preeclampsia

OBJECTIVES

As conventional laboratory tests fail to identify early preeclampsia, the aim of this study was to seek a marker of endothelial injury and select more sensitive tests for monitoring the activation of the coagulation cascade.

METHODS

Using routine blood sampling methods, PT, PTT, AT-III, proteins C and S, platelets, fibrinogen and fibronectin were measured in 18 preeclamptic patients and 17 controls with a normal pregnancy.

RESULTS

Preeclampsia was associated with higher fibronectin and lower AT-III, protein C and protein S. The platelet count, fibrinogen and PT were similar. PTT was longer in preeclampsia.

CONCLUSION

As AT-III revealed compensated coagulopathy and fibronectin demonstrated preeclamptic vascular lesions before these were apparent from routine tests, measuring these elements could contribute to the earlier diagnosis and better treatment of chronically-altered coagulation in preeclampsia.

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.

Predictors of COVID-19 Readmission Among Patients Previously Hospitalized for SARS-CoV-2

INTRODUCTION

Predictors of coronavirus disease 2019 (COVID-19)-related rehospitalization remain underexplored. This study aims to identify the main risk factors associated with rehospitalizations due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfections among residents of Lombardy, northern Italy.

METHODS

A retrospective observational study was conducted using two linked administrative databases covering demographic data, comorbidities, hospital records, and COVID-19 data of Lombardy residents. The study population included patients hospitalized for COVID-19 between February 2020 and August 2021. Rehospitalization was defined as a second COVID-19-related hospitalization occurring at least 90 days after the first admission. The Fine-Gray subdistribution hazard model was used to identify risk factors, accounting for death as a competing risk.

RESULTS

Out of 98,369 patients hospitalized for COVID-19 between February 1, 2020 and August 31, 2021, 72,593 were alive 90 days after admission and 610 of these (0.8%) were rehospitalized. A higher rehospitalization risk was observed in older male patients with multiple comorbidities. Renal failure, liver disease, and use of diuretics were significantly associated with rehospitalization risk, while female biological sex and the use of lipid-lowering drugs were associated with a lower risk.

CONCLUSIONS

This is the first study conducted on regional administrative databases to investigate COVID-19 rehospitalizations. Through the availability of a huge cohort, it provides a groundwork for optimizing care for individuals at higher risk for COVID-19-related rehospitalizations. It underlines the need for patient-management approaches that extend beyond the initial recovery. This stresses the importance of ongoing monitoring and personalized interventions for those at heightened risk not only of SARS-CoV-2 reinfection but also related rehospitalizations.