Nomogram for early major adverse event in infants after cardiac surgery: a retrospective study

doi:10.21203/rs.3.rs-4805564/v1

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Nomogram for early major adverse event in infants after cardiac surgery: a retrospective study

https://doi.org/10.21203/rs.3.rs-4805564/v1

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Background Early major adverse event has a high mortality rate after cardiac surgery. In this study, our goal was to investigate the risk factors associated with early MAE in infants after cardiac surgery, develop a prediction model, and assess its accuracy in predicting outcomes.

Methods A model was constructed incorporating 766 patients at our Hospital from January 2020 to December 2021. Participants were randomly divided into modelling and validation group using a 7:3 ratio. We utilized the least absolute shrinkage and selection operator regression analysis to screen the variables, and then conducted a multiple logistic regression analysis to create a prediction nomogram.

Results The risk factors of MAE were weight, aortic clamp time, postoperative 8th hour lactate, off-CPB blood glucose and postoperative 4 hours urine output. The Hosmer−Lemeshowtest demonstrated that the model was a good fit (χ²=6.105, p=0.636). The clinical decision curve analysis showed significantly better net benefit in the predictive model, as well as that in the validation cohort.

Conclusion The prediction model based on perioperative factors was developed to screen the occurrence of early MAE in infants after cardiac surgery. It provided physicians with an effective tool for the early prediction, and took timely preventive measures.

Health sciences/Risk factors

Health sciences/Medical research/Outcomes research

Health sciences/Medical research/Paediatric research

Major adverse event

congenital heart disease

cardiac surgery

infant

prediction nomogram

Congenital heart disease (CHD) is the most common birth defect. In China, the incidence of CHD was 17.32 cases per 1000 perinatal births [1], and a portion of patients experience severe clinical symptoms that necessitate surgery during their first year of life. The in-hospital mortality rates varied between 0.8% and 1.0% [2]. Thanks to advancements in medical care, the survival rates of children with critical CHD have significantly improved over the years. Early recovery and minimizing the occurrence of complications for postoperative patients are widely acknowledged as a crucial concern. A major adverse event (MAE) is a complication that can occur after cardiac surgery and has a significant impact on the quality of medical care [3–6].

Adverse events were a common occurrence during and after pediatric cardiac surgery [6, 7], and they had a significant impact on the length of stay in the ICU and the mortality rate [3–7]. Besides, pediatric cardiac surgery had very low error tolerance [8]. An accurate and functional assessment of patients’ risk of early MAE is fundamental for effective intervention. The objective of this study is to develop a prediction tool applicable to the CHD infants to help prevent patients from developing early MAE after cardiac surgery.

Study population

There were 766 infants with CHD admitted between January 2020 and December 2021. This study was conducted as a retrospective investigation of the data from infants who were treated at our Hospital. The study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committees of Fuwai Hospital, Beijing, China (IRB; approval no:2022 − 1859; approval date: 11/22/2022). The criteria for inclusion were as follows: (1) age ≤ 1 year old; (2) diagnosis of CHD; (3) underwent cardiac surgery with cardiopulmonary bypass; (4) informed consent and voluntary participation; and (5) complete clinical data. The exclusion criteria were as follows: (1) age > 1 year old; (2) those with serious preoperative disease such as multi-organic insufficiency, severe infection, metabolic diseases and chromosomal genetic disorders; (3) unwilling to participate in study; and (4) incomplete clinical data.

Data acquisition

There were 29 variables were collected: (1) General data: sex, age, weight, and height. (2) Laboratory examination: Alanine aminotransferase, Aspartate aminotransferase, Albumin, Creatinine, Urea nitrogen, Cystatin C, Lactate level (pre-CPB, on-CPB, off-CPB, Postoperative 0th hour, Postoperative 2th hour, Postoperative 4th hour, Postoperative 8th hour), Blood glucose level (pre-CPB, on-CPB, off-CPB, Postoperative 0th hour, Postoperative 4th hour). (3) Operative data: cardiopulmonary bypass time, Aortic clamp time, Vasoactive inotropic score (Postoperative 2th hour, Postoperative 4th hour, Postoperative 8th hour), Urine output (Intraoperative, Postoperative 4 hours).

Study design

All the patients were divided into two groups. Diagnosis of early MAE [3, 4, 9]: unplanned reoperation, acute renal failure, sudden circulatory arrest, emergency chest opening, need for extracorporeal membrane oxygenation, low cardiac output, resistant tachycardia, and death within 48 hours were considered as MAE. Diagnosis of acute renal failure was based on the RIFLE criteria [10]. Diagnosis of low cardiac output was based on the research in 2022[11].

R software(glmnet4.2.2) createDataPartition was used to randomly divide the CHD patients into training set and testing set according to the ratio of 7:3 (537 in training set, 229 in testing set). R software (glmnet4.2.2) was used to conduct the least absolute shrinkage and selection operator (LASSO) regression analysis and adjusting the variable screening and complexity. LASSO regression analysis results were used to conduct multifactor logistic regression analysis to screen out independent predictors(p<0.05). R software nomogram model to predict the occurrence of postoperative MAE.

The bootstrap method was strengthened for resampling 1000 times for internal validation. The area under the receiver operating characteristics curve (ROC) curve (AUC) was used to assess the diagnostic testing or the identification accuracy of predictive models. The calibration was used to reflect the calibration. Clinical decision curve analysis (DCA) was used to explore the clinical practicability of model. The Hosmer − Lemeshow test was used to evaluate the model’s goodness fit.

Statistical analysis

The categorical variables were represented as frequencies and percentages. Continuous variables were represented as median with quartile range(P25-P75) for skewed distribution, mean ± standard deviation for normal distribution. The Mann–Whitney U test was used to compare continuous variables. The χ2 test and Fisher's exact test were used to compare categorical variables. All statistical tests were two-tailed and a P value < 0.05 was considered statistically significant. All data were analyzed using SPSS 26.0.

General characteristics

The clinical information a total of 834 patients was obtained, 71 did not meet the inclusion criteria. Finally, a total of 766 CHD patients were included in this study (Fig. 1). Complex malformation was 590 cases (77.0%), and simple malformation was 176 cases (23.0%). 71 cases (9.3%) was neonatal cardiac surgery. At least one adverse event within 48 hours postoperatively occurred in 144 postoperative patients (18.8%). 622 (81.2%) patients had no adverse event. Resistant tachycardia (74/766, 9.7%) were the most common adverse event, followed by low cardiac output (52/766, 6.8%), renal failure (25/766, 3.3%), cardiac arrest (10/766, 1.3%), emergency chest opening (3/766, 0.4%), unplanned reoperation (2/766, 0.3%), ECMO (2/766, 0.3%), and death (1/766, 0.1%). The most common congenital heart disease was ventricular septal defect (309/766, 40.3%), followed by tetralogy of Fallot (81/766, 10.6%).

Figure 1. Flow chart for patient selection.

Comparison of Baseline Data

All patients were divided into training set and testing set according to the ratio of 7:3. 537 patients in the training cohort, 229 in the validation cohort. The baseline data of training set and testing set were show in Table 1. There was no significant difference between the two groups (p > 0.05).

Table 1

Baseline characteristics in the training set and testing set.
Variable	Training Set (n = 537)	Testing Set (n = 229)	p-value
Sex,n(%)			0.431
Male	302(56.0%)	121(53.0%)
Female	235(44.0%)	108(47.0%)
Age at the surgery(months)	6(3, 9)	5(3, 8)	0.168
Weight(kg)	6.6(5.3, 8)	6.5(5.1, 7.8)	0.196
Height(cm)	65(60, 70)	65(59, 70)	0.379
Alanine aminotransferase(IU/L)	22(16, 32)	22(16, 36)	0.365
Aspartate aminotransferase(IU/L)	47(38, 58)	47(39, 61)	0.428
Albumin(g/L)	40.7(38.3, 42.9)	40.5(37.6, 43.2)	0.762
Creatinine(umol/L)	28.78(23.08, 33.63)	28.35(24.12, 33.22)	0.788
Urea nitrogen(mmol/L)	3.07(2.14, 4.21)	3.03(2.33, 4.19)	0.702
Cystatin C(mg/L)	1.05(0.9, 1.26)	1.04(0.92, 1.23)	0.871
CPB time(min)	88(65, 124)	93(64, 122)	0.586
Aortic crossclamp time(min)	57(38, 82)	58(40, 83)	0.440
Lactate(mmol/L)
pre-CPB	0.8(0.7, 1.0)	0.8(0.7, 0.9)	0.352
on-CPB	1.5(0.9. 2.1)	1.4(1.0, 2.0)	0.740
off-CPB	1.7(1.3, 2.5)	1.7(1.3, 2.3)	0.390
Postoperative 0th hour	1.4(1.1, 2.0)	1.4(1,1, 2.0)	0.883
Postoperative 2th hour	1.4(1.1, 2.1)	1.6(1.1, 2.1)	0.127
Postoperative 4th hour	1.1(0.8, 1.7)	1.2(0.9, 1.8)	0.276
Postoperative 8th hour	1.2(0.9, 1.6)	1.2(0.9, 1.8)	0.276
Blood glucose(mmol/L)
pre-CPB	5.13(4.51, 5.77)	5.00(4.38, 5.74)	0.150
on-CPB	6.69(5.66, 7.92)	6.65(5.62, 8.02)	0.748
off-CPB	7.82(6.67, 9.07)	7.66(6.57, 8.67)	0.131
Postoperative 0th hour	7.89(6.70, 9.54)	7.86(6.77, 9.29)	0.906
Postoperative 4th hour	6.86(5.87, 8.36)	6.93(5.90, 8.27)	0.783
Vasoactive inotropic score (VIS)
Postoperative 2th hour	10.00(8.00, 12.00)	10.00(8.00, 12.00)	0.142
Postoperative 4th hour	9.00(7.67, 12.00)	8.00(7.00, 12.00)	0.966
Postoperative 8th hour	8(6.00, 11.34)	8.00(7.00, 12.00)	0.624
Urine output(ml)
Intraoperative	10.0(4.2, 19.2)	9.7(4.5, 18.3)	0.983
Postoperative 4 hours	6.0(4.1, 8.0)	6.4(4.3, 8.5)	0.358

In the training cohort, 98 patients (98/537, 18.2%) were included in the MAE group, and 439 patients (439/537, 81.8%) were included in the non-MAE group. Significant differences between MAE group and no MAE group included age at the surgery, weight, height, albumin, Cystatin C, CPB time, aortic clamp time, lactate levels (on-CPB, off-CPB, Postoperative 0th hour, Postoperative 2th hour, Postoperative 4th hour, Postoperative 8th hour), Blood glucose level (pre-CPB, on-CPB, off-CPB, Postoperative 0th hour, Postoperative 4th hour), Vasoactive inotropic score (Postoperative 2th hour, Postoperative 4th hour, Postoperative 8th hour), Urine output (Intraoperative, Postoperative 4 hours)(p < 0.05) (Table 2).

Table 2

Baseline characteristics of the training set in MAE group and non-MAE group.
Variable	MAE group (n = 98)	non-MAE group (n = 439)	p-value
Sex,n(%)			0.716
Male	53(54.0%)	249(57.0%)
Female	45(46.0%)	190(43.0%)
Age at the surgery(months)	4.0(2.0, 5.0)	6.0(4.0, 9.0)	< 0.001
Weight(kg)	5.48 ± 1.66	6.92 ± 1.88	< 0.001
Height(cm)	60(56, 65)	67(61, 71)	< 0.001
Alanine aminotransferase(IU/L)	22.0(15.0, 32.0)	22.0(16.0, 32.0)	0.443
Aspartate aminotransferase(IU/L)	47.5(38.0, 57.8)	46.0(38.0, 58.5	0.944
Albumin(g/L)	39.6(36.92, 41.30)	41.0(38.6, 43.2)	< 0.001
Creatinine(umol/L)	29.50(21.78, 34.83)	28.5(23.1, 33.3)	0.416
Urea nitrogen(mmol/L)	2.94(2.26, 4.28)	3.09(2.10, 4.21)	0.665
Cystatin C(mg/L)	1.19(0.98, 1.37)	1.03(0.88, 1.22)	< 0.001
CPB time(min)	119(81, 150)	83(61, 117)	< 0.001
Aortic crossclamp time(min)	74(53, 103)	53(36, 78)	< 0.001
Lactate(mmol/L)
pre-CPB	0.8(0.7, 1.0)	0.8(0.7, 1.0)	0.974
on-CPB	1.8(1.1, 2.4)	1.4(0.9, 2.0)	0.001
off-CPB	2.3(1.7, 3.1)	1.7(1.2, 2.3)	< 0.001
Postoperative 0th hour	2.0(1.4, 2.7)	1.4(1.0, 1.8)	< 0.001
Postoperative 2th hour	2.1(1.3, 3.1)	1.3(1.0, 1.9)	< 0.001
Postoperative 4th hour	1.7(1.1, 2.6)	1.1(0.8, 1.5)	< 0.001
Postoperative 8th hour	1.6(1, 2.4)	1.1(0.8, 1.5)	< 0.001
Blood glucose(mmol/L)
pre-CPB	5.34(4.59, 6.07)	5.08(4.48, 5.68)	0.028
on-CPB	7.27(6.28, 8.04)	6.56(5.56, 7.91)	0.005
off-CPB	8.30(6.83, 9.97)	7.72(6.64, 8.95)	0.016
Postoperative 0th hour	8.84(7.41, 10.96)	7.72(6.66, 9.27)	< 0.001
Postoperative 4th hour	7.42(6.14, 9.76)	6.74(5.75, 8.05)	< 0.001
Vasoactive inotropic score (VIS)
Postoperative 2th hour	10.00(8.00, 13.50)	10.00(8.00, 12.00)	0.014
Postoperative 4th hour	10.00(8.00, 13.5)	8.00(7.00, 11.00)	0.002
Postoperative 8th hour	10.00(8.00, 12.37)	8.00(6.00, 11.00)	0.002
Urine output(ml)
Intraoperative	14.1(6.8, 24.3)	9.0(3.8, 18.5)	< 0.001
Postoperative 4 hours	5.6(3.6, 7.5)	6.2(4.3, 8.1)	0.039

Screening of Characteristics

LASSO regression analysis was used to screen the variables, and variables were reduced to 9, including age, weight, aortic clamp time, postoperative 0th hour lactate, postoperative 8th hour lactate, on-CPB blood glucose, off-CPB blood glucose, postoperative 4th hour blood glucose, and postoperative 4 hours urine output (Fig. 2). Multivariate logistic regression was used to control the influence of con-funding factors. The results showed that five factors were independent predictors of MAE, including weight, aortic clamp time, postoperative 8th hour lactate, off-CPB blood glucose and postoperative 4 hours urine output (Table 3).

Table 3

Multivariate logistic regression analysis.
Variable	B	SE	Wald	P	OR(95%CI)
(Intercrept)	-0.502	0.822	0.373	0.541	0.606(0.121, 3.050)
Weight(kg)	-0.430	0.079	29.605	< 0.001	0.651(0.555, 0.756)
Aortic crossclamp time(min)	0.009	0.004	5.395	0.020	1.009(1.001, 1.016)
Postoperative 8th hour lactate	0.331	0.135	6.053	0.014	1.393(1.106, 1.850)
off-CPB blood glucose	0.137	0.063	4.694	0.030	1.147(1.012, 1.298)
Postoperative 4 hours urine output	-0.096	0.041	5.433	0.020	0.908(0.835, 0.982)

Risk prediction nomogram development and evaluation

Five factors from the logistic regression model were integrated to the nomogram (C-index = 0.710) (Fig. 2). For each patient, higher total points indicated a higher risk of MAE. In the training cohort, the AUC was 0.781(Fig. 3a). In the texting cohort, the AUC was 0.764 (Fig. 3b). These indicated that risk prediction nomogram is well discriminated. The calibration curve was close to the ideal diagonal line between two groups (Fig. 3c,d). The Hosmer − Lemeshow test demonstrated that the model was a good fit (χ² =6.105, P = 0.636). DCA showed significantly better net benefit in the predictive model, as well as that in the validation cohort (Fig. 3e). These data demonstrated that prediction nomogram had a significant potential for clinical practicability.

Figure 2. (a) Employing 10-fold cross-validation to draw vertical lines, and showing the variable coefficients. (b) minimum mean square error (λ = 0.019) and the standard error of the mininum diatance (λ = 0.064). (c) Nomogram for the perioperative prediction of MAE.

Figure 3. (a) Training set ROC and AUC. (b) Testing set ROC and AUC. (c) Training cohort of calibration curve. (d) Validation cohort of calibration curve. (e) Clinical decision curve analysis.

Major adverse events are clearly associated with a poor prognosis in infant after cardiac surgery [3–9]. As medical quality has advanced, pediatric heart centers have shifted their focus to promoting rapid recovery after surgery. Early detection of MAE in patients who are at risk remains a formidable task, but it could enable physicians to allocate additional resources towards the prevention and treatment of MAE in the high-risk population. Our study is the first to create a model that can predict the occurrence of postoperative early MAE in infants with congenital heart disease who have undergone cardiac surgery with cardiopulmonary bypass. Our findings indicated that certain factors, such as weight, aortic clamp time, postoperative 8th hour lactate, off-CPB blood glucose and postoperative 4 hours urine output, can be used to predict early major adverse events.

Weight is strongly associated with certain adverse outcomes in CHD infants[12, 13]. A low weight can be a potential indicator of a negative outcome during surgery [12]. Preoperative chronic cyanosis, congestive heart failure, and pulmonary hypertension often result in cardiac-related malnutrition due to insufficient nutrient intake [13]. After surgery, patients with low weight experienced a significant decrease in all parameters [14]. In addition, impaired gut function and limited fluid intake after surgery can further deteriorate nutritional status [15]. Mingjie et al. found that patients under 1-year-old had a higher incidence of preoperative malnutrition, leading to increased postoperative morbidity and mortality [14]. Rebecca et al. found that low weight might cause adverse short- and long-term outcomes, including increased the prevalence of postoperative complications, prolonged duration of ventilation and intensive care stay [16]. Our study also found that weight was a significant predictor of early MAE. Lower weight is associated with higher scores in the prediction model and an increased risk of early MAE. These findings highlight the significance of perioperative nutritional assessment, rehabilitation, and interventions, particularly in low weight infants.

The aortic cross-clamp used in cardiac surgery provided motionless and bloodless surgical area. However, aortic cross-clamp time has an inverse effect on postoperative outcomes [17]. Kate et al. conducted a retrospective analysis of 355 pediatric patients who underwent cardiac surgery with cardiopulmonary bypass aged less 1-year-old. They found a correlation between longer ICU stays and increased aortic cross-clamp time [18]. Matthew et al. analyzed the medical records of 221 infants and found a clear correlation between prolonged aortic cross-clamp time and an increase in postoperative complications [19]. The duration of aortic cross-clamp time emerged as the key factor in predicting early MAE in our study. Increased duration of aortic cross-clamp time is associated with a higher likelihood of experiencing major adverse events. Shultz et al. discovered that longer ACC durations were associated with more complex cardiac surgeries and extended ischemic times [20]. Myocardial injury and systemic inflammatory response after aortic cross-clamp can lead to serious early mortality and morbidity [21, 22].

Lactate is a well-investigated marker of adverse event. Perioperative hyperlactatemia was found to be linked to factors such as low cardiac output, inadequate oxygen support, and heightened metabolic demand [23]. Elevated blood lactate can predict early outcome in children after cardiac surgery with high specificity [24, 25]. In our training cohort, we observed an increase in intraoperative and postoperative lactate levels in both the MAE group and the non-MAE group. However, the increase was more pronounced in the MAE group. The highest lactate levels were observed at the off-CPB stage and started to decrease at postoperative 4th hour. An increase in intraoperative lactate level can be observed due to the use of intraoperative cardiopulmonary bypass and aortic cross-clamp. This increase may also extend into the early postoperative period [26]. Postoperative poor clinical status leads to a further increase in lactate levels[27]. In our prediction model, postoperative 8th hour lactate was the independent predictor of early MAE. We thought that postoperative 8th hour lactate level was a critical point. It is likely that survivors were able to eliminate intraoperative hyperlactatemia but those who continued to experience high lactate levels were affected by poor outcome [27].

The glucose level during cardiopulmonary bypass was found to be linked to the development of systemic inflammatory response syndrome and endocrine metabolic imbalance [28]. It was observed that many children undergoing cardiac surgery experienced intraoperative hyperglycemia, which unfortunately had a negative impact on their prognosis [29, 30]. In our study, off-CPB glucose emerged as a significant risk factor for early MAE. Studies by Gandhi et al, Yates et al, Matsumoto et al, and Zhi-Hua et al have all shown that there is a higher risk of postoperative complications with higher sugar levels [29–32]. It is important to maintain glycemic control during the CPB period in order to minimize postoperative complications [29, 30, 33]. Patients who maintained a CPB glucose level of no more than 8.1 mmol/L had a lower risk probability, as shown in a study [32]. Considering Asian origin have a higher insulin resistance [34], timely targeting age-adjusted hyperglycemia with insulin infusion could improve short outcome [35].

There are several limitations to our study. First, there was no gold standard inclusion or exclusion criteria for early MAE. The definition of early MAE come from the several previous study and consensus. Second, it is a retrospective single-center study, not a multi-center study. And internal bias and selection bias of this study is inevitable due to retrospective analysis. Third, external validity has not been verified. More multicenter large sample should be included in the future. Fourth, some potentially meaningful predictors, such as blood pressure, heart rate, body temperature, and ventilator conditions were not be assessed. Evidence-based methods to screen predictors should be used in the future. Fifth, more prospective case controlled studies are needed to further explain the association between risk factors and early MAE in infants.

In conclusion, this study developed a prediction nomogram for early major adverse events in infants with congenital heart disease following cardiac surgery. It identified several factors that can help predict these events, including weight, aortic clamp time, postoperative 8th hour lactate, off-CPB blood glucose and postoperative 4 hours urine output. It provided physicians with an effective tool for the early prediction, and enable the implementation of timely preventive measures.

Funding: This work was supported by the Clinical Research Foundation of the National Health Commission of the People’s Republic of China (grant numbers: 2022-GSP-GG-32 and 2022-GSP-QN-13).

Competing interests: The authors declare no competing interests.

Authors’ contributions:Fan Yang, Xia Li, and Zhiyuan Zhu contributed equally as co-first authors. Xu Wang: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing. Fan Yang, Xia Li, Zhiyuan Zhu: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft. Zhongyuan Lu: Data Curation, Writing - Original Draft. Shilin Wang: Visualization, Investigation. Chao Yue: Resources, Supervision. Leilei Duan: Software, Validation.

Acknowledgements: None.

Ethics approval: The study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committees of Fuwai Hospital, Beijing, China (IRB; approval no:2022-1859; approval date: 11/22/2022).

Consent to participate: Informed consent was obtained from all individual participants included in the study.

Consent for publication: Not applicable.

Data availability: Data are available on reasonable request from the corresponding authors (X.W.).

Conflict of interests: All authors declare that they have no conflict of interests.

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No competing interests reported.

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Nomogram for early major adverse event in infants after cardiac surgery: a retrospective study

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Abstract

Figures

Introduction

Methods

Study population

Data acquisition

Study design

Statistical analysis

Results

General characteristics

Comparison of Baseline Data

Screening of Characteristics

Risk prediction nomogram development and evaluation

Discussion

Conclusion

Declarations

References

Additional Declarations

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Version 1