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Lascarrou JB, Bougouin W, Chelly J, Bourenne J, Daubin C, Lesieur O, Asfar P, Colin G, Paul M, Chudeau N, Muller G, Geri G, Jacquier S, Pichon N, Klein T, Sauneuf B, Klouche K, Cour M, Sejourne C, Annoni F, Raphalen JH, Galbois A, Bruel C, Mongardon N, Aissaoui N, Deye N, Maizel J, Dumas F, Legriel S, Cariou A. Prospective comparison of prognostic scores for prediction of outcome after out-of-hospital cardiac arrest: results of the AfterROSC1 multicentric study. Ann Intensive Care 2023; 13:100. [PMID: 37819544 PMCID: PMC10567621 DOI: 10.1186/s13613-023-01195-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/26/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Out-of-hospital cardiac arrest (OHCA) is a heterogeneous entity with multiple origins and prognoses. An early, reliable assessment of the prognosis is useful to adapt therapeutic strategy, tailor intensity of care, and inform relatives. We aimed primarily to undertake a prospective multicentric study to evaluate predictive performance of the Cardiac Arrest Prognosis (CAHP) Score as compare to historical dataset systematically collected after OHCA (Utstein style criteria). Our secondary aim was to evaluate other dedicated scores for predicting outcome after OHCA and to compare them to Utstein style criteria. METHODS We prospectively collected data from 24 French and Belgium Intensive Care Units (ICUs) between August 2020 and June 2022. All cases of non-traumatic OHCA (cardiac and non-cardiac causes) patients with stable return of spontaneous circulation (ROSC) and comatose at ICU admission (defined by Glasgow coma score ≤ 8) on ICU admission were included. The primary outcome was the modified Rankin scale (mRS) at day 90 after cardiac arrest, assessed by phone interviews. A wide range of developed scores (CAHP, OHCA, CREST, C-Graph, TTM, CAST, NULL-PLEASE, and MIRACLE2) were included, and their accuracies in predicting poor outcome at 90 days after OHCA (defined as mRS ≥ 4) were determined using the area under the receiving operating characteristic curve (AUROC) and the calibration belt. RESULTS During the study period, 907 patients were screened, and 658 were included in the study. Patients were predominantly male (72%), with a mean age of 61 ± 15, most having collapsed from a supposed cardiac cause (64%). The mortality rate at day 90 was 63% and unfavorable neurological outcomes were observed in 66%. The performance (AUROC) of Utstein criteria for poor outcome prediction was moderate at 0.79 [0.76-0.83], whereas AUROCs from other scores varied from 0.79 [0.75-0.83] to 0.88 [0.86-0.91]. For each score, the proportion of patients for whom individual values could not be calculated varied from 1.4% to 17.4%. CONCLUSIONS In patients admitted to ICUs after a successfully resuscitated OHCA, most of the scores available for the evaluation of the subsequent prognosis are more efficient than the usual Utstein criteria but calibration is unacceptable for some of them. Our results show that some scores (CAHP, sCAHP, mCAHP, OHCA, rCAST) have superior performance, and that their ease and speed of determination should encourage their use. Trial registration https://clinicaltrials.gov/ct2/show/NCT04167891.
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Affiliation(s)
- Jean Baptiste Lascarrou
- AfterROSC Network Group, Paris, France.
- Université de Paris Cité, Inserm, Paris Cardiovascular Research Center, Paris, France.
- Service de Médecine Intensive Réanimation, University Hospital Center, 30 Boulevard Jean Monet, 44093, Nantes Cedex 9, France.
| | - Wulfran Bougouin
- AfterROSC Network Group, Paris, France
- Université de Paris Cité, Inserm, Paris Cardiovascular Research Center, Paris, France
- Médecine Intensive Réanimation, Hôpital Jacques Cartier, Massy, France
| | - Jonathan Chelly
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Toulon, Toulon, France
| | - Jeremy Bourenne
- AfterROSC Network Group, Paris, France
- Réanimation des Urgences et Déchocage, CHU La Timone, APHM, Marseille, France
| | - Cedric Daubin
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHU Caen, Caen, France
| | - Olivier Lesieur
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH La Rochelle, La Rochelle, France
| | - Pierre Asfar
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHU Angers, Angers, France
| | - Gwenhael Colin
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHD Vendée, La Roche-Sur-Yon, France
| | - Marine Paul
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Versailles, Le Chesnay, France
| | - Nicolas Chudeau
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Le Mans, Le Mans, France
| | - Gregoire Muller
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHR Orléans, Orléans, France
| | - Guillaume Geri
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, APHP, CHU Ambroise Pare, Boulogne-Billancourt, France
| | - Sophier Jacquier
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHU Tours, Tours, France
| | - Nicolas Pichon
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Brive-La-Gaillard, Bourges, France
| | - Thomas Klein
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHU Nancy, Nancy, France
| | - Bertrand Sauneuf
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Cherbourg-en-Cotentin, Cherbourg, France
| | - Kada Klouche
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHU Montpellier, Montpellier, France
| | - Martin Cour
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, Hospices Civils Lyon, Lyon, France
| | - Caroline Sejourne
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Bethune, Bethune, France
| | - Filippo Annoni
- AfterROSC Network Group, Paris, France
- Réanimation, ERASME, Brussels, Belgium
| | - Jean-Herle Raphalen
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, APHP, CHU Necker, Paris, France
| | - Arnaud Galbois
- AfterROSC Network Group, Paris, France
- Service de Réanimation Polyvalente, Hôpital Privé Claude Galien, Quincy-Sous-Sénart, France
| | - Cedric Bruel
- AfterROSC Network Group, Paris, France
- Service de Réanimation Polyvalente, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Nicolas Mongardon
- AfterROSC Network Group, Paris, France
- Service d'Anesthésie-Réanimation Chirurgicale, APHP, CHU Henri Mondor, Créteil, France
| | - Nadia Aissaoui
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, APHP, HEGP, Paris, France
| | - Nicolas Deye
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, APHP, CHU Lariboisière, Paris, France
| | - Julien Maizel
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CHU Amiens, Amiens, France
| | | | - Stephane Legriel
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, CH Versailles, Le Chesnay, France
| | - Alain Cariou
- AfterROSC Network Group, Paris, France
- Médecine Intensive Réanimation, APHP, CHU Cochin, Paris, France
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2
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Sun Y, He Z, Ren J, Wu Y. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning. BMC Anesthesiol 2023; 23:178. [PMID: 37231340 DOI: 10.1186/s12871-023-02138-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/13/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. METHODS The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. RESULTS Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram. CONCLUSIONS The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.
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Affiliation(s)
- Yiwu Sun
- Department of Anesthesiology, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, Sichuan, 635000, China.
| | - Zhaoyi He
- Department of Anesthesiology, The Third Affiliated Hospital of Harbin Medical University, No.150 Haping Road, Nangang District, Harbin, Heilongjiang, 150000, China
| | - Jie Ren
- Department of Anesthesiology, Guizhou Provincial People's Hospital, No.83 Zhongshan East Road, Nanming District, Guiyang, Guizhou, 550002, China
| | - Yifan Wu
- Department of Anesthesiology, Shanghai Sixth People's Hospital, No.600 Yishan Road, Xuhui District, Shanghai, 200030, China
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3
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Blatter R, Gökduman B, Amacher SA, Becker C, Beck K, Gross S, Tisljar K, Sutter R, Pargger H, Marsch S, Hunziker S. External validation of the PROLOGUE score to predict neurological outcome in adult patients after cardiac arrest: a prospective cohort study. Scand J Trauma Resusc Emerg Med 2023; 31:16. [PMID: 37016393 PMCID: PMC10074653 DOI: 10.1186/s13049-023-01081-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/24/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND The PROLOGUE score (PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages) is a novel prognostic model for the prediction of neurological outcome after cardiac arrest, which showed exceptional performance in the internal validation. The aim of this study is to validate the PROLOGUE score in an independent cohort of unselected adult cardiac arrest patients and to compare it to the thoroughly validated Out-of-Hospital Cardiac Arrest (OHCA) and Cardiac Arrest Hospital Prognosis (CAHP) scores. METHODS This study included consecutive adult cardiac arrest patients admitted to the intensive care unit (ICU) of a Swiss tertiary teaching hospital between October 2012 and July 2022. The primary endpoint was poor neurological outcome at hospital discharge, defined as a Cerebral Performance Category (CPC) score of 3 to 5 including death. RESULTS Of 687 patients included in the analysis, 321 (46.7%) survived to hospital discharge with good neurological outcome, 68 (9.9%) survived with poor neurological outcome and 298 (43.4%) died. The PROLOGUE score showed an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI 0.80 to 0.86) and good calibration for the prediction of the primary outcome. The OHCA and CAHP score showed similar performance (AUROC 0.83 and 0.84 respectively), the differences between the three scores were not significant (p = 0.495). In a subgroup analysis, the PROLOGUE score performed equally in out-of-hospital and in-hospital cardiac arrest patients whereas the OHCA and CAHP score performed significantly better in OHCA patients. CONCLUSION The PROLOGUE score showed good prognostic accuracy for the early prediction of neurological outcome in adult cardiac arrest survivors in our cohort and might support early goals-of-care discussions in the ICU. Trial registration Not applicable.
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Affiliation(s)
- René Blatter
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Bulus Gökduman
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Simon A Amacher
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland
- Department of Emergency Medicine, University Hospital Basel, Basel, Switzerland
| | - Christoph Becker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
- Department of Emergency Medicine, University Hospital Basel, Basel, Switzerland
| | - Katharina Beck
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Sebastian Gross
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Kai Tisljar
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland
| | - Raoul Sutter
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
| | - Hans Pargger
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
| | - Stephan Marsch
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
| | - Sabina Hunziker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland.
- Medical Faculty, University of Basel, Basel, Switzerland.
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4
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Ouyang Y, Cheng M, He B, Zhang F, Ouyang W, Zhao J, Qu Y. Interpretable machine learning models for predicting in-hospital death in patients in the intensive care unit with cerebral infarction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107431. [PMID: 36827826 DOI: 10.1016/j.cmpb.2023.107431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 07/20/2022] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Research on patients with cerebral infarction in the Intensive Care Unit (ICU) is still lacking. Our study aims to develop and validate multiple machine-learning (ML) models using two large ICU databases-Medical Information Mart for Intensive Care version III (MIMIC-III) and eICU Research Institute Database (eRI)-to guide clinical practice. METHODS We collected clinical data from patients with cerebral infarction in the MIMIC-III and eRI databases within 24 h of admission. The opinion of neurologists and the Least Absolute Shrinkage and Selection Operator regression was used to screen for relevant clinical features. Using eRI as the training set and MIMIC-III as the test set, we developed and validated six ML models. Based on the results of the model validation, we select the best model and perform the interpretability analysis on it. RESULTS A total of 4,338 patients were included in the study (eRI:3002, MIMIC-III:1336), resulting in a total of 18 clinical characteristics through screening. Model validation results showed that random forest (RF) was the best model, with AUC and F1 scores of 0.799 and 0.417 in internal validation and 0.733 and 0.498 in external validation, respectively; moreover, its sensitivity and recall were the highest of the six algorithms for both the internal and external validation. The explanatory analysis of the model showed that the three most important variables in the RF model were Acute Physiology Score-III, Glasgow Coma Scale score, and heart rate, and that the influence of each variable on the judgement of the model was consistent with medical knowledge. CONCLUSION Based on a large sample of patients and advanced algorithms, our study bridges the limitations of studies on this area. With our model, physicians can use the admission information of cerebral infarction patients in the ICU to identify high-risk groups among them who are prone to in-hospital death, so that they could be more alert to this group of patients and upgrade medical measures early to minimize the mortality of patients.
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Affiliation(s)
- Yang Ouyang
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Meng Cheng
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Bingqing He
- Department of Neurology, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Fengjuan Zhang
- Department of Neurology, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Wen Ouyang
- Department of Endocrinology, First People's Hospital of Changde City, 818 renmin Street, Changde 415000, China
| | - Jianwu Zhao
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China.
| | - Yang Qu
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China.
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5
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Amacher SA, Blatter R, Briel M, Appenzeller-Herzog C, Bohren C, Becker C, Beck K, Gross S, Tisljar K, Sutter R, Marsch S, Hunziker S. Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance. Crit Care 2022; 26:382. [PMID: 36503620 PMCID: PMC9741710 DOI: 10.1186/s13054-022-04263-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/10/2022] [Indexed: 12/14/2022] Open
Abstract
This work aims to assess the performance of two post-arrest (out-of-hospital cardiac arrest, OHCA, and cardiac arrest hospital prognosis, CAHP) and one pre-arrest (good outcome following attempted resuscitation, GO-FAR) prediction model for the prognostication of neurological outcome after cardiac arrest in a systematic review and meta-analysis. A systematic search was conducted in Embase, Medline, and Web of Science Core Collection from November 2006 to December 2021, and by forward citation tracking of key score publications. The search identified 1'021 records, of which 25 studies with a total of 124'168 patients were included in the review. A random-effects meta-analysis of C-statistics and overall calibration (total observed vs. expected [O:E] ratio) was conducted. Discriminatory performance was good for the OHCA (summary C-statistic: 0.83 [95% CI 0.81-0.85], 16 cohorts) and CAHP score (summary C-statistic: 0.84 [95% CI 0.82-0.87], 14 cohorts) and acceptable for the GO-FAR score (summary C-statistic: 0.78 [95% CI 0.72-0.84], five cohorts). Overall calibration was good for the OHCA (total O:E ratio: 0.78 [95% CI 0.67-0.92], nine cohorts) and the CAHP score (total O:E ratio: 0.78 [95% CI 0.72-0.84], nine cohorts) with an overestimation of poor outcome. Overall calibration of the GO-FAR score was poor with an underestimation of good outcome (total O:E ratio: 1.62 [95% CI 1.28-2.04], five cohorts). Two post-arrest scores showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the intensive care unit. A pre-arrest score showed acceptable prognostic accuracy and may support code status discussions.
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Affiliation(s)
- Simon A. Amacher
- grid.410567.1Intensive Care, University Hospital Basel, Basel, Switzerland ,grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - René Blatter
- grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Matthias Briel
- grid.6612.30000 0004 1937 0642Meta-Research Centre, Department of Clinical Research, University of Basel and University Hospital Basel, Basel, Switzerland ,grid.25073.330000 0004 1936 8227Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, Canada ,grid.6612.30000 0004 1937 0642Medical Faculty, University of Basel, Basel, Switzerland
| | | | - Chantal Bohren
- grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Christoph Becker
- grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland ,grid.410567.1Department of Emergency Medicine, University Hospital Basel, Basel, Switzerland
| | - Katharina Beck
- grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Sebastian Gross
- grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Kai Tisljar
- grid.410567.1Intensive Care, University Hospital Basel, Basel, Switzerland
| | - Raoul Sutter
- grid.410567.1Intensive Care, University Hospital Basel, Basel, Switzerland ,grid.6612.30000 0004 1937 0642Medical Faculty, University of Basel, Basel, Switzerland
| | - Stephan Marsch
- grid.410567.1Intensive Care, University Hospital Basel, Basel, Switzerland ,grid.6612.30000 0004 1937 0642Medical Faculty, University of Basel, Basel, Switzerland
| | - Sabina Hunziker
- grid.410567.1Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland ,grid.6612.30000 0004 1937 0642Medical Faculty, University of Basel, Basel, Switzerland
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6
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Blatter R, Amacher SA, Bohren C, Becker C, Beck K, Gross S, Tisljar K, Sutter R, Marsch S, Hunziker S. Comparison of different clinical risk scores to predict long-term survival and neurological outcome in adults after cardiac arrest: results from a prospective cohort study. Ann Intensive Care 2022; 12:77. [PMID: 35978065 PMCID: PMC9385915 DOI: 10.1186/s13613-022-01048-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022] Open
Abstract
Background Several scoring systems have been used to predict short-term outcome in patients with out-of-hospital cardiac arrest (OHCA), including the disease-specific OHCA and CAHP (Cardiac Arrest Hospital Prognosis) scores, as well as the general severity-of-illness scores Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II). This study aimed to assess the prognostic performance of these four scores to predict long-term outcomes (≥ 2 years) in adult cardiac arrest patients. Methods This is a prospective single-centre cohort study including consecutive cardiac arrest patients admitted to intensive care in a Swiss tertiary academic medical centre. The primary endpoint was 2-year mortality. Secondary endpoints were neurological outcome at 2 years post-arrest assessed by Cerebral Performance Category with CPC 1–2 defined as good and CPC 3–5 as poor neurological outcome, and 6-year mortality. Results In 415 patients admitted to intensive care, the 2-year mortality was 58.1%, with 96.7% of survivors showing good neurological outcome. The 6-year mortality was 82.5%. All four scores showed good discriminatory performance for 2-year mortality, with areas under the receiver operating characteristics curve (AUROC) of 0.82, 0.87, 0.83 and 0.81 for the OHCA, CAHP, APACHE II and SAPS II scores. The results were similar for poor neurological outcome at 2 years and 6-year mortality. Conclusion This study suggests that two established cardiac arrest-specific scores and two severity-of-illness scores provide good prognostic value to predict long-term outcome after cardiac arrest and thus may help in early goals-of-care discussions. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-022-01048-y.
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Affiliation(s)
- René Blatter
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Simon A Amacher
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland.,Intensive Care Unit, University Hospital Basel, Basel, Switzerland
| | - Chantal Bohren
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Christoph Becker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland.,Department of Emergency Medicine, University Hospital Basel, Basel, Switzerland
| | - Katharina Beck
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Sebastian Gross
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland
| | - Kai Tisljar
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland
| | - Raoul Sutter
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland.,Medical Faculty, University of Basel, Basel, Switzerland
| | - Stephan Marsch
- Intensive Care Unit, University Hospital Basel, Basel, Switzerland.,Medical Faculty, University of Basel, Basel, Switzerland
| | - Sabina Hunziker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031, Basel, Switzerland. .,Medical Faculty, University of Basel, Basel, Switzerland.
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7
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Liu N, Liu M, Chen X, Ning Y, Lee JW, Siddiqui FJ, Saffari SE, Ho AFW, Shin SD, Ma MHM, Tanaka H, Ong MEH. Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study. EClinicalMedicine 2022; 48:101422. [PMID: 35706500 PMCID: PMC9096672 DOI: 10.1016/j.eclinm.2022.101422] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/09/2022] [Accepted: 04/11/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Return of spontaneous circulation (ROSC) before arrival at the emergency department is an early indicator of successful resuscitation in out-of-hospital cardiac arrest (OHCA). Several ROSC prediction scores have been developed with European cohorts, with unclear applicability in Asian settings. We aimed to develop an interpretable prehospital ROSC (P-ROSC) score for ROSC prediction based on patients with OHCA in Asia. METHODS This retrospective study examined patients who suffered from OHCA between Jan 1, 2009 and Jun 17, 2018 using data recorded in the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. AutoScore, an interpretable machine learning framework, was used to develop P-ROSC. On the same cohort, the P-ROSC was compared with two clinical scores, the RACA and the UB-ROSC. The predictive power was evaluated using the area under the curve (AUC) in the receiver operating characteristic analysis. FINDINGS 170,678 cases were included, of which 14,104 (8.26%) attained prehospital ROSC. The P-ROSC score identified a new variable, prehospital drug administration, which was not included in the RACA score or the UB-ROSC score. Using only five variables, the P-ROSC score achieved an AUC of 0.806 (95% confidence interval [CI] 0.799-0.814), outperforming both RACA and UB-ROSC with AUCs of 0.773 (95% CI 0.765-0.782) and 0.728 (95% CI 0.718-0.738), respectively. INTERPRETATION The P-ROSC score is a practical and easily interpreted tool for predicting the probability of prehospital ROSC. FUNDING This research received funding from SingHealth Duke-NUS ACP Programme Funding (15/FY2020/P2/06-A79).
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Affiliation(s)
- Nan Liu
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
- Health Services Research Centre, Singapore Health Services, Singapore, Singapore
- SingHealth AI Health Program, Singapore Health Services, Singapore, Singapore
- SingHealth Duke-NUS Global Health Institute, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
- Corresponding author at: Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore.
| | - Mingxuan Liu
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Xinru Chen
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Yilin Ning
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Jin Wee Lee
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Fahad Javaid Siddiqui
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Seyed Ehsan Saffari
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
- National Neuroscience Institute, Singapore, Singapore
| | - Andrew Fu Wah Ho
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Sang Do Shin
- Department of Emergency Medicine, School of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Matthew Huei-Ming Ma
- Department of Emergency Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Hideharu Tanaka
- Graduate School of Emergency Medical System, Kokushikan University, Tokyo, Japan
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
- Health Services Research Centre, Singapore Health Services, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
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8
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Xu F, Li W, Zhang C, Cao R. Performance of Sequential Organ Failure Assessment and Simplified Acute Physiology Score II for Post-Cardiac Surgery Patients in Intensive Care Unit. Front Cardiovasc Med 2021; 8:774935. [PMID: 34938790 PMCID: PMC8685393 DOI: 10.3389/fcvm.2021.774935] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/01/2021] [Indexed: 01/23/2023] Open
Abstract
Background: The aim of this study is to assess the performance of Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score (SAPS II) on outcomes of patients with cardiac surgery and identify the cutoff values to provide a reference for early intervention. Methods: All data were extracted from MIMIC-III (Medical Information Mart for Intensive Care-III) database. Cutoff values were calculated by the receiver-operating characteristic curve and Youden indexes. Patients were grouped, respectively, according to the cutoff values of SOFA and SAPS II. A non-adjusted model and adjusted model were established to evaluate the prediction of risk. Comparison of clinical efficacy between two scoring systems was made by decision curve analysis (DCA). The primary outcomes of this study were in-hospital mortality, 28-day mortality, 90-day mortality, and 1-year mortality after cardiac surgery. The secondary outcomes included length of hospital stay and intensive care unit (ICU) stay and the incidence of acute kidney injury (AKI) within 7 days after ICU admission. Results: A total of 6,122 patients were collected and divided into the H-SOFA group (SOFA ≥ 7) and L-SOFA group (SOFA < 7) or H-SAPS II group (SAPS II ≥ 43) and L-SAPS II group (SAPS II < 43). In-hospital mortality, 28-day mortality, 90-day mortality, and 1-year mortality were higher, the length of hospital and ICU stay were longer in the H-SOFA group than in the L-SOFA group (p < 0.05), while the incidence of AKI was not significantly different. In-hospital mortality, 28-day mortality, 90-day mortality, 1-year mortality, and the incidence of AKI were all significantly higher in the H-SAPS II group than in the L-SAPS II group (p < 0.05). Hospital stay and ICU stay were longer in the H-SAPS II group than in the L-SAPS II group (p < 0.05). According to DCA, the SAPS II scoring system had more net benefits on assessing the long-term mortality compared with the SOFA scoring system. Conclusion: Exceeding the cutoff values of SOFA and SAPS II scores could lead to increased mortality and extended length of ICU and hospital stay. The SAPS II scoring system had a better discriminative performance of 90-day mortality and 1-year mortality in post-cardiac surgery patients than the SOFA scoring system. Emphasizing the critical value of the scoring system is of significance for timely treatment.
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Affiliation(s)
- Fei Xu
- Department of Anesthesiology, Chengdu Women's and Children's Central Hospital, Chengdu, China
| | - Weina Li
- Department of Anesthesiology, Chengdu Women's and Children's Central Hospital, Chengdu, China
| | - Cheng Zhang
- Department of Anesthesiology, Chengdu Women's and Children's Central Hospital, Chengdu, China
| | - Rong Cao
- Department of Anesthesiology, Chengdu Women's and Children's Central Hospital, Chengdu, China
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9
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Lima KP, Nogueira LDS, Barbosa G, Bonfim AKS, Sousa RMCD. Severity indexes of blunt trauma victims in intensive therapy: prediction capacity for mortality. Rev Esc Enferm USP 2021; 55:e03747. [PMID: 34076154 DOI: 10.1590/s1980-220x2020003203747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 12/16/2020] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVE To identify the predictive capacity for mortality of the indexes Revised Trauma Score, Rapid Emergency Medicine Score, modified Rapid Emergency Medicine Score, and Simplified Acute Physiology Score III in blunt trauma victims hospitalized in an intensive care unit and compare their performance. METHOD Retrospective cohort of patients with blunt trauma in an intensive care unit from medical records. Receiver Operating Characteristic and a 95% confidence interval of the area under the curve were analyzed to compare results. RESULTS Out of 165 analyzed patients, 66.7% have received surgical treatment. The mortality in the intensive care unit and in the hospital was 17.6% and 20.6%, respectively. For the mortality in the intensive care unit, the area under the curve varied from 0.672 to 0.738; however, better results have been observed in surgical patients (0.747 to 0.811). Similar results have been observed for in-hospital mortality. In all analyses, the areas under the curve of the indexes presented no significant difference. CONCLUSION The accuracy of the severity indexes was moderate, with an improved performance when applied to surgical patients. The four indexes presented a similar prediction for the analyzed outcomes.
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Affiliation(s)
- Kézia Porto Lima
- Universidade de São Paulo, Escola de Enfermagem, Programa de Pós-Graduação em Enfermagem na Saúde do Adulto, São Paulo, SP, Brasil.,Faculdade dos Carajás, Marabá, PA, Brasil
| | - Lilia de Souza Nogueira
- Universidade de São Paulo, Escola de Enfermagem, Programa de Pós-Graduação em Enfermagem na Saúde do Adulto, São Paulo, SP, Brasil
| | - Genesis Barbosa
- Universidade de São Paulo, Escola de Enfermagem, Programa de Pós-Graduação em Enfermagem na Saúde do Adulto, São Paulo, SP, Brasil.,Universidade Federal do Rio de Janeiro, Campus Professor Aloísio Teixeira, Macaé, RJ, Brasil
| | - Ane Karoline Silva Bonfim
- Universidade de São Paulo, Escola de Enfermagem, Programa de Pós-Graduação em Enfermagem na Saúde do Adulto, São Paulo, SP, Brasil
| | - Regina Marcia Cardoso de Sousa
- Universidade de São Paulo, Escola de Enfermagem, Programa de Pós-Graduação em Enfermagem na Saúde do Adulto, São Paulo, SP, Brasil
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10
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Nistal-Nuño B. Machine learning applied to a Cardiac Surgery Recovery Unit and to a Coronary Care Unit for mortality prediction. J Clin Monit Comput 2021; 36:751-763. [PMID: 33860407 DOI: 10.1007/s10877-021-00703-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 04/05/2021] [Indexed: 12/23/2022]
Abstract
Most established severity-of-illness systems used for prediction of intensive care unit (ICU) mortality were developed targeted at the general ICU population, based on logistic regression (LR). To date, no dynamic predictive tool for ICU mortality has been developed targeted at the Cardiac Surgery Recovery Unit (CSRU) and Coronary Care Unit (CCU) using machine learning (ML). CSRU and CCU adult patients from the MIMIC-III critical care database were studied. The ML methods developed extract ICU data during a 5-h window and demographic features to produce mortality predictions and were compared to six established severity-of-illness systems and LR. In a secondary experiment, additional procedure/surgery and ICU features were added to the models. The ML models developed were the Tree Ensemble (TE), Random Forest, XGBoost Tree Ensemble (XGB), Naive Bayes (NB), and Bayesian network. The discrimination, calibration and accuracy statistics were assessed. The AUROC values were superior for the ML models reaching 0.926 and 0.924 for the XGB, and 0.904 and 0.908 for the TE for ICU mortality prediction in the primary and secondary experiments respectively. Among the conventional systems, the serial SOFA obtained the highest AUROC (0.8405). The Brier score was better for the ML models except the NB over the conventional systems. The accuracy statistics less sensitive to unbalanced cohorts were higher for all the ML models. In conclusion, the predictive power of XGB was excellent, substantially outperforming the conventional systems and LR. The ML models developed in this work offer promising results that could benefit CSRU and CCU.
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Affiliation(s)
- Beatriz Nistal-Nuño
- Department of Anesthesiology, Complejo Hospitalario Universitario de Pontevedra, Mourente s/n, 36071, Pontevedra, Spain.
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11
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Kashyap R, Sherani KM, Dutt T, Gnanapandithan K, Sagar M, Vallabhajosyula S, Vakil AP, Surani S. Current Utility of Sequential Organ Failure Assessment Score: A Literature Review and Future Directions. Open Respir Med J 2021; 15:1-6. [PMID: 34249175 PMCID: PMC8227444 DOI: 10.2174/1874306402115010001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/13/2020] [Accepted: 01/13/2021] [Indexed: 02/08/2023] Open
Abstract
The Sequential Organ Failure Assessment (SOFA) score is commonly used in the Intensive Care Unit (ICU) to evaluate, prognosticate and assess patients. Since its validation, the SOFA score has served in various settings, including medical, trauma, surgical, cardiac, and neurological ICUs. It has been a strong mortality predictor and literature over the years has documented the ability of the SOFA score to accurately distinguish survivors from non-survivors on admission. Over the years, multiple variations have been proposed to the SOFA score, which have led to the evolution of alternate validated scoring models replacing one or more components of the SOFA scoring system. Various SOFA based models have been used to evaluate specific clinical populations, such as patients with cardiac dysfunction, hepatic failure, renal failure, different races and public health illnesses, etc. This study is aimed to conduct a review of modifications in SOFA score in the past several years. We review the literature evaluating various modifications to the SOFA score such as modified SOFA, Modified SOFA, modified Cardiovascular SOFA, Extra-renal SOFA, Chronic Liver Failure SOFA, Mexican SOFA, quick SOFA, Lactic acid quick SOFA (LqSOFA), SOFA in hematological malignancies, SOFA with Richmond Agitation-Sedation scale and Pediatric SOFA. Various organ systems, their relevant scoring and the proposed modifications in each of these systems are presented in detail. There is a need to incorporate the most recent literature into the SOFA scoring system to make it more relevant and accurate in this rapidly evolving critical care environment. For future directions, we plan to put together most if not all updates in SOFA score and probably validate it in a large database a single institution and validate it in multisite data base.
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Affiliation(s)
- Rahul Kashyap
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Khalid M Sherani
- Department of Internal Medicine, Jamaica Hospital Medical Center, Jamaica, NY 11418, USA.,Corpus Christi Medical Center, Corpus Christi, TX 78411, USA
| | - Taru Dutt
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic, Rochester MN, USA and Hennepin County Medical Center, Minneapolis, MN 55905, USA
| | - Karthik Gnanapandithan
- Department of Internal Medicine, Yale-New Haven Hospital and Yale University School of Medicine, New Haven, CT 06510, USA
| | - Malvika Sagar
- Department of Pediatrics, McLane Children's Hospital, Baylor Scott and White Health, Temple, TX 76502, USA
| | | | - Abhay P Vakil
- Department of Pediatrics, McLane Children's Hospital, Baylor Scott and White Health, Temple, TX 76502, USA.,Critical Care Medicine, Mayo Clinic College of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Salim Surani
- Corpus Christi Medical Center, Corpus Christi, TX 78411, USA.,Texas A&M University System Health Science Center, Bryan, TX 77807, USA
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12
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Johnsson J, Dankiewicz J, Walther S, Nielsen N. Hazards of adjustment in studies on temperature interventions. Resuscitation 2021; 160:140-141. [PMID: 33549693 DOI: 10.1016/j.resuscitation.2021.01.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Jesper Johnsson
- Lund University, Helsingborg Hospital, Department of Clinical Sciences Lund, Anesthesia and Intensive Care, Helsingborg, Sweden.
| | - Josef Dankiewicz
- Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Cardiology, Lund, Sweden
| | - Sten Walther
- Department of Cardiothoracic Anaesthesia and Intensive Care and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Niklas Nielsen
- Lund University, Helsingborg Hospital, Department of Clinical Sciences Lund, Anesthesia and Intensive Care, Helsingborg, Sweden
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13
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Grossestreuer AV, Yankama TT, Moskowitz A, Ngo L, Donnino MW. Use of SOFA score in cardiac arrest research: A scoping review. Resusc Plus 2020; 4:100040. [PMID: 34223317 PMCID: PMC8244435 DOI: 10.1016/j.resplu.2020.100040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/09/2020] [Accepted: 10/11/2020] [Indexed: 11/18/2022] Open
Abstract
Background The Sequential Organ Failure Assessment (SOFA) score is a commonly used severity-of-illness score in cardiac arrest research. Due to its nature, the SOFA score often has missing data. How much data is missing and how that missing data is handled is unknown. Objectives We conducted a scoping review on cardiac arrest studies using SOFA, focusing on missing data. Data sources PubMed, Embase, and Web of Science. Study selection All English-language peer-reviewed studies of cardiac arrest with SOFA as an outcome or exposure were included. Data extraction For each study, quantity of missing SOFA data, analytic strategy to handle missing SOFA variables, whether/to what degree mortality influenced the amount of missing SOFA scores), SOFA score modifications, and number of SOFA measurements was extracted. Data synthesis We included 66 studies published between 2006–2019. Five studies were randomized controlled trials, 26 were prospective cohort studies, and 25 were retrospective cohort studies. SOFA was used as an outcome in 36 (55%) and a primary outcome in 10 (15%). Nine studies (14%) mentioned the quantity of missing SOFA data, which ranged from 0 to 76% (median: 10% [IQR: 6%, 42%]). Twenty-seven (41%) studies reported a method to handle missing SOFA. The most common method used excluded subjects with missing data (81%). In the 50 studies using serial SOFA scores, 11 (22%) documented mortality prior to SOFA measurement; which ranged from 3% to 76% (median: 12% [IQR: 6%–35%]). Conclusions Missing data is common in cardiac arrest research using SOFA scores. Variability exists in reporting and handling missing SOFA variables.
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Affiliation(s)
- Anne V. Grossestreuer
- Center for Resuscitation Science, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Corresponding author at: Department of Emergency Medicine, Beth Israel Deaconess Medical Center, One Deaconess Road, Boston, MA 02215, USA.
| | - Tuyen T. Yankama
- Center for Resuscitation Science, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Ari Moskowitz
- Center for Resuscitation Science, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Long Ngo
- Department of Medicine, Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michael W. Donnino
- Center for Resuscitation Science, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
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14
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Zhang D, Yin C, Zeng J, Yuan X, Zhang P. Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inform Decis Mak 2020; 20:280. [PMID: 33121479 PMCID: PMC7596962 DOI: 10.1186/s12911-020-01297-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/19/2020] [Indexed: 01/09/2023] Open
Abstract
Background The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. Methods In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions.
Results We evaluate the performance of proposed models on 3 risk prediction tasks (i.e. in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. Conclusions The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.
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Affiliation(s)
- Dongdong Zhang
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH, 43210, USA.,School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, Hubei, China
| | - Changchang Yin
- Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH, 43210, USA
| | - Jucheng Zeng
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH, 43210, USA.,School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, Hubei, China
| | - Xiaohui Yuan
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, Hubei, China
| | - Ping Zhang
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH, 43210, USA. .,Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH, 43210, USA.
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15
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Johnsson J, Björnsson O, Andersson P, Jakobsson A, Cronberg T, Lilja G, Friberg H, Hassager C, Kjaergard J, Wise M, Nielsen N, Frigyesi A. Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2020; 24:474. [PMID: 32731878 PMCID: PMC7394679 DOI: 10.1186/s13054-020-03103-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 06/17/2020] [Indexed: 01/26/2023]
Abstract
Background Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). Methods Using the cohort of the TTM trial, we performed a post hoc analysis of 932 unconscious patients from 36 centres with OHCA of a presumed cardiac cause. The patient outcome was the functional outcome, including survival at 180 days follow-up using a dichotomised Cerebral Performance Category (CPC) scale with good functional outcome defined as CPC 1–2 and poor functional outcome defined as CPC 3–5. Outcome prediction and severity class assignment were performed using a supervised machine learning model based on ANN. Results The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data. Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance. When comparing the ANN model with a logistic regression-based model on the same cohort, the ANN model performed significantly better (p = 0.029). A simplified ANN model showed promising performance with an AUC above 0.852 when using three variables only: age, time to ROSC and first monitored rhythm. The ANN-stratified analyses showed similar intervention effect of TTM to 33 °C or 36 °C in predefined classes with different risk of a poor outcome. Conclusion A supervised machine learning model using ANN predicted neurological recovery, including survival excellently, and outperformed a conventional model based on logistic regression. Among the data available at the time of hospitalisation, factors related to the pre-hospital setting carried most information. ANN may be used to stratify a heterogenous trial population in risk classes and help determine intervention effects across subgroups.
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Affiliation(s)
- Jesper Johnsson
- Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Helsingborg Hospital, Lund University, Helsingborg, Sweden. .,Department of Anaesthesiology and Intensive Care, Helsingborg Hospital, Charlotte Yléns Gata 10, SE-251 87, Helsingborg, Sweden.
| | - Ola Björnsson
- Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden.,Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden
| | - Peder Andersson
- Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Skåne University Hospital, Lund University, Lund, Sweden
| | - Andreas Jakobsson
- Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences Lund, Intensive and Perioperative Care, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Christian Hassager
- Department of Cardiology, The Heart Centre, Rigshospitalet University Hospital and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jesper Kjaergard
- Department of Cardiology, The Heart Centre, Rigshospitalet University Hospital and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Matt Wise
- Department of Critical Care, University Hospital of Wales, Cardiff, UK
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Helsingborg Hospital, Lund University, Helsingborg, Sweden
| | - Attila Frigyesi
- Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden.,Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Skåne University Hospital, Lund University, Lund, Sweden
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17
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Karagoz S, Tekdos Seker Y, Cukurova Z, Hergunsel O. The Effectiveness of Scoring Systems in the Prediction of Diagnosis-Based Mortality. Ther Apher Dial 2019; 23:418-424. [PMID: 30520234 DOI: 10.1111/1744-9987.12780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Accepted: 11/30/2018] [Indexed: 12/01/2022]
Abstract
Scoring systems are used for mortality and morbidity rating in intensive care conditions, prognosis prediction, standardization of scientific data and the monitoring of clinical quality. The aim of this study was to retrospectively analyze the efficacy of APACHE II (Acute Physiology and Chronic Health Evaluation), APACHE IV and SAPS (Simplified Acute Physiology Score) III prognostic scorings in the prediction of mortality and disease severity of patients admitted to the Anesthesia and Reanimation Clinic Intensive Care Unit (ICU) in Bakırköy Dr. Sadi Konuk Training and Research Hospital according to general and specific diagnoses. A total of 1896 patient files were included in the study. With the exception of single system or head trauma patient groups, a statistically significant difference was found in the mortality prediction rates in all other diagnosis groups (P < 0.05). The discrimination calculated with AUROC fields was sufficient in all groups, and calibration was evaluated as good except for the neurological and neurosurgical patient group. In respect of standard mortality prediction, APACHE II and IV were good in cases of sepsis, and SAPS III made almost exact predictions for cardiovascular diseases, APACHE II for neurological diseases, and APACHE IV for gastrointestinal system diseases. From the results of this study, it was seen that different scoring systems vary in predictions according to the diagnoses, therefore, it can be recommended that the diagnosis should be taken into account more when applying scoring systems.
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Affiliation(s)
- Selda Karagoz
- Department of Anaesthesiology, University of Healthy Sciences, Bakırkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Yasemin Tekdos Seker
- Department of Anaesthesiology, University of Healthy Sciences, Bakırkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Zafer Cukurova
- Department of Anaesthesiology, University of Healthy Sciences, Bakırkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Oya Hergunsel
- Department of Anaesthesiology, University of Healthy Sciences, Bakırkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkey
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18
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Caputo ML, Baldi E, Savastano S, Burkart R, Benvenuti C, Klersy C, Cianella R, Anselmi L, Moccetti T, Mauri R, De Ferrari GM, Auricchio A. Validation of the return of spontaneous circulation after cardiac arrest (RACA) score in two different national territories. Resuscitation 2019; 134:62-68. [DOI: 10.1016/j.resuscitation.2018.11.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Revised: 11/08/2018] [Accepted: 11/12/2018] [Indexed: 01/19/2023]
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19
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Tang F, Xiao C, Wang F, Zhou J. Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open 2018; 1:87-98. [PMID: 31984321 PMCID: PMC6951928 DOI: 10.1093/jamiaopen/ooy011] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 03/30/2018] [Accepted: 04/02/2018] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVE The growing availability of rich clinical data such as patients' electronic health records provide great opportunities to address a broad range of real-world questions in medicine. At the same time, artificial intelligence and machine learning (ML)-based approaches have shown great premise on extracting insights from those data and helping with various clinical problems. The goal of this study is to conduct a systematic comparative study of different ML algorithms for several predictive modeling problems in urgent care. DESIGN We assess the performance of 4 benchmark prediction tasks (eg mortality and prediction, differential diagnostics, and disease marker discovery) using medical histories, physiological time-series, and demographics data from the Medical Information Mart for Intensive Care (MIMIC-III) database. MEASUREMENTS For each given task, performance was estimated using standard measures including the area under the receiver operating characteristic (AUC) curve, F-1 score, sensitivity, and specificity. Microaveraged AUC was used for multiclass classification models. RESULTS AND DISCUSSION Our results suggest that recurrent neural networks show the most promise in mortality prediction where temporal patterns in physiologic features alone can capture in-hospital mortality risk (AUC > 0.90). Temporal models did not provide additional benefit compared to deep models in differential diagnostics. When comparing the training-testing behaviors of readmission and mortality models, we illustrate that readmission risk may be independent of patient stability at discharge. We also introduce a multiclass prediction scheme for length of stay which preserves sensitivity and AUC with outliers of increasing duration despite decrease in sample size.
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Affiliation(s)
- Fengyi Tang
- Department of Computer Science and Engineering, Michigan State University College of Engineering, East Lansing, Michigan, USA
| | - Cao Xiao
- AI for Healthcare, IBM Research, Cambridge, Massachusetts, USA
| | - Fei Wang
- Department of Healthcare Policy and Research, Weill Cornell Medical School Cornell University, New York, New York, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University College of Engineering, East Lansing, Michigan, USA
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Choi JY, Jang JH, Lim YS, Jang JY, Lee G, Yang HJ, Cho JS, Hyun SY. Performance on the APACHE II, SAPS II, SOFA and the OHCA score of post-cardiac arrest patients treated with therapeutic hypothermia. PLoS One 2018; 13:e0196197. [PMID: 29723201 PMCID: PMC5933749 DOI: 10.1371/journal.pone.0196197] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 04/09/2018] [Indexed: 01/31/2023] Open
Abstract
Objective This study assessed the ability of the Acute Physiologic and Chronic Health Evaluation (APACHE) II score, Simplified Acute Physiology Score (SAPS) II, Sequential Organ Failure Assessment (SOFA) score, and out-of-hospital cardiac arrest (OHCA) score to predict the outcome of OHCA patients who underwent therapeutic hypothermia (TH). Methods This study included OHCA patients treated with TH between January 2010 and December 2013. The APACHE II score, SAPS II, and SOFA score were calculated at the time of admission and 24 h and 48 h after intensive care unit admission. The OHCA score was calculated at the time of admission. The area under the curve (AUC) of the receiver operating characteristic curve and logistic regression analysis were used to evaluate outcome predictability. Results Data from a total of 173 patients were included in the analysis. The APACHE II score at 0 h and 48 h, SAPS II at 48 h, and OHCA score had moderate discrimination for mortality (AUC: 0.715, 0.750, 0.720, 0.740). For neurologic outcomes, the APACHE II score at 0 h and 48 h, SAPS II at 0 h and 48 h, and OHCA score showed moderate discrimination (AUC: 0.752, 0.738, 0.771, 0.771, 0.764). The APACHE II score, SAPS II and SOFA score at various time points, in addition to the OHCA score, were independent predictors of mortality and a poor neurologic outcome. Conclusions The APACHE II score, SAPS II, SOFA score, and OHCA score have different capabilities in discriminating and estimating hospital mortality and neurologic outcomes. The OHCA score, APACHE II score and SAPS II at time zero and 48 h offer moderate predictive accuracy. Other scores at 0 h and 48 h, except for the SOFA score, are independently associated with 30-day mortality and poor cerebral performance.
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Affiliation(s)
- Jea Yeon Choi
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Jae Ho Jang
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, South Korea
- * E-mail:
| | - Yong Su Lim
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Jee Yong Jang
- Department of Emergency Medicine, Mokpo Hankook Hospital, Mokpo, Jeollanam-do, South Korea
| | - Gun Lee
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Hyuk Jun Yang
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Jin Seong Cho
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Sung Youl Hyun
- Department of Thoracic and Cardiovascular Surgery, Gachon University Gil Medical Center, Incheon, South Korea
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Organ support therapy in the intensive care unit and return to work in out-of-hospital cardiac arrest survivors–A nationwide cohort study. Resuscitation 2018; 125:126-134. [DOI: 10.1016/j.resuscitation.2018.01.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 12/22/2017] [Accepted: 01/01/2018] [Indexed: 01/26/2023]
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Pompermaier L, Steinvall I, Elmasry M, Thorfinn J, Sjöberg F. Burned patients who die from causes other than the burn affect the model used to predict mortality: a national exploratory study. Burns 2018; 44:280-287. [DOI: 10.1016/j.burns.2017.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 05/09/2017] [Accepted: 07/14/2017] [Indexed: 11/25/2022]
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Martinell L, Nielsen N, Herlitz J, Karlsson T, Horn J, Wise MP, Undén J, Rylander C. Early predictors of poor outcome after out-of-hospital cardiac arrest. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2017; 21:96. [PMID: 28410590 PMCID: PMC5391587 DOI: 10.1186/s13054-017-1677-2] [Citation(s) in RCA: 155] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Accepted: 03/22/2017] [Indexed: 12/24/2022]
Abstract
Background Early identification of predictors for a poor long-term outcome in patients who survive the initial phase of out-of-hospital cardiac arrest (OHCA) may facilitate future clinical research, the process of care and information provided to relatives. The aim of this study was to determine the association between variables available from the patient’s history and status at intensive care admission with outcome in unconscious survivors of OHCA. Methods Using the cohort of the Target Temperature Management trial, we performed a post hoc analysis of 933 unconscious patients with OHCA of presumed cardiac cause who had a complete 6-month follow-up. Outcomes were survival and neurological function as defined by the Cerebral Performance Category (CPC) scale at 6 months after OHCA. After multiple imputations to compensate for missing data, backward stepwise multivariable logistic regression was applied to identify factors independently predictive of a poor outcome (CPC 3–5). On the basis of these factors, a risk score for poor outcome was constructed. Results We identified ten independent predictors of a poor outcome: older age, cardiac arrest occurring at home, initial rhythm other than ventricular fibrillation/tachycardia, longer duration of no flow, longer duration of low flow, administration of adrenaline, bilateral absence of corneal and pupillary reflexes, Glasgow Coma Scale motor response 1, lower pH and a partial pressure of carbon dioxide in arterial blood value lower than 4.5 kPa at hospital admission. A risk score based on the impact of each of these variables in the model yielded a median (range) AUC of 0.842 (0.840–0.845) and good calibration. Internal validation of the score using bootstrapping yielded a median (range) AUC corrected for optimism of 0.818 (0.816–0.821). Conclusions Among variables available at admission to intensive care, we identified ten independent predictors of a poor outcome at 6 months for initial survivors of OHCA. They reflected pre-hospital circumstances (six variables) and patient status on hospital admission (four variables). By using a simple and easy-to-use risk scoring system based on these variables, patients at high risk for a poor outcome after OHCA may be identified early.
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Affiliation(s)
- Louise Martinell
- Department of Anaesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, SE-413 45, Gothenburg, Sweden.
| | - Niklas Nielsen
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Herlitz
- The Centre for Pre-hospital Research in Western Sweden, University College of Borås and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Thomas Karlsson
- Health Metrics at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Janneke Horn
- Department of Intensive Care, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Matt P Wise
- Adult Critical Care, University Hospital of Wales, Cardiff, UK
| | - Johan Undén
- Department of Intensive Care and Perioperative Medicine, Lund University, Malmö, Sweden
| | - Christian Rylander
- Department of Anaesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, SE-413 45, Gothenburg, Sweden
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Leão RN, Ávila P, Cavaco R, Germano N, Bento L. Therapeutic hypothermia after cardiac arrest: outcome predictors. Rev Bras Ter Intensiva 2016; 27:322-32. [PMID: 26761469 PMCID: PMC4738817 DOI: 10.5935/0103-507x.20150056] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 11/06/2015] [Indexed: 11/20/2022] Open
Abstract
OBJECTIVE The determination of coma patient prognosis after cardiac arrest has clinical, ethical and social implications. Neurological examination, imaging and biochemical markers are helpful tools accepted as reliable in predicting recovery. With the advent of therapeutic hypothermia, these data need to be reconfirmed. In this study, we attempted to determine the validity of different markers, which can be used in the detection of patients with poor prognosis under hypothermia. METHODS Data from adult patients admitted to our intensive care unit for a hypothermia protocol after cardiac arrest were recorded prospectively to generate a descriptive and analytical study analyzing the relationship between clinical, neurophysiological, imaging and biochemical parameters with 6-month outcomes defined according to the Cerebral Performance Categories scale (good 1-2, poor 3-5). Neuron-specific enolase was collected at 72 hours. Imaging and neurophysiologic exams were carried out in the 24 hours after the rewarming period. RESULTS Sixty-seven patients were included in the study, of which 12 had good neurological outcomes. Ventricular fibrillation and electroencephalographic theta activity were associated with increased likelihood of survival and improved neurological outcomes. Patients who had more rapid cooling (mean time of 163 versus 312 minutes), hypoxic-ischemic brain injury on magnetic resonance imaging or neuron-specific enolase > 58ng/mL had poor neurological outcomes (p < 0.05). CONCLUSION Hypoxic-ischemic brain injury on magnetic resonance imaging and neuron-specific enolase were strong predictors of poor neurological outcomes. Although there is the belief that early achievement of target temperature improves neurological prognoses, in our study, there were increased mortality and worse neurological outcomes with earlier target-temperature achievement.
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Affiliation(s)
- Rodrigo Nazário Leão
- Unidade de Urgência Médica, Centro Hospitalar de Lisboa Central, Lisboa, Portugal
| | - Paulo Ávila
- Unidade de Urgência Médica, Centro Hospitalar de Lisboa Central, Lisboa, Portugal
| | - Raquel Cavaco
- Unidade de Urgência Médica, Centro Hospitalar de Lisboa Central, Lisboa, Portugal
| | - Nuno Germano
- Unidade de Urgência Médica, Centro Hospitalar de Lisboa Central, Lisboa, Portugal
| | - Luís Bento
- Unidade de Urgência Médica, Centro Hospitalar de Lisboa Central, Lisboa, Portugal
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Lockey D, Lossius HM. Pre-hospital airway management: the data grows rapidly but controversy remains. Resuscitation 2014; 85:849-50. [PMID: 24830869 DOI: 10.1016/j.resuscitation.2014.05.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 05/04/2014] [Indexed: 10/25/2022]
Affiliation(s)
- David Lockey
- Institute of Pre-hospital Care, London's Air Ambulance, Barts Health NHS Trust, London E1 1BB, UK; North Bristol NHS Trust, Bristol BS16 1LE, UK.
| | - Hans Morten Lossius
- Department of Research and Development, The Norwegian Air Ambulance Foundation, Holterveien 24, P.O. Box 94, N-1441 Drøbak, Norway; Field of Pre-hospital Critical Care, Network for Medical Sciences, University of Stavanger, Kjell Arholmsgate 41, Stavanger 4036, Norway
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Kim SJ, Lim YS, Cho JS, Kim JJ, Park WB, Yang HJ. Acute Physiologic and Chronic Health Examination II and Sequential Organ Failure Assessment Scores for Predicting Outcomes of Out-of-Hospital Cardiac Arrest Patients Treated with Therapeutic Hypothermia. Korean J Crit Care Med 2014. [DOI: 10.4266/kjccm.2014.29.4.288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Sung Joon Kim
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Yong-Su Lim
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Jin Seong Cho
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Jin Joo Kim
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Won Bin Park
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Hyuk Jun Yang
- Department of Emergency Medicine, Gachon University Gil Medical Center, Incheon, Korea
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