scholarly journals Is Greater Than 0.5 MAC Inhalational Agent Use Post-Bypass Related to Need for Inotropic and/or Vasoconstrictor Support?

Hearts ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 213-223
Author(s):  
Tara A. Lenk ◽  
Carlos E. Guerra-Londono ◽  
Thomas E. Graul ◽  
Marc A. Murinson ◽  
Prabhdeep K. Hehar ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Surgery ◽  
Propensity Score ◽  
Regression Models ◽  
Independent Predictor ◽  
Care Hospital ◽  
Elective Cardiac Surgery ◽  
Logistic Regression Models ◽  
Adjusted Analysis ◽  
Inhalational Agent

Background and Aims: We hypothesized that maintaining a patient on moderate–high doses of potent inhalational agent for greater than 30 min during the post-bypass period would be an independent predictor of initiation and usage of either inotropic and/or vasopressor infusions. Setting and Design: This study is a retrospective design and approved by the institutional review board. The setting was a single-center, academic tertiary care hospital in Detroit, Michigan. Materials and Methods: Three-hundred, ninety-seven elective cardiac surgery patients were identified for chart review. Electronic medical records were reviewed to collect demographics and perioperative data. Statistics used include a propensity score regression adjusted analysis utilizing logistic regression models and a multivariable model. Results: A propensity score regression adjusted analysis was performed and then applied in both univariate and multivariate logistic regression models with a p value of <0.05 reaching statistical significance. Fifty-six percent of the participants had an exposure of greater than 30 min of a minimum alveolar concentration of isoflurane greater than 0.5 (ETISO ≥ 0.5MAC, 30 min) in the post-bypass period. After adjusting for propensity score, this was found to be a significant predictor of inotrope and/or vasoconstrictor use post-bypass (OR 2.49, 95% CI 1.15–5.38, p = 0.021). In the multivariate model, pulmonary hypertension (OR 5.9; 95% CI 1.33–26.28; p = 0.02), Euroscore II (2.73; 95% CI 1.35–5.5; p = 0.005), and cardiopulmonary bypass hours (OR 1.86; 95% CI 1.02–3.4; p = 0.042) emerged as significant. Conclusions: This study showed that an ETISO ≥ 0.5MAC, 30 min exposure during the immediate post-bypass period during elective cardiac surgery was an independent predictor of a patient being started on inotrope or vasoconstrictor infusions. Further research should consider a prospective design and examine depth of anesthesia during the post-bypass period.

scholarly journals Cardiac Surgical Outcome Prediction by Blood Pressure Variability Indices Poincaré plot and Coefficient of Variation: an Observational Study

2019 ◽  
Author(s):  
Senthil Packiasabapathy K ◽  
Varesh Prasad ◽  
Valluvan Rangasamy ◽  
David Popok ◽  
Xinling Xu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Renal Failure ◽  
Logistic Regression ◽  
Cardiac Surgery ◽  
Coefficient Of Variation ◽  
Blood Pressure Variability ◽  
Regression Models ◽  
Outcome Prediction ◽  
Prediction Ability ◽  
Logistic Regression Models

Abstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We aimed to test the performance of Poincaré plots and coefficient of variation (CV) independently by measuring intraoperative BP variability.Methods In this retrospective, observational, cohort study, 3687 adult patients undergoing cardiac surgery from 2008 to 2013 were included. Poincaré plots from BP data and descriptors SD1, SD2 by ellipse fitting technique were computed. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability.Results Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (SD1, SD2 and CV) performed poorly in predicting postoperative 30-day mortality and renal failure. They did not add any significant value to the conventional prediction model.Conclusions We demonstrate the feasibility of applying Poincaré plots for BP variability analysis. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

scholarly journals Cardiac Surgical Outcome Prediction by Blood Pressure Variability Indices Poincaré plot and Coefficient of Variation: an Observational Study

2020 ◽  
Author(s):  
Senthil Packiasabapathy K ◽  
Varesh Prasad ◽  
Valluvan Rangasamy ◽  
David Popok ◽  
Xinling Xu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Renal Failure ◽  
Logistic Regression ◽  
Cardiac Surgery ◽  
Coefficient Of Variation ◽  
Blood Pressure Variability ◽  
Regression Models ◽  
Outcome Prediction ◽  
Logistic Regression Models ◽  
C Statistic

Abstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes. Methods In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability. Results Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7]. Conclusions In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

Predictive Factors for Pneumonia Onset After Cardiac Surgery in Rio de Janeiro, Brazil

2007 ◽  
Vol 28 (4) ◽  
pp. 382-388 ◽  
Author(s):  
Marisa Santos ◽  
José Ueleres Braga ◽  
Renato Vieira Gomes ◽  
Guilherme L. Werneck
Keyword(s):  
Logistic Regression ◽  
Cardiac Surgery ◽  
Predictive Models ◽  
Rio De Janeiro ◽  
Regression Models ◽  
Tertiary Care ◽  
Classification And Regression Tree ◽  
Data Set ◽  
Logistic Regression Models ◽  
Cart Analysis

Objective.To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed.Design.Retrospective cohort study.Setting.Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil.Patients.Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, “test sample”) and the other used for validation (ie, “validation sample”).Results.The area under the receiver–operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid.Conclusion.Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

scholarly journals Biopsy in emergency gastroscopy does not increase the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding combined with suspected malignant gastric ulcer: a multicenter retrospective cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Quchuan Zhao ◽  
Tianyu Chi
Keyword(s):  
Logistic Regression ◽  
Gastric Ulcer ◽  
Cohort Study ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Retrospective Cohort ◽  
Regression Models ◽  
Upper Gastrointestinal Bleeding ◽  
Logistic Regression Models ◽  
Upper Gastrointestinal

Abstract Background Few studies have reported whether a biopsy in emergency gastroscopy (EG) increased the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) combined with suspected malignant gastric ulcer (SMGU). This study aims to conduct a multicenter retrospective cohort study using propensity score matching to verify whether a biopsy in EG increases the risk of rebleeding in patients diagnosed with Forrest I ANVUGIB combined with SMGU. Methods Using the data for propensity-matched patients, logistic regression models were fitted using rebleeding as the dependent variable. Survival time was defined as the length of time the patient experienced from visiting the emergency department to rebleeding. We used the Kaplan–Meier (KM) method to analyze the 30-day survival of the patients with and without a biopsy after matching, and the log-rank test was performed to examine the differences in survival. Results With the use of propensity score matching, 308 patients who underwent a biopsy in EG were matched with 308 patients who did not. In the five logistic regression models, there were no significant group differences in the risk of rebleeding in patients with Forrest I ANVUGIB combined with SMGU between the biopsy and no-biopsy groups. The probability of survival was not significantly different between the no-biopsy and biopsy groups. Conclusions In this multicenter, retrospective propensity score matching cohort study, compared with patients without a biopsy, patients with a biopsy during EG had no increased risk of rebleeding, and there was no significant difference in the rate of rebleeding.

scholarly journals Cardiac Surgical Outcome Prediction by Blood Pressure Variability Indices Poincaré plot and Coefficient of Variation: an Observational Study

2020 ◽  
Author(s):  
Senthil Packiasabapathy K ◽  
Varesh Prasad ◽  
Valluvan Rangasamy ◽  
David Popok ◽  
Xinling Xu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Renal Failure ◽  
Logistic Regression ◽  
Cardiac Surgery ◽  
Coefficient Of Variation ◽  
Blood Pressure Variability ◽  
Regression Models ◽  
Outcome Prediction ◽  
Logistic Regression Models ◽  
C Statistic

Abstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes. Methods In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability. Results Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7]. Conclusions In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

scholarly journals Immigration, perceived discrimination and mental health: evidence from Venezuelan population living in Peru

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Benoît Mougenot ◽  
Elard Amaya ◽  
Edward Mezones-Holguin ◽  
Alfonso J. Rodriguez-Morales ◽  
Báltica Cabieses
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Regression Models ◽  
Mental Health Problems ◽  
Perceived Discrimination ◽  
Health Problems ◽  
Logistic Regression Models ◽  
Venezuelan Population

Abstract Background The association between international migration and mental health is conditioned to several factors, and discrimination may play a significant role. Currently, Peru is one of the principal Venezuelan migrant-receiving countries in Latin America. There are around one million Venezuelan refugees and migrants in the country. This study evaluates the association between self-perceived discrimination and mental health problems in Venezuelan population living in Peru. Method We analyzed data from the Venezuelan Population Residing in Peru Survey 2018, a nationally representative urban sample aimed at collecting information on several dimensions of Venezuelan population wellbeing. We applied logistic regression models to assess the association between self-perceived discrimination and mental health problems. Moreover, we applied the propensity score matching method as a robustness check of our results. Results Of 9487 Venezuelans surveyed, 6806 included complete information. From this sample, 6.3% reported mental health problems related to fear, anger, anxiety, or stress. Logistic regression models showed that Venezuelans who perceived being discriminated against had 2.4 higher odds of presenting mental health problems than their non-discriminated counterparts. Moreover, propensity score matching models showed that Venezuelans who perceived being discriminated against increased by 3.5 percentage points their probability of presenting mental health problems compared to their non-discriminated counterparts. Conclusions There is evidence that self-perceived discrimination is associated with mental health deterioration in Venezuelan migrants living in Peru. Our findings are relevant in the current geopolitical context and could be useful in the decision making processes in international health.

scholarly journals Cardiac Surgical Outcome Prediction by Blood Pressure Variability Indices Poincaré plot and Coefficient of Variation: a Retrospective Study

2019 ◽  
Author(s):  
Senthil Packiasabapathy K ◽  
Varesh Prasad ◽  
Valluvan Rangasamy ◽  
David Popok ◽  
Xinling Xu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Renal Failure ◽  
Logistic Regression ◽  
Cardiac Surgery ◽  
Coefficient Of Variation ◽  
Blood Pressure Variability ◽  
Regression Models ◽  
Outcome Prediction ◽  
Logistic Regression Models ◽  
C Statistic

Abstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We aimed to test the performance of Poincaré plots and coefficient of variation (CV) independently by measuring intraoperative blood pressure variability. Methods In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability. Results Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7]. Conclusions In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient co-morbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

The Relationship Between AKI in Patients With STEMI and Short-Term Mortality: A Propensity Score Matching Analysis

Angiology ◽  
2021 ◽  
pp. 000331972199856
Author(s):  
Zhongyuan Meng ◽  
Yaxin Zhao ◽  
Xifeng Zheng ◽  
Yan He
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Regression Models ◽  
Medical Information ◽  
Short Term ◽  
Long Term Outcome ◽  
Logistic Regression Models ◽  
Propensity Score Matching Analysis ◽  
Short Term Mortality

Acute myocardial infarction (AMI) in patients with acute kidney injury (AKI) is associated with poor long-term outcome. However, the short-term prognosis of AKI in patients with ST-elevation AMI (STEMI) needs to be explored further. We assessed this relationship between these patients and short-term mortality in relation to AKI and chronic kidney disease (CKD). All data were extracted from the Medical Information Mart for Intensive Care III database. The primary outcome was 28-day mortality. Kaplan-Meier curves, logistic regression models, and propensity score matching analysis were used to evaluate the associations between AKI in patients with STEMI and outcomes. A total of 1031 patients with STEMI met the inclusion criteria. For 28-day mortality, in the multivariable logistic regression models, the odds ratio (95% CI) of group 2 (AKI but no CKD) and group 3 (AKI in the presence of CKD) were 3.24 (1.46-7.18) and 4.57 (1.83-11.37), respectively, compared with group 1 (no AKI and no CKD). Comorbid AKI increased the risk of short-term mortality among patients with STEMI, especially for those with AKI in the presence of CKD.

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