A Novel Nomogram for Predicting the Risk of Coronary Atherosclerosis in Patients with Gastroesophageal Reflux Disease A Nomogram Predicting Coronary Atherosclerosis

doi:10.21203/rs.3.rs-3815688/v1

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Research Article

A Novel Nomogram for Predicting the Risk of Coronary Atherosclerosis in Patients with Gastroesophageal Reflux Disease A Nomogram Predicting Coronary Atherosclerosis

https://doi.org/10.21203/rs.3.rs-3815688/v1

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Aims

This study developed a novel nomogram to predict the incidence of coronary atherosclerosis (CA) in patients with gastroesophageal reflux disease (GERD) and evaluated the predictive value of the nomogram.

Methods

13658 patients of gastroesophageal reflux disease from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analysed. The patients were randomly divided into two groups in a seven-to-three ratio to form a training cohort (n = 9560) and a validation cohort (n = 4098). Least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify associated risk variables. A nomogram was established to predict the rate of coronary atherosclerosis in patients with gastroesophageal reflux disease. The new model was assessed in terms of the concordance index (C-index), the area under the curve (AUC) of receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA).

Results

Least absolute shrinkage and selection operator regression analysis identified nine potential predictors of coronary atherosclerosis. Multivariate logistic regression analysis was used to evaluate the effects of these predictors and create a final model. The concordance index values were 0.750. The areas under the curves for the training and validation sets were 0.7500 and 0.7297, respectively.

Conclusion

The age, white blood cells (WBC), hemoglobin, mean corpsular hemoglobin (MCH), mean corpuscular volume (MCV), sodium, bicarbonate, creatinine and chloride were identified as predictors. Our nomogram is a reliable convenient approach for predicting coronary atherosclerosis in patients with gastroesophageal reflux disease.

nomogram

coronary atherosclerosis

gastroesophageal reflux

Gastroesophageal reflux disease (GERD) refers to the reflux of gastric and duodenal contents into the esophagus, causing damage to the esophageal mucosa and resulting in symptoms such as acid reflux and heartburn [1–3]. The prevalence of GERD worldwide is approximately 8% -33%, involving various age groups, genders, and occupational groups [4]. At present, the main treatment strategy for GERD is to use proton pump inhibitor (PPI) which brings a burden of billions of dollars to society every year [4, 5]. However, there are still many GERD patients who are not sensitive to PPI treatment [4, 6]. Further research on GERD is needed in the future.

Symptoms of GERD can present as atypical chest pain similar to the symptoms of coronary atherosclerosis (CA) [7]. The risk factors for GERD include age, gender, smoking, excessive alcohol consumption, increased BMI, social factors, psychosomatic diseases, etc., which overlap with those of CA [7–10]. Many patients with GERD combined with CA can be seen in the hospitals. They are at a higher risk of cardiovascular events than patients without CA [11]. There are some previous studies focusing on the relationship between GERD and CA, but the exact link between the two has not been clarified [7, 12, 13].

In oncology and pharmacy, nomograms are widely used to determine prognosis and make it possible to evaluate the occurrence rate of an event [14]. However, there are few predictive models for assessing the incidence of CA in GERD patients. This study used data from the “Medical Information Mart for Intensive Care” (MIMIC) database to develop and validate a nomogram to predict the incidence of CA in GERD patients.

Aim

This study aimed to determine the incidence of CA in patients with GERD.

Database

MIMIC-IV is a publicly available database containing data from 76,540 ICU stays from 2008 to 2019 in the Beth Israel Deaconess Medical Center [15, 16]. This database contains all baseline data of patients and most laboratory results that can be used for various studies [17]. It aims to eliminate obstacles to scientific research [18]. The version used is v2.2, and PostgreSQL v16 was used to search and extract data from it (http://www.postgresql.org/).

Study population

This is a retrospective analysis using data from GERD patients stored in MIMIC-IV. The inclusion criteria were as follows:

Patients with previous history of GERD.

Patients aged older than 18 years.

Patients admitted to the ICU for the first time.

Data collection and measurements

The characteristics used for our work included demographic data (e.g., age, gender, race and marital status), vital signs (e.g., heart rate, and blood pressure) and laboratory results (e.g., routine blood tests, chemistry and coagulation). All data were collected from 6 hours before admission to the hospital to 24 hours after admission to the hospital. Unmarried, married, divorced and widowed status was marked as 0, 1, 2 and 3, respectively. White people were marked as 0, and people of other races were marked as 1. Women were marked as 0, and men were marked as 1.

Statistical analysis

Statistical analysis was achieved by using R software (Version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria; https://www.r-project.org). The distribution of continuous variables was assessed using the Shapiro–Wilk test. Mean and standard deviation (SD) are reported for continuous variables having a normal distribution; median and interquartile range (IQR), for continuous variables having a skewed distribution; and numbers and percentages, for categorical variables. The Kruskal–Wallis H test was used to compare continuous data. The Fisher’s exact test was used to compare categorical variables. 70% of the data were allocated to the training set for developing the model, and the remaining 30% were allocated to the validation set for validation. Records with missing values were deleted or replaced by using chained equations as appropriate. The LASSO method, which accounts for multicollinearity and avoids model overfit, was used to select the best candidate variables for establishing the nomogram [19]. To avoid making the clinical prediction model too complex, the lambda value was set to ‘1se’ so that the error was within one standard error of the minimum. Sensitivity and specificity were used to evaluate the model’s performance. The calibration C index (bootstrap resampling performed 1000 times), the calibration curve and ROC curves were used to evaluate the discrimination and calibration of the model [20]. DCA was used to identify the clinical benefit of the model [21]. The AUC of the ROC curves values were drawn to assess the stability of the models [22]. All significance tests were two-sided, and a P value < 0.05 was considered statistically significant.

Clinical characteristics

Figure 1 is a flowchart showing the cohort selection process. This study included 13658 patients. Of these, 70% (9560) were randomly assigned to the training set and the remaining 30% (4098) were assigned to the validation set.

Table 1 summarizes the data of the enrolled variables. Of the 13658 patients with GERD, 2608 were admitted to the ICU with CA.

[Table 1 near here.] Table 1. The data of variables enrolled in this study.

[Table legend] resp_rate, respiratory rate; temp, temperature; sbp, systolic blood pressure; dbp, diastolic blood pressure; WBC, white blood cells; RBC, red blood cells; MCH, mean corpsular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RDW, red blood cell distribution width; PT, prothrombin time; PTT, partial thromboplastin time; INR, international normalized ratio; BUN, blood urea nitrogen; AG, anion gap.

Variable selection

After removing variables with missing values exceeding 5% of the total, 13658 patients were retained in the study. Variables with missing values not exceeding 5% were replaced by using chained equations. Finally, 34 variables were examined. Figure 2 shows ROC curves of all screened variables. Figure 3 is a heatmap involving all screened variables. Figure 4 illustrates AUC values of all variables. Twentyfold cross-validated LASSO regression identified 9 variables: age, WBC, hemoglobin, MCH, MCV, sodium, bicarbonate, creatinine and chloride. Figures 5 and 6 illustrate the variable selection process. These 9 variables were included in the multivariate logistic regression model for predicting the occurrence of CA, and their odd ratios and 95% confidence intervals (CIs) were 2.30(2.11 ~ 2.51), 0.92(0.86 ~ 0.97), 1.22(1.12 ~ 1.32), 2.34(2.07 ~ 2.65), 0.35(0.30 ~ 0.40), 0.62༈0.57 ~ 0.66༉, 1.61(1.51 ~ 1.72), 1.19(1.15 ~ 1.22) and 2.09(1.90 ~ 2.29), respectively.

Construction of a nomogram

A nomogram was established for predicting the occurrence of CA among GERD patients in the ICU (Fig. 7). The 9 variables identified above were assigned different initial scores and a total score was calculated. The probability of developing CA increased with the total score.

Assessment and validation

The calibration curve demonstrated high conformity between the predicted and observed probabilities (Fig. 8). ROC curves for the training and test sets were used to evaluate the discriminability of the model. The area under the ROC curve was 0.7500 in the training set and 0.7297 in the validation set (Figs. 9 and 10). The results indicated that our prediction model has good stability and accuracy.

Figure 11 shows that the higher the threshold value is, the greater the net benefit of the nomogram decision curve. This indicates the good clinical value of our nomogram.

This population-based study retrospectively investigated 13658 patients admitted to the ICU with GERD. A nomogram model was developed and validated to gauge the risk of CA. The nomogram contained 9 variables: age, WBC, hemoglobin, MCH, MCV, sodium, bicarbonate, creatinine and chloride. It demonstrated good clinical utility. The ROC analysis of the training cohort had an AUC of 0.7500 and that of the test cohort was 0.7297, suggesting that our model is predictive.

There are large prospective cohort studies showing that all gastrointestinal diseases increase the incidence of cardiovascular disease to a greater or lesser extent, with the relevance of GERD and CA being of particular interest [23, 24]. It has therefore been suggested that GERD may be an independent risk factor for cardiovascular disease [25]. Pathophysiological mechanisms that have been widely investigated for the link between GERD and CA include inflammation, autonomic function and fibrinolysis [26–28].

Age is a known risk factor for CA [29]. Older people are significantly more likely to develop or progress with CA than younger people [29, 30]. Age is also a risk factor for GERD [31, 32]. The likelihood of GERD patients developing CA gradually increases with age, a result consistent with our model. Previous studies have shown that inflammation is a key feature of atherosclerosis and its clinical manifestations, and white blood cell count is an indicator of inflammation [26]. It is common sense that the higher the white blood cell count, the greater the severity of CA [33–36]. But this is not the case with our nomogram results. The risk score is high when the white blood cell count is within a certain range. Follow-up studies on the relationship between different segments of the white blood cell count and the risk of CA are worthwhile. Our model shows that as hemoglobin rises, the likelihood of CA in GERD patients increases. However, previous studies have focused more on glycated hemoglobin and cardiovascular disease and have not looked at the relationship between hemoglobin and CA [37]. The reasons for this phenomenon need to be investigated further. Only a few studies have examined cardiovascular disease in relation to general haematological indices. The conclusions of the few remaining studies are often contradictory. Some studies conclude that higher MCH and MCV are associated with a higher risk of cardiovascular disease [38]. Others have concluded that lower MCV is associated with a higher risk of cardiovascular disease, whereas MCH is not associated with cardiovascular disease [39]. Our study concluded that higher MCH and lower MCV were positively associated with a higher incidence of CA. Elevated blood pressure due to high salt diet and high serum sodium level is a risk factor for coronary artery disease [40]. A number of studies based on this have also concluded that a higher serum sodium level is associated with a higher cardiovascular risk [41]. Other studies have demonstrated a negative correlation between blood sodium levels and the incidence of cardiovascular events [42, 43]. From our nomogram, we can see that our results support the conclusion of a negative correlation. However, the exact reason for the negative correlation remains to be explored. There are also conflicting conclusions about the relationship between bicarbonate and cardiovascular risk. There are studies suggesting that higher bicarbonate or lower bicarbonate is associated with higher cardiovascular risk [44, 45]. However, none of these trials had a large enough sample size to be convincing. Our study concluded that the odds of CA in patients with GERD increased with higher bicarbonate levels. In our study, the association between creatinine and CA was not as strong as other factors. A slight increase in creatinine may cause an increase in the incidence of CA. This result is in line with the results of the existing papers [46]. Researches on the relationship between blood chloride and cardiovascular risk are particularly scarce. The existing studies contradict our finding of higher cardiovascular risk accompanied with higher blood chloride [47]. This may be a new direction for research.

Clinicians need to evaluate patients with GERD before deciding on treatment plans. The identification of predictive factors and risk assessment is crucial to prevent the occurrence of CA. A nomogram is a total score based on the values of multiple predictor variables for an individual. Our nomogram includes nine variables that are readily available in regular clinical practice and can be assessed easily. Patients with GERD can also evaluate their situation using our nomogram.

Our study has some limitations. Firstly, all of our data came from the MIMIC-IV database. Incomplete records and possible data errors may have reduced the accuracy of our model. Secondly, this was a single-center retrospective study; only internal validation was performed, so external validation is needed to confirm the reliability of the results. Thirdly, blood lipids were not included in the study due to the lack of corresponding records in the MIMIC-IV database. Blood lipids are important risk factors for cardiovascular diseases [48], so further research on them is necessary. Fourthly, the types and severity of GERD were not considered in this study, which did not determine the risk of developing CA for each severity level of GERD. In addition, all types of CA were lumped together. Finally, our nomogram may predict the possibility of CA in patients with GERD, but cannot answer whether intervention was prompt.

A nomogram to predict the risk of CA in GERD patients was developed using data from the MIMIC-IV database. The model had good predictive ability. The nomogram can help to screen GERD patients for the risk of CA and could facilitate good clinical decisions.

CA coronary atherosclerosis

GERD gastroesophageal reflux disease

PPI proton pump inhibitor

MIMIC Medical Information Mart for Intensive Care

LASSO least absolute shrinkage and selection operator

AUC area under the curve

ROC receiver operating characteristic

DCA decision curve analysis

C-index Concordance index

RBC red blood cells

WBC white blood cells

MCV mean corpuscular volume

BUN blood urea nitrogen

INR international normalized ratio

PT prothrombin time

PTT partial thromboplastin time

RDW red blood cell distribution width

MCH mean corpsular hemoglobin

MCHC mean corpuscular hemoglobin concentration

Acknowledgements

We would like to thank all MIMIC project team members.

Ethics approval and consent to participate

This study is an analysis of data stored in a public database. All information is available and free for the public, so the agreement of the medical ethics committee board and the need for informed consent were not necessary.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used/or analyzed during the current study are available from the corresponding authors on reasonable request.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

No funding.

Author contributions

Ziyang Wu conceived, designed the study and drafted the manuscript. Chengchun Tang and Dong Wang revised the manuscript and approved the final version to be published. All authors read and approved the final manuscript.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

Table1.xlsx
Table 1. The data of variables enrolled in this study. [Table legend] resp_rate, respiratory rate; temp, temperature; sbp, systolic blood pressure; dbp, diastolic blood pressure; WBC, white blood cells; RBC, red blood cells; MCH, mean corpsular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RDW, red blood cell distribution width; PT, prothrombin time; PTT, partial thromboplastin time; INR, international normalized ratio; BUN, blood urea nitrogen; AG, anion gap.

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A Novel Nomogram for Predicting the Risk of Coronary Atherosclerosis in Patients with Gastroesophageal Reflux Disease A Nomogram Predicting Coronary Atherosclerosis

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Abstract

Aims

Methods

Results

Conclusion

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Introduction

Methods

Aim

Database

Study population

Data collection and measurements

Statistical analysis

Results

Clinical characteristics

Variable selection

Construction of a nomogram

Assessment and validation

Discussion

Conclusion

Abbreviations

Declarations

References

Table 1

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Supplementary Files

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