RESUMO
Acute kidney injury (AKI) is one of the most common complications after cardiac surgery, associated with increased mortality and morbidity. Near-infrared spectroscopy (NIRS) continuously measures regional oxygen saturation(rSO2) in real-time. This exploratory retrospective study aimed to investigate the association between intraoperative plantar rSO2 and postoperative AKI in cardiac surgery patients. Between August 2019 and March 2021, 394 patients were included. Plantar and cerebral rSO2 were monitored using NIRS intraoperatively. The primary outcome was AKI within 7 postoperative days. The nonlinear association between plantar rSO2, cerebral rSO2, and mean arterial blood pressure (MBP) and AKI was assessed, and plantar rSO2<45% was related to an increased risk of AKI. Multivariable logistic regression analyses revealed that longer duration and higher area under the curve below plantar rSO2<45% and MBP<65 mmHg were more likely to be associated with increased odds of AKI. In additional multivariable regression analyses, association between plantar rSO2<45% and AKI was still maintained after adjusting the duration or AUC of MBP<65 mmHg as a covariate. Cerebral rSO2 levels were not associated with AKI. Independent of MAP, intraoperative plantar rSO2 was associated with AKI after cardiac surgery. However, intraoperative cerebral rSO2 was not associated with AKI. Intraoperative plantar rSO2 monitoring may be helpful in preventing AKI.
Assuntos
Injúria Renal Aguda , Procedimentos Cirúrgicos Cardíacos , Humanos , Estudos Retrospectivos , Saturação de Oxigênio , Procedimentos Cirúrgicos Cardíacos/efeitos adversos , Procedimentos Cirúrgicos Cardíacos/métodos , Injúria Renal Aguda/etiologia , Monitorização Intraoperatória/métodos , OxigênioRESUMO
BACKGROUND: Acute kidney injury (AKI) is one of the most common complications in patients undergoing open abdominal aortic aneurysm (AAA) repair. Dopamine has been frequently used in these patients to prevent AKI. We aimed to clarify the relationship between intraoperative dopamine infusion and postoperative AKI in patients undergoing open AAA repair. METHODS: We analyzed 294 patients who underwent open AAA repair at a single tertiary center from 2009 to 2018, retrospectively. The primary outcome was the incidence of postoperative AKI, determined by the Kidney Disease Improving Global Outcomes definition, after open AAA repair. Secondary outcomes included survival outcome, hospital and intensive care unit length of stay, and postoperative renal replacement therapy (RRT). RESULTS: Postoperative AKI occurred in 21.8% (64 out of 294 patients) The risk of postoperative AKI by intraoperative dopamine infusion was greater after adjusting for risk factors (odds ratio [OR] 2.56; 95% confidence interval [CI], 1.09-5.89; P = 0.028) and after propensity score matching (OR 3.22; 95% CI 1.12-9.24; P = 0.030). On the contrary, intraoperative norepinephrine use was not associated with postoperative AKI (use vs. no use; 19.3 vs. 22.4%; P = 0.615). Patients who used dopamine showed higher requirement for postoperative RRT (6.8 vs. 1.2%; P = 0.045) and longer hospital length of stay (18 vs. 16 days, P = 0.024). CONCLUSIONS: Intraoperative dopamine infusion was associated with more frequent postoperative AKI, postoperative RRT, and longer hospital length of stay in patients undergoing AAA repair, when compared to norepinephrine. Further prospective randomized clinical trial may be necessary for this topic.
Assuntos
Injúria Renal Aguda , Aneurisma da Aorta Abdominal , Injúria Renal Aguda/epidemiologia , Injúria Renal Aguda/etiologia , Injúria Renal Aguda/prevenção & controle , Aorta Abdominal , Aneurisma da Aorta Abdominal/cirurgia , Dopamina/uso terapêutico , Humanos , Estudos RetrospectivosRESUMO
Neurological pupil index (NPi) calculated by automated pupillometry predicts clinical outcomes in critically ill patients. However, there are few data on intraoperative NPi and postoperative outcome after cardiac surgery. We evaluated the relationships between intraoperative NPi and clinical outcomes, such as delirium, in cardiac surgery patients. NPi was measured at baseline, after anesthesia induction, at 30 min intervals after initiation of cardiopulmonary bypass or anastomosis of coronary artery bypass graft, and at skin closure. Abnormal NPi was defined as one or more measurements of NPi < 3.0 during surgery. The worst intraoperative NPi was recorded, then multivariate logistic regression analysis was performed to evaluate the relationship between abnormal NPi and postoperative delirium following cardiac surgery. Among 123 included patients, postoperative delirium developed in 19.5% (24/123) of patients. Intraoperative abnormal NPi was significantly associated with postoperative delirium (odds ratio 6.078; 95% confidence interval 1.845-20.025; P = 0.003) after adjustment for Society of Thoracic Surgeons Predicted Risk of Mortality score, coronary artery disease, and use of calcium channel blockers. In conclusion, abnormal intraoperative NPi independently predicted postoperative delirium following cardiac surgery. Intraoperative application of pupillometry may have prognostic value for development of postoperative delirium, thereby enabling close surveillance and early intervention in high-risk patients.Registry number: ClinicalTrials.gov (NCT04136210).
Assuntos
Procedimentos Cirúrgicos Cardíacos , Delírio do Despertar , Humanos , Pupila , Procedimentos Cirúrgicos Cardíacos/efeitos adversos , Ponte de Artéria Coronária/efeitos adversos , Anastomose CirúrgicaRESUMO
In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.
Assuntos
Anestesia , Bases de Dados Factuais , Sinais Vitais , Humanos , Aprendizado de Máquina , Monitorização Fisiológica/métodosRESUMO
BACKGROUND: Most risk prediction models predicting short-term mortality after cardiac surgery incorporate patient characteristics, laboratory data, and type of surgery, but do not account for surgical experience. Considering the impact of case volume on patient outcome after high-risk procedures, we attempted to develop a risk prediction model for mortality after cardiac surgery that incorporates institutional case volume. METHODS: Adult patients who underwent cardiac surgery from 2009 to 2016 were identified. Patients who underwent cardiac surgery (n = 57,804) were randomly divided into the derivation cohort (n = 28,902) or the validation cohorts (n = 28,902). A risk prediction model for in-hospital mortality and 1-year mortality was developed from the derivation cohort and the performance of the model was evaluated in the validation cohort. RESULTS: The model demonstrated fair discrimination (c-statistics, 0.76 for in-hospital mortality in both cohorts; 0.74 for 1-year mortality in both cohorts) and acceptable calibration. Hospitals were classified based on case volume into 50 or less, 50-100, 100-200, or more than 200 average cardiac surgery cases per year and case volume was a significant variable in the prediction model. CONCLUSIONS: A new risk prediction model that incorporates institutional case volume and accurately predicts in-hospital and 1-year mortality after cardiac surgery was developed and validated.
Assuntos
Procedimentos Cirúrgicos Cardíacos , Adulto , Estudos de Coortes , Mortalidade Hospitalar , Humanos , Medição de Risco , Fatores de RiscoRESUMO
BACKGROUND: Serum vitamin B