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Fredrik Hessulf

Affiliated to Research

Department of Anesthesiology and Intensive Care
Mail
fredrik.hessulf@gu.se
Visiting address
Blå stråket 5, vån 3 SU/Sahlgrenska
41345 Göteborg
Postal address
Sahlgrenska Universitetssjukhuset
41345 Göteborg

About Fredrik Hessulf

About Fredrik Hessulf

Doctor of Medicine and Consultant in Anaesthesiology and Intensive Care. Instructor for medical students during the anesthesia module of the Medical Program at the University 91̽. Head of Research, Development, Education, and Innovation (FOUUI) at the dapartment of Anesthesia and Intensive Care, Sahlgrenska University Hospital/Mölndal. Secretary of ETOS, the Ethics Section of the Swedish Society of Anaesthesiology and Intensive Care Medicine (SFAI). Member of the Ethics Group of the Swedish Resuscitation Council.

Research

1) Intensive Care and Artificial Intelligence 

Predicting patient outcomes in intensive care is a critical yet challenging task. Several tools exist to estimate mortality risk, such as APACHE II, SOFA, and SAPS 3. With the advent of machine learning and artificial intelligence (AI), there is potential to develop more efficient and accurate prediction tools. Previous research has indicated that AI can improve mortality prediction compared to traditional methods.The Swedish Intensive Care Registry (SIR) provides a unique data source to explore this field, but no large-scale AI-based prognostic model has yet been developed using these data. The goal of this project is to build AI prediction models aimed at improving the care of intensive care patients.

PhD student: Tobias Siöland, Consultant in Anaesthesiology

2) Ethics and Intensive Care Determining what is ethically right is often more complex than deciding on the correct medical management. Despite this, only a small fraction of research and development resources are devoted to ethical questions compared to clinical research. This project explores ethical aspects of intensive care using data from the Swedish Intensive Care Registry.

Pre-doctoral student: Lisa Wiltz, Resident in Anaesthesiology

Resident projects: Lisa Persson, MD, and Sofia Dahlgren, MD

Medical student project: Maria Lönnquist, Medical Student

3) Orthogeriatric Anaesthesia

Patients undergoing surgery for acute hip fracture belong to the frailest and oldest segment of society. The aim of these projects is to improve the care of this vulnerable patient group.

Implementation of ultrasound-guided FIC block in the Geriatric Department, Sahlgrenska University Hospital: Yang Cong, Resident in Geriatrics

Optimized temperature control before, during, and after acute hip fracture surgery: Anna Jönsson, RN, CCRN, and Selma Dögg, Resident in Anaesthesiology

4) AI-Intox: Intoxications in the ICU

PhD student: Sara Lundin, Pharmacist

5) Orthopaedic Anaesthesia

PhD student: Anita Szell, Consultant Anaesthesiologist

Main supervisor: Professor Kristian Samuelsson, Orthopaedics

6) TRAIL-ICU

Multicenter study (Sahlgrenska/Mölndal, Mario Negri Institute in Milan, Amsterdam University).Integration of three European ICU registries and external validation of ICU prediction models using AI.

7) Cardiac Arrest 

Several collaborations with Associate Professor Araz Rawshani, Professor Johan Herlitz, and Associate Professor Johan Engdahl.

8) Treatment Limitations at the End of Life

Collaboration with Dr. Viveka Andersson and Professor Anders Bremer.

Former Students

Johan Cederquist: Medical Emergency Team at Sahlgrenska University Hospitals. Master's thesis, Medical Program, Term 10 (2024).

Sanna Olausson: Treatment limitations in the ICU at Sahlgrenska University Hospitals. Master's thesis, Medical Program, Term 10 (2024).

Tobias Siöland: ICURE. SSAI scientific project (2022–23), publication.

Nino Jönsson: Treatment limitations in the ICU in Halland (2022), publication.

 

Media

 

Key Publications

 

Hessulf, F., Bhatt, D. L., Engdahl, J., Lundgren, P., Omerovic, E., Rawshani, A., Helleryd, E., Dworeck, C., Friberg, H., Redfors, B., Nielsen, N., Myredal, A., Frigyesi, A., Herlitz, J., & Rawshani, A. (2023). Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model. EBioMedicine, 89, 104464.  IF 9.7

 

Siöland, T., Rawshani, A., Nellgård, B., Malmgren, J., Oras, J., Dalla, K., Cinà, G., Engerström, L., & Hessulf, F. (2024). ICURE: Intensive care unit (ICU) risk evaluation for 30-day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden. Acta Anaesthesiol Scand.  IF 1.9

 

Hessulf, F., Herlitz, J., Rawshani, A., Aune, S., Israelsson, J., Södersved-Källestedt, M. L., Nordberg, P., Lundgren, P., & Engdahl, J. (2020). Adherence to guidelines is associated with improved survival following in-hospital cardiac arrest. Resuscitation, 155, 13-21.  IF 6.5