Predictors of Prolonged Mechanical Ventilation in Adults After Acute Type-A Aortic Dissection Repair

A Predictive Model for Prolonged Mechanical Ventilation After Triple-Branched Stent Graft for Acute Type A Aortic Dissection

INTRODUCTION

The aim of this study is to develop a model for predicting the risk of prolonged mechanical ventilation (PMV) following surgical repair of acute type A aortic dissection (AAAD).

METHODS

We retrospectively collected clinical data from 381 patients with AAAD who underwent emergency surgery. Clinical features variables for predicting postoperative PMV were selected through univariate analysis, least absolute shrinkage and selection operator regression analysis, and multivariate logistic regression analysis. A risk prediction model was established using a nomogram. The model's accuracy and reliability were evaluated using the area under the curve of the receiver operating characteristic curve and the calibration curve. Internal validation of the model was performed using bootstrap resampling. The clinical applicability of the model was assessed using decision curve analysis and clinical impact curve.

RESULTS

Among the 381 patients, 199 patients (52.2%) experienced postoperative PMV. The predictive model exhibited good discriminative ability (area under the curve = 0.827, 95% confidence interval: 0.786-0.868, P < 0.05). The calibration curve confirmed that the predicted outcomes of the model closely approximated the ideal curve, indicating agreement between the predicted and actual results (with an average absolute error of 0.01 based on 1000 bootstrap resampling). The decision curve analysis curve demonstrated that the model has significant clinical value.

CONCLUSIONS

The nomogram model established in this study can be used to predict the risk of postoperative PMV in patients with AAAD. It serves as a practical tool to assist clinicians in adjusting treatment strategies promptly and implementing targeted therapeutic measures.

Establishment of a nomogram for predicting prolonged mechanical ventilation in cardiovascular surgery patients

AIMS

This study aimed to develop a nomogram model for predicting prolonged mechanical ventilation (PMV) in patients undergoing cardiovascular surgery.

METHODS AND RESULTS

In total, 693 patients undergoing cardiovascular surgery at an Affiliated Hospital of Nantong University between January 2018 and June 2020 were studied. Postoperative PMV was required in 147 patients (21.2%). Logistic regression analysis showed that delirium [odds ratio (OR), 3.063; 95% confidence interval (CI), 1.991-4.713; P < 0.001], intraoperative blood transfusion (OR, 2.489; 95% CI, 1.565-3.960; P < 0.001), obesity (OR, 2.789; 95% CI, 1.543-5.040; P = 0.001), postoperative serum creatinine level (mmol/L; OR, 1.012; 95% CI, 1.007-1.017; P < 0.001), postoperative serum albumin level (g/L; OR, 0.937; 95% CI, 0.902-0.973; P = 0.001), and postoperative total bilirubin level (μmol/L; OR, 1.020; 95% CI, 1.005-1.034; P = 0.008) were independent risk factors for PMV. The area under the receiver operating characteristic curve for our nomogram was found to be 0.770 (95% CI, 0.727-0.813). The goodness-of-fit test indicated that the model fitted the data well (χ2 = 12.480, P = 0.131). After the model was internally validated, the calibration plot demonstrated good performance of the nomogram, as supported by the Harrell concordance index of 0.760. Decision curve analysis demonstrated that the nomogram was clinically useful in identifying patients at risk for PMV.

CONCLUSION

We established a new nomogram model that may provide an individual prediction of PMV. This model may provide nurses, social workers, physicians, and administrators with an accurate and objective assessment tool to identify patients at high risk for PMV after cardiovascular surgery.

Risk factors for in-hospital mortality after total arch procedure in patients with acute type A aortic dissection

Object

Knowledge about the risk factors of in-hospital mortality for acute type A aortic dissection (ATAAD) patients who received total arch procedure is limited. This study aims to investigate preoperative and intraoperative risk factors of in-hospital mortality of these patients.

Methods

From May 2014 to June 2018, 372 ATAAD patients received the total arch procedure in our institution. These patients were divided into survival and death groups, and patients` in-hospital data were retrospectively collected. Receiver operating characteristic curve analysis was adopted to determine the optimal cut-off value of continuous variables. Univariate and multivariable logistic regression analyses were used to detect independent risk factors for in-hospital mortality.

Results

A total of 321 patients were included in the survival group and 51 in the death group. Preoperative details showed that patients in the death group were older (55.4 ± 11.7 vs. 49.3 ± 12.6, P = 0.001), had more renal dysfunction (29.4% vs. 10.9%, P = 0.001) and coronary ostia dissection (29.4% vs. 12.2%, P = 0.001), and decreased left ventricular ejection fraction (LVEF) (57.5 ± 7.9% vs. 59.8 ± 7.3%, P = 0.032). Intraoperative results showed that more patients in the death group experienced concomitant coronary artery bypass grafting (35.3% vs. 15.3%, P = 0.001) with increased cardiopulmonary bypass (CPB) time (165.7 ± 39.0 vs. 149.4 ± 35.8 min, P = 0.003), cross-clamp time (98.4 ± 24.5 vs. 90.2 ± 26.9 min, P = 0.044), and red blood cell transfusion (913.7 ± 629.0 vs. 709.7 ± 686.6 ml, P = 0.047). Logistic regression analysis showed that age >55 years, renal dysfunction, CPB time >144 min, and RBC transfusion >1,300 ml were independent risk factors for in-hospital mortality in patients with ATAAD.

Conclusion

In the present study, we identified that older age, preoperative renal dysfunction, long CPB time, and intraoperative massive transfusion were risk factors for in-hospital mortality in ATAAD patients with the total arch procedure.

Nomogram and Risk Calculator for Postoperative Tracheostomy after Heart Valve Surgery

Postoperative tracheostomy (POT) is an important indicator of critical illness, associated with poorer prognoses and increased medical burdens. However, studies on POTs after heart valve surgery (HVS) have not been reported. The objectives of this study were first to identify the risk factors and develop a risk prediction model for POTs after HVS, and second to clarify the relationship between POTs and clinical outcomes. Consecutive adults undergoing HVS from January 2016 to December 2019 in a single cardiovascular center were enrolled, and a POT was performed in 1.8% of the included patients (68/3853). Compared to patients without POTs, the patients with POTs had higher rates of readmission to the ICU and in-hospital mortality, as well as longer ICU and hospital stays. Five factors were identified to be significantly associated with POTs after HVS by our multivariate analysis, including age, diabetes mellitus, pulmonary edema, intraoperative transfusion of red blood cells, and surgical types. A nomogram and a risk calculator were constructed based on the five factors, showing excellent discrimination, calibration, and clinical utility. Three risk intervals were defined as low-, medium-, and high-risk groups according to the nomogram and clinical practice. The findings of this study may be helpful for early risk assessment and perioperative management.

Early identification of delayed extubation following cardiac surgery: Development and validation of a risk prediction model

Background

Successful weaning and extubation after cardiac surgery is an important step of postoperative recovery. Delayed extubation is associated with poor prognosis and high mortality, thereby contributing to a substantial economic burden. The aim of this study was to develop and validate a prediction model estimate the risk of delayed extubation after cardiac surgery based on perioperative risk factors.

Methods

We performed a retrospective cohort study of adult patients undergoing cardiac surgery from 2014 to 2019. Eligible participants were randomly assigned into the development and validation cohorts, with a ratio of 7:3. Variables were selected using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation. Multivariable logistic regression was applied to develop a predictive model by introducing the predictors selected from the LASSO regression. Receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA) and clinical impact curve were used to evaluate the performance of the predictive risk score model.

Results

Among the 3,919 adults included in our study, 533 patients (13.6%) experienced delayed extubation. The median ventilation time was 68 h in the group with delayed extubation and 21 h in the group without delayed extubation. A predictive scoring system was derived based on 10 identified risk factors based on 10 identified risk factors including age, BMI ≥ 28 kg/m², EF < 50%, history of cardiac surgery, type of operation, emergency surgery, CPB ≥ 120 min, duration of surgery, IABP and eGFR < 60 mL/min/1.73 m². According to the scoring system, the patients were classified into three risk intervals: low, medium and high risk. The model performed well in the validation set with AUC of 0.782 and a non-significant p-value of 0.901 in the Hosmer-Lemeshow test. The DCA curve and clinical impact curve showed a good clinical utility of this model.

Conclusions

We developed and validated a prediction score model to predict the risk of delayed extubation after cardiac surgery, which may help identify high-risk patients to target with potential preventive measures.

Prediction model for delirium in patients with cardiovascular surgery: development and validation

BACKGROUND

The aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery.

METHODS

From January 2017 to June 2020, we collected data from 838 patients who underwent cardiovascular surgery at the Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set at a 5:5 ratio. A nomogram model was established based on logistic regression. Discrimination and calibration were used to evaluate the predictive performance of the model.

RESULTS

The incidence of delirium was 48.3%. A total of 389 patients were in the modelling group, and 449 patients were in the verification group. Logistic regression analysis showed that CPB duration (OR [Formula: see text] 1.004, 95% CI: 1.001-1.008, [Formula: see text] 0.018), postoperative serum sodium (OR [Formula: see text] 1.112, 95% CI: 1.049-1.178, [Formula: see text] 0.001), age (OR [Formula: see text] 1.027, 95% CI: 1.006-1.048, [Formula: see text] 0.011), and postoperative MV (OR [Formula: see text] 1.019, 95% CI: 1.008-1.030, [Formula: see text] 0.001) were independent risk factors. The results showed that AUC[Formula: see text] was 0.712 and that the 95% CI was 0.661-0.762. The Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation ([Formula: see text] 6.200, [Formula: see text] 0.625). The results of verification showed that the AUC[Formula: see text] was 0.705, and the 95% CI was 0.657-0.752. The Hosmer-Lemeshow goodness of fit test results were [Formula: see text] 8.653 and [Formula: see text] 0.372, indicating that the predictive effect of the model is good.

CONCLUSIONS

The establishment of the model provides accurate and objective assessment tools for medical staff to start preventing postoperative delirium in a purposeful and focused manner when a patient enters the CSICU after surgery.

Blood Transfusion Predicts Prolonged Mechanical Ventilation in Acute Stanford Type A Aortic Dissection Undergoing Total Aortic Arch Replacement

Background

This research aimed to evaluate the impacts of transfusing packed red blood cells (pRBCs), fresh frozen plasma (FFP), or platelet concentrate (PC) on postoperative mechanical ventilation time (MVT) in patients with acute Stanford type A aortic dissection (ATAAD) undergoing after total arch replacement (TAR).

Methods

The clinical data of 384 patients with ATAAD after TAR were retrospectively collected from December 2015 to October 2017 to verify whether pRBCs, FFP, or PC transfusion volumes were associated with postoperative MVT. The logistic regression was used to assess whether blood products were risk factors for prolonged mechanical ventilation (PMV) in all three endpoints (PMV ≥24 h, ≥48 h, and ≥72 h).

Results

The mean age of 384 patients was 47.6 ± 10.689 years, and 301 (78.39%) patients were men. Median MVT was 29.5 (4–574) h (h), and 213 (55.47%), 136 (35.42%), and 96 (25.00%) patients had PMV ≥24 h, ≥48 h, and ≥72 h, respectively. A total of 36 (9.38%) patients did not have any blood product transfusion, the number of patients with transfusion of pRBCs, FFP, and PC were 334 (86.98%), 286 (74.48%), and 189 (49.22%), respectively. According to the multivariate logistic regression of three PMV time-endpoints, age was a risk factor [PMV ≥ 24 h odds ratio (ORPMV≥24) = 1.045, p = 0.005; ORPMV≥48 = 1.060, p = 0.002; ORPMV≥72 = 1.051, p = 0.011]. pRBC transfusion (ORPMV≥24 = 1.156, p = 0.001; ORPMV≥48 = 1.156, p &lt; 0.001; ORPMV≥72 = 1.135, p ≤ 0.001) and PC transfusion (ORPMV≥24 = 1.366, p = 0.029; ORPMV≥48 = 1.226, p = 0.030; ORPMV≥72 = 1.229, p = 0.011) were independent risk factors for PMV. FFP had no noticeable effect on PMV [ORPMV≥48 = 0.999, 95% confidence interval (CI) 0.998–1.000, p = 0.039; ORPMV≥72 = 0.999, 95% CI: 0.998–1.000, p = 0.025].

Conclusions

In patients with ATAAD after TAR, the incidence of PMV was very high. Blood products transfusion was closely related to postoperative mechanical ventilation time. pRBC and PC transfusions and age increased the incidence of PMV at all three endpoints.

Predictors and outcomes of postoperative tracheostomy in patients undergoing acute type A aortic dissection surgery

Background

Despite surgical advances, acute type A aortic dissection remains a life-threatening disease with high mortality and morbidity. Tracheostomy is usually used for patients who need prolonged mechanical ventilation in the intensive care unit (ICU). However, data on the risk factors for requiring tracheostomy and the impact of tracheostomy on outcomes in patients after Stanford type A acute aortic dissection surgery (AADS) are limited.

Methods

A retrospective single-institutional study including consecutive patients who underwent AADS between January 2016 and December 2019 was conducted. Patients who died intraoperatively were excluded. Univariate analysis and multivariate logistic regression analysis were used to identify independent risk factors for postoperative tracheostomy (POT). A nomogram to predict the probability of POT was constructed based on independent predictors and their beta-coefficients. The area under the receiver operating characteristic curve (AUC) was performed to assess the discrimination of the model. Calibration plots and the Hosmer–Lemeshow test were used to evaluate calibration. Clinical usefulness of the nomogram was assessed by decision curve analysis. Propensity score matching analysis was used to analyze the correlation between requiring tracheostomy and clinical prognosis.

Results

There were 492 patients included in this study for analysis, including 55 patients (11.2%) requiring tracheostomy after AADS. Compared with patients without POT, patients with POT experienced longer ICU and hospital stay and higher mortality. Age, cerebrovascular disease history, preoperative white blood cell (WBC) count and renal insufficiency, intraoperative amount of red blood cell (RBC) transfusion and platelet transfusion were identified as independent risk factors for POT. Our constructed nomogram had good discrimination with an AUC = 0.793 (0.729–0.856). Good calibration and clinical utility were observed through the calibration and decision curves, respectively. For better clinical application, we defined four intervals that stratified patients from very low to high risk for occurrence of POT.

Conclusions

Our study identified preoperative and intraoperative risk factors for POT and found that requiring tracheostomy was related to the poor outcomes in patients undergoing AADS. The established prediction model was validated with well predictive performance and clinical utility, and it may be useful for individual risk assessment and early clinical decision-making to reduce the incidence of tracheostomy.

Is obstructive sleep apnoea associated with hypoxaemia and prolonged ICU stay after type A aortic dissection repair? A retrospective study in Chinese population

Background

Although obstructive sleep apnoea (OSA) is prevalent among patients with aortic dissection, its prognostic impact is not yet determined in patients undergoing major vascular surgery. We aimed to investigate the association of OSA with hypoxaemia and with prolonged intensive care unit (ICU) stay after type A aortic dissection (TAAD) repair.

Methods

This retrospective study continuously enrolled 83 patients who underwent TAAD repair from January 1 to December 31, 2018. OSA was diagnosed by sleep test and defined as an apnoea hypopnea index (AHI) of ≥ 15/h, while an AHI of > 30/h was defined severe OSA. Hypoxaemia was defined as an oxygenation index (OI) of < 200 mmHg. Prolonged ICU stay referred to an ICU stay of > 72 h. Receiver operating characteristic curve analysis was performed to evaluate the predictive value of postoperative OI for prolonged ICU stay. Multivariate logistic regression was performed to assess the association of OSA with hypoxaemia and prolonged ICU stay.

Results

A total of 41 (49.4%) patients were diagnosed with OSA using the sleep test. Hypoxaemia occurred postoperatively in 56 patients (67.5%). Postoperatively hypoxaemia developed mostly in patients with OSA (52.4% vs. 83.0%, p = 0.003), and particularly in those with severe OSA (52.4% vs. 90.5%, p = 0.003). The postoperative OI could fairly predict a prolonged ICU stay (area under the receiver-operating characteristic curve, 0.72; 95% confidence intervals [CI] 0.60–0.84; p = 0.002). Severe OSA was associated with both postoperative hypoxaemia (odds ratio [OR] 6.65; 95% CI 1.56–46.26, p = 0.008) and prolonged ICU stay (OR 5.58; 95% CI 1.54–20.24, p = 0.009).

Conclusions

OSA was common in patients with TAAD. Severe OSA was associated with postoperative hypoxaemia and prolonged ICU stay following TAAD repair.

Risk factors for and outcomes of prolonged mechanical ventilation in patients received DeBakey type I aortic dissection repairment

Background

This study aimed to identify risk factors for prolonged mechanical ventilation (PMV) and its association with disease prognosis following acute DeBakey type I aortic dissection surgery.

Methods

A total of 582 patients who received emergency surgery for acute DeBakey type I aortic dissection from 2014 to 2018 were enrolled in this study. Mechanical ventilation period after surgery longer than 48 hours was defined as postoperative PMV. Multiple logistic regression analysis was used to identify risk factors for PMV. This study also compared short- and long-term outcomes in patients who developed PMV with patients who did not develop this complication. To identify and compare long-term cumulative survival rate, Kaplan-Meier survival curve was plotted.

Results

Among all enrolled patients, 259 (44.5%) received PMV treatment. Our data suggested that the length of intensive care unit and hospital stay were longer for patients who received PMV treatment. Thirty-day mortality was also higher in patients with PMV than in patients without PMV. Elevated leukocyte count and increased serum cystatin C level upon admission, lower preoperative platelet count and longer cardiopulmonary bypass (CPB) duration were identified as risk factors for PMV. Interestingly, our data suggested that there was no significant difference of survival rate between patients with or without PMV history.

Conclusions

PMV after DeBakey type I aortic dissection repair surgery was a common complication and associated with increased short-term mortality rate but did not affect long-term mortality rate. Elevated preoperative leukocyte count, increased preoperative serum cystatin C level, lower preoperative platelet count and longer CPB duration were risk factors for PMV.

Perioperative risk factors for delayed extubation after acute type A aortic dissection surgery

Background

Delayed extubation after cardiac surgery is associated with high morbidity and mortality, increased intensive care unit length of stay, and healthcare cost. Acute type A aortic dissection (ATAAD) generally results in prolonged mechanical ventilation due to the complexity of surgical management and some postoperative complications. This study aimed to elucidate the perioperative risk factors for delayed extubation in patients undergoing ATAAD surgery.

Methods

A retrospective cohort study including 239 patients who were diagnosed with ATAAD and underwent emergency surgery from October 2004 to January 2018 was performed. The potential perioperative risk factors for delayed extubation were collected. This study defined delayed extubation as the time to commence extubation being greater than 48 hours. The clinical data were analyzed with univariate and multivariate analyses to identify risk factors for delayed extubation following ATAAD surgery.

Results

The incidence of delayed extubation was 48.5% (n=116). Multiple logistic regression analysis showed perioperative risk factors for delayed extubation included preoperative cardiac tamponade [odds ratio (OR) 3.94, 95% confidence interval (CI) 1.39–11.17, P=0.010], central arterial cannulation (ascending aorta and proximal aortic arch) for cardiopulmonary bypass (CPB) (OR 4.04, 95% CI: 1.03–15.91, P=0.046), postoperative stroke (OR 10.58, 95% CI: 2.65–42.25, P=0.001), postoperative renal dysfunction that required temporary hemodialysis (OR 6.60 95% CI: 1.97–22.11, P=0.002), and re-exploration to stop bleeding (OR 2.65, 95% CI: 1.00–6.99, P=0.050).

Conclusions

Preoperative cardiac tamponade, central arterial cannulation for CPB, postoperative stroke, postoperative renal dysfunction that required temporary hemodialysis, and re-exploration to stop bleeding are perioperative risk factors for delayed extubation. Identification of the potential risk factors for delayed extubation may help optimize the perioperative management and improve postoperative outcomes of patients undergoing ATAAD surgery.

Perioperative risk factors predict one-year mortality in patients with acute type-A aortic dissection

Objective

The goal of this study was to analyze perioperative risk factors to predict one- year mortality after operation for acute type A aortic dissection (AAD).

Methods

A total of 121 consecutive patients undergoing Stanford type A AAD surgery in Beijing Anzhen Hospital were enrolled. Preoperative clinical and laboratory data from patients were collected.

Results

Multivariable Cox regression analysis showed that significant factors associated with increased one-year mortality were elder age (year) (hazard ratio (HR) 1.0985; 95% confidence interval (CI) 1.0334–1.1677), intraoperative blood transfusion ≥2000 mL (HR 8.8081; 95% CI 2.3319–33.2709), a higher level of serum creatinine (μmol/L) at postoperative one day (HR 1.0122; 95% CI 1.0035–1.0190) and oxygenation index (OI) < 200 (mmHg) at the end of surgery (HR 5.7575; 95% CI 1.1695–28.3458).

Conclusion

In this study, perioperative risk factors to predict one-year prognosis are age, intraoperative blood transfusion ≥2000 mL, postoperative OI < 200 mmHg and level of postoperative serum creatinine. The results aid in the comprehension of surgical outcomes and assist in the optimization of treatment strategies for those with perioperative risk factors to decrease one-year mortality.

Factors Associated with Low Admission Platelet Count in Adults with Acute Aortic Dissection

Purpose: Platelets are crucial components of the coagulation processes, and low admission platelet count (PLC) is associated with adverse clinical outcomes in patients with Stanford type A acute aortic dissection (AAD).

Methods: A total of 130 consecutive patients undergoing Stanford type A AAD surgery in Beijing Anzhen Hospital were enrolled between January 2013 and July 2014. Preoperative clinical and laboratory data from patients were collected. Multiple regression analyses were used to determine the independent factors of low admission platelets.

Results: Adjusted multiple regression analysis showed that age (β: −1.069, 95% confidence interval [CI]: −2.109, −0.029), sex (β: −29.973, 95% CI: −56.512, −3.433), tissue factor pathway inhibitor (TFPI; β: 0.197, 95% CI: 0.039, 0.354), fibrinogen degradation product (FDP) (β: −0.476, 95% CI: −0.879, −0.074), and attack time (β: 11.125, 95% CI: 7.963, 14.287) were significantly associated with admission PLC. Admission PLC increased with attack time up to the 3 days (β: 16.2, 95% CI: 12.1, 20.2).

Conclusions: We found that increasing age, male patients, patients with lower serum levels of TFPI and higher serum levels of FDP, and patients with a shorter attack time were significantly associated with lower PLC at admission. Moreover, the turning point of attack time is 3 days after the onset of dissection.

Serum Lactate as an Independent Predictor of In-Hospital Mortality in Intensive Care Patients

PURPOSE

The aim of this study was to check if serum lactate was independently associated with mortality among critically ill patients.

MATERIALS AND METHODS

This was a single-center, retrospective cohort study. All adult patients (>18 years of age) who had at least 1 measurement of lactate within 24 hours of admission to intensive care unit (ICU) between January 2002 and December 2017 were included in the analysis. Patients were stratified into 3 groups: those with a serum lactate of <2 mmol/L (normal level), 2 to 4 mmol/L (intermediate level), and >4 mmol/L (high level). The primary outcome was in-hospital mortality. Secondary outcomes included ICU and hospital lengths of stay and mechanical ventilation duration. To determine the association between lactate level and hospital mortality, bivariate and multivariate logistic regression analyses were performed.

RESULTS

Of the 16,447 patients admitted to the ICU, 8167 (49.65%) had normal levels, 4648 (28.26%) had an intermediate, and 3632 (22.09%) had high lactate levels. Hospital mortality was the highest in high lactate level, followed by the intermediate and the normal level group (47.4% vs 26.5% vs 19.6%; P < .0001). Intermediate and high lactate levels were independent predictors of hospital mortality (odds ratio [OR], 1.32; 95% confidence interval [CI]: 1.20-1.46, and 1.94; 95% CI, 1.75-2.16, respectively) as well as ICU mortality (OR, 1.47; 95% CI, 1.30-1.66 and 2.56; 95% CI, 2.27-2.88, respectively).

CONCLUSIONS

Intensive care unit serum lactate is associated with increased ICU and hospital mortality, independent of comorbidities, organ dysfunction, or hemodynamic status.