Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy

doi:10.21203/rs.3.rs-8127609/v1

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Research Article

Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy

https://doi.org/10.21203/rs.3.rs-8127609/v1

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Background

Delayed extubation after anesthesia can lead to adverse clinical outcomes. An investigation was undertaken to identify the risk factors contributing to a delay extubation of patients undergoing robot-assisted radical prostatectomy and to develop a visualized nomogram prediction model for clinical use.

Methods

A total of 624 patients were included and divided into training group, validation group, and external validation group. The training group was utilized to develop a nomogram, whereas the validation group and external validation group was used to assess its performance. LASSO regression was employed to refine variables and choose predictors, and a nomogram was constructed using multivariate logistic regression. The performance of the model was internally validated using calibration and receiver operating characteristic curves. Additionally, decision curve analysis and clinical impact curves were used to assess the clinical utility of the model.

Results

Patients between January 2022 and April 2024 were included and divided into training group (n = 389), validation group (n = 98), and external validation group (n = 137). Logistic regression identified cerebral infarction, pulmonary disease, coronary heart disease, age, and intraoperative hypotension as independent predictors of delayed extubation. A nomogram constructed based on these factors demonstrated excellent predictive performance, with area under the curve values of 0.763 (95% CI: 0.717–0.810) in the train group, 0.811 (95% CI: 0.726–0.897) in the validation group, and 0.769 (95% CI: 0.689–0.848) in the external validation group. Across all three group, the model demonstrated a good fit, as indicated by a non-significant Hosmer-Lemeshow test statistic, and the calibration curves indicated a strong alignment between the predictions and actual observations. Furthermore, decision curve analysis and clinical impact curve demonstrated the clinical efficiency and benefits of the prediction model.

Conclusion

This study identified key risk factors for delayed extubation and established an effective predictive nomogram with high discriminative ability and clinical applicability for predictive the risk of extubation delay in patients undergoing robot-assisted radical prostatectomy.

Trial registration

The Medical Ethics Committee of Nanjing Drum Tower Hospital granted ethical approval for this research(grant number: 2024-742-01)

Anesthesia

Nomogram

Robotic-assisted radical prostatectomy

Prostate cancer

Airway extubation

Tracheal extubation at the conclusion of surgery is a crucial step in recovery from general anesthesia. Delayed extubation after surgery can increase the risk of respiratory complications, including laryngeal injury, dysphagia, pneumonia, and glottic stenosis[1–4]. Previous research has substantiated that early removal of tracheal catheters in patients undergoing esophagectomy or cardiac surgery can reduce lung complications and promote hemodynamic stability[5, 6]. Moreover, delayed extubation imposes a heavier workload on medical staff and reduces the turnover rate in the operating room [7]. Therefore, identifying the risk factors associated with delayed extubation and establishing a clinical prediction model are vital. The timely recognition of high-risk individuals and subsequent administration of tailored interventions can effectively reduce the incidence of delayed extubation and prevent perioperative complications.

Prostate cancer significantly affects the male population globally, and is the second most prevalent cancer in men [8, 9]. It accounts for 7% of newly diagnosed cancer cases in men worldwide and increases to 15% in developed regions. Furthermore, the annual number of newly diagnosed cases exceeds 1.2 million, with over 350,000 deaths attributable to prostate cancer annually [10, 11]. These findings suggest that prostate cancer stands as a major contributor to cancer-specific deaths in men.

The treatment strategy with the highest efficacy for prostate cancer is currently radical resection, which is considered a gold-standard approach. Robot-assisted radical resection of prostate cancer is an emerging minimally invasive surgical technique that can effectively reduce operation time, surgical trauma, intraoperative blood loss, and postoperative complications compared with traditional laparoscopic radical resection; therefore, it is widely used in clinics [12, 13]. There is a strong correlation between advancing age and the incidence of prostate cancer, as evidenced by the fact that over 85% of new diagnoses are concentrated in individuals over 60 years of age [14]. A positive correlation has been observed between advanced patient age and an increased incidence of postoperative extubation delay. [15]. However, to date, no study has investigated the predictive factors for delayed extubation after robot-assisted radical resection of prostate cancer. Therefore, a retrospective analysis was conducted on the clinical data of patients who underwent robot-assisted radical prostatectomy, with the objectives of identifying risk factors for delayed extubation, developing a predictive nomogram, and internally validating the model to aid in clinical prognostication.

Study design and patients

This study is a single-center retrospective study, which was approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital (grant number: 2024-742-01). We retrospectively collected data from 750 patients who underwent robot-assisted radical resection for prostate cancer between January 2022 and April 2024. After exclusion, 624 patients were included in the study to investigate the factors associated with delayed extubation. Among them, 4according to a predefined 4:1 ratio, the cohort of 487 patients (January 2022 to October 2023) was randomly divided into training and validation groups and 112 patients from November 2023 to April 2024 were divided into the external validation group.

The inclusion criteria for this study were as follows: 1) No severe hearing or visual impairment prior to surgery. 2) No dementia, coma, or disturbance of consciousness prior to surgery. 3) The patient was diagnosed with prostate cancer and underwent robot-assisted radical resection of prostate cancer. 4) Patients were extubated in the operating room. The exclusion criteria were as follows: 1) Incomplete clinical data.

2) Patients who died within 24 h after surgery.The study flow diagram is shown in Fig. 1.

Delayed extubation

Extubation time was defined as the interval from the cessation of anesthetic administration to removal of the tracheal tube. The timing of extubation was comprehensively evaluated by a registered anesthesiologist and Post-anesthetic care unit nurses considering the patient's spontaneous breathing efforts, neurological status, motor function, and other relevant clinical indicators for extubation readiness[16]. Ultimately, the determination regarding tracheal catheter management (removal or retention) was made by a registered anesthesiologist Previous studies [17, 18] have shown that extubation time exceeding 1 h can lead to increased clinical complications. Therefore, the patients were divided into two groups: the conventional (extubation time ≤ 1 h) and delayed (extubation time > 1 h) groups.

Data collection

We collected patient data after screening for the inclusion and exclusion criteria. Preoperative examination data and basic patient information included preoperative white blood cell count, neutrophil percentage, glucose, creatinine, serum potassium, serum calcium, and blood pressure; ASA grade; age; body mass index; smoking status; anemia (preoperative Hb < 120 g/L); hypoproteinemia (total serum protein < 60 g/L or albumin < 35 g/L); alanine transaminase levels; and presence of hypertension, diabetes, cerebral infarction, pulmonary disease, and coronary heart disease. Intraoperative data included the volume of crystalloid and colloid infusions, intraoperative blood transfusion, urine volume, intraoperative blood loss, intraoperative glucose levels, intraoperative serum potassium levels, and MAP.

Statistical analysis

Continuous variables were summarized according to their distribution: normally distributed data as mean ± standard deviation and compared using the independent samples t-test, while non-normally distributed data as median with interquartile range and compared using the Mann-Whitney U test. Categorical variables were expressed as percentages (%) and compared with the chi-square test or Fisher’s exact test, as appropriate[19]. All variables were subjected to a LASSO regression analysis for variable selection and risk factor identification. Variables selected by LASSO were incorporated into a multivariable logistic regression model, and a nomogram was developed based on the results. We assessed the model's discriminatory power using ROC analysis, with performance quantified by AUC. Internal validation of the model was performed employing the bootstrapping technique with 500 replicates. Simultaneously, the Hosmer-Lemeshow test was utilized to evaluate the model's goodness-of-fit.The clinical utility of the model was analyzed using decision curve DCA and CIC. The data analysis was carried out in R (version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria). P-value below 0.05 was considered to indicate statistical significance.

Demographics of the training group

The study site was a tertiary hospital in China. A total of 750 patients undergoing robot-assisted radical prostatectomy between January 2022 and April 2024 were retrospectively collected. Because this was an observational study, 126 patients with incomplete clinical data were excluded from the group. After exclusion, 624 patients were included in the study, among them 487 patients from January 2022 to October 2023 were subsequently randomized into training and validation group at a 4:1 ratio using the random sampling function in R software. Of the training group, 204 (52.6%) experienced delayed extubation after surgery.

Table 1 summarizes the characteristics of the patients who participated in the study, comparing those who experienced delayed extubation with those who did not. Patients with delayed extubation were older than those without delayed extubation (P < 0.001) and had a higher incidence of anemia (P = 0.022), cerebral infarction (P = 0.003), pulmonary disease (P < 0.001), and coronary heart disease (P < 0.001). Simultaneously, in comparison to patients without delayed extubation, the incidence of intraoperative hypotension was significantly higher among patients with delayed extubation (P = 0.004).

Table 1

— Patients’ characteristics in the training group
Variable	Total(n = 389)	Delayed extubation		p
Variable	Total(n = 389)	No(n = 184)	Yes(n = 205)	p
ASA, n (%)				0.23
2	11 (3%)	8 (4%)	3 (1%)
3	361 (93%)	168 (91%)	193 (94%)
4	17 (4%)	8 (4%)	9 (4%)
preoperative white blood cell count (×10⁹/L) Median (Q1,Q3)	5.6 (4.6, 6.5)	5.7 (4.77, 6.8)	5.5 (4.5, 6.4)	0.089
preoperative neutrophil percentage (%), Mean ± SD	59.6 (54.5, 65)	59.8 (53.8, 64.82)	59.4 (55.5, 65.1)	0.774
anemia, n (%)				0.022
No	343 (88%)	170 (92%)	173 (84%)
Yes	46 (12%)	14 (8%)	32 (16%)
hypoproteinemia, n (%)				0.581
No	331 (85%)	159 (86%)	172 (84%)
Yes	58 (15%)	25 (14%)	33 (16%)
Preoperative glucose (mmol/L), Median (Q1,Q3)	4.78 (4.41, 5.39)	4.74 (4.45, 5.32)	4.8 (4.38, 5.56)	0.945
preoperative creatinine (µmol/L), Median (Q1,Q3)	70 (62, 78)	69 (62, 76)	71 (62.1, 81)	0.159
Preoperative serum potassium (mmol/L), Median (Q1,Q3)	3.92 (3.71, 4.15)	3.92 (3.71, 4.14)	3.94 (3.71, 4.16)	0.677
Preoperative serum calcium (mmol/L), Median (Q1,Q3)	2.34 (2.26, 2.42)	2.35 (2.29, 2.42)	2.33 (2.25, 2.43)	0.129
alanine transaminase (U/L), Median (Q1,Q3)	17.5 (13, 23.8)	18.1 (13, 24)	16.8 (13, 23.7)	0.714
hypertension, n (%)				0.034
No	157 (40%)	85 (46%)	72 (35%)
Yes	232 (60%)	99 (54%)	133 (65%)
diabetes, n (%)				0.244
No	315 (81%)	154 (84%)	161 (79%)
Yes	74 (19%)	30 (16%)	44 (21%)
cerebral infarction, n (%)				0.003
No	315 (81%)	161 (88%)	154 (75%)
Yes	74 (19%)	23 (12%)	51 (25%)
pulmonary disease, n (%)				< 0.001
No	226 (58%)	133 (72%)	93 (45%)
Yes	163 (42%)	51 (28%)	112 (55%)
coronary heart disease, n (%)				< 0.001
No	345 (89%)	177 (96%)	168 (82%)
Yes	44 (11%)	7 (4%)	37 (18%)
age, Median (Q1,Q3)	71 (66, 76)	69 (63.75, 74)	73 (68, 78)	< 0.001
BMI (Kg/m²), Median (Q1,Q3)	24.02 (22.49, 25.71)	24.3 (22.52, 25.95)	23.67 (22.49, 25.34)	0.356
smoking, n (%)				1
No	274 (70%)	130 (71%)	144 (70%)
Yes	115 (30%)	54 (29%)	61 (30%)
crystalloid infusion volume (ml), Median (Q1,Q3)	1000 (1000, 1500)	1000 (1000, 1300)	1000 (1000, 1500)	0.337
colloids infusion volume (ml), Median (Q1,Q3)	1000 (500, 1000)	500 (500, 1000)	1000 (500, 1000)	0.051
Intraoperative infusion volume (ml), Median (Q1,Q3)	2000 (1500, 2000)	2000 (1500, 2000)	2000 (1500, 2000)	0.633
intraoperative blood transfusion (ml), n (%)				0.063
No	384 (99%)	184 (100%)	200 (98%)
Yes	5 (1%)	0 (0%)	5 (2%)
urine volume (ml), Median (Q1,Q3)	400 (300, 700)	400 (300, 600)	400 (250, 800)	0.74
intraoperative blood loss (ml), Median (Q1,Q3)	100 (100, 100)	100 (100, 100)	100 (100, 100)	0.855
intraoperative glucose (mmol/L), Median (Q1,Q3)	5.9 (5.23, 6.73)	6 (5.2, 6.82)	5.75 (5.23, 6.7)	0.473
intraoperative serum potassium (mmol/L), Median (Q1,Q3)	3.57 (3.37, 3.8)	3.6 (3.35, 3.8)	3.55 (3.4, 3.8)	0.791
intraoperative hypotension, n (%)				0.004
MAP > 80% of baseline	103 (26%)	57 (31%)	46 (22%)
MAP 70%-80༅ of baseline	139 (36%)	70 (38%)	69 (34%)
MAP < 70% of baseline	113 (29%)	50 (27%)	63 (31%)
MAP < 65mmHg	34 (9%)	7 (4%)	27 (13%)

Model development

This study utilized the LASSO regression model to analyze 27 variables obtained from patients, and 5 variables were identified as significant predictors due to non-zero coefficients. These selected variables were age, cerebral infarction, pulmonary disease, coronary heart disease and intraoperative hypotension (Fig. 2).

The five variables selected from the LASSO regression analysis were used to performed a multivariate logistic regression for build a predictive model for delayed extubation. The results from this model identified that age (OR: 1.064, 95% CI: 1.031–1.098, P < 0.001), cerebral infarction (OR: 1.875, 95% CI: 1.035–3.399, P < 0.038), pulmonary disease (OR: 3.213, 95% CI: 2.027–5.092, P < 0.001), coronary heart disease (OR: 4.878, 95% CI: 2.029–11.728, P < 0.001), MAP 70%–80% of baseline (OR: 1.219, 95% CI: 0.689–2.154, P = 0.496), MAP < 70% of baseline (OR: 1.576, 95% CI: 0.867–2.863, P = 0.135), and MAP < 65 mmHg (OR: 3.802, 95% CI: 1.356–10.657, P = 0.011) were independent predictive variables of delayed extubation (Table 2). Based on the multivariate logistic regression, a clinical prediction nomogram was developed (Fig. 3A). Each patient received a points assignment for the presence of each risk factor, with the aggregate score then being transformed into an estimated probability of delayed extubation. The probability of delayed extubation for a randomly selected patient was 83.9%, and the patient actually encountered extubation delay clinically, suggesting that the clinical applicability of the scoring system is favorable (Fig. 3B) .

Table 2

Multivariate analysis of the predictive factors in the training group.
Variable	B	OR(95%CI)	P
cerebral infarction	0.629	1.875(1.035–3.399)	0.038
pulmonary disease	1.167	3.213(2.027–5.092)	<0.001
coronary heart disease	1.585	4.878(2.029–11.728)	<0.001
age	0.062	1.064(1.031–1.098)	<0.001
intraoperative hypotension
MAP 70%-80༅ of baseline	0.198	1.219(0.689–2.154)	0.496
MAP < 70% of baseline	0.455	1.576(0.867–2.863)	0.135
MAP < 65mmHg	1.336	3.802(1.356–10.657)	0.011

Evaluation and validation of the nomogram model

To evaluate the predictive performance and clinical utility of the model, ROC curve analysis was employed. AUC was 0.763 (95% CI: 0.717–0.810) for the training group (Fig. 4A), 0.811 (95% CI: 0.726–0.897) for the validation group ( Fig. 4B), and 0.769 (95% CI: 0.689–0.848) for the external validation group (Fig. 4C). These results indicate that the nomogram model exhibits strong predictive accuracy for delayed extubation.

The Hosmer–Lemeshow test yielded non-significant P-values of 0.707 in the training group, 0.401 in the validation group, and 0.735 in the external validation group, indicating a satisfactory goodness of fit for the model.

After 500 bootstrap resampling iterations, the mean absolute error between the simulated and actual curves was 0.011 in the training group (Fig. 5A), 0.047 in the validation group (Fig. 5A), and 0.046 in the external validation group (Fig. 5A). These results suggest that the trend trajectories of the two curves were closely aligned and exhibited strong consistency.

DCA demonstrated that the nomogram model yielded higher net benefits compared to the treat-none and treat-all strategies across the training (Fig. 6A), validation (Fig. 6B), and external validation (Fig. 6C) groups. Across all group, the nomogram showed a consistently high net benefit (training: 23%-99%; validation: 21%-89%; external validation: 18%-90%). This supports its utility as an effective predictor of delayed extubation risk and a guide for clinical decision-making.

CIC (Fig. 7A-C) was generated by predicting risk stratification among 1000 individuals using bootstrap resampling. The red curve traces the count of patients classified as high-risk for delayed extubation at varying threshold probabilities, whereas the blue curve tracks the concomitant number of true positives. Across all three group, the model demonstrated consistent effectiveness in identifying high-risk patients throughout nearly the entire spectrum of threshold probabilities, indicating robust clinical applicability.

Prolonged mechanical ventilation is associated with adverse clinical outcomes, higher morbidity, and longer hospital stay [20]. Prostate cancer is the most common cancer among men, particularly older men [21]. Some studies have indicated that advanced age may lead to delayed extubation [15], However, to the best of our knowledge, no study has investigated delayed extubation after anesthesia in patients undergoing robot-assisted radical prostatic cancer surgery. Therefore, a preoperative assessment system is required to predict the risk of delayed extubation in patients undergoing robot-assisted radical prostate cancer surgery.

We developed and validated a predictive model for delayed extubation, which showed good discrimination and calibration for individualized prediction. Our predictive model was based on 5 predictive factors: age, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension.

Consistent with previous studies [1, 22–25], our research also identified age, anemia, coronary heart disease, colloid infusion volume, and intraoperative blood transfusion as risk factors associated with delayed extubation. Although anemia did not reach statistical significance in the multivariate analysis, a significant association was observed in univariate analysis. Similarly, colloid infusion volume showed a suggestive difference in univariate analysis but failed to achieve statistical significance. A trend was also noted for intraoperative blood transfusion; however, likely due to the limited number of positive cases, it did not reach a statistically significant level.

Compared with previous studies, our results also identified pulmonary disease, cerebral infarction, and intraoperative hypotension as significant predictors of delayed extubation. Tong et al [15] reported that a lower FEV₁/FVC ratio was an independent risk factor for delayed extubation, while Rasera et al [26] found that elevated end-tidal carbon dioxide was associated with extubation failure, collectively underscoring the impact of pulmonary function on extubation outcomes. In line with these findings, our study confirmed pulmonary disease as an independent predictor. Chen et al [27] found that patients with a history of cerebral infarction before surgery had a higher risk of extubation failure, highlighting the role of central nervous system in postoperative extubation. Our analysis similarly showed an association between previous cerebral infarction and delayed extubation. Additionally, intraoperative hypotension may contribute to impaired cerebral perfusion and disrupted neural regulation, thereby increasing the risk of delayed extubation.

The nomogram was developed to enhance the visualization of the model and promote its clinical applicability. The predictive model exhibited strong discriminatory ability, with AUC of 0.763, 0.811, and 0.769 in the training, validation, and external validation groups, respectively. Both the Hosmer-Lemeshow test and calibration curves indicated good model consistency across all three groups. Furthermore, the DCA and CIC graphically confirmed that a significant net clinical benefit for the nomogram. This finding further validated the model's applicability for clinical decision-making.

The majority of patients undergoing robot-assisted radical prostatectomy are elderly men, who demonstrate a higher probability of delayed extubation. However, there is currently a scarcity of research focused on this specific clinical scenario. To address this gap, this study developed a nomogram prediction model for delayed extubation based on the following predictors: age, anemia, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension.

Despite its contributions, this study has several limitations. First, as a single-center investigation, it may be influenced by center-specific biases, and the generalizability of our findings could be limited. Therefore, future multicenter prospective studies are warranted to validate these results. Second, the relatively small number of cases involving intraoperative blood transfusion may have reduced the statistical power to detect significant associations, potentially affecting the reliability of this specific result.

This study showed that age, anemia, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension were significantly associated with delayed extubation. Furthermore, the developed nomogram facilitated the prediction of the risk of delayed extubation, which can assist clinical practitioners in formulating personalized treatment plans for each patient.

MAP

Intraoperative mean arterial pressure

LASSO

Least absolute shrinkage and selection operator

ROC

Receiver operating characteristic

AUC

Area under the curve

DCA

Decision curve analysis

CIC

Clinical impact curve

FEV₁

Forced expiratory volume in 1 second

FVC

Forced vital capacity

Acknowledgements

We extend our sincere gratitude to our colleagues in the Department of Anesthesiology, Nanjing Drum Tower Hospital, for their support of this work.

Contributions

Xiaoping Gu contributed to the study protocol and manuscript preparation. Tantan Fang was responsible for data collection and performed the statistical analysis. ChuanFei Liu assisted in the statistical analysis and was involved in interpreting the results.

Animal studies

Not applicable.

Funding

Not applicable.

Author details

¹Department of Anesthesiology, Nanjing Drum Tower Hospital, Nanjing, China; ²Department of Anesthesiology,The Affiliated Hospital of Nanjing University Medical School, Nanjing, China

Corresponding authors

Xiao-Ping Gu , Department of Anesthesiology, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, 210008, Nanjing, China.

Data availability

Upon reasonable request, the corresponding author can provide the datasets used and/or analyzed in this study.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital with the reference number 2024-742-01, and in compliance with the Helsinki Declaration. Given that this study was conducted using anonymized observational data, individual informed consent for participation was waived by the Committee on Medical Ethics of Nanjing Drum Tower Hospital.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Trial registration

Figures

Introduction

Materials and methods

Study design and patients

Delayed extubation

Data collection

Statistical analysis

Results

Demographics of the training group

Model development

Evaluation and validation of the nomogram model

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1