RESEARCH ARTICLE


Predicting Readmission of Cardiovascular Patients Admitted to the CCU using Data Mining Techniques



Marzie Salimi1, #, Peivand Bastani2, #, Mahdi Nasiri3, Mehrdad Karajizadeh4, #, Ramin Ravangard5, *
1 Student Research Committee, Department of Health Services Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
2 College of Health and Human Sciences, Charles Darwin University, Alice Springs, NT, 0870, Australia
3 Research Center for Design and Fabrication of Advanced Electronic Devices, Shiraz, Iran
4 Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
5 Health Human Resources Research Center, Department of Health Services Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran


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Creative Commons License
© 2023 Salimi et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Health Services Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Tel: 00987132340774; E-mail: ra_ravangard@yahoo.com
#These authors have contributed equally to this work.


Abstract

Background:

Cardiovascular (CV) diseases account for a large number of readmissions.

Objective:

Using data mining techniques, we aimed to predict the readmission of CV patients to Coronary Care Units of 4 public hospitals in Shiraz, Iran, within 30 days after discharge.

Methods:

To identify the variables affecting the readmission of CV patients in the present cross-sectional study, a comprehensive review of previous studies and the consensus of specialists and sub-specialists were used. The obtained variables were based on 264 readmitted and non-readmitted patients. Readmission was modeled with predictive algorithms with an accuracy of >70% using the IBM SPSS Modeler 18.0 software. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology provided a structured approach to planning the project.

Results:

Overall, 47 influential variables were included. The Support Vector Machine (SVM), Chi-square Automatic Interaction Detection (CHIAD), artificial neural network, C5.0, K-Nearest Neighbour, logistic regression, Classification and Regression (C&R) tree, and Quest algorithms with an accuracy of 98.60%, 89.60%, 89.90%, 88.00%, 85.90%, 79.90%, 78.60%, and 74.40%, respectively, were selected. The SVM algorithm was the best model for predicting readmission. According to this algorithm, the factors affecting readmission were age, arrhythmia, hypertension, chest pain, type of admission, cardiac or non-cardiac comorbidities, ejection fraction, undergoing coronary angiography, fluid and electrolyte disorders, and hospitalization 6-9 months before the current admission.

Conclusion:

According to the influential variables, it is suggested to educate patients, especially the older ones, about following physician advice and also to teach medical staff about up-to-date options to reduce readmissions.

Keywords: Forecasting, Patient readmission, Cardiovascular diseases, Coronary care units, Data mining, Physicians.



1. INTRODUCTION

Increasing demand for health services, on the one hand, and limited resources, on the other hand, are major issues for health services. Therefore, it is suggested that health system policymakers and managers should aim for the optimal use oflimited resources available, including hospital beds, and increasing clinical effectiveness. One of the key indicators for assessing healthcare outcomes is the evaluation of readmission rates [1]. In general, readmission refers to rehospitalization within a specific time frame (usually within 30 days after discharge) in the same hospital or other ones on a planned or unplanned basis [2].

Currently, cardiovascular (CV) diseases account for a significant proportion of readmission cases, and a sharp increase in readmission rates for these diseases is worrying [3]. CV diseases are the most common cause of death in most countries, including Iran, and the leading cause of disability [4]. About 17.9 million people lost their lives in 2019 due to CV diseases, accounting for 32% of deaths worldwide [5]. Over three-fourths of the deaths from CV diseases occur in low- and middle-income countries [5], where people suffering from these diseases do not have adequate access to appropriate and effective hospital services, including Coronary Care Units (CCUs) [6, 7]. Therefore, today, there is an urgent need for CCUs more than ever, and this is why hospitals are seeking to increase the number of their CCU beds [8]. Discharge from this unit at the earliest appropriate time will reduce the cost of healthcare facilities and increase access to intensive care for critically ill patients. However, the early discharge of CCU patients may increase the likelihood of readmission and worsen the disease process [9].

Determining the factors affecting the readmission of CV patients will provide an opportunity to meet the needs of the patients and the problems related to the services provided. The results of numerous studies demonstrated that readmission of CV patients was affected by several factors, including demographic and social ones, such as age [10-13], gender [14, 15], type of health insurance coverage [16, 17], length of stay [18-21], type of admission [22, 23], discharge destination [19, 24-26], ethnicity [13, 27], and discharge status [25, 28], as well as some clinical factors like cardiac or non-cardiac comorbidities [29, 30], body mass index (BMI) [13, 31, 32], infection [33-36], fluid and electrolyte disorders [37, 38], surgery [13, 39], chest pain [40, 41], and depression [13, 42].

On the other hand, due to the large volume of available data and their complexity, the ever-increasing need for using an efficient, effective, and reliable tool for discovering useful knowledge in this data is required in order to determine the factors affecting readmissions. This goal can be achieved through the use of data mining algorithms. Data mining has good potential to create a knowledge-rich environment that can significantly increase the quality of clinical decisions [43]. Hence, some studies have used different data mining algorithms, such as logistic regression [44-48], support vector machine (SVM) [48-50], artificial neural network [47, 48], random forest [48, 49], and decision tree [48] to identify the factors affecting readmission of CV patients. However, in these studies, different findings have been reported.

Due to the diversity of techniques and their accuracy, authors used different data mining algorithms in the present study to identify the best algorithm for predicting the readmission of CV patients admitted to the CCUs of public hospitals in Shiraz, Iran, within 30 days after discharge in 2020.

2. MATERIALS AND METHODS

This was a cross-sectional study conducted on the patients admitted to the CCUs of public hospitals (4 hospitals) in Shiraz, Iran, in 2020 to predict the readmission of hospitalized CV patients through the use of data mining techniques. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was used to provide a structured approach to planning the data mining project [51]. As it was an iteration loop model, some steps might be performed several times to achieve the desired result in modeling. The CRISP-DM method included the following steps:

2.1. Business Understanding

At this stage, a comprehensive review of the articles in English on determining the factors affecting readmission of CV patients indexed in Web of Science, PubMed, Scopus, and Embase databases and published between 2000.01.01 and 2020.12.30 was carried out. The articles were searched using the following keywords:

“Factor OR Determinant OR Element” AND” Readmission OR Rehospitalization OR Re_admission OR Readmit OR Re_hospitalization” AND” Heart disease OR Cardiac disease OR Cardiovascular disease OR Cardiovascular patient OR Coronary artery disease OR Cardio surgeries OR Coronary artery bypass.

Finally, 45 variables were obtained. Then, using the consensus of 8 specialists and subspecialists working in the CCUs of the studied hospitals, 5 more variables were added, and a total of 50 variables were ultimately obtained (Appendices A and B).

2.2. Data Understanding

The patients who experienced readmission within 30 days after discharge were identified. The data relating to their demographic and clinical variables, the variables related to the service providers, and the data related to the variables were respectively obtained from the patients’ records, the Statistics unit of Shiraz University of Medical Sciences, and through interviews with the patients. The number of readmitted patients studied was equal to that of non-readmitted ones, and a total of 264 patients were examined. In other words, there were 132 readmitted patients, all of whom were examined through the census method. There were also 7362 non-readmitted patients in the four hospitals studied, of whom 132 were selected through the stratified random sampling method proportional to the size and simple random sampling method. The number of patients not readmitted to the CCUs of studied hospitals was determined based on the stratified random sampling method proportional to size. Within each hospital, these samples were selected through the simple random sampling method using their medical record numbers.

2.3. Data Preparation

At this stage, two variables of hospital ownership and being a teaching hospital were excluded from the study because all of the hospitals under study were teaching hospitals. In addition, the variable of training the patient on how to take medications was excluded due to the provision of education to all patients by nurses and the existence of the training sheet in all medical records. As a result, among the variables obtained from the comprehensive review of the articles and the consensus of the specialists and subspecialists, 47 variables were examined.

Moreover, since the body mass index (BMI) had not been recorded in some medical records, it was calculated through height and weight values ​​extracted from these records.

Furthermore, a medical records technician classified principal diagnoses into seven main categories of CV diseases based on the International Classification of Diseases, Tenth Revision (ICD-10) (Appendix A).

2.4. Modeling

The readmission of the CV patients admitted to the CCUs of the studied hospitals within 30 days after discharge was odellin using the following algorithms through the use of the IBM SPSS Modeler 18.0 software:

C5.0 [52], logistic regression [53], Decision list [54], Discriminant [55], Bayesian Network [53], K-Nearest Neighbors (KNN) [56], Random Forest [53], Support Vector Machine (SVM) [56], Chi-square Automatic Interaction Detection (CHAID) [57], Quantile Estimation after Supervised Training (Quest) [58], Classification and Regression (C&R) Tree [58], and Artificial Neural Network (ANN) [59].

The algorithms with an accuracy of >70%, namely SVM, CHIAD, artificial neural network, C5.0, KNN, logistic regression, C&R tree, and Quest algorithms, were selected.

The input variables of these models included 47 identified variables.

Before odelling, the data were classified into three categories: training, testing, and validation. The validation data were used to check the overfitting of each model. The data from one of the hospitals, which included 49 patients (18.6%) (23 readmitted and 26 non-readmitted), were considered validation data and were not used in the training and testing stages. The data from the remaining three hospitals were randomly divided between the training (70%) and testing (30%) classes.

The calibration of the studied algorithms can be seen in Appendix C.

2.5. Evaluation

To evaluate the algorithms, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value measures were used, and to select the best algorithm for readmission prediction, the area under the Receiver Operating Characteristic (ROC) curve was also calculated.

3. RESULTS

The final 47 variables obtained from the comprehensive review of the published articles and the consensus of specialists and subspecialists are presented in appendices A and B. The values of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of each data mining algorithm used in the present study to predict readmission of CV patients are presented in Table 1 by training, testing, and validation data.

Table 1. Evaluation and validation of data mining algorithms in predicting readmission of cardiovascular patients.
Model Classification Accuracy (%) Sensitivity (%) Specificity (%) Positive Predictive Value (%) Negative Predictive Value (%)
SVM algorithm Training 98.10 50.38 89.04 89.18 91.54
Testing 97.80 52.38 90.90 91.66 88.33
External validation 100.00 100.00 100.00 100.00 100.00
Mean 98.60
CHIAD algorithm Training 96.00 49.20 87.67 87.32 86.48
Testing 78.90 53.06 69.69 72.22 67.64
External validation 93.90 50.00 85.84 85.84 83.48
Mean 89.60
Artificial neural network algorithm Training 98.20 50.73 93.05 93.24 95.71
Testing 82.10 61.53 60.60 71.11 83.33
External validation 89.40 42.94 92.38 90.12 73.48
Mean 89.90
C5.0 algorithm Training 97.10 47.40 97.26 96.96 89.87
Testing 76.00 53.70 75.75 78.37 75.75
External validation 91.00 52.10 85.84 86.84 90.09
Mean 88.00
KNN algorithm Training 90.90 44.91 90.27 88.33 77.38
Testing 74.90 46.93 78.78 76.66 65.00
External validation 91.90 54.94 78.09 81.30 90.10
Mean 85.90
Logistic regression algorithm Data set 79.90 77.30 82.60 82.50 77.27
C&R tree algorithm Training 76.00 43.24 86.30 82.75 72.41
Testing 73.70 50.00 81.81 81.81 72.97
External validation 86.10 55.74 72.64 76.98 86.51
Mean 78.60
Quest algorithm Training 69.50 33.66 91.78 85.00 63.80
Testing 75.50 34.46 96.96 95.23 65.30
External validation 78.30 45.45 84.90 82.41 72.58
Mean 74.40
Abbreviations: SVM: Support Vector Machine, ANN: Artificial Neural Network, RBF: Radial Basis Function, CHAID: CHi-square Automatic Interaction Detection, KNN: K-Nearest Neighbour, QUEST: QUantile Estimation after Supervised Training, C&R: Classification and Regression.
Fig. (1). The Receiver Operating Characteristic (ROC) curve of the Support Vector Machine (SVM) algorithm.

Fig. (2). Factors affecting the readmission of cardiovascular patients admitted to Coronary Care Units (CCUs) based on the Support Vector Machine (SVM) algorithm.

The results showed that the SVM algorithm was the best algorithm for predicting the readmission of CV patients (with 98.60% accuracy), while the Quest algorithm had the least accuracy (74.40%) (Table 1 and Fig. 1).

Based on the results of the SVM algorithm, the factors affecting the readmission of CV patients admitted to the studied CCUs were age, arrhythmia, hypertension, chest pain, type of admission (elective or non-elective), cardiac or non-cardiac comorbidities, ejection fraction, undergoing coronary angiography, fluid and electrolyte disorders, and hospitali- zation 6-9 months before the current admission (Fig. 2).

4. DISCUSSION

Readmission of CV patients is an important issue that can be investigated from various aspects, including the financial burden imposed on the patients and medical centers, the occupation of very valuable beds in the CCUs, and the lack of optimal use of facilities when providing services to CV patients [60]. This study aimed to predict the readmission for CV patients admitted to the CCUs of public hospitals in Shiraz, Iran, within 30 days after discharge in 2020 by using data mining techniques.

The results of the present study showed that the SVM algorithm, with an accuracy of 98.60%, was the best model for predicting readmission. The findings of the studies conducted by Yousefi et al. [61] and Zheng et al. [50] confirmed these results.

However, Golas et al. [62] reported that the accuracy of the deep unified networks (DUNs) model (76.4%) was higher than that of other models used to predict readmission of heart failure patients. In their cohort study, Wallmann et al. predicted the 30-day cardiovascular-disease-related readmissions from 2003 to 2009 at a Spanish academic tertiary care center using a predictive model developed by logistic regression and reported that the sensitivity, specificity, positive predictive value, and negative predictive value were 0.66, 0.7, 0.10 and 0.98, respectively [44]. Similarly, Desautels et al. conducted a study to identify patients who were likely to suffer unplanned ICU readmission at a single academic, tertiary care hospital in the UK from October 2014 to August 2016 on all patients who visited an ICU at some point during their stay using a logistic regression and showed that the accuracy of the logistic regression was 70.95% [45]. The results of these studies are inconsistent with those of the present study. Some reasons for the differences between the accuracy of the models in these studies and the present one can be differences in the population, hospital wards as well as the models used.

On the other hand, the study by Najafi-Vosough et al. indicated that the random forests algorithm was more accurate in predicting the readmission of heart failure patients than the SVM model [49]. The reason for the difference between their finding and that of the present study could be the difference in the type of variables and data studied. In their study, Futoma et al. compared data mining models to predict early hospital readmissions and suggested that the random forests and deep neural networks were better predictors of the readmission risk compared to the SVM algorithm [47], which is not in line with the results of the present study. The difference between the results could be due to the use of deep learning and some other techniques by Futoma et al. and the lack of using them in the present study. Another reason could be the difference in the studied populations (i.e., not only the patients with CV disease but also with obstructive pulmonary disease, pneumonia, and knee arthroplasty).

Awan et al. used machine learning methods to predict the readmission of heart failure patients and performed their modeling with the multilayer perceptron, logistic regression, random forest, decision tree, and support vector machine models. In their study, the multilayer perceptron model was selected as the best with the highest accuracy [48]. The reasons for the difference between the results of their study and the present research could be the imbalance of the data in their study as well as the difference in the studied populations (i.e., patients with a mean age of 81 years).

According to the results of the present study, the factors affecting readmission in the SVM algorithm included fluid and electrolyte disorders, arrhythmia, hypertension, chest pain, age, undergoing coronary angiography, type of admission (elective and non-elective), cardiac ejection fraction percentage, hospitalization 6-9 months before the current admission, and cardiac or non-cardiac comorbidities.

The present study indicated that fluid and electrolyte disorders were an effective factor in the readmission of CV patients. This is in line with the results of the studies conducted by Ranna et al. [38], Vogel et al. [27], and Lima et al. [11]. Due to the reduced cardiac ejection fraction percentage in patients with systolic heart failure, blood flow to the kidneys is also reduced, and kidney function is usually impaired. Kidney dysfunction in patients with heart failure is associated with the worsening of the symptoms of the disease and an increase in the length of hospital stay [63].

Also, usually, a large number of patients with CV diseases experience atrial fibrillation after heart surgery and are prone to persistent arrhythmias after discharge [64]. Therefore, it could be concluded that postoperative atrial fibrillation is common, and this is in line with the results of the studies conducted by Maniar et al., Iribarne et al., Arora et al., and Cedars et al. [34, 36, 65, 66].

In the present study, hypertension was identified as a factor affecting readmission, which is consistent with the results of the studies conducted by Kim et al. [67] and Bavishi et al. [68]. Hypertension is known as a gradual and quiet killer that can cause diseases, such as stroke. Due to the critical condition of patients studied in the present study, readmission is very common among people who also have hypertension. However, it is not in line with the results of the studies by Aranda Jr et al. [69] and Ferraris et al. [70], who stated that hypertension during hospitalization had an inverse relationship with readmission. The difference in the results could be due to differences in the studied populations and samples. Aranda et al. studied a population consisting of Medicare patients from the Centers for Medicare and Medicaid Services, and Ferraris et al. studied patients who underwent heart surgery.

Chest pain is one of the main symptoms of CV diseases [71] and is an effective factor in the readmission of CV patients in the present study. Given that chest pain can be a warning sign of cardiac ischemia, lack of adequate care for such patients might cause recurrent pain and readmission. The studies by Pellitier et al. [40] and Jarvinen et al. [41] confirmed the results of the present study.

The present study suggested that age was one of the factors affecting the readmission of CV patients. The results of the studies by Benuzilla et al. [72] and Bavishi et al. [68] confirmed this finding. Older people might not have all of their medical needs met before discharge. In addition, they may suffer from comorbidities and need more support and care in the community, and if this is not taken into account, they will be more likely to be readmitted.

The results of the SVM algorithm in the present study showed that undergoing coronary angiography was an influential factor in readmission because coronary angiography could cause complications. The femoral approach was the standard access site for coronary angiography, the use of which for angiography might cause complications, such as bleeding, hematoma, and embolism of the lower parts of the catheter entrance, due to the trauma to the artery wall [73, 74]. These complications could lead to readmission. The results of a study by Dunlay et al. confirmed these findings [75].

Based on the results of the present study, comorbidities (either cardiac or non-cardiac) are a factor determining the readmission of CV patients. It might be concluded that due to the lack of complete management and control of comorbidities and insufficient attention to them by patients and the severity of these diseases, CV patients with comorbidities usually need hospital care and readmission. According to the findings of previous studies, non-cardiac comorbidities [26, 76-83] and cardiac comorbidities [17, 20, 26, 68, 85-88] have been factors influencing the readmission of CV patients.

The present study had some limitations, including its cross-sectional design, small sample size as well as the non-use of complete pharmacological and laboratory data of the studied patients.

CONCLUSION

According to the results of the present study, SVM was the best algorithm in terms of accuracy. Based on the results of this algorithm, the factors affecting the readmission were age, arrhythmia, hypertension, chest pain, type of admission (elective and non-elective), cardiac or non-cardiac comorbidities, ejection fraction, undergoing coronary angiography, fluid and electrolyte disorders, and hospitalization 6-9 months before the current admission. Therefore, it is recommended to teach the patients, especially the older ones, about the regular use of the drugs and following physician advice after discharge. It is also suggested to train medical staff about the relevant and up-to-date options in order to reduce the likelihood of patient readmissions.

AUTHOR CONTRIBUTIONS

RR, PB, and MN designed the study. MS collected the required data. MK and MS analyzed the data. All authors interpreted the data. RR and MS wrote the first draft of the manuscript. All authors reviewed and edited the manuscript and approved the final version of the manuscript.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This study was approved by the Shiraz University of Medical Sciences Ethics Committee (Code: IR.SUMS.REC.1398.272).

HUMAN AND ANIMAL RIGHTS

No animals were used in this research. All procedures performed in studies involving human participants were in accordance with the ethical standards of institutional and/or research committee and with the 1975 Declaration of Helsinki, as revised in 2013.

CONSENT FOR PUBLICATION

To participate in this study, all the patients gave their written informed consent, and all were assured of the confidentiality of their responses.

STANDARDS OF REPORTING

STROBE guidelines were followed.

AVAILABILITY OF DATA AND MATERIALS

The datasets used and/or analyzed during the current study are available from the corresponding author [R.R] upon reasonable request.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

FUNDING

The present article was extracted from the thesis written by Marzie Salimi and was financially supported by Shiraz University of Medical Sciences (grant No. 97-01-07-18833).

ACKNOWLEDGMENTS

The present article was extracted from the thesis written by Marzie Salimi and was financially supported by Shiraz University of Medical Sciences grant No. 97-01-07-18833. The researchers would like to thank the studied patients for their kind cooperation with the researchers in collecting and analysing the data.

APPENDIX

Appendix A: Qualitative variables identified in the business understanding step.
Variables Data variable categories Variable definition Type References Variable Data variable categories Variable definition Type References
1 Gender Female = 0 Male & female Input [14, 15] 22 Having cardiac or non-cardiac comorbidities No = 0 Having cardiac or non-cardiac comorbidities at the time of admission Input [29, 30, 89]
Male = 1 Yes = 1
2 Marital status Married = 0 Patient’s marital status at the time of admission to the CCU Input [29, 90] 23 Having peripheral artery diseases No = 0 Diseases that affect blood vessels outside the heart and brain, such as the arms, legs, etc. Input [83]
Single = 1 Yes = 1
3 Education level Illiterate = 0 Patient education level at the time of admission to the CCU Input [91] 24 Having chest pain No = 0 Having chest pain, according to the patient's report and the contents of the patient’s medical records Input [40, 41, 68, 92]
Elementary = 1 Yes = 1
Middle school = 2 25 Having depression No = 0 Having patient depression, according to the physician's diagnosis and the patient’s medical records Input [42]
Diploma = 3 Yes = 1
Academic education = 4 26 Pregnancy No = 0 Being pregnant, according to the patient’s report and the contents of the patient’s medical records Input [89]
Yes = 1
4 Occupation Self-employed = 0 Being self-employed, housewife, employee, or retired Input [92] 27 Having infection No = 0 Having patient infection at the time of admission to and during hospitalization in the CCU Input [33-36]
Housewife = 1 Yes = 1
Employee = 2 28 Undergoing dialysis No = 0 Having undergone dialysis at the time of admission to the CCU Input [32]
Retired = 3 Yes = 1
5 Housing status Owner = 0 Being an owner or tenant at the time of admission to the CCU Input [93] 29 Poor right ventricular function No = 0 Poor right ventricular function according to the echocardiographic results Input [94]
Tenant = 1 Yes = 1
6 Governmental health insurance coverage No = 0 Having governmental health insurance coverage [17, 16] 30 Undergoing coronary angiography No = 0 Having or not having undergone coronary angiography at the time of hospitalization Input Specialists and sub-specialists consensus
Yes = 1 Input Yes = 1
31 Having arrhythmia No = 0 Having abnormal heart rhythm Input [30]
7 Place of residence Urban area = 0 Being a resident of a city or a village Input [95] Yes = 1
Rural area = 1 32 Training the patient on how to take medications No = 0 Existence of the drug use instruction sheet in the patient’s medical records Input [92]
8 Nationality Iranian = 0 Being Iranian or a foreigner Input [13, 27] Yes = 1
Foreigner = 1 33 Prescribing special medicines No = 0 Type of special medicines prescribed by physicians for after discharge, such as beta-blockers and anticoagulants Input [29, 79, 96]
9 Taking herbal medicines No = 0 Taking herbal medicines after discharge Input Specialists' and sub-specialists' consensus Yes = 1
Yes = 1 34 Fluids and electrolyte disorders No = 0 Disorders of factors, such as creatinine, potassium, etc. Input [37, 38]
10 Following a special diet suitable for cardiovascular patients No = 0 Following a special diet suitable for cardiovascular patients after discharge Input Specialists' and sub-specialists' consensus Yes = 1
Yes = 1 35 Hematocrit level Normal = 0 Normal: 40% to 52% Abnormal: <40 Input [39, 97]
11 Discharge on weekend No = 0 Discharge on the weekend (Thursday and Friday) Input [98, 99] Abnormal = 1
Yes = 1
12 Destination after discharge Home = 0 Destination of patients after discharge from hospital (home or other medical centers or non-medical centers, such as nursing homes) Input [19, 24-26] 36 Hemoglobin levels Normal = 0 Normal: 12 to 16.5 g/dl Input [36, 100, 101]
Other medical centers = 1 Abnormal = 1
Non-medical centers = 2
13 Type of admission Elective = 0 Patient status at the time of admission Input [22, 23] 37 Red blood cell levels Normal = 0 Normal: 4.50 to 5.90 ml / m3 Input [102]
Non-elective (emergency) = 1 Abnormal = 1
14 Discharge season Spring = 0 The season when the patient was discharged Input
Summer = 1 [103] 38 Blood cholesterol levels Normal = 0 200 mg / dL is normal, and above this value is abnormal. Input [103]
Fall = 2 Abnormal = 1
Winter = 3
15 Hospitalization 6 to 9 months before the current admission No = 0 Hospitalization 6-9 months before the current admission Input [79, 92, 98] 39 Principal diagnoses Cardiac ischemia = 0 Principal diagnoses by physicians at the time of admission to the hospital Input Specialists' and sub-specialists' consensus
Yes = 1 Other heart diseases = 1
16 Physician’s Certificate Specialist = 0 Under-treatment by a specialist or a sub-specialist Input [104] Cerebrovascular diseases = 2
Sub-specialist = 1 Blood pressure diseases = 3
17 Hospital ownership Public = 0 Being public or private Input [105] Chronic rheumatic disease = 4
18 Type of hospital Teaching = 0 Being teaching or non-teaching Input [106] Complications of prostheses, implants, and grafts = 5
19 Having hypertension No = 0 Blood pressure> 14 mm Hg Input [27, 68] Abnormal clinical signs not classified elsewhere = 6
Yes = 1 40 Heartbeat Normal = 0 Normal: 60 to 100 beats/ min Input [107]
Abnormal = 1
41 Smoking No = 0 Consumption of hookah, cigarette, etc. Input [87, 108]
20 Having hyperlipidemia No = 0 Increased levels of lipids in the blood Input [109] Yes = 1
Yes = 1 42 Discharge status With the physician’s permission = 0 Discharge with physician’s permission or voluntarily with personal consent Input (23, 24
21 Readmission No = 0 The patient returns to the hospital within 30 days after discharge Target [1, 2] Voluntarily with personal consent =1
Yes = 1 43 Surgery No = 0 Having undergone heart surgery during hospitalization Input [13, 39]
Yes = 1
Appendix B: Quantitative variables identified in the business understanding step.
Reference Variable definition Variable
[10-13] Patient’s age at the admission time Age 1
[105] Patient’s weight at the admission time Weight 2
[18-21] Length of stay in hospital Length of stay 3
[53] Number of active and available hospital beds Hospital bed 4
[110] The ratio of nurses to the CCU beds The ratio of nurses to the CCU beds 5
Specialists' and sub-specialists' consensus The ratio of physicians to the CCU beds The ratio of physicians to the CCU beds 6
[13, 31, 32] kg/m2, where kg is a patient’s weight in kilograms, and m2 is his/her height in meter square Body Mass Index 7
[15, 86] It is a measure used by physicians to calculate the percentage of blood coming out of the ventricles in each contraction. Ejection fraction (EF) percentage 8

 
 
 
 
 


Appendix C: Calibration of the studied algorithms.
Model Specification Value Model Specification Value
SVM Use partitioned data True KNN Use partitioned data True
Partition Partition Partition Partition
Stopping criteria 1.0E-03 What type of analysis do you want to perform? Predict a target field
Kernel type RBF What is your objective? Balance speed and accuracy
Regularization parameter 10 Focal face value 0
Regression precision (ε) 0.1 Normalize range input True
RBF gamma 0.1 Distance computation Euclidean metric
Gamma 1 Prediction for range target The mean of the nearest neighbor value
Bias 0 Automatically select K True
Degree 3 K 3
Algorithm SVM Minimum 3
Model type Classification Maximum 5
NN N Neural network model Multilayer perceptron (MLP) Number to select 10
Hidden layer Automatically compute the number of units Minimum change 0.01
Hidden layer 1 1 Number of folds 10
Hidden layer 2 0 C&R Tree Use partitioned data True
Random seed 229176228 Partition partition
Replicate results True Calculate predictor importance True
Minute 15 Level below root 5
Use maximum training time (per component model) True Model Expert
Overfit prevention set (%) 30 Maximum surrogate 5
Missing value in predictors Delete list wise The minimum change in the impurity 0.0
Method Neural network Impurity measure for categorical target Gini
CHAID Use partitioned data True Stopping criteria Use percentage
Partition Partition Minimum records parents branch (%) 2
Calculate predictor importance True Minimum records child branch (%) 1
Method CHAID Prune tree True
Level below root 5 Prior probabilities Based on training data
Alpha for splitting 0.05 QUEST Use partitioned data True
Alpha for merging 0.05 Partition Partition
Epsilon for convergence 0.001 Calculate predictor importance True
Use Bonferroni adjustment true Level below root 5
Maximum iterations for convergence 100 Mode Expert
Chi-square method Pearson Maximum surrogates 5
Stopping criteria Use percentage Alpha for splitting 0.05
Minimum records parents branch (%) 2 Stopping criteria Use percentage
Minimum record child branch (%) 1 Minimum records parents branch (%) 2
C5.0 Use partitioned data True Minimum record child branch (%) 1
Partition Partition Prune tree True
Calculate predictor importance True Prior probabilities Based on training data
Output type Decision tree Algorithm QUEST
Mode Simple Model type Classification
Favor Accuracy
Tree depth 10
Expected noise (%) 0
Model type Classification
Abbreviations: SVM: Support Vector Machine, ANN: Artificial Neural Network, RBF: Radial Basis Function, CHAID: CHi-square Automatic Interaction Detection, KNN: K-Nearest Neighbour, QUEST: QUantile Estimation after Supervised Training, C&R: Classification and Regression

REFERENCES

[1] Sajadi S, Hosseini M, Alimohammadzadeh K. Assessment of prevalence of readmission in admitted ward of tehran oil companys hospital in 2016. Nurs Midwifery Res J 2018; 16(1): 1-11.
[2] Hatam N, Askarian M, Khamzade F, Jafari P, Bastani P. Readmission risk factors in patients of general surgery wards in shiraz hospitals: Applying LACE index as a predictive indicator. Academic Journal of Surgery 2016; 2(3-4): 33-8.
[3] Heydari A, Ziaee ES, Ebrahimzade S. The frequency of rehospitalization and its contributing factors in patient with cardiovascular diseases hospitalized in selected hospitals in mashhad in 2010. Horizon Med Sci 2011; 17(2): 65-71.
[4] Najafi F, Nalini M, Nikbakht MR. Changes in risk factors and exercise capacity after cardiac rehabilitation and its effect on hospital readmission. Iran Red Crescent Med J 2014; 16(5): e4899.
[5] Cardiovascular diseases. World Health Organization 2022.
[6] Devi G, Kumar S. Cardiovascular disease and physical activity. Int J Phy Edu Spo 2018; 3(01): 67-70.
[7] Areekal B, Bhaskar A, Antony L, et al. Prevalence of risk factors for cardiovascular disease among adults older than 30 years in a rural area in central Kerala, India. Int J Med Sci Public Health 2015; 4(12): 1655-9.
[8] Damiani G, Salvatori E, Silvestrini G, et al. Influence of socioeconomic factors on hospital readmissions for heart failure and acute myocardial infarction in patients 65 years and older: evidence from a systematic review. Clin Interv Aging 2015; 10: 237-45.
[9] Kaben A, Corrêa F, Reinhart K, Settmacher U, Gummert J, Kalff R. Readmission to a surgical intensive care unit: Incidence, outcome and risk factors. Crit Care 2008; 12(5): R123.
[10] Selthofer-Relatić K, Tomić M, Stažić A, Arambašić J, Kralik K, Mirat J. Prognostic indicators for first and repeated hospitalizations in heart failure patients with reduced left ventricular ejection fraction. Coll Antropol 2020; 44(3): 139-45.
[11] Lima FV, Kolte D, Louis DW, et al. Thirty-day readmission after endovascular or surgical revascularization for chronic mesenteric ischemia: Insights from the Nationwide Readmissions Database. Vasc Med 2019; 24(3): 216-23.
[12] Lemor A, Hernandez GA, Patel N, et al. Predictors and etiologies of 30-day readmissions in patients with non-ST-elevation acute coronary syndrome. Catheter Cardiovasc Interv 2019; 93(3): 373-9.
[13] Hannan EL, Zhong Y, Lahey SJ, et al. 30-day readmissions after coronary artery bypass graft surgery in New York State. JACC Cardiovasc Interv 2011; 4(5): 569-76.
[14] Kumar N, Simek S, Garg N, et al. Thirty-day readmissions after hospitalization for hypertensive emergency. Hypertension 2019; 73(1): 60-7.
[15] Romano S, Mansour IN, Kansal M, et al. Left ventricular global longitudinal strain predicts heart failure readmission in acute decompensated heart failure. Cardiovasc Ultrasound 2017; 15(1): 6.
[16] Benavidez OJ, He W, Lahoud-Rahme M. Readmissions following congenital heart surgery in infants and children. Pediatr Cardiol 2019; 40(5): 994-1000.
[17] Damrauer SM, Gaffey AC, DeBord SA, Fairman RM, Nguyen LL. Comparison of risk factors for length of stay and readmission following lower extremity bypass surgery. J Vasc Surg 2015; 62(5): 1192-1200.e1.
[18] Kogan A, Cohen J, Raanani E, et al. Readmission to the intensive care unit after “fast-track” cardiac surgery: risk factors and outcomes. Ann Thorac Surg 2003; 76(2): 503-7.
[19] Tripathi B, Arora S, Kumar V, et al. Hospital complications and causes of 90-day readmissions after implantation of left ventricular assist devices. Am J Cardiol 2018; 122(3): 420-30.
[20] Hariri E, Hansra B, Barringhaus KG, et al. Trends, predictors, and outcomes associated with 30-day hospital readmissions after percutaneous coronary intervention in a high-volume center predominantly using radial vascular access. Cardiovasc Revasc Med 2020; 21(12): 1525-31.
[21] Kogon B, Woodall K, Kanter K, Alsoufi B, Oster M. Reducing readmissions following paediatric cardiothoracic surgery: A quality improvement initiative. Cardiol Young 2015; 25(5): 935-40.
[22] Saab S, Noureddine S, Dumit NY. Readmission rates and emergency department visits after coronary artery bypass graft surgery and related factors. J Med Liban 2014; 61(3): 155-60.
[23] Johnson BH, Smoyer-tomic KE, Siu K, et al. Readmission among hospitalized patients with nonvalvular atrial fibrillation. Am J Health Syst Pharm 2013; 70(5): 414-22.
[24] Shah RM, Zhang Q, Chatterjee S, et al. Incidence, cost, and risk factors for readmission after coronary artery bypass grafting. Ann Thorac Surg 2019; 107(6): 1782-9.
[25] Kwok CS, Parwani PJ, Fischman DL, et al. Nonspecific chest pain and 30-day unplanned readmissions in the United States (From the Nationwide Readmission Database). Am J Cardiol 2019; 123(8): 1343-50.
[26] Arora S, Lahewala S, Tripathi B, et al. Causes and predictors of readmission in patients with atrial fibrillation undergoing catheter ablation: A national population-based cohort study. J Am Heart Assoc 2018; 7(12): e009294.
[27] Vogel TR, Smith JB, Kruse RL. Hospital readmissions after elective lower extremity vascular procedures. Vascular 2018; 26(3): 250-61.
[28] Feng TR, White RS, Gaber-Baylis LK, Turnbull ZA, Rong LQ. Coronary artery bypass graft readmission rates and risk factors - A retrospective cohort study. Int J Surg 2018; 54(Pt A): 7-17.
[29] Wang N, Gallagher R, Sze D, Hales S, Tofler G. Predictors of frequent readmissions in patients with heart failure. Heart Lung Circ 2019; 28(2): 277-83.
[30] Dakour Aridi HN, Locham S, Nejim B, Ghajar NS, Alshaikh H, Malas MB. Indications, risk factors, and outcomes of 30-day readmission after infrarenal abdominal aneurysm repair. J Vasc Surg 2018; 67(3): 747-758.e7.
[31] Benetis R, Širvinskas E, Kumpaitiene B, Kinduris S. A case-control study of readmission to the intensive care unit after cardiac surgery. Med Sci Monit 2013; 19(1): 148-52.
[32] Shahian DM, He X, O’Brien SM, et al. Development of a clinical registry-based 30-day readmission measure for coronary artery bypass grafting surgery. Circulation 2014; 130(5): 399-409.
[33] Rehman SM, Elzain O, Mitchell J, et al. Risk factors for mediastinitis following cardiac surgery: the importance of managing obesity. J Hosp Infect 2014; 88(2): 96-102.
[34] Arora S, Hendrickson MJ, Strassle PD, et al. Trends in costs and risk factors of 30-day readmissions for transcatheter aortic valve implantation. Am J Cardiol 2020; 137: 89-96.
[35] Alimadadi E, Abbasinia M, Mohammadbeigi A, Abbasi M. Risk factors of readmission after coronary artery bypass graft surgery: A case-control study. Nursing Practice Today 2020; 7(4): 295-301.
[36] Iribarne A, Chang H, Alexander JH, et al. Readmissions after cardiac surgery: Experience of the national institutes of health/canadian institutes of health research cardiothoracic surgical trials network. Ann Thorac Surg 2014; 98(4): 1274-80.
[37] Trooboff SW, Magnus PC, Ross CS, et al. A multi-center analysis of readmission after cardiac surgery: Experience of The Northern New England Cardiovascular Disease Study Group. J Card Surg 2019; 34(8): 655-62.
[38] Rana S, Tran T, Luo W, Phung D, Kennedy RL, Venkatesh S. Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Aust Health Rev 2014; 38(4): 377-82.
[39] Duwayri Y, Goss J, Knechtle W, et al. The readmission event after vascular surgery: Causes and costs. Ann Vasc Surg 2016; 36: 7-12.
[40] Pelletier R, Choi J, Winters N, et al. Sex differences in clinical outcomes after premature acute coronary syndrome. Can J Cardiol 2016; 32(12): 1447-53.
[41] Järvinen O, Huhtala H, Laurikka J, Tarkka MR. Higher age predicts adverse outcome and readmission after coronary artery bypass grafting. World J Surg 2003; 27(12): 1317-22.
[42] Oxlad M, Stubberfield J, Stuklis R, Edwards J, Wade TD. Psychological risk factors for cardiac-related hospital readmission within 6 months of coronary artery bypass graft surgery. J Psychosom Res 2006; 61(6): 775-81.
[43] Hajizadeh E, Ardakani HD, Shahrabi J. Application of data mining techniques in stock markets: A survey. J Econ Int Finance 2010; 2(7): 109-18.
[44] Wallmann R, Llorca J, Gómez-Acebo I, Ortega ÁC, Roldan FR, Dierssen-Sotos T. Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data. Int J Cardiol 2013; 164(2): 193-200.
[45] Desautels T, Das R, Calvert J, et al. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: A cross-sectional machine learning approach. BMJ Open 2017; 7(9): e017199.
[46] Zhou H, Della PR, Roberts P, Goh L, Dhaliwal SS. Utility of models to predict 28-day or 30-day unplanned hospital readmissions: An updated systematic review. BMJ Open 2016; 6(6): e011060.
[47] Futoma J, Morris J, Lucas J. A comparison of models for predicting early hospital readmissions. J Biomed Inform 2015; 56: 229-38.
[48] Awan SE, Bennamoun M, Sohel F, Sanfilippo FM, Dwivedi G. Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC Heart Fail 2019; 6(2): 428-35.
[49] Najafi-Vosough R, Faradmal J, Hosseini SK, Moghimbeigi A, Mahjub H. Predicting hospital readmission in heart failure patients in Iran: A comparison of various machine learning methods. Healthc Inform Res 2021; 27(4): 307-14.
[50] Zheng B, Zhang J, Yoon SW, Lam SS, Khasawneh M, Poranki S. Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Syst Appl 2015; 42(20): 7110-20.
[51] Clark A. The machine learning audit-CRISP-DM Framework. ISACA J 2018; 1: 42-7.
[52] Lakshmi BN, Indumathi TS, Ravi N. A study on C. 5 decision tree classification algorithm for risk predictions during pregnancy. Procedia Technol 2016; 24: 1542-9.
[53] Yazdani A, Zahmatkeshan M, Ravangard R, Sharifian R, Shirdel M. Supervised machine learning approach to covid-19 detection based on clinical data. Med J Islam Repub Iran 2022; 36: 110-6.
[54] Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int J Comput Appl 2011; 17(8): 43-8.
[55] Chiang D, Chiang HC, Chen WC, Tsai FJ. Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: A new method. BJU Int 2003; 91(7): 661-6.
[56] Shanbehzadeh M, Yazdani A, Shafiee M, Kazemi-Arpanahi H. Predictive modeling for COVID-19 readmission risk using machine learning algorithms. BMC Med Inform Decis Mak 2022; 22(1): 139.
[57] Choi HY, Kim EY, Kim J. Prognostic factors in diabetes: Comparison of Chi-square automatic interaction detector (CHAID) decision tree technology and logistic regression. Medicine 2022; 101(42): e31343.
[58] F Asadi, C Salehnasab, L Ajori. Supervised algorithms of machine learning for the prediction of cervical cancer. J Biomed Phys Eng 2020; 10(4): 513-22.
[59] Yazdani A, Safaei AA, Safdari R, Zahmatkeshan M. Diagnosis of breast cancer using decision tree, artificial neural network and naive bayes to provide a native model for fars province. Payavard Salamat 2019; 13(3): 241-50.
[60] Farshidi H. Causes of readmission in ischemic heart disease patients. Hormozgan Med J 2004; 8(2): 67-71.
[61] Yousefi M, Bagheri A, Shah BA, Faridi MM. Modeling prediction of readmission to heart hospital using data mining. Sixth International Conference on Industrial and Systems Engineering (ICISE 2020).
[62] Golas SB, Shibahara T, Agboola S, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: A retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak 2018; 18(1): 44.
[63] Zeighami MSH, AsgharzadehHaghighi S. The study of electrolyte abnormalities and renal dysfunction on elderly patients with systolic heart failure. Iran J Crit Care Nurs 2011; 3(4): 155-66.
[64] Mahouri AR, Heshmati F, Nourouzinia H, Mahdizadeh H, Hasani E, Shokoufeh A. Evaluation of the complication leading to prolonged intensive care unit stay after open heart surgery. Studies in Medical Sciences 2008; 19(2): 139-44.
[65] Maniar HS, Bell JM, Moon MR, et al. Prospective evaluation of patients readmitted after cardiac surgery: Analysis of outcomes and identification of risk factors. J Thorac Cardiovasc Surg 2014; 147(3): 1013-20.
[66] Cedars AM, Burns S, Novak EL, Amin AP. Rehospitalization is a major determinant of inpatient care costs in adult congenital heart disease. J Am Coll Cardiol 2016; 67(10): 1254-5.
[67] Kim LK, Yeo I, Cheung JW, et al. Thirty-day readmission rates, timing, causes, and costs after st-segment-elevation myocardial infarction in the United States: A national readmission database analysis 2010-2014. J Am Heart Assoc 2018; 7(18): e009863.
[68] Bavishi C, Lemor A, Trivedi V, et al. Etiologies and predictors of 30-day readmissions in patients undergoing percutaneous mechanical circulatory support-assisted percutaneous coronary intervention in the United States: Insights from the Nationwide Readmissions Database. Clin Cardiol 2018; 41(4): 450-7.
[69] Aranda JM Jr, Johnson JW, Conti JB. Current trends in heart failure readmission rates: analysis of medicare data. Clin Cardiol 2009; 32(1): 47-52.
[70] Ferraris VA, Ferraris SP, Harmon RC, Evans BD. Risk factors for early hospital readmission after cardiac operations. J Thorac Cardiovasc Surg 2001; 122(2): 278-86.
[71] Fakhr-Movahedi A, Ebrahimian A, Mirmohammadkhani M, Ghasemi S. Relationship between chest pain severity and physiological indexes in patients with coronary artery disease. Tehran Univ Med J 2016; 74(2): 140-5.
[72] Benuzillo J, Caine W, Evans RS, Roberts C, Lappe D, Doty J. Predicting readmission risk shortly after admission for CABG surgery. J Card Surg 2018; 33(4): 163-70.
[73] Yeganehkhah MR, Vafaeimanesh J, Akbari H, Amiri Z, Naraghipoor Z, Pazoki S. Preventive effects of sodium bicarbonate on contrast-induced nephropathy in high-risk patients undergoing coronary angiography. Qom Univ Med Sci J 2015; 9(9): 9-1.
[74] Sallam M, Al-Hadi H, Rathinasekar S, Chandy S. Comparative study of the radial and femoral artery approaches for diagnostic coronary angiography. Sultan Qaboos Univ Med J 2009; 9(3): 272-8.
[75] Dunlay SM, Weston SA, Killian JM, Bell MR, Jaffe AS, Roger VL. Thirty day hospital readmissions following acute myocardial infarction: A community study. Ann Intern Med 2012; 157(1): 11.
[76] Frigola-Capell E, Comin-Colet J, Davins-Miralles J, Gich-Saladich I, Wensing M, Verdú-Rotellar JM. Trends and predictors of hospitalization, readmissions and length of stay in ambulatory patients with heart failure. Rev Clin Esp 2013; 213(1): 1-7.
[77] Gyalai-Korpos I, Ancusa O, Dragomir T, Tomescu MC, Marincu I. Factors associated with prolonged hospitalization, readmission, and death in elderly heart failure patients in western Romania. Clin Interv Aging 2015; 10: 561-8.
[78] Iannuzzi JC, Chandra A, Kelly KN, Rickles AS, Monson JRT, Fleming FJ. Risk score for unplanned vascular readmissions. J Vasc Surg 2014; 59(5): 1340-1347.e1.
[79] Saito M, Negishi K, Marwick TH. Meta-analysis of risks for short-term readmission in patients with heart failure. Am J Cardiol 2016; 117(4): 626-32.
[80] Kutyifa V, Rice J, Jones R, et al. Impact of non-cardiovascular disease burden on thirty-day hospital readmission in heart failure patients. Cardiol J 2018; 25(6): 691-700.
[81] Wood M, Sweeney T, Trayah M, Civalier M, McMillian W. The impact of transitions of care pharmacist services and identification of risk predictors in heart failure readmission. J Pharm Pract 2021; 34(4): 567-72.
[82] Toraman F, Senay S, Gullu U, Karabulut H, Alhan C. Readmission to the intensive care unit after fast-track cardiac surgery: An analysis of risk factors and outcome according to the type of operation. Heart Surg Forum 2010; 13(4): E212-7.
[83] Harmon D, Rathousky J, Choudhry F, et al. Readmission risk factors and heart failure with preserved ejection fraction. J Am Osteopath Assoc 2020; 120(12): 831-8.
[84] Feng TR, White RS, Ma X, Askin G, Pryor KO. The effect of obstructive sleep apnea on readmissions and atrial fibrillation after cardiac surgery. J Clin Anesth 2019; 56: 17-23.
[85] Nicolini F, Vezzani A, Fortuna D, et al. Gender differences in outcomes following isolated coronary artery bypass grafting: Long-term results. J Cardiothorac Surg 2016; 11(1): 144.
[86] Guedeney P, Huchet F, Manigold T, et al. Incidence of, risk factors for and impact of readmission for heart failure after successful transcatheter aortic valve implantation. Arch Cardiovasc Dis 2019; 112(12): 765-72.
[87] Gupta S, Cogswell RJ, Roy SS, et al. Impact of 30 day readmission after left ventricular assist device implantation. ASAIO J 2019; 65(3): 252-6.
[88] Jarvie JL, Metz TD, Davis MB, Ehrig JC, Kao DP. Short-term risk of cardiovascular readmission following a hypertensive disorder of pregnancy. Heart 2018; 104(14): 1187-94.
[89] Takabayashi K, Kitaguchi S, Iwatsu K, et al. Living alone and gender differences in rehospitalization for heart failure after discharge among acute heart failure patients. Int Heart J 2020; 61(6): 1245-52.
[90] Khawaja FJ, Shah ND, Lennon RJ, et al. Factors associated with 30-day readmission rates after percutaneous coronary intervention. Arch Intern Med 2012; 172(2): 112-7.
[91] Kwok CS, Brown DL, Van Spall HGC, et al. Non-specific chest pain and subsequent serious cardiovascular readmissions. Int J Cardiol 2019; 291: 1-7.
[92] Bradford C, Shah BM, Shane P, Wachi N, Sahota K. Patient and clinical characteristics that heighten risk for heart failure readmission. Res Social Adm Pharm 2017; 13(6): 1070-81.
[93] Balla S, Alqahtani F, Alhajji M, Alkhouli M. Cardiovascular outcomes and rehospitalization rates in homeless patients admitted with acute myocardial infarction. Mayo Clin Proc 2020; 95(4): 660-8.
[94] Lella LK, Sales VL, Goldsmith Y, et al. Reduced right ventricular function predicts long-term cardiac re-hospitalization after cardiac surgery. PLoS One 2015; 10(7): e0132808.
[95] Chave M, Marques-Vidal P. Factors associated with readmission of patients with congenital heart disease in a swiss university hospital. Pediatr Cardiol 2017; 38(4): 650-5.
[96] Colavecchia AC, Putney DR, Johnson ML, Aparasu RR. Discharge medication complexity and 30-day heart failure readmissions. Res Social Adm Pharm 2017; 13(4): 857-63.
[97] Rubin DJ, Golden SH, McDonnell ME, Zhao H. Predicting readmission risk of patients with diabetes hospitalized for cardiovascular disease: A retrospective cohort study. J Diabetes Complications 2017; 31(8): 1332-9.
[98] Mackie AS, Ionescu-Ittu R, Pilote L, Rahme E, Marelli AJ. Hospital readmissions in children with congenital heart disease: A population-based study. Am Heart J 2008; 155(3): 577-84.
[99] Zaremski L, Mazori A, Shulman E, et al. Atrial fibrillation and the weekend effect regarding cardioversion, length of stay, readmission, and mortality. Crit Pathw Cardiol 2018; 17(3): 111-3.
[100] Khayat R, Abraham W, Patt B, et al. Central sleep apnea is a predictor of cardiac readmission in hospitalized patients with systolic heart failure. J Card Fail 2012; 18(7): 534-40.
[101] Shehata N, Forster A, Li L, et al. Does anemia impact hospital readmissions after coronary artery bypass surgery? Transfusion 2013; 53(8): 1688-97.
[102] Giakoumidakis K, Eltheni R, Patelarou A, Patris V, Kuduvalli M, Brokalaki H. Incidence and predictors of readmission to the cardiac surgery intensive care unit: A retrospective cohort study in Greece. Ann Thorac Med 2014; 9(1): 8-13.
[103] Wang N, Farrell M, Hales S, et al. Prevalence and seasonal variation of precipitants of heart failure hospitalization and risk of readmission. Int J Cardiol 2020; 316: 152-60.
[104] Wu YM, Liu CC, Yeh CC, Sung LC, Lin CS, Cherng YG. Hospitalization outcome of heart diseases between patients who received medical care by cardiologists and non-cardiologist physicians: A propensity-score matched study. PloS one 2020; 15(7): e0235207.
[105] Azhar AS. Unplanned hospital readmissions following congenital heart diseases surgery. Saudi Med J 2019; 40(8): 802-9.
[106] Brown JR, Chang CH, Zhou W, MacKenzie TA, Malenka DJ, Goodman DC. Health system characteristics and rates of readmission after acute myocardial infarction in the United States. J Am Heart Assoc 2014; 3(3): e000714.
[107] Choi ES, Wiseman T, Betihavas V. Biomedical, Socioeconomic and Demographic Predictors of Heart Failure Readmissions: A Systematic Review. Heart Lung Circ 2021; 30(6): 817-36.
[108] Steuer J, Blomqvist P, Granath F, et al. Hospital readmission after coronary artery bypass grafting: Are women doing worse? Ann Thorac Surg 2002; 73(5): 1380-6.
[109] Shah M, Patnaik S, Patel B, et al. The day of the week and acute heart failure admissions: Relationship with acute myocardial infarction, 30-day readmission rate and in-hospital mortality. Int J Cardiol 2017; 249: 292-300.
[110] Wei H, Sewell KA, Woody G, Rose MA. The state of the science of nurse work environments in the United States: A systematic review. Int J Nurs Sci 2018; 5(3): 287-300.