Development of prognostic models for predicting 90-day neurological function and mortality after cardiac arrest.

BACKGROUND: The survivors of cardiac arrest experienced vary extent of hypoxic ischemic brain injury causing mortality and long-term neurologic disability. However, there is still a need to develop robust and reliable prognostic models that can accurately predict these outcomes. OBJECTIVES: To establish reliable models for predicting 90-day neurological function and mortality in adult ICU patients recovering from cardiac arrest. METHODS: We enrolled patients who had recovered from cardiac arrest at Binhaiwan Central Hospital of Dongguan, from January 2018 to July 2021. The study's primary outcome was 90-day neurological function, assessed and divided into two categories using the Cerebral Performance Category (CPC) scale: either good (CPC 1-2) or poor (CPC 3-5). The secondary outcome was 90-day mortality. We analyzed the relationships between risk factors and outcomes individually. A total of four models were developed: two multivariable logistic regression models (models 1 and 2) for predicting neurological function, and two Cox regression models (models 3 and 4) for predicting mortality. Models 2 and 4 included new neurological biomarkers as predictor variables, while models 1 and 3 excluded. We evaluated calibration, discrimination, clinical utility, and relative performance to establish superiority between the models. RESULTS: Model 1 incorporates variables such as gender, site of cardiopulmonary resuscitation (CPR), total CPR time, and acute physiology and chronic health evaluation II (APACHE II) score, while model 2 includes gender, site of CPR, APACHE II score, and serum level of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1). Model 2 outperforms model 1, showcasing a superior area under the receiver operating characteristic curve (AUC) of 0.97 compared to 0.83. Additionally, model 2 exhibits improved accuracy, sensitivity, and specificity. The decision curve analysis confirms the net benefit of model 2. Similarly, models 3 and 4 are designed to predict 90-day mortality. Model 3 incorporates the variables such as site of CPR, total CPR time, and APACHE II score, while model 4 includes APACHE II score, total CPR time, and serum level of UCH-L1. Model 4 outperforms model 3, showcasing an AUC of 0.926 and a C-index of 0.830. The clinical decision curve analysis also confirms the net benefit of model 4. CONCLUSIONS: By integrating new neurological biomarkers, we have successfully developed enhanced models that can predict 90-day neurological function and mortality outcomes more accurately.

Validation of the NoSAS Score for the Screening of Sleep-Disordered Breathing: A Hospital-Based Retrospective Study in China.

STUDY OBJECTIVES: This study was conducted to validate the NoSAS score in clinical populations and to compare it with the Berlin, STOP, and STOP-Bang questionnaires, as well as the Epworth Sleepiness Scale (ESS), in screening for sleep-disordered breathing (SDB). METHODS: A retrospective analysis was conducted of all patients aged 18 to 80 years who had completed a full-night polysomnography (PSG) at the sleep center of the First Affiliated Hospital of Guangzhou Medical University from October 2012 to November 2016. Patients who had incomplete or unanswered questionnaires were excluded. The data for the NoSAS score, ESS, STOP, STOP-Bang, and Berlin questionnaires were collected, after which the NoSAS score was compared against the other questionnaires for SDB screening. RESULTS: A total of 2,208 participants were enrolled in this study. The NoSAS scores, which ranged from 0 to 17 and allocated a threshold of 8 points, identified individuals at risk of clinically significant SDB (defined as an apnea-hypopnea index [AHI] cutoff of ≥ 20 events/h), with an area under the curve (AUC) of 0.707. The NoSAS score performed significantly better than the STOP (AUC 0.655) and STOP-Bang (AUC 0.704) questionnaires and the ESS (AUC 0.642), and it was at par with the Berlin (AUC 0.697) scores for SDB screening. A significant correlation was found between the AHI and NoSAS score (r = .386, P < .001). CONCLUSIONS: The NoSAS score is a simple, efficient, and easy method for screening SDB in the clinical setting, especially in moderate to severe SDB. It demonstrates a moderately high level of sensitivity for SDB.