scholarly journals An Early Predictive Scoring Model for In-Hospital Cardiac Arrest of Emergent Hemodialysis Patients

2021 ◽  
Vol 10 (15) ◽  
pp. 3241
Author(s):  
Shih-Hao Chen ◽  
Ya-Yun Cheng ◽  
Chih-Hao Lin
Keyword(s):  
Cardiac Arrest ◽  
Risk Group ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Hemodialysis Patients ◽  
High Risk Group ◽  
Predictive Score ◽  
Primary Group ◽  
Sum Scores ◽  
Hospital Cardiac Arrest

Background: Patients undergoing hemodialysis are prone to cardiac arrests. Methods: This study aimed to develop a risk score to predict in-hospital cardiac arrest (IHCA) in emergency department (ED) patients undergoing emergency hemodialysis. Patients were included if they received urgent hemodialysis within 24 h after ED arrival. The primary outcome was IHCA within three days. Predictors included three domains: comorbidity, triage information (vital signs), and initial biochemical results. The final model was generated from data collected between 2015 and 2018 and validated using data from 2019. Results: A total of 257 patients, including 52 with IHCA, were analyzed. Statistical analysis selected significant variables with higher sensitivity cutoff, and scores were assigned based on relative beta coefficient ratio: K > 5.5 mmol/L (score 1), pH < 7.35 (score 1), oxygen saturation < 85% (score 1), and mean arterial pressure < 80 mmHg (score 2). The final scoring system had an area under the curve of 0.78 (p < 0.001) in the primary group and 0.75 (p = 0.023) in the validation group. The high-risk group (defined as sum scores ≥ 3) had an IHCA risk of 47.2% and 41.7%, while the low-risk group (sum scores < 3) had 18.3% and 7%, in the primary and validation databases, respectively. Conclusions: This predictive score model for IHCA in emergent hemodialysis patients could help healthcare providers to take necessary precautions and allocate resources.

Abstract 55: The CAHP (Cardiac Arrest Hospital Prognosis) Score: Predicting Neurological Outcome After Out-of-Hospital Cardiac Arrest

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Carole Maupain ◽  
Wulfran Bougouin ◽  
Lamhaut Lionel ◽  
Nicolas Deye ◽  
Daniel Jost ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
High Risk ◽  
Neurological Outcome ◽  
Risk Group ◽  
Unfavorable Outcome ◽  
Validation Dataset ◽  
Group Score ◽  
Poor Neurological Outcome ◽  
Hospital Cardiac Arrest ◽  
Prognosis Score

Background: Out-of-hospital cardiac arrest (OHCA) carries a very poor prognosis. Early prognostication of patients admitted in ICU after resuscitated OHCA is a key issue but remains challenging. The aim of that study was to establish a new scoring system to predict poor neurological outcome in these patients. Materials and Methods: The CAHP (Cardiac Arrest Hospital Prognosis) score was developed from the Sudden Death Expertise Center registry (SDEC, Paris, France). Objective risk factors were weighted on the basis of a logistic regression analysis. The primary outcome was poor neurological outcome defined as Cerebral Performance Category 3, 4 or 5. Thresholds were defined to distinguish low, moderate and high-risk groups. The CAHP score was then validated in an external dataset (Parisian OHCA Registry). Score calibration and discrimination characteristics were assessed in the validation dataset. Results: The developmental dataset included 819 patients admitted in ICU from May 2011 to December 2012. After logistic regression, 7 variables were independently associated with poor neurological outcome: age, initial shockable rhythm, time form collapse to basic life support (BLS), time from BLS to return of spontaneous circulation (ROSC), location of cardiac arrest, epinephrine dose during resuscitation and arterial pH at admission. These variables were included in the CAHP score. 3 risks groups were identified: a low risk group (score ≤ 150, 39 % of unfavorable outcome), medium risk group (score 150-200, 81% of unfavorable outcome) and high-risk group (CAHP score ≥ 200, 100 % of unfavorable outcome). AUC of the CAHP score was 0.93. In the external validation dataset, discrimination value of the CAHP score was consistent with an AUC of 0.85. Conclusion: The CAHP score is a simple and objective tool for early assessment of prognosis in patients admitted to ICU after OHCA. Moreover it allows to stratify the probability of poor neurological outcome by identifying a very high-risk category of patients (score ≥ 200).

scholarly journals The Ability of the Nottingham Hip Fracture Score to Predict Mobility, Length of Stay and Mortality in Hospital, and Discharge Destination in Patients Admitted with a Hip Fracture

2020 ◽  
Vol 107 (4) ◽  
pp. 319-326 ◽  
Author(s):  
Radcliffe Lisk ◽  
Keefai Yeong ◽  
David Fluck ◽  
Christopher H. Fry ◽  
Thang S. Han
Keyword(s):  
Hip Fracture ◽  
Length Of Stay ◽  
Adverse Events ◽  
Nursing Care ◽  
Risk Group ◽  
Area Under The Curve ◽  
High Risk Group ◽  
Discharge Destination ◽  
Increased Risk ◽  
Surgery Rates

Abstract The Nottingham Hip Fracture Score (NHFS) has been developed for predicting 30-day and 1-year mortality after hip fracture. We hypothesise that NHFS may also predict other adverse events. Data from 666 patients (190 men, 476 women), aged 60.2–103.4 years, admitted with a hip fracture to a single centre from 1/10/2015 and 7/12/2017 were analysed. The ability of NHFS to predict mobility within 1 day after surgery, length of stay (LOS) find mortality, and discharge destination was evaluated by receiver operating characteristic curves and two-graph plots. The area under the curve (95% confidence interval [CI]) for predicting mortality was 67.4% (58.4–76.4%), prolonged LOS was 59.0% (54.0–64.0%), discharge to residential/nursing care was 62.3% (54.0–71.5%), and any two of failure to mobilise, prolonged LOS or discharge to residential/nursing care was 64.8% (59.0–70.6%). NHFS thresholds at 4 and 7 corresponding to the lower and upper limits of intermediate range where sensitivity and specificity equal 90% were identified for mortality and prolonged LOS, and 4 and 6 for discharge to residential/nursing care, which were used to create three risk categories. Compared with the low risk group (NHFS = 0–4), the high risk group (NHFS = 7–10 or 6–10) had increased risk of in-patient mortality: rates = 2.0% versus 7.1%, OR (95% CI) = 3.8 (1.5–9.9), failure to mobilise within 1 day of surgery: rates = 18.9% versus 28.3%, OR = 1.7 (1.0–2.8), prolonged LOS (> 17 days): rates = 20.3% versus 33.9%, OR = 2.2 (1.3–3.3), discharge to residential/nursing care: rates = 4.5% vs 12.3%, OR = 3.0 (1.4–6.4), and any two of failure to mobilise, prolonged LOS or discharge to residential/nursing care: rates = 10.5% versus 28.6%, 3.4 (95% CI 1.9–6.0), and stayed 4.1 days (1.5–6.7 days) longer in hospital. High NHFS associates with increased risk of mortality, prolonged LOS and discharge to residential/nursing care, lending further support for its use to identify adverse events.

scholarly journals Prognostic value of automated pupillometry: an unselected cohort from a cardiac intensive care unit

2019 ◽  
Vol 9 (7) ◽  
pp. 779-787 ◽  
Author(s):  
Laust Obling ◽  
Christian Hassager ◽  
Charlotte Illum ◽  
Johannes Grand ◽  
Sebastian Wiberg ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
Area Under The Curve ◽  
Youden Index ◽  
Cardiac Intensive Care Unit ◽  
Infrared Pupillometry ◽  
Cardiac Intensive Care ◽  
Neurological Pupil Index ◽  
Hospital Cardiac Arrest

Background: Patients admitted to a cardiac intensive care unit are often unconscious with uncertain prognosis. Automated infrared pupillometry for neurological assessment in the intensive care unit may provide early prognostic information. This study aimed to determine the prognostic value of automated pupillometry in different subgroups of patients in a cardiac intensive care unit with 30-day mortality as the primary endpoint and neurological outcome as the secondary endpoint. Methods: A total of 221 comatose patients were divided into three groups: out-of-hospital cardiac arrest, in-hospital cardiac arrest and others (i.e. patients with cardiac diagnoses other than cardiac arrest). Automated pupillometry was serially performed until discharge or death and pupil measurements were analysed using the neurological pupil index algorithm. We applied receiver operating characteristic curves in univariable and multivariable logistic regression models and a calculated Youden index identified neurological pupil index cut-off values at different specificities. Results: In out-of-hospital cardiac arrest patients higher neurological pupil index values were independently associated with lower 30-day mortality. The univariable model for 30-day mortality had an area under the curve of 0.87 and the multivariable model achieved an area under the curve of 0.94. The Youden index identified a neurological pupil index cut-off in out-of-hospital cardiac arrest patients of 2.40 for a specificity of 100%. For patients with in-hospital cardiac arrest and other cardiac diagnoses, we found no association between neurological pupil index values and 30-day mortality, and the univariable models showed poor predictive values. Conclusion: Automated infrared pupillometry has promising predictive value after out-of-hospital cardiac arrest, but poor predictive value in patients with in-hospital cardiac arrest or cardiac diagnoses unrelated to cardiac arrest. Our data suggest a possible neurological pupil index cut-off of 2.40 for poor outcome in out-of-hospital cardiac arrest patients.

scholarly journals Unscheduled return visits by patients to a german emergency department are a high risk group for initial wrong diagnosis

2018 ◽  
Vol 17 (4) ◽  
pp. 178-181
Author(s):  
Felix Ludwig ◽  
◽  
Wilhelm Behringer ◽  
Steffen Herdtle ◽  
Christian Hohenstein ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Risk Group ◽  
Vital Signs ◽  
High Risk Group ◽  
Normal Limits ◽  
Wrong Diagnosis ◽  
Return Visits ◽  
Special Patient Population ◽  
Special Patient

The aim was to classify patients who returned unscheduled to an emergency department within 7 days. We categorized the patients’ cases arbitrarily according to the underlying cause of the return. The main causes for returning unscheduled were: “patient related” (24,2%), “illness related” (35,4%), “physician related” (18,3%), “system related” (3,8%) and “other” (21,7%). We also analyzed missed diagnoses, as the literature describes this special patient population as a high risk group. 15,4% of all return cases had a wrong diagnosis. No typical risk constellation/symptom could be found. Vital signs or blood values were within normal limits as well.

scholarly journals External validation of simplified out-of-hospital cardiac arrest and cardiac arrest hospital prognosis scores in a Japanese population: A multicentre retrospective cohort study

2020 ◽  
Author(s):  
Keita Shibahashi ◽  
Kazuhiro Sugiyama ◽  
Yusuke Kuwahara ◽  
Takuto Ishida ◽  
Atsushi Sakurai ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Japanese Population ◽  
Goodness Of Fit ◽  
External Validation ◽  
Predictive Score ◽  
Calibration Plot ◽  
Goodness Of Fit Test ◽  
Neurological Outcomes ◽  
Lemeshow Test ◽  
Hospital Cardiac Arrest

Abstract Background Out-of-hospital cardiac arrest (OHCA) is a global medical problem. The newly-developed simplified out-of-hospital cardiac arrest (sOHCA) and cardiac arrest hospital prognosis (sCAHP) scores used for prognostication of patients admitted alive have not been validated externally. This study was, thus, conducted to externally validate sOHCA and sCAHP scores in a Japanese population. Methods Adult patients resuscitated and admitted to hospitals after intrinsic OHCA (n=2,428, age ≥18 years) were selected from a prospectively collected Japanese database (January 2012–March 2013). We validated sOHCA and sCAHP scores with reference to the original ones in predicting 1-month unfavourable neurological outcomes based on discrimination and calibration measures. Discrimination and calibration were assessed using area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test with calibration plot, respectively. Results One-month unfavourable neurological outcome was observed in 82% of patients. Score availability was significantly higher in the simplified scores than in the original ones and was highest in the sCAHP score (76%). The AUCs of simplified scores were not significantly different from those of original ones, whereas the AUC of the sCAHP score was significantly higher than that of the sOHCA score (0.88 vs. 0.81, P <0.001). Goodness-of-fit was poor in the sOHCA score (ν= 8, χ 2 =19.1, Hosmer-Lemeshow test: P =0.014) but not in the sCAHP score (ν= 8, χ 2 =13.5, Hosmer-Lemeshow test: P =0.10). Conclusion Performance of original and simplified OHCA and CAHP scores in predicting neurological outcomes in successfully resuscitated OHCA patients were acceptable. Based on the highest availability, similar discrimination, and good calibration, the sCAHP score was the better candidate for clinical implementation. The validated predictive score can help patients’ families, healthcare providers, and researchers by accurately stratifying patients.

scholarly journals Does Combining Biomarkers and Brain Images Provide Improved Prognostic Predictive Performance for Out-Of-Hospital Cardiac Arrest Survivors before Target Temperature Management?

2020 ◽  
Vol 9 (3) ◽  
pp. 744 ◽  
Author(s):  
Seung Ha Son ◽  
In Ho Lee ◽  
Jung Soo Park ◽  
In Sool Yoo ◽  
Seung Whan Kim ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Neuron Specific Enolase ◽  
Area Under The Curve ◽  
Temperature Management ◽  
Target Temperature Management ◽  
High Signal ◽  
Target Temperature ◽  
Brain Images ◽  
Prognostic Performance ◽  
Hospital Cardiac Arrest

We examined whether combining biomarkers measurements and brain images early after the return of spontaneous circulation improves prognostic performance compared with the use of either biomarkers or brain images for patients with cardiac arrest following target temperature management (TTM). This retrospective observational study involved comatose out-of-hospital cardiac arrest survivors. We analyzed neuron-specific enolase levels in serum (NSE) or cerebrospinal fluid (CSF), grey-to-white matter ratio by brain computed tomography, presence of high signal intensity (HSI) in diffusion-weighted imaging (DWI), and voxel-based apparent diffusion coefficient (ADC). Of the 58 patients, 33 (56.9%) had poor neurologic outcomes. CSF NSE levels showed better prognostic performance (area under the curve (AUC) 0.873, 95% confidence interval (CI) 0.749–0.950) than serum NSE levels (AUC 0.792, 95% CI 0.644–0.888). HSI in DWI showed the best prognostic performance (AUC 0.833, 95% CI 0.711–0.919). Combining CSF NSE levels and HSI in DWI had better prognostic performance (AUC 0.925, 95% CI 0.813–0.981) than each individual method, followed by the combination of serum NSE levels and HSI on DWI and that of CSF NSE levels and the percentage of voxels of ADC (AUC 0.901, 95% CI 0.792–0.965; AUC 0.849, 95% CI 0.717–0.935, respectively). Combining CSF/serum NSE levels and HSI in DWI before TTM improved the prognostic performance compared to either each individual method or other combinations.

Abstract 9056: The Association Between Thoracic Injuries Due to Chest Compression and Short-Term Prognosis in Patients With Non-traumatic Out-of-Hospital Cardiac Arrest

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Aya Katasako ◽  
Shoji Kawakami ◽  
Hidenobu Koga ◽  
Kenichi Kitahara ◽  
Keiichiro Komiya ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Chest Compression ◽  
Survival Rates ◽  
Area Under The Curve ◽  
Spontaneous Circulation ◽  
Rank Test ◽  
Thoracic Injuries ◽  
Short Term ◽  
Roc Curve Analysis ◽  
Hospital Cardiac Arrest

Background: The current guidelines emphasize that high-quality chest compression is essential for improving the survival in out-of-hospital cardiac arrest (OHCA) patients. However, it may lead to thoracic injuries which is a potential factor of poor prognosis. Method: Between June 2017 to July 2019, we collected Utstein-style data on 384 consecutive adult patients with non-traumatic OHCA who were transferred to our hospital. Full-body CT scan was performed and thoracic injuries were defined as rib fracture, sternum fracture, hemorrhagic pleural effusion, pneumothorax, sternum posterior bleeding, mediastinal hematoma, or mediastinal emphysema. We identified the predictors for thoracic injuries and evaluated the relationship between thoracic injuries and prognosis. Results: Patients with thoracic injuries (Group-T) were 234 (76%). The duration of chest compression in Group-T was 43 min, which was significantly longer than that in patients without thoracic injuries (Group-N, 32 min, p<0.001). ROC curve analysis identified a duration of chest compression of 35 minutes as the optimal cut off for predicting thoracic injuries (area under the curve 0.73). Multivariate analysis revealed that age (OR: 1.03, 95%CI: 1.01-1.05, p=0.005) and duration of chest compression (OR: 1.07, 95%CI: 1.04-1.09, p<0.001) were independent predictors of thoracic injuries. The rate of obtaining return of spontaneous circulation (ROSC), 30-day survival and favorable neurologic outcome were larger in Group-N than Group-T. In patients with achieving ROSC, Kaplan-Meier curves showed a significantly higher cumulative survival rates in Group-N compared to that in Group-T during follow-up of 30 days (Log-rank test p=0.009). Conclusion: Age and duration of chest compression were independent predictors for thoracic injuries due to chest compression in non-traumatic OHCA patients. Moreover, the presence of thoracic injuries was associated with poor short-term prognosis.

Abstract 10992: Machine Learning to Predict In-Hospital Cardiac Arrest in Patients Admitted from the Emergency Department with COVID-19 and Suspected Pneumonia

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Tsung-Chien Lu ◽  
Eric H Chou ◽  
CHIH-HUNG WANG ◽  
Amir Mostafavi ◽  
Mario Tovar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Early Warning Score ◽  
Gradient Boosting ◽  
Patients At Risk ◽  
End Of Life Decision ◽  
Hospital Cardiac Arrest ◽  
Suspected Pneumonia

Introduction: There are only scarce models developed for stratifying the risk of cardiac arrest from COVID-19 patients presenting to the ED with suspected pneumonia. By using the machine learning (ML) approach, we aimed to develop and validate the ML models to predict in-hospital cardiac arrest (IHCA) in patients admitted from the ED. Hypothesis: We hypothesized that ML approach can serve as a valuable tool in identifying patients at risk of IHCA in a timely fashion. Methods: We included the COVID-19 patients admitted from the EDs of five hospitals in Texas between March and November 2020. All adult (≥ 18 years) patients were included if they had positive RT-PCR for SARS-CoV-2 and also received CXR examination for suspected pneumonia. Patients’ demographic, past medical history, vital signs at ED triage, CXR findings, and laboratory results were retrieved from the EMR system. The primary outcome (IHCA) was identified via a resuscitation code. Patients presented as OHCA or without any blood testing were excluded. Nonrandom splitting strategy based on different location was used to divide the dataset into the training (one urban and two suburban hospitals) and testing cohort (one urban and one suburban hospital) at around 2-to-1 ratio. Three supervised ML models were trained and performances were evaluated and compared with the National Early Warning Score (NEWS) by the area under the receiver operating characteristic curve (AUC). Results: We included 1,485 records for analysis. Of them, 190 (12.8%) developed IHCA. Of the constructed ML models, Random Forest outperformed the others with the best AUC result (0.930, 95% CI: 0.896-0.958), followed by Gradient Boosting (0.929, 95% CI: 0.891-0.959) and Extra Trees classifier (0.909, 95% CI: 0.875-0.943). All constructed ML models performed significantly better than by using the NEWS scoring system (AUC: 0.787, 95% CI: 0.725-0.840). The top six important features selected were age, oxygen saturation at triage, and lab data of APTT, lactic acid, and LDH. Conclusions: The ML approach showed excellent discriminatory performance to identify IHCA for patients with COVID-19 and suspected pneumonia. It has the potential to save more life or provide end-of-life decision making if successfully implemented in the EMR system.

Abstract 220: Missed Opportunity for Medical Emergency Team Activation in Pediatric In-Hospital Cardiac Arrest

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Natalie Jayaram ◽  
Maya L Chan ◽  
Fengming Tang ◽  
Paul S Chan
Keyword(s):  
Cardiac Arrest ◽  
Medical Emergency Team ◽  
Vital Signs ◽  
National Registry ◽  
Medical Emergency ◽  
Missed Opportunities ◽  
Emergency Team ◽  
Variable Success ◽  
Get With The Guidelines ◽  
Hospital Cardiac Arrest

Background: Prior studies of Medical Emergency Teams (METs) in pediatric hospitals have shown inconsistent results in terms of their ability to improve outcomes. Whether the variable success is due to differential utilization of METs among hospitals is unknown. Methods: Within the Get With The Guidelines-Resuscitation Registry (GWTG-R), we identified children (age <18 years) with an in-hospital cardiac arrest (IHCA) on the general inpatient or telemetry floors from 2007 to 2014. In cases of IHCA where MET evaluation did not occur, we examined the frequency of “missed” opportunities for activation of the MET based upon the presence of one or more abnormal vital signs. We also examined the variability in utilization of the MET among those hospitals with at least ten cases of IHCA. Results: Of 215 children from 23 hospitals sustaining an IHCA, 48 (22.3%) had a preceding MET evaluation. Children with MET evaluation prior to IHCA were older (6.8 ± 6.5 vs. 3.1 ± 4.7, p < 0.001) and were more likely to have metabolic/electrolyte abnormalities (9/48 [18.8%] vs. 9/167 [5.4%], p=0.006), sepsis (8/48 [16.7%] vs. 8/167 [4.8%], p=0.01), or malignancy (11/48 [22.9%] vs. 9/167 [5.4%], p<0.001) at the time of their IHCA. Hospital utilization of the MET varied substantially (median 20%; inter-quartile range [IQR]: 3.4%-29.8%; range: 0%-36.4%). Among patients who did not have a MET called prior to their IHCA, 78/141 (55.3%) had at least one abnormal vital sign that should have triggered a MET. Conclusion: In a large, national registry, we found that the majority of pediatric IHCA cases are not preceded by a MET evaluation despite meeting criteria that should have triggered a MET. Improved utilization of the MET by all hospitals could lead to fewer pediatric IHCA and improved outcomes following pediatric IHCA.

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