Pathophysiology, Predictive Modeling, and Long-Term Cardiovascular Outcomes in Severe Preeclampsia

Camilla Edvinsson

Pathophysiology, Predictive Modeling, and Long-Term Cardiovascular Outcomes in Severe Preeclampsia

Research output: Thesis › Doctoral Thesis (compilation)

260 Downloads (Pure)

Original language	English
Qualification	Doctor
Awarding Institution	Department of Clinical Sciences, Lund
Supervisors/Advisors	Hansson, Stefan, Supervisor Nielsen, Niklas, Assistant supervisor Hein, Anette, Assistant supervisor, External person
Award date	2025 May 9
Place of Publication	Lund
Publisher	Lund University, Faculty of Medicine
ISBN (Print)	978-91-8021-697-5
Publication status	Published - 2025

Bibliographical note

Defence details
Date: 2025-05-09
Time: 09:00
Place: Medicinhistoriska muséet, Bergaliden 20, Helsingborg. Join by Zoom: https://lu-se.zoom.us/j/61155898278
External reviewer(s)
Name: Juul Wikkelsö, Anne
Title: Associate Professor
Affiliation: University of Copenhagen

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Subject classification (UKÄ)

Gynaecology, Obstetrics and Reproductive Medicine
Anesthesiology and Intensive Care

Free keywords

Preeclampsia/pregnancy
Intensive Care
Biomarkers
Machine Learning
Cardiovascular disease (CVD)
Endothelial damage

Access to Document

Pathophysiology, Predictive Modeling, and Long-Term Cardiovascular Outcomes in Severe PreeclampsiaFinal published version, 8.09 MB

2 Article

Predicting intensive care need in women with preeclampsia using machine learning–a pilot study
Edvinsson, C., Björnsson, O., Erlandsson, L. & Hansson, S. R., 2024, In: Hypertension in Pregnancy. 43, 1, 2312165.
Research output: Contribution to journal › Article › peer-review

Open Access
Intensive care patients with preeclampsia – Clinical risk factors and biomarkers for oxidative stress and angiogenic imbalance as discriminators for severe disease
Edvinsson, C., Hansson, E., Nielsen, N., Erlandsson, L. & Hansson, S. R., 2022 Dec, In: Pregnancy Hypertension. 30, p. 88-94 7 p.
Research output: Contribution to journal › Article › peer-review

Open Access

Cite this

@phdthesis{dec05f63383643aa9bba08e179769f77,

title = "Pathophysiology, Predictive Modeling, and Long-Term Cardiovascular Outcomes in Severe Preeclampsia",

keywords = "Preeclampsia/pregnancy, Intensive Care, Biomarkers, Machine Learning, Cardiovascular disease (CVD), Endothelial damage",

author = "Camilla Edvinsson",

note = "Defence details Date: 2025-05-09 Time: 09:00 Place: Medicinhistoriska mus{\'e}et, Bergaliden 20, Helsingborg. Join by Zoom: https://lu-se.zoom.us/j/61155898278 External reviewer(s) Name: Juul Wikkels{\"o}, Anne Title: Associate Professor Affiliation: University of Copenhagen",

year = "2025",

language = "English",

isbn = "978-91-8021-697-5",

series = "Lund University, Faculty of Medicine Doctoral Dissertation Series",

publisher = "Lund University, Faculty of Medicine",

number = "2025:44",

type = "Doctoral Thesis (compilation)",

school = "Department of Clinical Sciences, Lund",

}

TY - THES

T1 - Pathophysiology, Predictive Modeling, and Long-Term Cardiovascular Outcomes in Severe Preeclampsia

AU - Edvinsson, Camilla

N1 - Defence details Date: 2025-05-09 Time: 09:00 Place: Medicinhistoriska muséet, Bergaliden 20, Helsingborg. Join by Zoom: https://lu-se.zoom.us/j/61155898278 External reviewer(s) Name: Juul Wikkelsö, Anne Title: Associate Professor Affiliation: University of Copenhagen

PY - 2025

Y1 - 2025

KW - Preeclampsia/pregnancy

KW - Intensive Care

KW - Biomarkers

KW - Machine Learning

KW - Cardiovascular disease (CVD)

KW - Endothelial damage

M3 - Doctoral Thesis (compilation)

SN - 978-91-8021-697-5

T3 - Lund University, Faculty of Medicine Doctoral Dissertation Series

PB - Lund University, Faculty of Medicine

CY - Lund

ER -

Pathophysiology, Predictive Modeling, and Long-Term Cardiovascular Outcomes in Severe Preeclampsia

Bibliographical note

UN SDGs

Subject classification (UKÄ)

Free keywords

Access to Document

Research output

Predicting intensive care need in women with preeclampsia using machine learning–a pilot study

Intensive care patients with preeclampsia – Clinical risk factors and biomarkers for oxidative stress and angiogenic imbalance as discriminators for severe disease

Cite this