A novel nomogram for predicting the implementation of ultra-fast-track cardiac anesthesia for minimally invasive cardiac surgery

doi:10.21203/rs.3.rs-5236976/v1

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A novel nomogram for predicting the implementation of ultra-fast-track cardiac anesthesia for minimally invasive cardiac surgery

https://doi.org/10.21203/rs.3.rs-5236976/v1

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Background

Ultra-fast-track cardiac anesthesia (UFTCA) is a crucial component of Enhanced Recovery After Cardiac Surgery (ERACS). However, research on the factors influencing UFTCA implementation remains limited. This study aimed to identify predictors of UFTCA in right-thoracoscopic minimally invasive cardiac surgery (MICS) and develop a nomogram to forecast UFTCA implementation.

Methods

This retrospective study included 947 patients who underwent right-thoracoscopic MICS from January 2021 to July 2023. Patients were randomly divided into derivation (70%) and validation (30%) cohorts. Univariable logistic regression analysis was used for variable selection, followed by a multivariable logistic regression model to determine significant predictors and construct a nomogram for forecasting UFTCA implementation. The model's discrimination, calibration, and clinical usefulness were evaluated using the validation cohort.

Results

Multivariate analysis identified six independent predictors of UFTCA implementation: operation type, fascial plane chest wall blocks (FPCWB), intraoperative sufentanil and dexamethasone dosage, operation later than 8 p.m., and cardiopulmonary bypass (CPB) duration. The nomogram demonstrated good discriminative ability, with areas under the receiver operating characteristic (ROC) curve of 0.869 and 0.862 for the derivation and validation sets, respectively. The calibration plot showed close alignment with the ideal diagonal line, and the decision curve analysis (DCA) confirmed the model's clinical practical significance.

Conclusion

This study developed and validated a nomogram that could predict the implementation of UFTCA in patients undergoing MICS. The identified predictors, including operation type, FPCWB, intraoperative sufentanil and dexamethasone dosage, operation later than 8 p.m., and CPB duration, could guide clinicians in decision-making to facilitate UFTCA implementation.

Health sciences/Risk factors

Health sciences/Medical research/Experimental models of disease

Enhanced Recovery after Surgery

ultra-fast-track cardiac anesthesia

minimally invasive cardiac surgery

predictive modeling

perioperative management

The concept and principles of Enhanced Recovery After Surgery (ERAS) are now generally accepted around the world¹. ERAS has demonstrated its great strengths, but the higher mortality and complication rates make it difficult to develop ERAS programs for cardiac surgery patients. Early on, Enhanced Recovery after Cardiac Surgery (ERACS) was developed on the basis of fast-track anesthesia^2,3. Fast-track cardiac anesthesia (FTCA)was defined as extubation within 6–8 hours after surgery, which had achieved significant clinical benefits^4,5. Building on this foundation, the concept of Ultra-fast-track cardiac anesthesia (UFTCA) was developed, defined as an approach to extubate immediately after cardiac surgery^6,7.

Due to the increasing number of patients requiring cardiac surgery, accelerating patients' postoperative recovery, reducing their medical burden, and promoting the full utilization of limited medical resources become more urgent. Several studies have shown that UFTCA significantly reduces the length of stay in the hospital and intensive care unit (ICU), and even reduces the costs for patients compared to conventional cardiac anesthesia. ^5,8,9. However, the UFTCA remains highly controversial and questionable, especially its practice for highly selected patients and surgery types^7,10–12. Moreover, it is also a great challenge for the anesthesiologist as well as for the department's collaboration.

Our center began administering UFTCA under Right-Thoracoscopic Minimally Invasive Cardiac Surgery (MICS) in 2021. MICS is an integral part of ERACS^13,14, with numerous studies showing that MICS can reduce trauma, blood loss, and postoperative complications compared to traditional sternotomy^15–17. Currently, there are relatively few studies that have a combination of MICS and UFTCA, and even no document has researched predictors for UFTCA under MICS. By thoroughly analyzing a large set of medical case histories, our research seeks to uncover the potential determinants and develop a nomogram that can predict the adoption of UFTCA in the context of right-thoracoscopic minimally invasive cardiac surgery.

This study was approved by the institutional review board (IRB) of the Zhejiang Provincial People's Hospital (Approval No: QT2024007). We confirmed that all experiments were performed in accordance with relevant guidelines and regulations. Due to the retrospective design of the study, the IRB of the Zhejiang Provincial People's Hospital waived the requirement for written informed consent.

2.1 Participants

A total of 967 patients underwent Right-Thoracoscopic Minimally Invasive Cardiac Surgery at a single academic institution between January 1, 2021, and July 30, 2023. Since 2021, the Center has been implementing the UFTCA. The study data were obtained entirely from our institution's electronic medical record system, which captured both the patients' preoperative characteristics and intraoperative details.

Patients meeting the following criteria will be excluded: cardiogenic shock, septic shock, extracorporeal membrane oxygenation (ECMO) support, left ventricular assist device (LVAD) implantation, and significant data incompleteness. Finally, 947 patients were screened and enrolled (Fig. 1).

2.2 Anesthesia and Extubation

All patients were preoperatively evaluated by an anesthesiologist. A standardized anesthesia protocol was used. Patients receive preoperative education before entering the operating room. Upon entry, routine monitoring, invasive arterial blood pressure, and central venous catheterization are conducted. Anesthesia induction is performed using etomidate, propofol, sufentanil, and cisatracurium. After anesthesia induction, a 0.375% ropivacaine solution mixed with 1 µg/kg dexmedetomidine (60 ml total) is used for fascial plane chest wall blocks (FPCWB) at three points: the serratus anterior and the fascial planes of the pectoral muscles, including PECS I and PECS II blocks. Anesthesia maintenance is achieved with continuous infusion of propofol and remifentanil. For patients undergoing UFTCA, the endotracheal tube is removed within one hour postoperatively, provided extubation criteria are met, and the patient is transferred to the ICU. In the CGA group, anesthesia maintenance drugs are administered continuously until the patient is transferred to the ICU.

After a comprehensive evaluation, the anesthesia maintenance medication was adjusted for patients who were considered to implement UFTCA. Patients were deemed suitable for extubation: (1) Stable hemodynamics with minimal vasopressor support [norepinephrine ≤ 0.05µg/(kg·min), dobutamine ≤ 5.0µg(kg·min)] and absence of refractory arrhythmias. (2) Exhibited minimal bleeding (drainage volume < 50 mL/hour), hemoglobin ≥ 80g/L, and ACT restored to preoperative baseline. (3) Arterial blood gas analysis displayed adequate oxygenation under spontaneous breathing (SPO2 ≥ 95% with FiO2 100%, PaO2 ≥ 60mmHg, PaCO2 35 ~ 45mmHg, pH 7.35 ~ 7.45) with no electrolyte imbalances. (4) Being fully alert with appropriate reversal of neuromuscular blockade and able to follow commands such as sticking out tongue, opening eyes, making a fist, and lifting the head. (5) Maintained normothermia (body temperature ≥ 36°C). The patients were subsequently transferred to the ICU after extubation. If extubation wasn't achievable within 30 minutes after the operation, patients were transferred to the anesthesia recovery room; if not possible within 60 minutes, UFTCA was abandoned, and patients were transferred to the ICU.

2.3 Statistical Analysis

Statistical analyses for this study were conducted using R version 4.3.3. P value less than 0.05 (P < 0.05) was considered statistically significant. Continuous data were summarized using medians and ranges, reflecting the inherent characteristics of the dataset. For categorical data, we utilized frequencies and percentages to convey the distribution of each category. To compare patient groups, the Mann-Whitney U test was applied to continuous variables, while chi-square tests were utilized for analyzing categorical variables. Traditional machine learning programs suggest a segmentation ratio of 7:3 for relatively small amounts of data (on the order of 10,000). Therefore, for model derivation, 663 patients were selected for the derivation set, and the remaining 284 patients were assigned to the internal validation set.

We used Univariate regression analysis. Variables with p < 0.05 were included in Multivariate logistic analysis. After a comprehensive evaluation of the above two steps, we finally looked for statistically significant indicators. Then, we constructed a nomogram. The scores corresponding to the results of each variable were summed to obtain the total score, which is the score of the successful implementation of UFTCA. The model was validated by internal validation. We used discrimination and calibration to evaluate the derivation and validation sets separately. The ability to discriminate (referring to the model's ability to discriminate those who successfully implement UFTCA) was evaluated using the receiver operating characteristic (ROC) curve, along with the area under the curve (AUC) as an indicator. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test, which compares the observed probabilities with those predicted by the model. A value of P > 0.05 indicated satisfactory calibration. Moreover, we used decision curve analysis (DCA) to help us understand the value of modeling in clinical decision-making.

3.1 Patients Baseline Clinical Characteristics

A total of 967 patients who underwent Right-Thoracoscopic Minimally Invasive Cardiac Surgery in our hospital from January 2021 to July 2023 were included, and 20 cases were excluded, including 5 cases of cardiogenic shock, 5 cases of septic shock, 2 cases requiring ECMO support, 3 cases of LVAD implantation, and 5 cases with incomplete clinical data (Fig. 1). Of the 947 patients ultimately enrolled in the study, 663 cases served as derived sets, and 284 patients were internal validation sets. The demographic and clinical characteristics of patients are summarized in Supplementary Tables S1. UFTCA was successfully performed in 439 of the total patients (46.36%).

3.2 Model Development

In the derivation set, univariate analysis identified 25 variables that showed a significant association with the implementation of UFTCA (Supplementary Tables S2). Multivariate analysis identified that operation types (Odds ratio (OR) = 5.05, 95%CI, 1.57 to 16.23, p = 0 .007), FPCWB(OR = 0.15, 95%CI, 0.08 to 00.30, p < 0 .001), intraoperative sufentanil dosage (OR = 1.04, 95%CI, 1.03 to 1.05, p < 0 .001) and dexamethasone (OR = 0.92, 95%CI, 0.87 to 0.98, p = 0 .012) dosage, CPB duration (OR = 1.02, 95%CI, 1.01 to 1.03, p = 0 .001), and Operation later than 8 p.m. (OR = 2.38, 95%CI, 1.40 to 4.07, p = 0 .001) were independent predictors of UFTCA administration (Table 1). A nomogram was developed using these six candidates to predict the probability of successful UFTCA implementation (Fig. 2).

Table 1

Multivariable Logistic Regression Model of UFTCA implementation
Variables	β	S.E	Z	P	OR (95%CI)
Intercept	-5.26	1.72	-3.05	0.002	0.01 (0.00 ~ 0.15)
Age	0.02	0.01	1.72	0.086	1.02 (1.00 ~ 1.04)
ASA Class
2					1.00 (Reference)
3	-0.68	0.93	-0.74	0.461	0.50 (0.08 ~ 3.11)
4	0.38	0.96	0.40	0.691	1.46 (0.22 ~ 9.60)
NYHA Class
2					1.00 (Reference)
3	0.17	0.25	0.69	0.488	1.19 (0.73 ~ 1.93)
4	0.03	0.46	0.07	0.948	1.03 (0.42 ~ 2.54)
EF	-0.03	0.01	-1.86	0.062	0.97 (0.95 ~ 1.00)
Coronary Artery Disease	0.46	0.33	1.40	0.160	1.58 (0.83 ~ 2.99)
Atrial Fibrillation	0.05	0.30	0.18	0.860	1.05 (0.58 ~ 1.90)
Pulmonary Hypertension	-0.01	0.26	-0.03	0.975	0.99 (0.59 ~ 1.66)
Stroke	0.20	0.49	0.41	0.680	1.22 (0.47 ~ 3.17)
Diabetes	0.65	0.49	1.32	0.187	1.91 (0.73 ~ 5.00)
Creatinine	0.00	0.00	1.38	0.168	1.00 (1.00 ~ 1.01)
Prothrombin time	0.05	0.03	1.88	0.061	1.05 (1.00 ~ 1.11)
Fibrinogen	0.19	0.14	1.35	0.175	1.21 (0.92 ~ 1.60)
D-dimer	0.00	0.00	1.79	0.073	1.00 (1.00 ~ 1.00)
Operation types
LVOT Obstruction					1.00 (Reference)
Isolated MVR	0.72	0.37	1.94	0.052	2.06 (0.99 ~ 4.26)
Isolated TVR	0.81	0.81	1.00	0.316	2.26 (0.46 ~ 11.06)
Isolated AVR	0.27	0.42	0.63	0.530	1.30 (0.57 ~ 2.99)
DVR	0.30	0.39	0.76	0.447	1.35 (0.63 ~ 2.89)
Multiple valve surgery	1.62	0.60	2.72	0.007	5.05 (1.57 ~ 16.23)
Atrial Septal Defect Repair	-1.46	0.75	-1.95	0.051	0.23 (0.05 ~ 1.01)
Aortic replacement	0.46	0.63	0.72	0.471	1.58 (0.46 ~ 5.46)
Aortic valve combined with ascending aortic replacement	-0.14	0.88	-0.16	0.873	0.87 (0.15 ~ 4.92)
Other	1.20	0.69	1.74	0.082	3.33 (0.86 ~ 12.95)
Fascial plane chest wall blocks	-1.88	0.34	-5.50	< .001	0.15 (0.08 ~ 0.30)
Length of surgery	0.00	0.00	1.70	0.090	1.00 (1.00 ~ 1.01)
CPB Duration	0.02	0.01	3.25	0.001	1.02 (1.01 ~ 1.03)
Aortic clamp time	-0.01	0.01	-1.42	0.155	0.99 (0.98 ~ 1.00)
Sufentanil	0.04	0.01	7.10	< .001	1.04 (1.03 ~ 1.05)
Remifentanil	-0.10	0.13	-0.75	0.451	0.91 (0.71 ~ 1.17)
Methylprednisolone	0.00	0.00	0.80	0.423	1.00 (1.00 ~ 1.00)
Dexamethasone	-0.08	0.03	-2.52	0.012	0.92 (0.87 ~ 0.98)
Liquid Infusion	0.00	0.00	0.55	0.585	1.00 (1.00 ~ 1.00)
Blood Output	0.00	0.00	0.24	0.812	1.00 (1.00 ~ 1.00)
Operation later than 8 p.m.	0.87	0.27	3.18	0.001	2.38 (1.40 ~ 4.07)
Abbreviation: ASA, American Society of Anesthesiologists; AVR, aortic valve replacement; CI, Confidence Interval CPB, Cardiopulmonary Bypass; DVR, Double Valve Replacement; EF, Ejection Fraction; LVOT, Left Ventricular Outflow Tract; MVR, mitral valve replacement; NYHA, New York Heart Association; OR, Odds Ratio; TVR, Tricuspid Valve Replacement

3.3 Model performance and internal validation

The nomogram's area under the curve (AUC) was 0.869 (95% CI: 0.842–0.895) for the derivation set and 0.862 (95% CI: 0.842–0.895) for the validation set (Fig. 3a and b). The calibration curves in the two sets consistently showed a close match between the predicted values and the observed values, validating the excellent calibration of the developed predictive model. (Fig. 4a and b). The Hosmer–Lemeshow test produced non-significant P values of 0.86 and 0.247 for the derivation and validation sets, respectively. These results indicate strong concordance between the predicted and observed implementation of UFTCA. DCA was employed to assess the clinical utility of the model (Fig. 5a and b). The analysis revealed that employing the nomogram's risk estimates led to higher net benefits for patients across a broad spectrum of decision thresholds, ranging from 10–95% probability. This also implied that the nomogram constructed for this research possessed strong discriminative performance, well-calibrated predictions, meaningful clinical applicability, and the capacity to be applied to diverse patient cohorts.

Both UFTCA and MICS are important components of ERACS. However, the implementation of UFTCA is not currently widely practiced in MICS. Since 2021, our center has been the administrator of the UFTCA under MICS, and with satisfactory results⁹. The study provided a validated nomogram for successful UFTCA implementation to help clinical decision-making. In this study, six independent predictors were identified and used to construct a novel nomogram for estimating UFTCA implementation. This nomogram demonstrated good AUC and calibration during internal validation. Additionally, the DCA confirmed the nomogram's clinical utility. This indicated that making intervention decisions using the predictive model was advantageous when the threshold risk ranged from 10–95%.

Studies of immediate extubation after cardiac surgery focused on neonates and children^18–20. Relatively little research has been done on predictors of UFTCA administration in adults. Several studies have identified potential predictors of immediate postoperative extubation^21,22, including age, body mass index (BMI), diabetes, operation types, fentanyl dose, paravertebral blockade, and intraoperative transfusion. We also searched for predictors related to the administration of FTCA under cardiac surgery^23,24, such as recent acute coronary syndrome, preoperative renal function, and cardiopulmonary bypass time. By comparison, our study showed that intraoperative sufentanil and dexamethasone dose, CPB duration, pectoral muscle fascial plane blockade usage, operation types, and operation ended after 8 p.m. were independent factors for UFTCA implementation. Among these predictors, intraoperative dexamethasone dose and operation ended after 8 p.m. were rarely mentioned in previous literatures.

Dexamethasone is commonly used for its anti-inflammatory and against postoperative nausea and vomiting (PONV) effects. Glucocorticoid therapy has demonstrated significant beneficial effects on measures of alveolar-capillary membrane permeability, as well as on the mediators of inflammation and tissue repair²⁵. A study showed that 20 mg dexamethasone applied early significantly decreased both the duration of mechanical ventilation and mortality in patients with acute lung injury²⁶. In our right-thoracoscopic MICS procedure, the right lung needs to be deflated, which would likely lead to re-expansion lung injury. The use of dexamethasone may mitigate lung injury and reduce pulmonary complications. Moreover, dexamethasone was effective in preventing PONV^27,28, reducing sore throat and hoarseness after tracheal tube removal²⁹. These demonstrated that dexamethasone may facilitate the successful performance of UFTCA. Another important finding of this study was the influence of surgical endpoint on UFTCA implementation. There was evidence that night-time surgery may have a higher rate of postoperative complications as well as adverse events than daytime surgery³⁰. Also, if the surgeon worked continuously from day to night, the incidence of postoperative complications would increase greatly³¹. This suggested that an appropriate workload could ensure the work status of the surgeon and increase the security of the patient during the hospitalization. In addition, anesthesiologists need to pay more attention to the implementation of UFTCA which may increase the anesthesiologist's workload and lead them to choose more conservative anesthetic protocols.

FTCA and UFTCA were based on administering relatively small doses of short-acting opioids, which were supplemented with either propofol or other anesthetic agents^32,33. As a result, a significant reduction in sufentanil dose was an independent predictor of UFTCA implementation. However, reduction of intraoperative opioid use is controversial, because it may lead to an increased need for postoperative analgesia^34,35. FPCWB was performed in UFTCA in our center to alleviate postoperative analgesia. Our previous observational study showed that the incidence of ICU rescue analgesia was lower in patients who used FPCWB than in those who did not⁹. Continuous deep FPCWB was shown to improve postoperative pain and reduce postoperative opioid requirements from the research of Toscano et al³⁶. CPB duration was an independent predictor of UFTCA implementation in this study. CPB, a non-physiological circulation employed during cardiac surgery, can lead to pulmonary endothelial damage, and increased pulmonary vascular resistance. There were many studies showing that CPB duration was an independent risk factor for complications after cardiac surgery, such as renal injury and cerebral microemboli^37,38. CPB duration may be correlated with the complexity of procedure. The result showed that multiple valve surgery was an independent risk factor of UFTCA implementation. Patients undergoing a single procedure are more likely to be successfully extubated than complex procedures.

This study had several limitations. First, this model is about the implementation of UFTCA in MICS and could not work for all types of cardiac surgery. Second, the preferences of anesthesiologists and surgeons for UFTCA could not be controlled during the observational period. Thirdly, this study is a retrospective analysis conducted at a single center and does not include external data validation. To confirm the model’s validity, further prospective studies across multiple centers are necessary. Driven by the progress in machine learning, a wider array of feature variable filtering approaches have been developed. Expanding the number of variables considered and applying different screening strategies can enable the construction of enhanced predictive models.

In this study, Univariate and Multivariate logistic analysis were combined to screen 6 predictors and constructed a nomogram to predict the implementation of UFTCA. Among them, operation types, intraoperative sufentanil dosage, CPB duration and Operation later than 8 p.m. are hazard factors; FPCWB and intraoperative dexamethasone dosage are beneficial factors. The nomogram performed well when evaluated for its discriminative ability, calibration, and practical clinical use. The findings indicate its potential to help decision-making to implement UFTCA in patients undergoing MICS.

Author contributions: All authors contributed to the study and the manuscript. Tian Jiang and Meijuan Yan designed the study and wrote the first draft of the manuscript; Linting Xu and Hanwei Wei provided the statistical analysis. Data collection and analysis were performed by Haozhou Wang, Yihui Zhang, Qinghui Zheng, Xiaokan Lou, and Jinchen Guo. All authors read and approved the final manuscript.

Competing interests: The authors declare no competing interests.

Data availability: The data underlying this article will be shared on reasonable request to the corresponding author.

Consent for publication: All authors consent for the publication of this study.

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No competing interests reported.

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A novel nomogram for predicting the implementation of ultra-fast-track cardiac anesthesia for minimally invasive cardiac surgery

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Abstract

Background

Methods

Results

Conclusion

Figures

1 Introduction

2 Materials and Method

2.1 Participants

2.2 Anesthesia and Extubation

2.3 Statistical Analysis

3 Results

3.1 Patients Baseline Clinical Characteristics

3.2 Model Development

3.3 Model performance and internal validation

4 Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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