scholarly journals Early prognostication of neurological outcome by heart rate variability in adult patients with out-of-hospital sudden cardiac arrest

Critical Care ◽  
2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Hiroshi Endoh ◽  
Natuo Kamimura ◽  
Hiroyuki Honda ◽  
Masakazu Nitta
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Cardiac Arrest ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Neurological Outcome ◽  
Sudden Cardiac Arrest ◽  
Poor Outcome ◽  
Hrv Analysis

Abstract Background Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family’s intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM. Methods Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 °C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1–2) and poor outcome (GOS 3–5) groups. Results Seventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (α1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (α1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (α1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (α1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%. Conclusions The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

scholarly journals Time to Appropriate Antibiotic Therapy is an Independent Indicator of Poor Outcome in Children with Nosocomial Klebsiella Pneumoniae Bloodstream Infection

2020 ◽  
Author(s):  
Jie Cheng ◽  
Qinyuan Li ◽  
Guangli Zhang ◽  
Huiting Xu ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Antibiotic Therapy ◽  
Bloodstream Infection ◽  
Logistic Regression Analysis ◽  
Cutoff Point ◽  
Cart Analysis ◽  
Poor Outcome

Abstract Objectives: To evaluate the effects of time to appropriate therapy (TTAT) on outcomes in children with nosocomial K. pneumoniae bloodstream infection, and to find an optimal time window for empiric antibiotics administration. Methods: Children with nosocomial K. pneumoniae bloodstream infection hospitalized in Children’s Hospital of Chongqing Medical University from April 2014 to December 2019 were enrolled retrospectively. TTAT cutoff point and risk factors were determined and analyzed by Classification and Regression Tree (CART) analysis and Logistic Regression analysis. Results: Overall, sixty-seven patients were enrolled. The incidence of septic shock and mortality was 17.91% (12/67) and 13.43% (9/67), respectively. The CART-derived TTAT cutoff point was 10.7 hours. The multivariate logistic regression analysis indicated delayed therapy (TTAT ≥ 10.7 h), PRISM III scores ≥ 10, early TTP (TTP ≤ 13 h), and need for invasive mechanical ventilation were independent risk factors of septic shock (OR 9.87, 95% CI 1.46-66.59, P = 0.019; OR 9.69, 95% CI 1.15-81.39, P = 0.036; OR 8.28, 95% CI 1.37-50.10, P = 0.021; OR 6.52, 95% CI 1.08-39.51, P = 0.042; respectively) and in-hospital mortality (OR 22.19, 95% CI 1.25-393.94, P = 0.035; OR 40.06, 95% CI 2.32-691.35, P = 0.011; OR 22.60, 95% CI 1.78-287.27, P = 0.016; OR 12.21, 95% CI 1.06-140.67, P = 0.045; respectively). Conclusions: TTAT is an independent predictor of poor outcome in children with nosocomial K. pneumoniae bloodstream infection. Initial appropriate antibiotic therapy should begin within 10.7 hours from the onset of bloodstream infection.

scholarly journals Time to Appropriate Antibiotic Therapy is an Independent Indicator of Poor Outcome in Children with Nosocomial Klebsiella Pneumoniae Bloodstream Infection

2021 ◽  
Author(s):  
Jie Cheng ◽  
Qinyuan Li ◽  
Guangli Zhang ◽  
Huiting Xu ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Antibiotic Therapy ◽  
Bloodstream Infection ◽  
Logistic Regression Analysis ◽  
Cutoff Point ◽  
Cart Analysis ◽  
Poor Outcome

Abstract We aim to evaluate the effects of time to appropriate therapy (TTAT) on outcomes in children with nosocomial K. pneumoniae bloodstream infection, and to find an optimal time window for empiric antibiotics administration. Children with nosocomial K. pneumoniae bloodstream infection hospitalized in Children’s Hospital of Chongqing Medical University from April 2014 to December 2019 were enrolled retrospectively. TTAT cutoff point and risk factors were determined and analyzed by Classification and Regression Tree (CART) analysis and Logistic Regression analysis. Overall, sixty-seven patients were enrolled. The incidence of septic shock and mortality was 17.91% (12/67) and 13.43% (9/67), respectively. The CART-derived TTAT cutoff point was 10.7 hours. The multivariate logistic regression analysis indicated delayed therapy (TTAT ≥ 10.7 h), PRISM III scores ≥ 10, early TTP (TTP ≤ 13 h), and need for invasive mechanical ventilation were independent risk factors of septic shock (OR 9.87, 95% CI 1.46-66.59, P = 0.019; OR 9.69, 95% CI 1.15-81.39, P = 0.036; OR 8.28, 95% CI 1.37-50.10, P = 0.021; OR 6.52, 95% CI 1.08-39.51, P = 0.042; respectively) and in-hospital mortality (OR 22.19, 95% CI 1.25-393.94, P = 0.035; OR 40.06, 95% CI 2.32-691.35, P = 0.011; OR 22.60, 95% CI 1.78-287.27, P = 0.016; OR 12.21, 95% CI 1.06-140.67, P = 0.045; respectively). Conclusions: TTAT is an independent predictor of poor outcome in children with nosocomial K. pneumoniae bloodstream infection. Initial appropriate antibiotic therapy should begin within 10.7 hours from the onset of bloodstream infection.

Identifying predictive factors of rapid response/code blue: A retrospective study using logistic regression analysis

2020 ◽  
Author(s):  
Brittany Thomas ◽  
Herschel Knapp ◽  
Frances Patmon
Keyword(s):  
Heart Rate ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Logistic Regression Analysis ◽  
Medical Records ◽  
Community Hospital ◽  
Rapid Response ◽  
Care Providers ◽  
Single Centre

Abstract Background: Rapid response calls and cardiac arrests are often preceded by observable signs of clinical deterioration often hours prior to the adverse event.Objectives: The purpose of this retrospective study was to identify risk factors that provide predictive value in determining the likelihood of a Rapid Response Call on adult telemetry patients at a single-centre community hospital.Design: This was a retrospective study based on secondary data analysis. After approval by the Institutional Review Board was obtained (CANV DHIRB-2018-362), we utilized the electronic medical record system to extract de-identified quantitative data from patient medical records.Setting: This study utilized medical records from patients on the Telemetry unit at a single-centre, 230-bed community hospital.Participants: The sample consisted of 250 randomized de-identified medical records from both patients who did and did not require a rapid response between January and December, 2018. Patients who were less than 18 years of age and those who were transferred to another facility or to another hospital were excluded from the analyses.Methods: The variables that were collected included age, gender, race, primary admitting medical diagnosis, hemoglobin, potassium, magnesium, creatinine, lactic acid, and urine output. Additional variables collected in four-hour increments included the vital signs: temperature, heart rate, oxygen saturation, respirations, systolic and diastolic blood pressure, and level of consciousness which was scored using the adult Glasgow Coma Scale. Logistic regression analysis was used to identify which of these variables were statistically significant in predicting patient deterioration.Results: The following predictors were statistically significant (a = 0.05 with 95% Confidence Intervals [CI]): For every one beat increase in heart rate 4 hours prior to a RRT, the odds of a RRT increased by 4.9% (p=0.003) (CI=95% 1.016, 1.084). For every one increase in respirations, the odds of a RRT increased by 42.8% (p=0.004) (95% CI 1.11, 1.82), 8 hours before the RRT, and by 47% (p=0.002) (95% CI 1.15, 1.87), 12 hours before a RRT. African Americans had 20.6 times the odds of experiencing an RRT compared to Caucasians (p<0.001) (95% CI 3.4, 124.6), Hispanics had 56.6 times the odds of experiencing a RRT compared to Caucasians (p<0.001) (95% CI 11.4, 280.4), and other races had 6.3 times the odds of a RRT compared to Caucasians (p=0.044) (95% CI 1.05, 38.5).Conclusions: Such predictors can be used to identify early signs of deterioration that can alert health care providers to early intervention.

Abstract 318: Association Carbon Dioxide Level During Cardiopulmonary Resuscitation with Neurological Outcome After Out-of-hospital Cardiac Arrests

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Tasuku Matsuyama ◽  
Tetsuhisa Kitamura ◽  
Taku Iwami ◽  
Bon Ohta
Keyword(s):  
Carbon Dioxide ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Neurological Outcome ◽  
Multivariable Logistic Regression Analysis ◽  
Blood Gas ◽  
Hospital Arrival ◽  
Co2 Level ◽  
Favorable Neurological Outcome

Background: Little evidence exists to guide appropriate ventilation strategies during CPR. We aimed to investigate the association between carbon dioxide (CO2) pressure measured on hospital arrival from those who were receiving ongoing resuscitation and neurological outcome after out-of-hospital cardiac arrest (OHCA). Method: This nationwide hospital-based prospective observational study (The JAAM-OHCA registry) carried out between June, 2014 and December, 2017 in Japan. From this registry, we included adult OHCA patients aged ≥18 years with blood gas sampled on hospital arrival during CPR. The main exposure was CO2 level in blood gas analysis during CPR. Based on the CO2 level on hospital arrival, included patients were classified into four quartiles (Q1-Q4) defined as Q1 (<66.0 mmHg), Q2 (66.1-87.2 mmHg), Q3 (87.3-113.5 mmHg), and Q4 (≥113.6 mmHg). The primary outcome of this study was one-month survival with favorable neurological outcome defined by cerebral performance category 1 or 2. We adjusted for potential confounders with multivariable logistic regression analysis. Results: During the study period, a total of 21,137 patients were included in our analysis. The overall proportion of favorable neurological outcome was 1.0% (207/21137). The highest proportion of favorable neurological outcome was observed in the Q1 group (2.4% [140/5244]), followed by Q2 (0.7% [37/5284]), Q3 (0.4% [21/5296]), and Q4 (0.2% [9/5313]). In the multivariable logistic regression analysis, we found that the Q4 group had the significantly lower proportion of favorable neurological outcome than the Q1 group (adjusted odds ratio 0.25; 95% confidence interval 0.16-0.55). The adjusted probability of favorable neurological outcome decreased in a stepwise fashion across increasing quartiles (P<0.001). Conclusion: This study observed the association between lower CO2 level on hospital arrival and favorable neurological outcome after OHCA in a dose-dependent manner.

scholarly journals Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
R. Shashikant ◽  
P. Chetankumar
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Cardiac Arrest ◽  
Random Forest ◽  
Decision Tree ◽  
Machine Learning Technique ◽  
Forest Model ◽  
Learning Technique

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

scholarly journals Relationship between CHA2DS2-VASC score on admission to emergency department and in-hospital major adverse cardiovascular events in patients diagnosed with ST-elevation myocardial infarction

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
DUYGU Inan ◽  
DUYGU Genc ◽  
BARIS Simsek ◽  
OZAN Tanik ◽  
EVLIYA Akdeniz ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
P Value ◽  
Killip Class ◽  
No Reflow ◽  
St Elevation ◽  
The Relationship

Abstract Funding Acknowledgements Type of funding sources: None. Inotroduction . CHA₂DS₂-VASc scoring system, which includes traditional risk factors of coronary artery disease, is actually created to determine the risk of thromboembolism in patients with atrial fibrillation. In this study; the value of CHA₂DS₂-VASc score, which can be calculated easily on admission, was evaluated for predicting in-hospital adverse outcomes in ST elevation miyocardial infarction (STEMI) patients without atrial fibrillation. Method This was a single center cross-sectional study. 1933 STEMI patients enrolled to the study. Primary end points include in-hospital death, cardiopulmonary arrest and cerebrovascular accident and were identified as MACE Results MACE rate was 10% (193 patients), in-hospital mortality rate was 9% (169 patients).In proportional logistic regression analysis, CHA₂DS₂-VASc score was an independent predictor for MACE (OR and CI 95%, 2.31[1.37-3.90]; p value:0.0016). In the regression analysis, the CHA₂DS₂-VASc score was taken as an uncatagorized continuous variable, and the relationship between the CHA₂DS₂-VASc score and MACE was observed to be linear. Additionally heart rate (OR and 95% CI, 1.56 [0.97- 2.50]; p value: 0.0242), killip class on admission (OR and 95% CI, 24.19[10.74-54.46]; p value &lt;0.0001), creatinine level on admission (OR and 95% CI, 1.54 [1.10-2.16]; p value: 0.0024), peak CK-MB level (OR and 95% GA, 1.63 [0.98-2.70]; p value: 0.0001) and presence of no-reflow (OR and 95% CI, 2.45 [1.25-4.80]; p value: 0.0085) were indendified as other independent predictors of MACE. Conclusion CHA₂DS₂-VASc score was observed as an indepented predictor for MACE in STEMI patients. To evaluate the relationship between CHA₂DS₂-VASc score and outcomes, the linear analysis of the CHA₂DS₂-VASc score without categorization in prediction model is used and this is the main difference of our study from others. Table-1 Variables Odss Ratio (OR) and 95% CI p value CHA₂DS₂-VASc ( 0 to 3) 2.31 (1.37-3.90) p = 0,0016 Heart Rate (Beats per minute) ( 68 to 94) 1.56 (0.97-2.50) p =0.0242 Systolic Blood Pressure (mmHg) ( 115 to 156) 0.83 (0.51-1.34) p = 0.3523 Killip Class ( I to IV) 24.19 (10.74-54.46) p &lt; 0.0001 Hemoglobin (g/dL) ( 12 to 15) 0.96 (0.54-1.70) p = 0.4066 Creatinine ( mg/dL) (0.74 to 1.0) 1.54 (1.10-2.16) p = 0.0024 Peak CK-MB (IU/L) (40.8 to 165.1) 1.63 (0.98-2.70) p = 0.0001 No-reflow (yes) 2.45 (1.25-4.80) p = 0.0085 Independent predictors of MACE in STEMI patients according to penalized proportional odds logistic regression analysis Abstract Figure. Partial impact plots of predictors

Predictors of poor clinical outcome despite complete reperfusion in acute ischemic stroke patients

2020 ◽  
Vol 13 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Noel van Horn ◽  
Helge Kniep ◽  
Hannes Leischner ◽  
Rosalie McDonough ◽  
Milani Deb-Chatterji ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Stroke Severity ◽  
Stroke Patients ◽  
Poor Clinical Outcome ◽  
Poor Outcome

BackgroundIn patients suffering from acute ischemic stroke from large vessel occlusion (LVO), mechanical thrombectomy (MT) often leads to successful reperfusion. Only approximately half of these patients have a favorable clinical outcome. Our aim was to determine the prognostic factors associated with poor clinical outcome following complete reperfusion.MethodsPatients treated with MT for LVO from a prospective single-center stroke registry between July 2015 and April 2019 were screened. Complete reperfusion was defined as Thrombolysis in Cerebral Infarction (TICI) grade 3. A modified Rankin scale at 90 days (mRS90) of 3–6 was defined as ‘poor outcome’. A logistic regression analysis was performed with poor outcome as a dependent variable, and baseline clinical data, comorbidities, stroke severity, collateral status, and treatment information as independent variables.Results123 patients with complete reperfusion (TICI 3) were included in this study. Poor clinical outcome was observed in 67 (54.5%) of these patients. Multivariable logistic regression analysis identified greater age (adjusted OR 1.10, 95% CI 1.04 to 1.17; p=0.001), higher admission National Institutes of Health Stroke Scale (NIHSS) (OR 1.14, 95% CI 1.02 to 1.28; p=0.024), and lower Alberta Stroke Program Early CT Score (ASPECTS) (OR 0.6, 95% CI 0.4 to 0.84; p=0.007) as independent predictors of poor outcome. Poor outcome was independent of collateral score.ConclusionPoor clinical outcome is observed in a large proportion of acute ischemic stroke patients treated with MT, despite complete reperfusion. In this study, futile recanalization was shown to occur independently of collateral status, but was associated with increasing age and stroke severity.

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