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.

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 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 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.

Increased blood pressure variability after endovascular thrombectomy for acute stroke is associated with worse clinical outcome

2018 ◽  
Vol 10 (9) ◽  
pp. 823-827 ◽  
Author(s):  
Alicia E Bennett ◽  
Michael J Wilder ◽  
J Scott McNally ◽  
Jana J Wold ◽  
Gregory J Stoddard ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Blood Pressure Variability ◽  
Regression Models ◽  
Ordinal Logistic Regression ◽  
Logistic Regression Models ◽  
Intravenous Tissue Plasminogen Activator ◽  
Point Increase ◽  
Tissue Plasminogen

Background and purposeBlood pressure variability has been found to contribute to worse outcomes after intravenous tissue plasminogen activator, but the association has not been established after intra-arterial therapies.MethodsWe retrospectively reviewed patients with an ischemic stroke treated with intra-arterial therapies from 2005 to 2015. Blood pressure variability was measured as standard deviation (SD), coefficient of variation (CV), and successive variation (SV). Ordinal logistic regression models were fitted to the outcome of the modified Rankin Scale (mRS) with univariable predictors of systolic blood pressure variability. Multivariable ordinal logistic regression models were fitted to the outcome of mRS with covariates that showed independent predictive ability (P<0.1).ResultsThere were 182 patients of mean age 63.2 years and 51.7% were female. The median admission National Institutes of Health Stroke Scalescore was 16 and 47.3% were treated with intravenous tissue plasminogen activator. In a univariable ordinal logistic regression analysis, systolic SD, CV, and SV were all significantly associated with a 1-point increase in the follow-up mRS (OR 2.30–4.38, all P<0.002). After adjusting for potential confounders, systolic SV was the best predictor of a 1-point increase in mRS at follow-up (OR 2.63–3.23, all P<0.007).ConclusionsIncreased blood pressure variability as measured by the SD, CV, and SV consistently predict worse neurologic outcomes as measured by follow-up mRS in patients with ischemic stroke treated with intra-arterial therapies. The SV is the strongest and most consistent predictor of worse outcomes at all time intervals.

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.

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.

Abstract WP39: Intraprocedural Systolic Blood Pressure Mean and Variability During Endovascular Thrombectomy Not Associated With Neurologic Outcome

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Mohammad Anadani ◽  
Adam De Havenon ◽  
Linda M Baki ◽  
Alejandro M Spiotta
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Systolic Blood Pressure ◽  
Regression Models ◽  
Primary Outcome ◽  
Neurologic Outcome ◽  
Anterior Circulation ◽  
Endovascular Thrombectomy ◽  
Logistic Regression Models ◽  
Adjusted Logistic Regression

Background: Prior research has shown that increased systolic blood pressure variability (BPV) is associated with worse neurologic outcome after endovascular thrombectomy (EVT). Those studies have typically included BPV from 24-72 hours after stroke onset, but none have focused specifically on the BPV during EVT. Methods: We included acute ischemic stroke patients who underwent EVT for anterior circulation large vessel occlusion from 2 stroke centers. All patients had a minimum of 10 blood pressure readings during EVT. The primary outcome is mRS 0-2 (good outcome) and the secondary outcome is death, both as close to 90 days as possible. We fit adjusted logistic regression models to our outcomes with the predictors of intraprocedural systolic mean, standard (SD), and coefficient of variation (CV). Results: We included 303 patients with a mean (SD) age of 65.7 years and 53.5% were female. The primary outcome of mRS 0-2 was met by 39.9% and 27.4% died. Systolic mean, SD, and CV did not differ in patients with mRS 0-2 versus 3-6 (Table 1) nor for patients who died versus were alive (all p>0.5). In the adjusted logistic regression models, systolic mean, SD, and CV were not associated with either mRS 0-2 or death at follow-up (Tables 2 & 3). Conclusions: Blood pressure variation during endovascular thrombectomy was not associated with the functional outcome or death in patients with anterior circulation strokes.

scholarly journals Hypertension control and risk of colonic diverticulosis

2019 ◽  
Vol 12 ◽  
pp. 175628481985573
Author(s):  
Li-Xian Yeo ◽  
Tzu-Hsiang Tseng ◽  
Wei-Liang Chen ◽  
Tung-Wei Kao ◽  
Li-Wei Wu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Regression Models ◽  
Antihypertensive Medication ◽  
Positive Association ◽  
Antihypertensive Drug Therapy ◽  
Logistic Regression Models ◽  
Epidemiologic Evidence ◽  
Asymptomatic Patients

Background: The prevalence of diverticulosis has increased in our aging population, but the risk factors for diverticulosis are not fully understood. The role of hypertension in the risk of diverticulosis remains uncertain. This study investigated whether hypertension is associated with asymptomatic colorectal diverticulosis. Methods: This study enrolled asymptomatic patients who received a colonoscopy as part of a health check. Hypertension was defined by actual measured blood pressure. Logistic regression models were used to examine the relationship between hypertension and diverticulosis. In addition, we established three logistic regression models for covariate adjustment, and further stratified patients with hypertension into three subgroups based on their type of hypertension. Results: The study group consisted of 2748 participants, including 141 participants with diverticulosis and 2607 participants without diverticulosis. After adjustments for potential covariates, the odds ratio (OR) for having diverticulosis was 1.83 (95% confidence interval, 1.21–2.75, p = 0.004) in the hypertension group compared with the group without hypertension. In subgroup analyses, hypertension without antihypertensive medication use, and hypertension despite the use of antihypertensive medication were also significantly associated with the occurrence of asymptomatic diverticulosis (OR = 1.73, p = 0.028; OR = 2.07, p = 0.013, respectively). Current normal blood pressure under antihypertensive drug therapy was not associated with diverticulosis (OR = 1.74, p = 0.092). Conclusions: Our findings suggest a positive association between hypertension and diverticulosis. Participants with poorly controlled blood pressure were found to have a higher risk of asymptomatic diverticulosis. Our study presents epidemiologic evidence for future prevention strategies against diverticulosis.

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