TIMECARD score: An easily operated prediction model of unfavorable neurological outcomes in out-of-hospital cardiac arrest patients with targeted temperature management.

BACKGROUND: Targeted temperature management (TTM) is recommended for comatose out-of-hospital cardiac arrest (OHCA) survivors. Several prediction models have been proposed; however, most of these tools require data conversion and complex calculations. Early and easy predictive model of neurological prognosis in OHCA survivors with TTM warrant investigation. MATERIALS AND METHODS: This multicenter retrospective cohort study enrolled 408 non-traumatic adult OHCA survivors with TTM from the TaIwan network of targeted temperature ManagEment for CARDiac arrest (TIMECARD) registry during January 2014 to June 2019. The primary outcome was unfavorable neurological outcome at discharge. The clinical variables associated with unfavorable neurological outcomes were identified and a risk prediction score-TIMECARD score was developed. The model was validated with data from National Taiwan University Hospital. RESULTS: There were 319 (78.2%) patients presented unfavorable neurological outcomes at hospital discharge. Eight independent variables, including malignancy, no bystander cardiopulmonary resuscitation (CPR), non-shockable rhythm, call-to-start CPR duration >5 min, CPR duration >20 min, sodium bicarbonate use during resuscitation, Glasgow Coma Scale motor score of 1 at return of spontaneous circulation, and no emergent coronary angiography, revealed a significant correlation with unfavorable neurological prognosis in TTM-treated OHCA survivors. The TIMECARD score was established and demonstrated good discriminatory performance in the development cohort (area under the receiver operating characteristic curve [AUC] = 0.855) and validation cohorts (AUC = 0.918 and 0.877, respectively). CONCLUSION: In emergency settings, the TIMECARD score is a practical and simple-to-calculate tool for predicting neurological prognosis in OHCA survivors, and may help determine whether to initiate TTM in indicated patients.

The CSP (Cardiogenic Shock Prognosis) Score: A Tool for Risk Stratification of Cardiogenic Shock.

Background: Cardiogenic shock (CS) is a critical condition and the leading cause of mortality after acute myocardial infarction (AMI). Scores that predict mortality have been established, but a patient's clinical course is often nonlinear. Thus, factors present during acute care management may be explored. This study intended to develop a risk-predictive model for patients with CS. Methods: In this observational study, adult patients who received inotropic support at the Emergency Room (ER) from January 2017 to August 2020 and were admitted to the cardiac care unit (CCU) with a diagnosis of CS were enrolled in this study. Patients with out-of-hospital cardiac arrest, inotropic support for bradycardia, and survival <24 h after ER arrival were excluded. A total of 311 patients were enrolled and categorized into derivation (n = 243) and validation (n = 68) cohorts. Results: A history of coronary artery disease, multiple inotrope use, ejection fraction <40%, lower hemoglobin concentration, longer cardiopulmonary resuscitation duration, albumin infusion, and renal replacement therapy were identified as independent prognostic factors for in-hospital mortality. The cardiogenic shock prognosis (CSP) score was established as a nomogram and three risk groups were identified: low-risk (score 115, 0% of mortality), medium-risk (score 116-209, 8.75% of mortality), and high-risk (score 210, 66.67% of mortality). The area-under-the-curve (AUC) of the CSP score was 0.941, and the discrimination value in the validation cohort was consistent (AUC = 0.813). Conclusions: The CSP score represents a risk-predictive model for in-hospital mortality in patients with CS in acute care settings. Patients identified as the high-risk category may have a poor prognosis.

The Differences of CPR duration between Shockable and Nonshockable Rhythms in Predicting the Benefit of Target Temperature Management.

BACKGROUND: Among cardiac arrest (CA) survivors, whether the combination of duration of cardiopulmonary resuscitation (CPR) and shockable/nonshockable rhythms during resuscitation can help predict the benefit of targeted temperature management (TTM) remains un-investigated. MATERIALS AND METHODS: This multicenter retrospective cohort study enrolled 479 nontraumatic adult CA survivors with TTM and CPR duration < 60 min during January 2014 to June 2019 from the Taiwan network of targeted temperature ManagEment for CARDiac arrest (TIMECARD) registry. The differences of CPR duration between shockable and nonshockable rhythms in predicting outcomes in the studied population was evaluated. RESULTS: We observed that 205 patients (42.8%) survived to hospital discharge and 100 patients (20.9%) presented favorable neurological outcomes at discharge. The enrolled patients were further re-classified into four groups according to shockable/nonshockable rhythms and CPR duration. Patients with shockable rhythms and shorter CPR duration had better survival-to-discharge (adjusted odds ratio [OR] = 2.729, 95% confidence interval [CI] = 1.384-5.383, P = 0.004) and neurological recovery (adjusted OR = 9.029, 95%CI = 3.263-24.983, P  < 0.001) than did those with nonshockable rhythms and longer CPR duration. CONCLUSION: The CPR duration for predicting outcomes differs between CA patients with shockable and nonshockable rhythms. The combination of shockable/nonshockable rhythms and CPR duration may help predict the prognosis in CA survivors undergoing TTM.

Aging-induced dynamics for statically indeterminate systems.

Statically indeterminate systems are experimentally demonstrated to be in fact dynamical. Take the classic problems of a beam with three supporting points, granules in a silo, and a ladder leaning against a wall, for instance; their reaction forces are found to vary logarithmically for over 10^{4}s with an increment or decrement of more than 10%. This seemingly contradictory mixture of dynamics for a static system is shown to derive from the evolution of microcontact area with the ground and/or wall due to the aging effect.

Barriers to bystanders defibrillation: A national survey on public awareness and willingness of bystanders defibrillation☆.

BACKGROUND: After years of setting up public automated external defibrillators (AEDs), the rate of bystander AED use remains low all over the world. This study aimed to assess the public awareness and willingness of bystanders to use AEDs and to investigate the awareness on the Good Samaritan Law (GSL) and the factors associated with the low rate of bystander AED use. METHODS: Using stratified random sampling, national telephone interviews were conducted using an author-designed structured questionnaire. The results were weighted to match the census data in Taiwan. The factors associated with public awareness and willingness of bystanders to use AEDs were analysed by logistic regression. RESULTS: Of the 1073 respondents, only 15.2% had the confidence to recognise public AEDs, and 5.3% of them had the confidence to use the AED. Concerns on immature technique and legal issues remain the most common barriers to AED use by bystanders. Moreover, only 30.8% thought that the public should use AEDs at the scene. Few respondents (9.6%) ever heard of the GSL in Taiwan, and less than 3% understood the meaning of GSL. Positive awareness on AEDs was associated with high willingness of bystanders to use AEDs. Respondents who were less likely to use AEDs as bystanders were healthcare personnel and women. CONCLUSION: The importance of active awareness and the barriers to the use of AEDs among bystanders seemed to have been underestimated in the past years. The relatively low willingness to use AEDs among bystander healthcare providers and women needs further investigation.

Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation.

BACKGROUND: Doctors must care for many patients simultaneously, and it is time-consuming to find and examine all patients' medical histories. Discharge diagnoses provide hospital staff with sufficient information to enable handling multiple patients; however, the excessive amount of words in the diagnostic sentences poses problems. Deep learning may be an effective solution to overcome this problem, but the use of such a heavy model may also add another obstacle to systems with limited computing resources. OBJECTIVE: We aimed to build a diagnoses-extractive summarization model for hospital information systems and provide a service that can be operated even with limited computing resources. METHODS: We used a Bidirectional Encoder Representations from Transformers (BERT)-based structure with a two-stage training method based on 258,050 discharge diagnoses obtained from the National Taiwan University Hospital Integrated Medical Database, and the highlighted extractive summaries written by experienced doctors were labeled. The model size was reduced using a character-level token, the number of parameters was decreased from 108,523,714 to 963,496, and the model was pretrained using random mask characters in the discharge diagnoses and International Statistical Classification of Diseases and Related Health Problems sets. We then fine-tuned the model using summary labels and cleaned up the prediction results by averaging all probabilities for entire words to prevent character level-induced fragment words. Model performance was evaluated against existing models BERT, BioBERT, and Long Short-Term Memory (LSTM) using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) L score, and a questionnaire website was built to collect feedback from more doctors for each summary proposal. RESULTS: The area under the receiver operating characteristic curve values of the summary proposals were 0.928, 0.941, 0.899, and 0.947 for BERT, BioBERT, LSTM, and the proposed model (AlphaBERT), respectively. The ROUGE-L scores were 0.697, 0.711, 0.648, and 0.693 for BERT, BioBERT, LSTM, and AlphaBERT, respectively. The mean (SD) critique scores from doctors were 2.232 (0.832), 2.134 (0.877), 2.207 (0.844), 1.927 (0.910), and 2.126 (0.874) for reference-by-doctor labels, BERT, BioBERT, LSTM, and AlphaBERT, respectively. Based on the paired t test, there was a statistically significant difference in LSTM compared to the reference (P<.001), BERT (P=.001), BioBERT (P<.001), and AlphaBERT (P=.002), but not in the other models. CONCLUSIONS: Use of character-level tokens in a BERT model can greatly decrease the model size without significantly reducing performance for diagnoses summarization. A well-developed deep-learning model will enhance doctors' abilities to manage patients and promote medical studies by providing the capability to use extensive unstructured free-text notes.

Targeted temperature management and emergent coronary angiography are associated with improved outcomes in patients with prehospital return of spontaneous circulation.

BACKGROUND/PURPOSE: Whether targeted temperature management (TTM) and emergent coronary angiography (CAG) remain associated with better outcomes in patients with prehospital return of spontaneous circulation (ROSC) was unknown. METHODS: This retrospective cohort study enrolled 81 adult, nontraumatic out-of-hospital cardiac arrest patients who had good pre-arrest neurological function and achieved prehospital ROSC during 2012 to August 2017. The outcomes were survival-to-discharge and neurological recovery at discharge. RESULTS: Fifty-five patients (67.9%) survived to hospital discharge (the survivor group) and twenty-six (32.1%) failed (the non-survivor group). A total of 47 patients (58.0%) presented favorable neurological outcomes [Cerebral Performance Category (CPC) score = 1-2, the favorable group], and 34 patients (42.0%) presented unfavorable neurological outcomes (CPC score = 3-5, the poor group). The survivor group had more patients with TTM (45.5% vs. 19.2%, p = 0.023) and emergent CAG (76.4% vs. 19.2%, p < 0.001) than the non-survivor group, and similar findings were noted in the neurological outcomes (TTM: 44.7% vs. 26.5%, p = 0.094; CAG: 80.9% vs. 26.5%, p < 0.001). TTM remained associated with increased survival-to-discharge [odds ratio (OR) = 14.635, 95% confidence interval (CI) = 1.296-165.305, p = 0.030] and a trend toward good neurological recovery (OR = 4.551, 95%CI = 0.963-21.517, p = 0.056). After excluding patients with rapid neurological recovery after ROSC (n = 70), TTM was associated with good neurological outcomes (OR = 4.534, 95% CI = 1.075-19.127, p = 0.040). Emergent CAG had the trend associated with survival-to-discharge (OR = 9.599, 95%CI = 0.764-120.634, p = 0.080) and was significantly associated with good neurological outcomes (OR = 21.785, 95%CI = 2.004-236.836, p = 0.011). CONCLUSION: In patients with prehospital ROSC, both TTM and emergent CAG were associated to improved survival and neurological outcomes.

Public knowledge, attitudes and willingness regarding bystander cardiopulmonary resuscitation: A nationwide survey in Taiwan.

BACKGROUND: A low bystander cardiopulmonary resuscitation (CPR) rate is one of the factors associated with low cardiac arrest survival. This study aimed to assess knowledge, attitudes, and willingness towards performing CPR and the barriers for implementation of bystander-initiated CPR. METHODS: Telephone interviews were conducted using an author-designed and validated structured questionnaire in Taiwan. After obtaining a stratified random sample from the census, the results were weighted to match population data. The factors affecting bystander-initiated CPR were analysed using logistic regression. RESULTS: Of the 1073 respondents, half of them stated that they knew how to perform CPR correctly, although 86.7% indicated a willingness to perform CPR on strangers. The barriers to CPR performance reported by the respondents included fear of legal consequences (44%) and concern about harming patients (36.5%). Most participants expressed a willingness to attend only an hour-long CPR course. Respondents who were less likely to indicate a willingness to perform CPR were female, healthcare providers, those who had no cohabiting family members older than 65 years, those who had a history of a stroke, and those who expressed a negative attitude toward CPR. CONCLUSION: The expressed willingness to perform bystander CPR was high if the respondents possessed the required skills. Attempts should be made to recruit potential bystanders for CPR courses or education, targeting those respondent subgroups less likely to express willingness to perform CPR. The reason for lower bystander CPR willingness among healthcare providers deserves further investigation.

Utility of a blood culture time to positivity-incorporated scoring model in predicting vascular infections in adults with nontyphoid Salmonella bacteremia.

BACKGROUND: Vascular infections (VI) are potentially catastrophic complications of nontyphoid Salmonella (NTS). We aimed to develop a scoring model incorporating information from blood culture time to positivity (TTP-NTSVI) and compared the prediction capability for VI among adults with NTS bacteremia between TTP-NTSVI and a previously published score (Chen-NTSVI). METHODS: This retrospective cohort study enrolled 217 adults with NTS bacteremia ≧ 50 years old. We developed a TTP-NTSVI score by multiple logistic regression modeling to identify independent predictors for imaging-confirmed VI and assigned a point value weighting by the corresponding natural logarithm of the odds ratio for each model predictor. Chen-NTSVI score includes hypertension, male sex, serogroup C1, coronary arterial disease (CAD) as positive predictors, and malignancy and immunosuppressive therapy as negative predictors. The prediction capability of the two scores was compared by area under the receiver operating characteristic curve (AUC). RESULTS: The mean age was 68.3 ± 11.2 years-old. Serogroup D was the predominant isolate (155/217, 71.4%). Seventeen (7.8%) patients had VI. Four independent predictors for VI were identified: male sex (24.9 [2.59-239.60]; 6) (odds ratio [95% confidence interval]; assigned score point), peripheral arterial occlusive disease (9.41 [2.21-40.02]; 4), CAD (4.0 [1.16-13.86]; 3), and TTP <10 h (4.67 [1.42-15.39]; 3). Youden's index showed best cutoff value of ≧7 with 70.6% sensitivity and 82.5% specificity. TTP-NTSVI score had higher AUC than Chen-NTSVI (0.851 vs 0.741, P = 0.039). CONCLUSION: While the previously reported scoring model performed well, a TTP-incorporated scoring model was associated with improved capability in predicting NTSVI.

Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission.

OBJECTIVE Isolation of multidrug-resistant gram-negative bacteria (MDR-GNB) from patients in the community has been increasingly observed. A prediction model for MDR-GNB colonization and infection risk stratification on hospital admission is needed to improve patient care. METHODS A 2-stage, prospective study was performed with 995 and 998 emergency department patients enrolled, respectively. MDR-GNB colonization was defined as isolates resistant to 3 or more classes of antibiotics, identified in either the surveillance or early (≤48 hours) clinical cultures. RESULTS A score-assigned MDR-GNB colonization prediction model was developed and validated using clinical and microbiological data from 995 patients enrolled in the first stage of the study; 122 of these patients (12.3%) were MDR-GNB colonized. We identified 5 independent predictors: age>70 years (odds ratio [OR], 1.84 [95% confidence interval (CI), 1.06-3.17]; 1 point), assigned point value in the model), residence in a long-term-care facility (OR, 3.64 [95% CI, 1.57-8.43); 3 points), history of cerebrovascular accidents (OR, 2.23 [95% CI, 1.24-4.01]; 2 points), hospitalization within 1 month (OR, 2.63 [95% CI, 1.39-4.96]; 2 points), and recent antibiotic exposure (OR, 2.18 [95% CI, 1.16-4.11]; 2 points). The model displayed good discrimination in the derivation and validation sets (area under ROC curve, 0.75 and 0.80, respectively) with the best cutoffs of<4 and ≥4 points for low- and high-risk MDR-GNB colonization, respectively. When applied to 998 patients in the second stage of the study, the model successfully stratified the risk of MDR-GNB infection during hospitalization between low- and high-risk groups (probability, 0.02 vs 0.12, respectively; log-rank test, P<.001). CONCLUSION A model was developed to optimize both the decision to initiate antimicrobial therapy and the infection control interventions to mitigate threats from MDR-GNB. Infect Control Hosp Epidemiol 2017;38:1216-1225.