Preoperative Hypercapnia as a Predictor of Hypotension During Anesthetic Induction in Lung Transplant Recipients

Mizota, Toshiyuki; Matsukawa, Shino; Fukagawa, Hiroshi; Daijo, Hiroki; Tanaka, Tomoharu; Chen, Fengshi; Date, Hiroshi; Fukuda, Kazuhiko

doi:10.1053/j.jvca.2014.10.027

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Finally, ABG demonstrating increased PaCO 2 >55 mm Hg was associated with an increased incidence of hypotension (75%) compared with patients with PaCO 2 <55 mm Hg (31%) during induction of anesthesia for lung transplantation surgery. 11 After adjusting for confounding factors, Mizota et al 11 showed that the odds ratio for the relationship between preoperative hypercapnia and hypotension during anesthetic induction was 12.54 (95% CI = 3.10-66.66). Hypercapnia indicates reduced lung compliance 12 and the presence of hypercarbia in PAH patients may work negatively to precipitate hemodynamic compromise during induction for lung transplantation.…”

Section: Predicting Induction Eventsmentioning

confidence: 99%

Pulmonary Hypertensive Crisis on Induction of Anesthesia

Schisler

Marquez

Hilmi

et al. 2016

Semin Cardiothorac Vasc Anesth

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Anesthesia for lung transplantation remains one of the highest risk surgeries in the domain of the cardiothoracic anesthesiologist. End-stage lung disease, pulmonary hypertension, and right heart dysfunction as well as other comorbid disease factors predispose the patient to cardiovascular, respiratory and metabolic dysfunction during general anesthesia. Perhaps the highest risk phase of surgery in the patient with severe pulmonary hypertension is during the induction of anesthesia when the removal of intrinsic sympathetic tone and onset of positive pressure ventilation can decompensate a severely compromised cardiovascular system. Severe hypotension, cardiac arrest, and death have been reported previously. Here we present 2 high-risk patients for lung transplantation, their anesthetic induction course, and outcomes. We offer suggestions for the safe management of anesthetic induction to mitigate against hemodynamic and respiratory complications.

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Section: Predicting Induction Eventsmentioning

confidence: 99%

Pulmonary Hypertensive Crisis on Induction of Anesthesia

Schisler

Marquez

Hilmi

et al. 2016

Semin Cardiothorac Vasc Anesth

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“…One contribution is the adoption of the ICV as a PIHI index by merging the various hemodynamic changes after intubation. In previous literatures, most of the attentions were focused on the postoperative outcomes [ 6 – 8 , 13 , 16 – 18 , 20 ] and perioperative binary judgement on specific symptom using classification models [ 10 , 11 , 15 , 19 ]. However, few studies have attempted to quantify the post-intubation anesthetic risk with continuous value and build up the prediction model.…”

Section: Discussionmentioning

confidence: 99%

“…Data-driven analysis and machine-learning models have become possible with increasingly structured data, such as preoperative electronic health/medical records [6]. Recently, various models have been developed to predict postoperative mortality [6,7], postoperative pain [8], surgical acceptance [9], hypotension during anesthesia [10][11][12], surgery time [13,14], intraoperative bradycardia [15], postoperative delirium [16,17] and other risk outcomes [18][19][20]. Nevertheless, there is a paucity of endeavors dedicated to preoperative prediction of postintubation hemodynamic instability (PIHI), despite its critical significance in enhancing the perioperative anesthesia quality and safety, as well as mitigating postoperative risks [21].…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Te,

Zhu,

et al. 2024

BMC Anesthesiol

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Background Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. Methods In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). Results The ICV values were proved to be consistent with the anesthetists’ ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511–0.0513), RMSE (0.0792, 95% CI: 0.0790–0.0794), and MAPE (0.2086, 95% CI: 0.2077–0.2095) and the largest R2 (0.9047, 95% CI: 0.9043–0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. Conclusions Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.

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Anesthetic management of lung transplantation: impact of presenting disease

Martin

Fritz

Wilkey

2020

Current Opinion in Anaesthesiology

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Purpose of review Recent literature has described the emerging role of anesthesiologists as key members of the lung transplantation team and the impact of anesthetic management on outcomes. This review examines the impact of presenting cause of end-stage lung disease (ESLD) on anesthetic management. Recent findings The four primary causes of ESLD are suppurative, obstructive, or restrictive processes, and pulmonary hypertension. Our recent review of perioperative literature revealed new data regarding preoperative risk stratification, intraoperative management, and postoperative use of extracorporeal membrane oxygenation (ECMO) support. Major studies specifically about anesthetic management are lacking; however, the principles studied are readily applicable to the design of a perioperative care plan. The most compelling data have resulted in a revolutionary change in terms of intraoperative support and postoperative planning for pulmonary hypertension patients. Historically treated with cardiopulmonary bypass, significant data have been reported describing the successful use of ECMO both as an intraoperative support with superior outcomes, as well as postoperative support for improved stability during biventricular remodeling post graft implantation. Summary The application of these updated findings should assist anesthesiologists as they develop internal protocols and external guidelines to integrate within multidisciplinary teams caring for the lung transplant patient.

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Preoperative Hypercapnia as a Predictor of Hypotension During Anesthetic Induction in Lung Transplant Recipients

Abstract: Hypotension during anesthetic induction is common in lung transplant recipients, and is independently predicted by preoperative hypercapnia.

Cited by 4 publications

References 14 publications

Pulmonary Hypertensive Crisis on Induction of Anesthesia

Pulmonary Hypertensive Crisis on Induction of Anesthesia

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Anesthetic management of lung transplantation: impact of presenting disease

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