scholarly journals Relationship Between First 24-h Mean Body Temperature and Clinical Outcomes of Post-cardiac Surgery Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Fei Xu ◽  
Cheng Zhang ◽  
Chao Liu ◽  
Siwei Bi ◽  
Jun Gu
Keyword(s):  
Cardiac Surgery ◽  
Body Temperature ◽  
Clinical Outcomes ◽  
Observational Research ◽  
Mean Body Temperature ◽  
Cox Analysis ◽  
The Relationship ◽  
Mimic Iii ◽  
Post Cardiac Surgery

Background: This study was aimed to investigate the relationship between first 24-h mean body temperature and clinical outcomes of post cardiac surgery patients admitted to intensive care unit (ICU) in a large public clinical database.Methods: This is a retrospectively observational research of MIMIC III dataset, a total of 6,122 patients included. Patients were divided into 3 groups according to the distribution of body temperature. Multivariate cox analysis and logistic regression analysis were used to investigate the association between abnormal temperature, and clinical outcomes.Results: Hypothermia (<36°C) significantly associated with increasing in-hospital mortality (HR 1.665, 95%CI 1.218–2.276; p = 0.001), 1-year mortality (HR 1.537, 95% CI 1.205–1.961; p = 0.001), 28-day mortality (HR 1.518, 95% CI 1.14–2.021; p = 0.004), and 90-day mortality (HR 1.491, 95% CI 1.144–1.943; p = 0.003). No statistical differences were observed between short-term or long-term mortality and hyperthermia (>38°C). Hyperthermia was related to the extended length of ICU stay (p < 0.001), and hospital stay (p < 0.001).Conclusion: Hypothermia within 24h after ICU admission was associated with the increased mortality of post cardiac surgery patients. Enhanced monitoring of body temperature within 24h after cardiac surgery should be taken into account for improving clinical outcomes.

scholarly journals Long Term Renal Outcomes in Children with Acute Kidney Injury Post-Cardiac Surgery

2021 ◽  
Author(s):  
Sidharth Kumar Sethi ◽  
Rajesh Sharma ◽  
Aditi Gupta ◽  
Abhishek Tibrewal ◽  
Romel Akole ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Cardiac Surgery ◽  
Kidney Injury ◽  
Renal Outcomes ◽  
Post Cardiac Surgery

Machine Learning Methods for Predicting Long-term Mortality in Patients after Cardiac Surgery

2021 ◽  
Author(s):  
Yue Yu ◽  
Chi Peng ◽  
Zhiyuan Zhang ◽  
Kejia Shen ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Early Warning Systems ◽  
Gradient Boosting ◽  
Distribution Width ◽  
Extreme Gradient Boosting ◽  
Mimic Iii ◽  
Long Term Mortality

Abstract Background Establishing a mortality prediction model of patients undergoing cardiac surgery might be useful for clinicians for alerting, judgment, and intervention, while few predictive tools for long-term mortality have been developed targeting patients post-cardiac surgery. Objective We aimed to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients after cardiac surgery during a 4-year follow-up. Methods The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, prognostic scoring systems, and treatment information on the first day of ICU admission. 4-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA). Results Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination (RFE) and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings. Conclusions The Ada model performs best in predicting long-term mortality after cardiac surgery among the eight ML models. The ML-based algorithms might have significant application in the development of early warning systems for patients following operations.

Abstract WMP60: Serial Body Temperature Evaluation and Long-Term Clinical Outcomes in Stroke

Stroke ◽  
2018 ◽  
Vol 49 (Suppl_1) ◽  
Author(s):  
Emily Margolin ◽  
Jeffrey L Saver ◽  
Sidney Starkman ◽  
David S Liebeskind ◽  
Scott Hamilton ◽  
...  
Keyword(s):  
Body Temperature ◽  
Clinical Outcomes

scholarly journals Application of strain and other echocardiographic parameters in the evaluation of early and long-term clinical outcomes after cardiac surgery revascularization

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Anna Gozdzik ◽  
Krzysztof Letachowicz ◽  
Barbara Barteczko Grajek ◽  
Tomasz Plonek ◽  
Marta Obremska ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Clinical Outcomes ◽  
Echocardiographic Parameters

scholarly journals Long-Term Clinical Outcomes of Cardiac Surgery for Kidney Transplant Patients

2020 ◽  
Vol 26 (2) ◽  
pp. 84-87 ◽  
Author(s):  
Masahide Komagamine ◽  
Tomohiro Nishinaka ◽  
Yuki Ichihara ◽  
Satoshi Saito ◽  
Hiroshi Niinami
Keyword(s):  
Cardiac Surgery ◽  
Clinical Outcomes ◽  
Kidney Transplant ◽  
Transplant Patients ◽  
Kidney Transplant Patients

scholarly journals Peri-Operative Cerebral Regional Oxygen Saturation Variability and Short-Term Outcomes Following Cardiac Surgery in Neonates

2021 ◽  
Author(s):  
Maryam O. Abubakar ◽  
Santina A. Zanelli ◽  
Michael C. Spaeder
Keyword(s):  
Cardiac Surgery ◽  
Single Ventricle ◽  
Long Term Effects ◽  
Short Term ◽  
Neurodevelopmental Outcomes ◽  
Arch Obstruction ◽  
Neonatal Cardiac Surgery ◽  
The Relationship ◽  
Operative Monitoring

Abstract Decreased post-operative cerebral region oxygenation saturation (crSO2) variability, a surrogate for cerebral autoregulation, correlates with poor neurodevelopmental outcomes in neonates who undergo cardiac surgery. The goal of this study is to investigate the relationship between pre- and post-operative crSO2 variability in neonates requiring neonatal cardiac surgery for congenital heart disease (CHD). The variability of averaged 1-min crSO2 values was calculated for a minimum of 12h before and for the first 48h following cardiac surgery with cardiopulmonary by-pass in neonates between November 2019 and May 2021. The crSO2 variability increased by 9% with each additional postnatal day in the pre-operative monitoring period (p=0.009). There was a 40% decrease in crSO2 variability between the pre-and post-operative monitoring periods (p<0.001). There were no associations between the degree of decrease in crSO2 variability and CHD classification (aortic arch obstruction or single ventricle physiology). The crSO2 variability improves with each additional postnatal day but then decreases by almost half following cardiac surgery in neonates. We did not observe any association between pre-operative crSO2 variability and post-operative ventilator-free days, post-operative ICU days, or mortality.The long-term effects or significance of reduced crSO2 require further exploration.

scholarly journals HUBUNGAN PENGGUNAAN ALAT PELINDUNG DIRI (APD) DENGAN KADAR SGOT DAN SGPT DALAM DARAH PADA PETANI PADI

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Iga Maliga ◽  
Rafi'ah Rafi'ah
Keyword(s):  
Quantitative Research ◽  
Health Conditions ◽  
Observational Research ◽  
Protective Equipment ◽  
Agricultural Activities ◽  
Cross Sectional ◽  
Exact Test ◽  
Rice Farmers ◽  
The Relationship

The use of personal protective equipment (PPE) when spraying pesticides is a must in agricultural activities. It greatly affects the long-term health conditions of farmers. This study aimed to see the relationship between the use of PPE with SGOT and SGPT levels in the blood of rice farmers. This study used quantitative research with an analytic observational research design using a cross-sectional approach. Blood samples were taken for farmers and carried out the process of filling out questionnaires and observation sheets. The results of the questionnaire were processed with SPSS 16.0, with Fisher's exact test. The sample of the study was 40 rice farmers taken purposively. The results showed that the levels of SGOT and SGPT were more in the normal category. The analysis showed that there was no relationship between AST and ALT levels with the use of PPE. It can be seen from the significance value of more than 0.05. As many as 38.5% complained about recurring complaints. After spraying, dizziness, dizziness, watery eyes, frequent spitting, namely vision, coughing, and ergonomic complaints in the form of back pain and lumbago. The conclusion in this study is that there is no relationship between the use of PPE with AST and ALT levels in the blood.

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