|Year : 2023 | Volume
| Issue : 3 | Page : 125-135
Predictors of 30-day re-admission in cardiac patients at heart hospital, Qatar
Hajar A Hajar Albinali, Rajvir Singh, Abdul Rahman Al Arabi, Awad Al Qahtani, Nidal Asaad, Jassim Al Suwaidi
Department of Adult Cardiology, Heart Hospital, Hamad Medical Corporation, Doha, Qatar
|Date of Submission||09-Oct-2022|
|Date of Acceptance||11-May-2023|
|Date of Web Publication||05-Jul-2023|
Dr. Hajar A Hajar Albinali
Department of Adult Cardiology, Heart Hospital, Hamad Medical Corporation, Doha 3050
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Cardiovascular disease patients are more likely to be readmitted within 30 days of being discharged alive. This causes an enormous burden on health-care systems in terms of poor care of patients and misutilization of resources.
Aims and Objective: This study aims to find out the risk factors associated with 30-day readmission in cardiac patients at Heart Hospital, Qatar.
Methods: A total of 10,550 cardiac patients who were discharged alive within 30 days at the heart hospital in Doha, Qatar, from January 2015 and December 2019 were analyzed. The bootstrap method, an internal validation statistical technique, was applied to present representative estimates for the population.
Results: Out of the 10,550 cardiac patients, there were 8418 (79.8%) index admissions and 2132 (20.2%) re-admitted at least once within 30 days after the index admission. The re-admissions group was older than the index admission group (65.6 ± 13.2 vs. 56.0 ± 13.5, P = 0.001). Multinomial regression analysis showed that females were 30% more likely to be re-admitted than males (adjusted odds ratio [aOR] 1.30, 95% confidence interval [CI]: 1.11–1.50, P = 0.001). Diabetes (aOR 1.36, 95% CI: 1.20–1.53, P = 0.001), chronic renal failure (aOR 1.93, 95% CI: 1.66–2.24, P = 0.001), previous MI (aOR 3.22, 95% CI: 2.85–3.64, P = 0.001), atrial fibrillation (aOR 2.17, 95% C.I. : 1.10-2.67, P = 0.01), cardiomyopathy (aOR 1.72, 95% CI 1.47–2.02, P = 0.001), and chronic heart failure (aOR 1.56, 95% C.I.: 1.33-1.82, P = 0.001) were also independent predictors for re-admission in the regression model. C-statistics showed these variables could predict 82% accurately hospital readmissions within 30 days after being discharged alive.
Conclusion: The model was more than 80% accurate in predicting 30-day readmission after being discharged alive. The presence of five or more risk factors was found to be crucial for readmissions within 30 days. The study may help design interventions that may result in better outcomes with fewer resources in the population.
Keywords: 30-day readmission, cardiac patients, multinomial logistic regression analysis
|How to cite this article:|
Albinali HA, Singh R, Al Arabi AR, Al Qahtani A, Asaad N, Al Suwaidi J. Predictors of 30-day re-admission in cardiac patients at heart hospital, Qatar. Heart Views 2023;24:125-35
|How to cite this URL:|
Albinali HA, Singh R, Al Arabi AR, Al Qahtani A, Asaad N, Al Suwaidi J. Predictors of 30-day re-admission in cardiac patients at heart hospital, Qatar. Heart Views [serial online] 2023 [cited 2023 Oct 3];24:125-35. Available from: https://www.heartviews.org/text.asp?2023/24/3/125/380495
| Introduction|| |
Cardiovascular disease (CVD) remains the leading cause of hospitalization, and CVD patients are more likely to be readmitted after discharge. CVD has a significant clinical impact, with high morbidity and mortality rates, poor quality of life, and a tremendous demand on health-care systems. CVD puts a significant financial strain on health-care systems worldwide.
However, readmissions have been targeted by physicians, payers, and policymakers to reduce health-care expenditures and improve the quality of care for CVD patients.
According to a previous study from the University of Minnesota, roughly one-third of CVD patients were readmitted within 30 days of discharge. These hospital readmissions increase in-hospital mortality and treatment costs for the patients and healthcare. When patients are readmitted due to cardiac reasons, their chances of mortality are higher.
Factors contributing to hospital readmissions include hospital system problems, patient and community system challenges, socioeconomic factors, and individual educational qualifications. Female gender, living alone, increased age, advanced stage of illness, a more extended stay during the initial hospitalization, emergency room (ER) use, and severity of the disease are also contributing factors to hospital readmissions.
A study conducted at the University of Kentucky found that risk factors for early hospital readmission after cardiac operations included women with diabetes, chronic lung disease, renal impairment, and preoperative atrial fibrillation (AF). Another study on predictors of 30-day hospital readmission following coronary artery bypass showed that female sex and diabetes were associated with more than twice the risk of 30-day readmission. Female sex, length of stay ≥ 5 days, acute deep venous thrombosis, liver disease, systemic thromboembolism, Charlson comorbidity index ≥ 3, chronic kidney disease, deficiency anemias, AF, prior MI, and intra-aortic balloon pump (IABP) were found risk factors for increasing 30 days readmissions. Although some readmissions are unavoidable and staged, many studies have indicated that a considerable proportion of readmissions can be avoided.,, The approach to reducing hospital readmission rates in Cardiology is multifactorial. However, several hospitals have implemented measures to reduce preventable complications from hospitalization, improve the quality of patient discharge, improve the transition of care post-hospitalization, and improve communication and coordination of care with other healthcare providers. Despite these efforts, identifying patients at high risk of readmission has proven difficult.
To the best of our knowledge, no data shows risk factors associated with 30-day readmission in cardiac patients in Qatar. Understanding these factors may identify high-risk patients and promote targeted care-management strategies to prevent actual 30-day readmissions.
| Methods|| |
The study used retrospective cohort data of patients with CVD indicators discharged alive within 30 days of the Coronary Care Unit (CCU) registry at Heart Hospital, Hamad Medical Corporation (HMC), a tertiary hospital in Qatar. IRB approves the CCU data registry, Medical Research Center, HMC (MRC#11355/11).
Heart Hospital at HMC caters to more than 95% of patients seeking cardiovascular medical and surgical care for the nationals and residents of Qatar. The physicians collected each patient's data at the time of discharge on a predefined coded record form.
The collected data were checked and validated by the research coordinator in the department. Approximately 40% of the Qatar population are Arabs, including Qatari nationals and 60% of nonarabs are mainly from West and South Asia, including the Philippines, India, Pakistan, Nepal, and Bangladesh. 30-day hospital readmission is defined according to the Centers for Medicare and Medicaid Services, USA.
Twelve thousand and two hundred and eighty-six patients of age ≥18 years were hospitalized between January 2015 and December 2019. Eight hundred and seventy-three noncardiac patients were excluded from the analysis. One hundred and thirty-one patients died of the remaining 11,413 patients, and 10,550 cardiac patients were discharged alive within 30 days. The 10,550 cardiac patients were divided into two groups-the index admission group consists of 8418 patients hospitalized once within 30 days, and the readmission group consists of 2132 patients hospitalized twice or more within 30 days.
The frequency with percentages was calculated for categorical variables, and mean, standard deviation, or Interquartile range as appropriate were calculated for interval variables for 30-day discharged alive cardiac patients.
Chi-square tests were used to see the categorical variables' association between re-admission and the index admission group. Student's t-tests or Mann–Whitney U-tests were applied to see significant mean/median level differences between the two groups for interval variables.
Multinomial logistic regression assumes independence among dependent variable categories but does not assume normality, linearity, and homoscedasticity, making it appropriate to use in cluster data. Hence, multinomial logistic regression was used to find out associated risk factors for 30-day re-admission following cardiac patients discharged alive compared to the index admission.
Two multinomial logistic regressions were performed having binary outcome variables (Index admission and readmission) and (index admission, 2 times and ≥3 times readmission). Our results were similar for multivariable regression analyses, and only one multinomial logistic regression with binary outcome results was presented. Efron introduced the bootstrap method for internal validation of the traditional multinomial regression model was used to obtain a bias-corrected 95% confidence interval (CI) of β coefficients by resampling 200 samples. β coefficients with bias in the coefficients and bootstrap 95% CI of the coefficients were presented. Two-tailed P (≤ 0.05) was considered for the statistically significant level. SPSS 28.0 Statistical package, IBM Corp. Release 2021, IBM SPSS statistics for windows, Version 28.0, Armonk, NY, USA: IBM Corporation was used for the analysis.
| Results|| |
The study analyzed a total of 10,550 cardiac patients who were discharged alive within 30 days at the heart hospital from January 2015 to December 2019. There were 8418 (79.8%) index admissions and 2132 (20.2%) hospitalized at least twice or more within 30 days after index admissions. Old age patients (65.6 ± 13.2 vs. 56.0 ± 13.5, P = 0.001), females (768 [36.0%] vs. 1462 [17.4%], P = 0.001) and Qatari Nationals (1065 [50.0%] vs. 1864 [22.1%], P = 0.001) were found more in the re-admission group than the index admission group. Also, hospital stay in days was more in the re-admission group compared to the index admission group, but there was no difference in CCU days admissions between the groups [Table 1].
|Table 1: Baseline characteristics of cardiac patients 30-day discharged alive after index admission|
Click here to view
[Table 2] describes risk factors, complaints, and findings of cardiac patients 30-day discharged alive. Re-admissions were more frequent in comparison to the index-admissions in patients having diabetes mellitus (DM) (1504 [70.5%] vs. 3862 [45.9%], P = 0.001), Hypertension (HTN) (1536 [72.0%] vs. 4141 [49.2%], P = 0.001), Cholesterol (440 [20.6% vs. 1391 [16.5%], P = 0.001), obesity (183 [8.6%] vs. 404 [4.8%], P = 0.001), chronic renal failure (511 [24.0%] vs. 563 [6.7%], P = 0.001), previous MI (838 [39.3%] vs. 1085 [12.9%], P = 0.001), previous stroke (82 [3.8%] vs. 165 [2%], P = 0.001), Peripheral arterial disease (PAD) (18 [0.8%] vs. 25 [0.3%], P = 0.001), chronic heart failure (CHF) (71 [3.3%] vs. 187 [2.2%], P = 0.003) whereas; re-admissions were less frequent in comparison to index admissions in smoking (299 [14.0%] vs. 2318 [27.5%], P = 0.001), family history (FH) (37 [1.7%] vs. 250 [3.0%], P = 0.002), coronary angiography single vessel (VD) disease (109 [5.1%) vs. 1694 [20.1%], P = 0.001), coronary angiography 2VD (120 [5.6%] vs. 1271 [15.1%], P = 0.001) and coronary angiography 3VD (232 [10.9%] vs. 1320 [15.7%], P = 0.001). Triglycerides (TG) and shock were not significant between the two.
|Table 2: Risk factors, complaints and complications of cardiac patients 30-day discharged alive after index admission|
Click here to view
Shortness of breath (SOB) and palpitation complaints at admission were more in the re-admission group than the index admission group respectively (1063 [49.9%] vs. 1710 [20.3%], P = 0.001) and (176 [8.3%] vs. 533 [6.3%], P = 0.002). There was no difference between the two for dizziness. Chest pain was more in index admission cases than the re-admissions.
AF was found more in the readmission group than the index admission group (94 [4.4%] vs. 194 [2.3%], P = 0.001), whereas; there was no difference in Ventricular tachycardia, Ventricular fibrillation (VF), arrest, and other findings.
[Table 3] shows the final diagnosis of cardiac patients 30-day discharged alive at Heart Hospital. Variables AF (403 [18.9%] vs. 503 [5.9%], P = 0.001), A. Flutter (105 [4.9%] vs. 200 [2.4%], P = 0.001), LBBB (57 [2.7%] vs. 83 [1.0%], P = 0.001), AI (106 [5.0%] vs. 299 [3.6%], P = 0.002), Cardiomyopathy (409 [19.2%] vs. 625 [7.4%], P = 0.001), HTN (285 [13.4%] vs. 785 [9.3%], P = 0.001), CHF (528 [24.8%] vs. 630 [7.5%], P = 0.001) and pulmonary HTN (17 [0.8%] vs. 21 [0.2%], P = 0.001) were more in re-admission group whereas; STEMI and NSTEMI were found more in the index admission group. There was no statistical difference between the two for the variables PVC, VF, arrest, CAD angina, shock, pericardial disease, and syncope.
|Table 3: Final diagnosis of cardiac patients 30-day discharged alive after index admission at Heart Hospital|
Click here to view
[Table 4] describes the procedures of cardiac patients discharged alive within 30 days at Heart Hospital. transcatheter aortic valve implantation (16 [0.8%] vs. 33 [0.4%], P = 0.03), PPM cardiac resynchronisation therapy (26 [1.2%] vs. 31 [0.4%], P = 0.001), Nuclear study (130 [6.1%] vs. 276 [3.3%], P = 0.001) and dialysis (30 [1.4%] vs. 35 [0.4%], P = 0.001), were performed more in readmission group than the index admission group whereas Cath RT LT heart (229 [10.7%] vs. 1644 [19.5%], P = 0.001), PCI/PTCA (548 [25.7%] vs. 5812 [69%], P = 0.001), Echo (1309 [61.4%] vs. 5485 [65.2%], P = 0.001), stress test (8 [0.4%] vs. 81 [1.0], P = 0.008), perfusion echo (25 [1.2%] vs. 38 [0.5%], P = 0.001), cardiac computed tomography (57 [2.7%] vs. 406 [4.8%], P = 0.001), cardia magnetic resonance imaging (21 [1.0%] vs. 207 [2.5%], P = 0.001), IABP (4 [0.2%] vs. 43 [0.5%], P = 0.04) and Thrombolysis (8 [0.4%] vs. 150 [1.8%], P = 0.001) were performed more in Index admissions. There was no difference between the variables of Swan Ganz, thrombectomy, temporary pacer, Valvuloplasty, Holter, respirator, hypothermia, pericardiocentesis, CPR, cardioversion, cardiac surgery, CABG, PPM single, PPM Dual, EP study diagnostic, and EP study ablation. [Figure 1] showed that readmissions were minimum in 2017 and maximum in 2018.
|Table 4: Procedures of cardiac patients discharged alive within 30 days after index admission at Heart Hospital|
Click here to view
|Figure 1: Trend of 30 days re-admission rates at Heart Hospital, HMC, Qatar. HMC: Hamad medical corporation|
Click here to view
Multinomial logistic regression was conducted with significant independent variables identified in univariate analysis. The results are presented in [Table 5] after adjusting variables such as the number of days spent in the hospital, the year of admission, the number of ER admissions; risk factors-HTN, smoking, cholesterol, TG, FH; and admission complaints-chest pain, dizziness, palpitations; and complications-AF, VF, CHF.
|Table 5: Risk factors associated to 30-day re-admission following cardiac patients discharged alive after index admission|
Click here to view
Over 61-year-old patients were two times more likely to experience re-admissions than those under 50 years of age (aOR 1.65, 95% C.I.: 1.41-1.94, P=0.001). The re-admission rates did not differ significantly between 51 and 60 years of age and ≤50 years of age. Females were 30% more likely to be re-admitted than their male counterparts (adjusted odds ratio [aOR] 1.30, 95% CI: 1.11–1.50, P = 0.001). Qatari nationals were re-admitted twice as often as non-Qatari residents (aOR 2.12, 95% CI; 1.89–2.38, P = 0.001). Variables DM (aOR 1.36, 95% CI: 1.20–1.53, P = 0.001), chronic renal failure (aOR 1.93, 95% CI: 1.66–2.24, P = 0.001), previous MI (aOR 3.22, 95% CI: 2.85–3.64, P = 0.001), SOB (aOR 1.80, 95%C.I.: 1.58-2.67, P = 0.001), final diagnosis AF (aOR 2.17, 95% C.I.: 1.10-2.67, P=0.01), cardiomyopathy (aOR 1.72, 95% CI 1.47–2.02, P = 0.001), and CHF (aOR 1.56, 95% C.I.: 1.33-1.82, P=0.001) were found statistically significant independent risk factors for re-admission. C-statistics (0.82, 95% CI: 0.81–0.83, P = 0.001) and receiver operating characteristic curve [Figure 2] indicated that these variables could predict re-admission to the hospital 82% accurately.
|Figure 2: ROC curve using predictive probabilities of a regression model. ROC: Receiver operating characteristic|
Click here to view
Validation of the regression model
In many cases, the performance of a predictive model is overestimated or underestimated if it is based solely on the sample of subjects. To overcome this problem, a realistic model that is bootstrapped (an internal validation method for the predictive model) was applied for stable estimates with low biases. [Table 6] describes Bootstrap (200 re-sampling) multinomial logistic regression analysis. Bootstrap results showed estimates bias between − 0.007 and 0.007, suggesting only an error margin of below 5% in the bootstrap 95% CIs of the traditional model.
|Table 6: Bootstrap (200 re-sampling) multinomial logistic regression analysis|
Click here to view
Predictive probabilities for the re-admission within 30 days after index admission
[Table 7] shows the predictive probabilities of readmissions according to the regression model. Based on our data, we found more than an 80% chance of 30-day re-admission for cardiac patients at Heart Hospital presenting with the following variables: age ≥51 years, female sex, presence of diabetes, chronic renal failure, previous MI, SOB, AF, cardiomyopathy, and CHF at index admission. The probability (0.07) of readmission within 30 days discharged alive after index admission was minimal where no risk factor was involved, and it went to 0.88 for eight or more risk factors. Results showed that the presence of any five or more risk factors was found to be crucial for readmissions within 30 days.Transfer clinical implications here:
|Table 7: Predictive probabilities of readmissions according to the regression model|
Click here to view
- Between January 2015 and December 2019, approximately 20% of cardiac admissions were readmitted within 30 days of being discharged alive after the index admission
- Age, female sex, diabetes, chronic renal failure, previous MI, cardiomyopathy, and CHF were significant predictors associated with 30-day readmission after being discharged alive
- Every second cardiac patient with five risk factors was readmitted within 30 days of being discharged alive after the index admission
- In the study, more than 80% of readmissions within 30 days of index admission were predicted by the regression model.
| Discussion|| |
Hospital readmission of CVD patients is on the rise worldwide and is frequently monitored as a quality indicator for the healthcare system. Hospital readmissions contribute a significant financial burden on the patients and the healthcare sector, mainly the associated treatment costs and the utilization of hospital resources., In the present study, we found that the age of the CVD patients was more in the readmission group than in the index admission group. Approximately 1 in 5 older adults hospitalized for Acute Myocardial Infraction (AMI) is readmitted to the hospital within 30 days of discharge mainly due to the risk of functional impairments and significantly impaired mobility, which increases adverse events in several AMI cohorts. Berkman et al. also reported that older patients with cardiac disease are at high risk for physical deterioration during posthospital recovery and suffer frequent early readmissions. Previous studies have also shown that age is linked to aortic stiffness and vascular calcification,, which are significant predictors of CVD. Furthermore, a history of CVD is associated with endothelial dysfunction, inflammation, and oxidative stress, which contribute to CVD events.,
Our study also showed more females, Qatari Nationals, ER admission patients, and elective admissions in the readmission group than in the index admission group. The hospital stay was also found more in the readmission group than in the index admission group. The previous study showed that women had a higher rate of heart failure and recurrent ACS readmission diagnoses than men. This supports the possibility that women are at a higher risk for recurrent acute cardiac events and readmissions, likely from either the natural sequela of the disease or the complications of the treatment procedures. Furthermore, most of the patients are of the ex-pat population in Qatar, and many traveled to their home countries after their initial discharge from the hospital. This could be why more Qatari nationals are in the re-admission group.
A recent study on risk factors for recent admission of patients with heart failure showed that the increase in the annual number of unscheduled visits to the ER implicated an increased risk of readmissions.
We also found that re-admissions were more frequent in patients having DM, HTN, Cholesterol, obesity, chronic renal failure, previous MI, and PAD. Earlier studies in the Framingham cohort and the Contemporary Elderly Cohort identified the presence of diabetes as a significant risk factor for heart failure. Another study reported that the re-admission rate of heart failure patients with diabetes was 49.1%, and the risk model indicated that having diabetes increased the risk of re-admission within 12 months by a factor of 3.8.
HTN is also among the highest prevalent diseases in the Arab population, as 40%–70% of heart failure patients have a history of HTN.,,, Lee et al. demonstrated that HTN, diabetes, metabolic syndrome, and atherosclerotic disease contribute to the readmission of heart failure patients. Kidney disease is also a risk factor for CVD readmission in patients within 30 days after AMI and other diseases.,,, Another study on peritoneal dialysis patients showed that advanced age, a history of CVD, and a lower albumin level were independently associated with a higher risk of 1st-year CVD readmission.
A retrospective observational cohort study among young and middle-aged adults showed that 11.2% were readmitted after an index hospitalization for AMI and 23.4% readmitted after an index hospitalization for HF.
Predicting the likelihood of readmission is complex and fraught with difficulties, as most existing models, whether for facility comparison or clinical purposes, have a low prediction accuracy. Hence, we aimed for a better model that could assess the readmission risk and the probability of patients getting readmitted within 30 days of their discharge.
In our study, variables such as age, female sex, diabetes, chronic renal failure, previous MI, SOB, AF, cardiomyopathy, and CHF were associated with a more than 80% chance of 30-day re-admission for cardiac patients. We also found that one in every two patients discharged alive within 30 days was readmitted if presented with five or more risk factors.
According to the bootstrap method, estimates presented in the prediction model had an error margin below 5%, indicating that the traditional model can be treated as a realistic model for the population.
| Conclusion|| |
Age, women, diabetes, chronic renal failure, previous MI, cardiomyopathy, and CHF were significant risk factors associated with 30-day readmission discharged alive after index admission.
The predictive model was able to predict more than 80% accurately 30-day readmission discharge alive after index admission in the population. The study may help design interventions potentially resulting in better outcomes and lower costs.
In modeling future outcomes, a statistical technique is used to estimate likely effects. Results were validated using the bootstrap method to correct for bias in the traditional regression model's estimates. The results are generalizable and can be applied cost-effectively throughout the country and in the Gulf region.
Social and behavioral variables were not available in the registry data. Although large data were available for the analysis, the limitation of retrospective cohort data cannot be ignored. As most of the population of Qatar is made up of immigrants who come to earn a living, the study results have limited generalizability to the Gulf region only.
Data sharing statement
Data are available at Cardiology Research Center, Medical Research Center, HMC.
We appreciate Heart Hospital staff who are continuously assisting in data collection and reviewing the registry records.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Xu H, Farmer HR, Granger BB, Thomas KL, Peterson ED, Dupre ME. Perceived versus actual risks of 30-day readmission in patients with cardiovascular disease. Circ Cardiovasc Qual Outcomes 2021;14:e006586.
Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al.
Global burden of cardiovascular diseases and risk factors, 1990-2019: Update from the GBD 2019 study. J Am Coll Cardiol 2020;76:2982-3021.
Thom T, Haase N, Rosamond W, Howard VJ, Rumsfeld J, Manolio T, et al.
Heart disease and stroke statistics – 2006 update: A report from the American Heart Association statistics committee and stroke statistics subcommittee. Circulation 2006;113:e85-151.
Ibrahim AM, Dimick JB. A decade later, lessons learned from the hospital readmissions reduction program. JAMA Netw Open 2019;2:e194594.
Wetmore JB, Molony JT, Liu J, Peng Y, Herzog CA, Collins AJ, et al.
Readmissions following a hospitalization for cardiovascular events in dialysis patients: A retrospective cohort study. J Am Heart Assoc 2018;7:e007231.
Li J, Huang N, Zhong Z, Joe P, Wang D, Ai Z, et al.
Risk factors and outcomes of cardiovascular disease readmission within the first year after dialysis in peritoneal dialysis patients. Ren Fail 2021;43:159-67.
Frishman WH, Alpert JS. Reducing hospital readmissions for cardiovascular disease: Is it feasible? Am J Med 2013;126:753-4.
Weissman JS, Stern RS, Epstein AM. The impact of patient socioeconomic status and other social factors on readmission: A prospective study in four Massachusetts hospitals. Inquiry 1994;31:163-72.
Ferraris VA, Ferraris SP, Harmon RC, Evans BD. Risk factors for early hospital readmission after cardiac operations. J Thorac Cardiovasc Surg 2001;122:278-86.
Stewart RD, Campos CT, Jennings B, Lollis SS, Levitsky S, Lahey SJ. Predictors of 30-day hospital readmission after coronary artery bypass. Ann Thorac Surg 2000;70:169-74.
Shah M, Patil S, Patel B, Agarwal M, Davila CD, Garg L, et al.
Causes and predictors of 30-day readmission in patients with acute myocardial infarction and cardiogenic shock. Circ Heart Fail 2018;11:e004310.
Berenson RA, Paulus RA, Kalman NS. Medicare's readmissions-reduction program – A positive alternative. N Engl J Med 2012;366:1364-6.
Bernheim SM, Grady JN, Lin Z, Wang Y, Wang Y, Savage SV, et al.
National patterns of risk-standardized mortality and readmission for acute myocardial infarction and heart failure. Update on publicly reported outcomes measures based on the 2010 release. Circ Cardiovasc Qual Outcomes 2010;3:459-67.
Kaboli PJ, Go JT, Hockenberry J, Glasgow JM, Johnson SR, Rosenthal GE, et al.
Associations between reduced hospital length of stay and 30-day readmission rate and mortality: 14-year experience in 129 veterans Affairs hospitals. Ann Intern Med 2012;157:837-45.
Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: Current strategies and future directions. Annu Rev Med 2014;65:471-85.
Efron B. Bootstrap methods: Another look at the jackknife. Ann Statist 1979;7:1-26.
Komajda M, Schöpe J, Wagenpfeil S, Tavazzi L, Böhm M, Ponikowski P, et al.
Physicians' guideline adherence is associated with long-term heart failure mortality in outpatients with heart failure with reduced ejection fraction: The QUALIFY international registry. Eur J Heart Fail 2019;21:921-9.
Upadhyay S, Stephenson AL, Smith DG. Readmission rates and their impact on hospital financial performance: A study of Washington Hospitals. Inquiry2019;56:46958019860386.
Dodson JA, Hajduk AM, Murphy TE, Geda M, Krumholz HM, Tsang S, et al.
Thirty-day readmission risk model for older adults hospitalized with acute myocardial infarction. Circ Cardiovasc Qual Outcomes 2019;12:e005320.
Berkman B, Millar S, Holmes W, Bonander E. Predicting elderly cardiac patients at risk for readmission. Soc Work Health Care 1991;16:21-38.
Prichard S. Cardiovascular risk in peritoneal dialysis. In: Ronco C, Dell'Aquila R, Rodighiero MP, editors. Contributions to Nephrology. Basel: KARGER; 2003. p. 82-90. Available from: https://www.karger.com/Article/FullText/71427
. [Last accessed on 2022 Jan 27].
Townsend RR, Wimmer NJ, Chirinos JA, Parsa A, Weir M, Perumal K, et al.
Aortic PWV in chronic kidney disease: A CRIC ancillary study. Am J Hypertens 2010;23:282-9.
Steiner S, Schaller G, Puttinger H, Födinger M, Kopp CW, Seidinger D, et al.
History of cardiovascular disease is associated with endothelial progenitor cells in peritoneal dialysis patients. Am J Kidney Dis 2005;46:520-8.
Steven S, Frenis K, Oelze M, Kalinovic S, Kuntic M, Bayo Jimenez MT, et al.
Vascular inflammation and oxidative stress: Major triggers for cardiovascular disease. Oxid Med Cell Longev 2019;2019:7092151.
Dai L, Golembiewska E, Lindholm B, Stenvinkel P. End-stage renal disease, inflammation, and cardiovascular outcomes. In: Ronco C, editor. Contributions to Nephrology. Switzerland:S. Karger AG; 2017. p. 32-43. Available from: https://www.karger.com/Article/FullText/479254
. [Last accessed on 2022 Jan 27].
Lam L, Ahn HJ, Okajima K, Schoenman K, Seto TB, Shohet RV, et al.
Gender differences in the rate of 30-day readmissions after percutaneous coronary intervention for acute coronary syndrome. Womens Health Issues 2019;29:17-22.
Sadeq A, Sadeq A, Sadeq A, Alkhidir IY, Aburuz S, Mellal AA, et al.
Risk factors for hospital readmission of patients with heart failure: A cohort study. J Pharm Bioallied Sci 2020;12:335-43.
Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham heart study and the epidemiology of cardiovascular disease: A historical perspective. Lancet 2014;383:999-1008.
Kalogeropoulos A, Georgiopoulou V, Kritchevsky SB, Psaty BM, Smith NL, Newman AB, et al.
Epidemiology of incident heart failure in a contemporary elderly cohort: The health, aging, and body composition study. Arch Intern Med 2009;169:708-15.
Sulaiman K, Panduranga P, Al-Zakwani I, Alsheikh-Ali AA, AlHabib KF, Al-Suwaidi J, et al.
Clinical characteristics, management, and outcomes of acute heart failure patients: Observations from the Gulf acute heart failure registry (Gulf CARE). Eur J Heart Fail 2015;17:374-84.
Alhabeeb W, Elasfar A, AlBackr H, AlShaer F, Almasood A, Alfaleh H, et al.
Clinical characteristics, management and outcomes of patients with chronic heart failure: Results from the heart function assessment registry trial in Saudi Arabia (HEARTS-chronic). Int J Cardiol 2017;235:94-9.
AlHabib KF, Elasfar AA, AlBackr H, AlFaleh H, Hersi A, AlShaer F, et al.
Design and preliminary results of the heart function assessment registry trial in Saudi Arabia (HEARTS) in patients with acute and chronic heart failure. Eur J Heart Fail 2011;13:1178-84.
AlHabib KF, Elasfar AA, Alfaleh H, Kashour T, Hersi A, AlBackr H, et al.
Clinical features, management, and short- and long-term outcomes of patients with acute decompensated heart failure: Phase I results of the HEARTS database. Eur J Heart Fail 2014;16:461-9.
Lee JH, Kim MS, Kim EJ, Park DG, Cho HJ, Yoo BS, et al.
KSHF guidelines for the management of acute heart failure: Part I. Definition, epidemiology and diagnosis of acute heart failure. Korean Circ J 2019;49:1-21.
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004;351:1296-305.
Wang H, Zhao T, Wei X, Lu H, Lin X. The prevalence of 30-day readmission after acute myocardial infarction: A systematic review and meta-analysis. Clin Cardiol 2019;42:889-98.
Kuo FC, Lin PC, Lu YD, Lee MS, Wang JW. Chronic kidney disease is an independent risk factor for transfusion, cardiovascular complication, and thirty-day readmission in minimally invasive total knee arthroplasty. J Arthroplasty 2017;32:1630-4.
Altibi AM, Prousi G, Agarwal M, Shah M, Tripathi B, Ram P, et al.
Correction to: Readmission-free period and in-hospital mortality at the time of first readmission in acute heart failure patients-NRD-based analysis of 40,000 heart failure readmissions. Heart Fail Rev 2020;25:551.
Ranasinghe I, Wang Y, Dharmarajan K, Hsieh AF, Bernheim SM, Krumholz HM. Readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia among young and middle-aged adults: A retrospective observational cohort study. PLoS Med 2014;11:e1001737.
Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al.
Risk prediction models for hospital readmission: A systematic review. JAMA 2011;306:1688-98.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]