Health Care Costs After Cardiac Arrest in the United States

Damluji, Abdulla; Al‐Damluji, Mohammed Salim; Pomenti, Sydney; Zhang, Tony J.; Cohen, Mauricio G.; Mitrani, Raul D.; Moscucci, Mauro; Myerburg, Robert J.

doi:10.1161/circep.117.005689

Cited by 35 publications

(23 citation statements)

References 21 publications

(23 reference statements)

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“…Costs associated with care after critically ill patients, and especially after comatose patients, are high and continue to increase [35][36][37]. In ICU patients treated for acute renal failure, the mean cost for one sixmonth survivor was €117,000 expressed in 2013 euros [38].…”

Section: Healthcare-associated Costsmentioning

confidence: 99%

Outcomes and healthcare-associated costs one year after intensive care-treated cardiac arrest

et al. 2018

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Section: Healthcare-associated Costsmentioning

confidence: 99%

Outcomes and healthcare-associated costs one year after intensive care-treated cardiac arrest

et al. 2018

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“… 8 9 10 As the hospital cost of caring for cardiac arrest survivors is considerable. 19 20 21 our study may have a role in reducing the socioeconomic burden associated with potentially futile treatment.…”

Section: Discussionmentioning

confidence: 90%

Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models

et al. 2021

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Background We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results A total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612–0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction perfo...

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“…The financial burden for patients who received ICPR after IHCA increased from 2011 to 2019 in South Korea. The health care cost following ICPR also increased steadily in the United States [ 14 ]. In South Korea, the mean total cost of hospitalization at ICPR was USD 6753.50 (SD: USD 10,295.10), and approximately 90% of the total cost was covered by the public health insurance program.…”

Section: Discussionmentioning

confidence: 99%

Trends in In-Hospital Cardiopulmonary Resuscitation from 2010 through 2019: A Nationwide Cohort Study in South Korea

Song

2022

JPM

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We aimed to examine recent trends in in-hospital cardiopulmonary resuscitation in South Korea from 2010 to 2019. A population-based sample of all adult patients who experienced in-hospital cardiopulmonary resuscitation between 1 January 2010 and 31 December 2019, was included. In all, 298,676 patients who received in-hospital cardiopulmonary resuscitation were included in the survival analysis. In 2010, 60.7 per 100,000 adults experienced in-hospital cardiopulmonary resuscitation. A similar rate was observed until 2015. The rate increased to 83.5 per 100,000 adults in 2016 and gradually increased to 92.1 per 100,000 adults in 2019. Among all patients, 78,783 (26.2%) were discharged alive after in-hospital cardiopulmonary resuscitation. The 6-month and 1-year survival rates were 9.8% and 8.7%, respectively. In 2010, the mean total cost of hospitalization was USD 5822.80 (United States Dollar) (standard deviation; SD: USD 7493.4), which increased to USD 7886.20 (SD: USD 13,071.6) in 2019. The rate of in-hospital cardiopulmonary resuscitation and cost of care have significantly increased since 2010, while the 6-month and 1-year rates of survival post in-hospital resuscitation remain low.

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Health Care Costs After Cardiac Arrest in the United States

Cited by 35 publications

References 21 publications

Outcomes and healthcare-associated costs one year after intensive care-treated cardiac arrest

Outcomes and healthcare-associated costs one year after intensive care-treated cardiac arrest

Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models

Trends in In-Hospital Cardiopulmonary Resuscitation from 2010 through 2019: A Nationwide Cohort Study in South Korea

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