Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model

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Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model

Hessulf, Fredrik, 1986 (författare): Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Anesthesiology and Intensive Care Medicine, Sahlgrenska University Hospital, Mölndal, Sweden

Bhatt, Deepak L. (författare): Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY, United States

Engdahl, Johan (författare): University of Gothenburg,Karolinska Institutet,Karolinska Institutet, Department of Medicine, Karolinska University Hospital Danderyd, Stockholm, Sweden

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Lundgren, Peter (författare): Gothenburg University,Göteborgs universitet,University of Borås,Högskolan i Borås,Akademin för vård, arbetsliv och välfärd,a Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine

Omerovic, Elmir, 1968 (författare): Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden; Wallenberg Laboratory for Cardiovascular and Metabolic Research, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden

Rawshani, Aidin, 1991 (författare): Gothenburg University,Göteborgs universitet,University of Gothenburg,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Laboratory for Cardiovascular and Metabolic Research, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; The Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, 413 45, Sweden

Helleryd, Edvin (författare): Gothenburg University,Göteborgs universitet,University of Gothenburg,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,a Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

Dworeck, Christian (författare): Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden

Friberg, Hans (författare): Lund University,Lunds universitet,Anestesiologi och intensivvård,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Centrum för hjärtstopp,Forskargrupper vid Lunds universitet,SWECRIT,Anesthesiology and Intensive Care,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Center for cardiac arrest,Lund University Research Groups,Department of Clinical Sciences, Anesthesia & Intensive Care, Lund University, Skåne University Hospital, Malmö, Sweden

Redfors, Björn (författare): Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden; Wallenberg Laboratory for Cardiovascular and Metabolic Research, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden

Nielsen, Niklas (författare): Lund University,Lunds universitet,Centrum för hjärtstopp,Forskargrupper vid Lunds universitet,SEBRA Sepsis and Bacterial Resistance Alliance,Center for cardiac arrest,Lund University Research Groups,Department of Clinical Sciences, Anaesthesia and Intensive Care, Helsingborg Hospital, Lund University, Lund, Sweden

Myredal, Anna (författare): Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,a Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden;

Frigyesi, Attila (författare): Lund University,Lunds universitet,Intensivvårdsepidemiologi,Forskargrupper vid Lunds universitet,Intensive Care Epidemiology,Lund University Research Groups,Department of Clinical Medicine, Anaesthesiology and Intensive Care, Lund University, Lund, SE-22185, Sweden

Herlitz, Johan, 1949- (författare): Gothenburg University,Göteborgs universitet,Högskolan i Borås,Akademin för vård, arbetsliv och välfärd,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden,PreHospen,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine

Rawshani, Araz, 1986 (författare): Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden; The Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden

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Elsevier BV, 2023
2023
Engelska.
Ingår i: eBioMedicine. - : Elsevier BV. - 2352-3964. ; 89

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Tidskriftsartikel (refereegranskat)

Abstract Ämnesord

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Background: A prediction model that estimates survival and neurological outcome in out-of-hospital cardiac arrest patients has the potential to improve clinical management in emergency rooms.Methods: We used the Swedish Registry for Cardiopulmonary Resuscitation to study all out-of-hospital cardiac arrest (OHCA) cases in Sweden from 2010 to 2020. We had 393 candidate predictors describing the circumstances at cardiac arrest, critical time intervals, patient demographics, initial presentation, spatiotemporal data, socioeconomic status, medications, and comorbidities before arrest. To develop, evaluate and test an array of prediction models, we created stratified (on the outcome measure) random samples of our study population. We created a training set (60% of data), evaluation set (20% of data), and test set (20% of data). We assessed the 30-day survival and cerebral performance category (CPC) score at discharge using several machine learning frameworks with hyperparameter tuning. Parsimonious models with the top 1 to 20 strongest predictors were tested. We calibrated the decision threshold to assess the cut-off yielding 95% sensitivity for survival. The final model was deployed as a web application.Findings: We included 55,615 cases of OHCA. Initial presentation, prehospital interventions, and critical time intervals variables were the most important. At a sensitivity of 95%, specificity was 89%, positive predictive value 52%, and negative predictive value 99% in test data to predict 30-day survival. The area under the receiver characteristic curve was 0.97 in test data using all 393 predictors or only the ten most important predictors. The final model showed excellent calibration. The web application allowed for near-instantaneous survival calculations.Interpretation: Thirty-day survival and neurological outcome in OHCA can rapidly and reliably be estimated during ongoing cardiopulmonary resuscitation in the emergency room using a machine learning model incorporating widely available variables.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Nyckelord

Machine learning
Out-of-hospital cardiac arrest
Prediction model
Web application
Människan i vården
The Human Perspective in Care
Machine learning
Out-of-hospital cardiac arrest
Prediction model
Web application

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Av författaren/redakt...: Hessulf, Fredrik ...; Bhatt, Deepak L.; Engdahl, Johan; Lundgren, Peter; Omerovic, Elmir, ...; Rawshani, Aidin, ...; visa fler...; Helleryd, Edvin; Dworeck, Christi ...; Friberg, Hans; Redfors, Björn; Nielsen, Niklas; Myredal, Anna; Frigyesi, Attila; Herlitz, Johan, ...; Rawshani, Araz, ...; visa färre...

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MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Klinisk medicin; och Kardiologi

Artiklar i publikationen: eBioMedicine

Av lärosätet: Högskolan i Borås; Karolinska Institutet; Lunds universitet; Göteborgs universitet

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