Skip to main content

Temporal trends and outcomes of heart transplantation in Spain (2002–2021): propensity score matching analysis to compare patients with and without type 2 diabetes



The impact of Type 2 Diabetes (T2D) on the outcomes of heart transplantation (HT) has not yet been clearly established. The objectives of this study were to examine the trends in the prevalence of T2D among individuals who underwent a HT in Spain from 2002 to 2021, and to compare the clinical characteristics and hospitalization outcomes between HT recipients with and without T2D.


We used the national hospital discharge database to select HT recipients aged 35 and older. The International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) were used to identify patients with and without T2D. We also recorded comorbidities, complications of HT, and procedures. Propensity score matching (PSM) and Cox regression were used to analyze the effect of T2D on in-hospital mortality (IHM).


Between 2002 and 2021, a total of 4429 HTs (T2D, 19.14%) were performed in Spain. The number of HTs in patients with T2D decreased from 2002 to 2005 (n = 171) to 2014–2017 (n = 154), then rose during 2018–2021 (n = 186). Complications of HT increased in patients with and without T2D over the study period (26.9% and 31.31% in 2002–2005 vs. 42.47% and 45.01% in 2018–2021, respectively). The results of the PSM showed that pneumonia and Gram-negative bacterial infections were less frequent in patients with T2D and that these patients less frequently required hemodialysis, extracorporeal membrane oxygenation (ECMO), and tracheostomy. They also had a shorter hospital stay and lower IHM than patients without diabetes. The variables associated with IHM in patients with T2D were hemodialysis and ECMO. IHM decreased over time in people with and without T2D. The Cox regression analysis showed that T2D was associated with lower IHM (HR 0.77; 95% CI 0.63–0.98).


The number of HTs increased in the period 2018–2021 compared with 2002–2005 in patients with and without T2D. Over time, complications of HT increased in both groups studied, whereas IHM decreased. The presence of T2D is associated with lower IHM.


Type 2 diabetes (T2D) frequently affects patients with advanced heart failure, often concomitantly with other medical conditions such as obesity, thromboembolic complications, renal dysfunction, and immunosuppression, thereby increasing the risk of infection [1, 2]. Accordingly, given that heart transplantation (HT) is an alternative in advanced heart failure, T2D is deemed a risk factor that could potentially influence its outcome [3].

No consensus has been reached with respect to the impact of T2D on complications after HT: some studies report no significant increase in the risk of infection, rejection, or heightened post-transplant complications in recipients with T2D [4,5,6], whereas others have suggested the contrary [7,8,9]. These inconsistencies may be due to variations in study design, sample size, follow-up duration, and post-transplant outcomes [4,5,6,7,8,9].

Multiple studies have indicated that the number of HT in patients with diabetes has recently increased or stabilized across different regions [10]. In Spain, a population-based study conducted between 2001 and 2015 found that the admission rates for HT declined over time among individuals with T2D (from 2.05 to 1.19 cases per 100,000 inhabitants; P < 0.001) [11]. Data on survival rates following HT in patients with diabetes remain inconsistent. Some studies report lower survival rates among HT recipients with diabetes compared to their non-diabetic counterparts [8], while other studies find similar rates in both groups or even higher among HT recipients without diabetes [6, 12,13,14]. Previous investigations in Spain have shown the effect of the COVID-19 pandemic on the use of heart procedures and hospital outcomes in people with diabetes [15, 16]. However, more studies are needed on HT in this population during the pandemic.

Therefore, the objectives of our investigation were as follows: (i) to evaluate temporal trends in incidence, clinical features, complications, and hospital outcomes among patients with T2D and non-diabetic patients who underwent HT in Spain from 2002 to 2021; (ii) to compare study variables between recipients with T2D and non-diabetic patients using propensity score matching (PSM); (iii) to identify variables associated with in-hospital mortality (IHM) among patients with and without T2D; and (iv) to ascertain the influence of T2D on IHM.


Study design, study population, and data assessment

To achieve the proposed objectives, we conducted an observational, retrospective, population-based study. The study period ran from January 1, 2002, to December 31, 2021. The study was based on the Spanish National Hospital Discharge Database (RAE-CMBD, Registro de Actividad de Atención Especializada-Conjunto Mínimo Básico de Datos), which collects individual patient information (e.g., sex, age, admission, and discharge date), diagnosis (1 to 20), and procedures (0 to 20), as well as the discharge destination (home, voluntary discharge, social institution, deceased). Diagnosis includes those conditions present at admission or diagnosed during hospitalization. Procedures include therapeutic or diagnostic procedures conducted during admission. The International Classification of Diseases Ninth Revision (ICD-9-CM) was used by the RAE-CMBD for coding from 2002 to 2015, and the Tenth Revision (ICD-10) was used from 2016 to 2021 [17]. All codes applied in this investigation are shown in Supplementary Table 1.

The study population included patients aged ≥ 35 years with an ICD-9 and ICD-10 code for HT in any position of the RAE-CMBD procedure fields. It was subsequently stratified according to diabetes status as T2D patients (ICD-9 codes: 250.x0 and 250.x2; ICD-10 codes: E11.x) and non-diabetic patients, in any diagnostic position. Patients with type 1 diabetes (T1D) codes (ICD-9 codes: 250.x1 and 250.x3; ICD-10 codes: E10.x) or missing data for essential variables (age, sex, date of admission or discharge, and discharge destination) were excluded. Recipients younger than 35 years were not included because the prevalence of Type 2 Diabetes (T2D) is extremely low in this age group in our country, so these patients may have Type 1 Diabetes (T1D) [18, 19]. For the purposes of this study, we also excluded all patients who underwent multi-organ transplantation (n = 77), including those who received both a heart and kidney transplant (n = 18), as well as those who underwent re-transplantation (n = 89).

Study covariates

The study covariates collected for each HT recipient were age, sex and year of surgery. To achieve our objectives, the study years were grouped into five time periods, each consisting of four years (2002–2005, 2006–2009, 2010–2013, 2014–2017, and 2018–2021).

To assess the presence of comorbidity the Charlson Comorbidity Index (CCI) was used, excluding diabetes and heart diseases, based on a methodology described elsewhere for ICD based databases [20, 21]. The CCI is commonly used in epidemiological investigation to control the risk of in-hospital mortality according to 19 conditions present at the time of hospital admission. [20, 21]. The CCI was categorized in CCI = 0, CCI = 1 and CCI ≥ 2 and analyzed as a continuous variable.

Regardless of the diagnostic position in the database, comorbid conditions including obesity and pulmonary hypertension was assessed. The concomitant heart conditions potentially leading to HT analyzed were valvular heart disease, dilated cardiomyopathy, ischemic heart disease, and congenital heart disease. Data on complications of HT and the presence of pneumonia were also collected. Complications of HT included, rejection, failure, infection, cardiac allograft vasculopathy and “other” or “unspecified” complications of HT.

To identify possible infections caused by specific microorganisms, we searched the database for the following infections: Staphylococcus bacteremia, Streptococcus bacteremia, Gram-negative bacteremia, Pseudomonas aeruginosa infection, and cytomegalovirus infection. According to the RAE-CMBD methodology, only microorganisms confirmed through culture or positive results on genetic testing (PCR) can be recorded.

The use of hemodialysis, extracorporeal membrane oxygenation (ECMO), and tracheostomy were evaluated regardless of the position of procedures in the database.

The ICD9 and ICD10 codes for comorbidities, procedures and complications are shown in Supplementary Tables 1 and 2.

Length of hospital stay (LOHS) was defined as the number of days between admission and discharge. IHM was the proportion of deaths during the hospital admission for HT. Finally, the type of hospital admission, whether categorized as “Urgent” or “Programmed,“ was described and analyzed.

Propensity score matching

The PSM method consisted of selecting for each patient with a T2D code a patient without a T2D code and with the same or closest propensity score (PS) obtained through multivariable logistic regression. We followed this approach to match the structure of the confounding factors for both groups. We used year of hospitalization, sex, age, and all comorbidities present on admission, including obesity and pulmonary hypertension, as matching conditions to calculate the PS [22].

The chosen matching method was one-to-one using calipers with a width equal to 0.2 of the standard deviation of the logit of the PS. Estimating the absolute standardized difference before and after matching enabled us to evaluate the quality of the PSM process. Populations are considered to be well balanced when the absolute standardized differences are < 10% after PSM [22]. A love plot was generated to visualize how populations become comparable after PSM. PSM has been previously used by other authors to compare the outcomes of HT according to clinical conditions and procedures [23,24,25,26,27,28,29,30].

Statistical analysis

A statistical analysis was conducted to describe and compare the total number and covariates of patients who underwent HT (with T2D and without T2D). The results obtained in the descriptive analysis were expressed as frequencies and percentages for categorical variables and as mean and standard deviation or median with interquartile range (IQR) for quantitative variables.

The temporal trend was analyzed using the Cochran-Mantel-Haenszel statistic or Cochran-Armitage test for categorical variables and a linear regression t test or Jonckheere-Terpstra test for continuous variables.

Categorical variables were compared using the Fisher exact test. Continuous variables were compared using the t test or the Mann-Whitney test, as required.

In our investigation, we aim to test multiple hypotheses concurrently within a single study. This approach increases the risk of a Type I error, making it necessary to adjust the P-value accordingly [31]. To address this, we have employed the Bonferroni adjustment. As a result, the significance level must be divided by the number of hypothesis tests conducted. In each table, we have included a footnote specifying the P-value that should be considered significant after applying the Bonferroni adjustment.

We used multivariable Cox regression to identify the variables associated with IHM among patients who underwent HT according to diabetes status. Models were constructed including sex, age, year, comorbidities (CCI and all the specific clinical conditions analyzed), and obesity as covariates. The results for these models are shown with the hazard ratio (HR) and 95% confidence interval (CI).

We used Stata version 14 to perform the statistical analysis (Stata, College Station, TX, USA). Statistical significance was set at p < 0.05 (2-tailed).

Sensitivity analysis

Using the entire database (patients with and without diabetes), we aimed to confirm the result of PSM for the effect of T2D on IHM. To do so we constructed a multivariable Cox regression model with IHM as the dependent variable adjusted for all the variables significantly associated with IHM in the bivariate analysis.

Ethics statement

The RAE-CMBD is owned by the Spanish Ministry of Health and can be accessed upon request [32]. We sent the protocol for this investigation to the Ministry, which approved it and provided us with the anonymized database. According to Spanish legislation, written consent from the patients is not required, as this is an administrative registry.


Between the years 2002 and 2021, a total of 4429 HTs were performed in Spain. Of these, 19.14% (n = 848) were in patients with a code for T2D. The prevalence of T2D has remained stable over time, with values slightly below 20% (p = 0.388).

Temporal trends in the number and characteristics of heart recipients according to diabetes status

As displayed in Table 1, there was a small increase in the number of HTs among recipients with T2D between 2002 and 2005 (n = 171) and 2018–2021 (n = 186). Women accounted for between 13% and 15.7% of patients, respectively. Over time, mean age increased (57.68 years in 2002–2005 vs. 59.27 years in 2018–2021; p = 0.025), as did comorbidity based on the mean CCI (p < 0.001), the frequency of obesity (7.6% vs. 12.37%; p = 0.020), and pulmonary hypertension (14.04% vs. 16.13%; p = 0.022).

Table 1 Clinical characteristics of hospital admissions for heart transplantation among patients with and without type 2 diabetes mellitus (T2D) in Spain, 2002–2021

In patients with T2D, dilated cardiomyopathy was the heart condition most frequently coded (60.26%), followed by ischemic heart disease (56.49%). However, the frequency of ischemic heart disease decreased between 2002 and 2005 and 2018–2021 (59.06% vs. 52.15%; p = 0.020), and that of the other heart conditions remained stable.

Trends were similar in patients with and without T2D, although in the latter, higher values were recorded for the number of women undergoing HT (16.64% in 2002–2005 vs. 26.92% in 2018–2021; p < 0.001) and the frequency of valvular heart disease, ischemic heart disease, and congenital heart disease (all p < 0.001).

Temporal trends in the prevalence of complications, procedures, and hospital outcomes among heart recipients according to diabetes status

As can been seen in Table 2, the proportion of recipients with T2D who experienced complications of HT increased significantly from 26.9% in 2002–2005 to 42.47% in 2018–2021 (p < 0.001). Significant increases over time were observed for the incidence of Staphylococcus bacteremia (2.92% vs. 10.22%; p < 0.001), Gram-negative bacteremia (2.92% vs. 4.84%; p < 0.001), and Pseudomonas aeruginosa (1.75% vs. 7.53%; p = 0.015). The procedures whose frequencies increased significantly over time were hemodialysis (5.85% vs. 17.74%; p < 0.001) and ECMO (0% vs. 17.74%; p < 0.001). IHM decreased, albeit not significantly, from 14.62 to 11.29% (p = 0.419), and the LOHS rose from a median of 24 days in 2002–2005 to 28 days in 2018–2021 (p < 0.001).

Table 2 Complications, isolate pathogens, procedures and outcomes of hospital admissions for heart transplantation among patients with and without type 2 diabetes (T2D) in Spain, 2002–2021

Regarding non-diabetic patients, the percentage of those with complications recorded in the discharge report increased significantly (p < 0.001), as did the presence of pneumonia and pathogens such as Gram-negative microorganisms, Pseudomonas aeruginosa, and cytomegalovirus (all p < 0.001). The frequency of hemodialysis, ECMO, and tracheostomy also increased significantly (all p < 0.001).

In non-T2D recipients, LOHS increased over time (26 days in 2002–2005 vs. 33 days in 2018–2021; p < 0.001). However, the IHM decreased from 17.17% in 2002–2005 to 15.38% in 2018–2021 (non-significant, p = 0.248).

Comparison of characteristics, clinical variables, and hospital outcomes between heart recipients according to diabetes status

Before PSM, when all patients hospitalized from 2002 to 2021 were grouped (see Table 3), there were more women in the non-diabetic group than in the T2D group (23.22% vs. 14.74%; p < 0.001). However, recipients with T2D were significantly older than non-T2D patients (58.91 years vs. 54.99 years; p < 0.001) and had more comorbid conditions according to the mean CCI (1.8 vs. 1.63; p < 0.001). Specifically, recipients with T2D more frequently had peripheral vascular disease and kidney disease (21.37% and 18.45% vs. 16.22% and 10.22%, respectively). Moreover, recipients with T2D were more frequently obese (7.42% vs. 3.51%; p < 0.001) and had ischemic heart disease (56.92% vs. 43.06%; p < 0.001). Nonetheless, non-T2D patients more frequently had valvular heart disease, congenital heart disease, complications of HT, pneumonia, Staphylococcus bacteremia, Gram-negative bacteremia, and cytomegalovirus infection. After PSM, the differences observed between patients with and without T2D became non-significant, except for pneumonia (6.97% in the non-diabetic group vs. 3.04% in the T2D group; p < 0.001) and Gram-negative bacteremia (7.87% in the non-diabetic group vs. 5.4% in the T2D group; p = 0.036).

Table 3 Comparison of characteristic, comorbidities, transplant complications and hospital outcomes among patients with and without type 2 diabetes (T2D) who underwent a heart transplantation in Spain from 2002 to 2021, before and after propensity score matching (PSM)

After PSM, recipients with T2D less frequently required hemodialysis (9.45% vs. 13.72%; p = 0.005), ECMO (6.41% vs. 10.01%; p = 0.006), and tracheostomy (4.39% vs. 6.97%; p < 0.001) than non-T2D patients.

The median LOHS was 28 days for non-T2D patients and 23 days for recipients with T2D (p < 0.001). IHM among non-T2D patients remained over 4% higher than among recipients with T2D (18.34% vs. 12.26%; p = 0.018).

The proportion of recipients with Type 2 Diabetes (T2D) who were coded for “Urgent” admission was significantly higher than that of those without diabetes (60.97% vs. 50.94%; p < 0.001).

Figure 1 shows the absolute standardized differences before and after PSM. A significant imbalance could be ruled out, since the absolute standardized differences after PSM were below 10% for all the variables included in the PS [22].

Fig. 1
figure 1

Love plot showing the comparison of covariate values for patients with and without diabetes: absolute standardized differences before and after propensity score matching (PSM). Footnote: CCI Charlson comorbidity index. COPD Chronic obstructive pulmonary disease. HT Heart transplantation ECMO extracorporeal membrane oxygenation LOHS: length of hospital stay;

Variables associated with in-hospital mortality among heart recipients according to diabetes status

Table 4 shows the results of the multivariable Cox regression analysis performed to identify variables associated with IHM after HT in patients with T2D, without T2D, and for all patients who underwent this procedure.

Table 4 Multivariable Cox regression analysis of the factors associated with mortality during hospital admission for heart transplantation in Spain, 2002–2021 according to diabetes status

For all three populations analyzed, the variable associated with lower IHM was more recent surgery; those associated with a higher IHM were older age, presenting complications of HT, and hemodialysis or ECMO during hospitalization.

The results of the sensitivity analysis confirmed those found after PSM. As can be seen in Table 4, when all patients hospitalized from 2002 to 2021 were included in the Cox multivariable model, the presence of T2D was associated with a lower IHM after HT (HR 0.77; 95%CI 0.63–0.98).


The findings of this nationwide retrospective study, which is based on the diabetes status of over 4400 patients who underwent HT in Spain between 2002 and 2021, revealed several significant insights. First, a slight increase in the number of HTs among patients with T2D was observed when the last period (2018–2021) was compared with the first one (2002–2005). Second, IHM has decreased over the last 20 years for individuals with and without T2D. Third, IHM after HT was lower in patients with T2D than in non-diabetic individuals after adjustment based on PSM and multivariable Cox regression analysis.

Consistent with de Miguel-Yanes et al., we recorded a decrease in the number of HT procedures in patients with T2D from 2002 to 2005 to 2014–2017 [11]. Nevertheless, despite the overall decrease in transplants reported during the COVID-19 pandemic, a slight increase was observed in the last period of the study (2018–2021) [33, 34]. We posit that this result is a consequence of the increasing age of patients on the waiting lists for HT [35, 36] and that this increased age and comorbidity may have escalated the need for HT. Future studies are required to confirm this hypothesis.

The frequency of complications of HT rose in patients with and without diabetes between 2002 and 2005 and 2018–2021. Furthermore, we observed an increase in microorganisms causing bacteremia, such as Staphylococcus, Gram-negative bacteria, and Pseudomonas aeruginosa, and in the use of procedures such as hemodialysis and ECMO. This can be attributed to the high comorbidity typical of transplant recipients, immunosuppressive therapy, kidney disease, and hypertension [37], resulting in increased hospital stay in both groups in our study. Nevertheless, despite the higher number of complications and infections, we witnessed a significant reduction in IHM over the study period. Data from the Spanish Heart Transplant Registry for the general population also show a progressive improvement in HT outcomes in terms of survival in recent years, likely due to better control of primary graft failure and acute rejection [38].

Several epidemiological studies had previously indicated that recipients with T2D undergoing HT differ from other types of patients [7]. A recent study reported that patients with diabetes experienced poorer graft survival following transplantation (HR 1.17; 95% CI 1.08–1.26, P < 0.001), although the authors scored graft survival to further stratify the risk of patients with diabetes and found that graft survival was similar in low-risk diabetic patients and non-diabetic patients (HR 0.91; 95% CI 0.82–1.01) [23]. In our study, individuals with T2D were less likely to have pneumonia and Gram-negative bacterial infections or undergo hemodialysis, ECMO, and tracheostomy procedures. They also had a lower LOHS and lower IHM. This suggests that, owing to their pre-existing conditions at the time of transplantation, patients with diabetes have a lower risk of reduced graft survival [6] or have well-controlled postoperative blood sugar [39], although we do not have sufficient data to substantiate this argument.

As expected, after multivariable adjustments, we discovered that the use of hemodialysis and ECMO were risk factors for mortality in T2D patients who underwent HT. Shoji et al. [40] found that post-transplant dialysis patients had a higher risk of all-cause mortality (adjusted hazard ratio [aHR]: 5.2, 95% CI: 4.7–5.7, p < 0.001), and Lim et al. [41] recently observed that patients requiring ECMO as a bridge to HT were characterized by higher rates of preoperative multi-organ failure and early mortality than those who were extubated.

In line with previously published results [11, 42], and despite the fact that scores predicting early survival following HT do not consider diabetes a significant factor compared to other, more significant clinical factors [43], we found that the presence of T2D was associated with a lower risk of IHM during admission for HT. A key explanation for our results is that they likely reflect an improvement in both pre-transplant and post-transplant management of diabetes and vascular risk factors [6, 44].

The strengths of our study include the use of a national population database (RAE-CMBD) over a 20-year period and a methodology that has been used elsewhere [11]. Furthermore, the RAE-CMBD collects virtually all hospitalizations in Spain (> 95%) and our data are consistent with those published by the National Transplant Organization of Spain [45].

Our study is subject to a series of limitations. First, like other observational and retrospective studies that rely on ICD codes from discharge databases, our investigation may be affected by low sensitivity and specificity. This can depend on the physicians’ proficiency in coding hospital procedures and diagnoses, which can, in turn, influence data quality. Coding is performed for administrative rather than research purposes, primarily focusing on economic considerations. Second, our study does not consider clinical or performance parameters and cannot assess events that may occur in patients after hospital discharge. Third, ICD codes do not provide information on disease severity, functional status, the type of T2D treatment (e.g., insulin or antidiabetic drugs), or the reasoning behind the intensity of the treatment provided. Fourth, in the RAE-CMBD database, the number of ICD codes that physicians can record is limited to 20 diagnoses and 20 procedures per patient. Additionally, the cause of death is not specified in the database, and sociodemographic variables such as race, education level, and social class are not collected. Fifth, our study excluded patients who had undergone multi-organ transplantation and re-transplantation. However, this represents a very low proportion of HT patients (< 4%), as multi-organ transplantation and re-transplantation are rare procedures in our country [46]. In our opinion, excluding these cases would have minimal impact on our results but should be considered for future investigations. Finally, ICD codes in the database do not consider donor demographics and clinical conditions. As a result, we could not incorporate variables describing recipient, donor, and transplant factors, including body mass index (BMI), peak panel reactive antibodies, human leukocyte antigen mismatches, cold ischemia time, immunosuppressant therapy, induction therapy, mechanical circulatory support devices used before surgery, previous sternotomies, and clinical factors such as wound or sternotomy infections, delayed graft function, or episodes of rejection.

Despite these limitations, hospital discharge databases based on ICD codes have been used widely in epidemiological research by many authors from various countries. Such databases have proven useful for studying temporal trends as well as the characteristics, procedures, and hospital outcomes of HT recipients [9, 11, 37, 47,48,49,50].


In conclusion, between 2002 and 2017, the number of HT procedures in Spain decreased in patients with T2D but increased slightly in the last study period (2018–2021). Lower values were recorded in recipients with T2D than in non-T2D patients for presence of pneumonia and Gram-negative bacteremia and for the use of procedures such as hemodialysis, ECMO, and tracheostomy. In addition, LOHS and IHM were lower in recipients with T2D. The main risk factors for IHM following HT in recipients with T2D were complications of HT and the need for hemodialysis and ECMO. IHM following a heart transplant has decreased in Spain in individuals with and without T2D. Finally, the presence of T2D was associated with a lower risk of IHM than in non-diabetic patients.

Data Availability

According to the contract signed with the Spanish Ministry of Health, which provided access to the databases from the RAE-CMBD, we cannot share the databases with any other investigator, and we have to destroy the databases once the investigation has concluded. Consequently, we cannot upload the databases to any public repository. However, any investigator can apply for access to the databases by filling out the questionnaire available at All other relevant data are included in the paper.



Charlson Comorbidity Index


Extracorporeal membrane oxygenation


Heart transplantation


International Classification of Diseases Ninth Revision


International Classification of Diseases Tenth Revision


In-hospital mortality


Length of hospital stay


Propensity score


Propensity score matching


Spanish National Hospital Discharge Database


Type 2 diabetes


  1. Cimato TR, Jessup M. Recipient selection in cardiac transplantation: contraindications and risk factors for mortality. J Heart Lung Transplant. 2002;21:1161–73.

    Article  PubMed  Google Scholar 

  2. Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389:2239–51.

    Article  CAS  PubMed  Google Scholar 

  3. Rivinius R, Gralla C, Helmschrott M, Darche FF, Ehlermann P, Bruckner T, et al. Pre-transplant type 2 diabetes Mellitus is Associated with higher graft failure and increased 5-Year Mortality after Heart Transplantation. Front Cardiovasc Med. 2022;9:890359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Badellino MM, Cavarocchi NC, Narins B, Jessup M, Alpern JB, McClurken JB et al. Cardiac transplantation in diabetic patients. Transplant Proc. 1990;22:2384-8.

  5. Ladowski JS, Kormos RL, Uretsky BF, Griffith BP, Armitage JM, Hardesty RL. Heart transplantation in diabetic recipients. Transplantation. 1990;49:303–5.

    Article  CAS  PubMed  Google Scholar 

  6. Russo MJ, Chen JM, Hong KN, Stewart AS, Ascheim DD, Argenziano M, et al. Survival after heart transplantation is not diminished among recipients with uncomplicated diabetes mellitus: an analysis of the United Network of Organ sharing database. Circulation. 2006;114:2280–7.

    Article  PubMed  Google Scholar 

  7. Marelli D, Laks H, Patel B, Kermani R, Marmureanu A, Patel J, et al. Heart transplantation in patients with diabetes mellitus in the current era. J Heart Lung Transplant. 2003;22:1091–7.

    Article  PubMed  Google Scholar 

  8. Klingenberg R, Gleissner C, Koch A, Schnabel PA, Sack FU, Zimmermann R, et al. Impact of pre-operative diabetes mellitus upon early and late survival after heart transplantation: a possible era effect. J Heart Lung Transplant. 2005;24:1239–46.

    Article  PubMed  Google Scholar 

  9. Isath A, Sherif AA, Siroky GP, Bandyopadhyay D, Rao SD, Krittanawong C, et al. Trends, Burden, and impact of arrhythmias on Cardiac Transplant Recipients: a 16-year Nationwide Study. Curr Probl Cardiol. 2023;48:101504.

    Article  PubMed  Google Scholar 

  10. Dharmavaram N, Hess T, Jaeger H, Smith J, Hermsen J, Murray D, et al. National Trends in Heart Donor usage rates: are we efficiently transplanting more hearts? J Am Heart Assoc. 2021;10:e019655.

    Article  PubMed  PubMed Central  Google Scholar 

  11. de Miguel-Yanes JM, Jiménez-García R, de Miguel-Díez J, Hernández-Barrera V, Méndez-Bailón M, Muñoz-Rivas N, et al. In-hospital outcomes for solid organ transplants according to type 2 diabetes status: an observational, 15-year study in Spain. Int J Clin Pract. 2018;e13283.

  12. Higgins J, Pflugfelder PW, Kostuk WJ. Increased morbidity in diabetic cardiac transplant recipients. Can J Cardiol. 2009;25:e125–9.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bedanova H, Ondrasek J, Cerny J, Orban M, Spinarova L, Hude P, et al. Impact of diabetes mellitus on survival rates after heart transplantation. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2009;153:283–7.

    Article  PubMed  Google Scholar 

  14. Saraiva J, Sola E, Prieto D, Antunes MJ. Diabetes as an outcome predictor after heart transplantation. Interact Cardiovasc Thorac Surg. 2011;13:499–504.

    Article  PubMed  Google Scholar 

  15. Lopez-de-Andres A, Jimenez-Garcia R, Carabantes-Alarcon D, Hernández-Barrera V, de-Miguel-Yanes JM, de-Miguel-Diez J, et al. Use of Cardiac Procedures in people with diabetes during the COVID pandemic in Spain: Effects on the In-Hospital mortality. Int J Environ Res Public Health. 2023;20:844.

    Article  PubMed  PubMed Central  Google Scholar 

  16. de Miguel-Yanes JM, Jimenez-Garcia R, de Miguel-Diez J, Hernández-Barrera V, Carabantes-Alarcon D, Zamorano-Leon JJ, et al. Impact of type 2 diabetes Mellitus on the incidence and outcomes of COVID-19 needing Hospital Admission according to sex: Retrospective Cohort Study using Hospital Discharge Data in Spain, Year 2020. J Clin Med. 2022;11:2654.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. de Sanidad M, Servicios Sociales e Igualdad. Real Decreto 69/2015, de 6 de febrero, por el que se regula el Registro de Actividad de Atención Sanitaria Especializada. (Spanish National Hospital Discharge Database) BOE 2015; 35: 10789-809. Available at: (Accessed on 31 May 2023).

  18. Soriguer F, Goday A, Bosch-Comas A, Bordiú E, Calle-Pascual A, Carmena R, et al. Prevalence of diabetes mellitus and impaired glucose regulation in Spain: the Study. Diabetologia. 2012;55:88–93.

    Article  CAS  PubMed  Google Scholar 

  19. Moulis G, Ibañez B, Palmaro A, Aizpuru F, Millan E, Lapeyre-Mestre M, et al. Cross-national health care database utilization between Spain and France: results from the EPICHRONIC study assessing the prevalence of type 2 diabetes mellitus. Clin Epidemiol. 2018;10:863–74.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9.

    Article  PubMed  Google Scholar 

  21. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comor-bidity index predicted in-hospital mortality. J Clin Epidemiol. 2004;57:1288–94.

    Article  PubMed  Google Scholar 

  22. Austin PC. Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. Stat Med. 2011;30:1292–301.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Akintoye E, Alvarez P, Salih M, Sellke F, Briasoulis A. Outcomes of diabetic patients with end-stage heart failure listed for heart transplantation: a propensity-matched analysis. Clin Transpl. 2022;36:e14590.

    Article  Google Scholar 

  24. Edelson JB, Zhang X, Goldstone AB, Rossano JW, O’Connor MJ, Gaynor JW, et al. Reduced incidence of cardiac rejection in multi-organ transplants: a propensity matched study. Clin Transpl. 2023;37:e15019.

    Article  Google Scholar 

  25. Butts R, Davis M, Savage A, Burnette A, Kavarana M, Bradley S, et al. Effect of induction therapy on graft survival in primary Pediatric Heart transplantation: a propensity score analysis of the UNOS Database. Transplantation. 2017;101:1228–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zheng Y, Xu L, Cai Z, Tu J, Liu Y, Wang Y, et al. The predictive role of intraoperative blood Transfusion Components in the prognosis of Heart Transplantation. Front Cardiovasc Med. 2022;9:874133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Nishida H, Salerno C, Onsager D, Song T, Nguyen A, Grinstein J, et al. Comparing short-term/long-term outcomes of heart transplants that occur inside and outside of normal working hours. ESC Heart Fail. 2022;9:2484–90.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Tzani A, Van den Eynde J, Doulamis IP, Kuno T, Kampaktsis PN, Alvarez P, et al. Impact of induction therapy on outcomes after heart transplantation. Clin Transpl. 2021;35:e14440.

    Article  CAS  Google Scholar 

  29. Zhou Y, Sun Y, Zhang J, Cai J, Dong N, Li F. Predictors and outcomes of heart transplantation utilizing donors with different brain death mode: a propensity-score matching study from China. Int J Cardiol;322:58–64.

  30. Shudo Y, Cohen JE, Lingala B, He H, Zhu Y, Woo YJ. Impact of increased-risk donor hearts on transplant outcomes: a propensity-matched analysis. J Thorac Cardiovasc Surg. 2019;157:603–10.

    Article  PubMed  Google Scholar 

  31. Lee S, Lee DK. What is the proper way to apply the multiple comparison test? Korean J Anesthesiol. 2018;71:353–60.

    Article  PubMed  PubMed Central  Google Scholar 

  32. de Sanidad M. Consumo y Bienestar Social. Solicitud de extracción de datos – Extraction request (Spanish Na-tional Hospital Discharge Database) Available at: (Accessed on 31 May 2023).

  33. González-Vílchez F, Almenar-Bonet L, Crespo-Leiro MG, Gómez-Bueno M, González-Costello J, Pérez-Villa F, et al. 32nd Official Report of the heart failure association of the Spanish Society of Cardiology. Rev Esp Cardiol (Engl Ed). 2021;74:962–70.

    Article  PubMed  Google Scholar 

  34. Hallett A, Motter JD, Frey A, Higgins RS, Bush EL, Snyder J, et al. Trends in Heart and Lung Transplantation in the United States across the COVID-19 pandemic. Transpl Direct. 2021;7(10):e759.

    Article  Google Scholar 

  35. DeFilippis EM, Sinnenberg L, Reza N, Givertz MM, Kittleson MM, Topkara VK, et al. Trends in US Heart Transplant Waitlist Activity and volume during the Coronavirus Disease 2019 (COVID-19) pandemic. JAMA Cardiol. 2020;5:1048–52.

    Article  PubMed  Google Scholar 

  36. Colvin M, Smith JM, Ahn Y, Skeans MA, Messick E, Goff R, et al. OPTN/SRTR 2019 Annual Data Report: heart. Am J Transplant. 2021;21(Suppl 2):356–440. PMID: 33595196.

    Article  PubMed  Google Scholar 

  37. Salah HM, Minhas AMK, Khan MS, Abramov D, Agarwal R, Fudim M. Trends and characteristics of hospitalizations in patients with Heart Transplant. Curr Probl Cardiol. 2022;47:101210.

    Article  PubMed  Google Scholar 

  38. González-Vílchez F, Almenar-Bonet L, Crespo-Leiro MG, Segovia-Cubero J, González-Costello J, Arizón D, Prado JM, et al. Spanish Heart Transplant Registry. 30th Official Report of the Spanish Society of Cardiology Working Group on Heart failure (1984–2018). Rev Esp Cardiol (Engl Ed). 2019;72:954–62.

    Article  PubMed  Google Scholar 

  39. Garcia C, Wallia A, Gupta S, Schmidt K, Malekar-Raikar S, Johnson Oakes D, et al. Intensive glycemic control after heart transplantation is safe and effective for diabetic and non-diabetic patients. Clin Transpl. 2013;27:444–54.

    Article  Google Scholar 

  40. Shoji S, Kuno T, Kohsaka S, Amiya E, Asleh R, Alvarez P, et al. Incidence and long-term outcome of heart transplantation patients who develop postoperative renal failure requiring dialysis. J Heart Lung Transplant. 2022;41:356–64.

    Article  PubMed  Google Scholar 

  41. Lim JH, Lee SY, Ju MH, Kim SH, Choi JH, Chon MK, et al. Direct extracorporeal membrane oxygenation bridged Heart Transplantation: the importance of multi-organ failure. Int J Heart Fail. 2023;5:91–9.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Moro JA, Martínez-Dolz L, Almenar L, Martínez-Ortiz L, Chamorro C, García C, et al. Impacto de la diabetes mellitus en el paciente con trasplante cardiaco [Impact of diabetes mellitus on heart transplant recipients]. Rev Esp Cardiol. 2006;59:1033–7.

    Article  PubMed  Google Scholar 

  43. Singh TP, Almond CS, Semigran MJ, Piercey G, Gauvreau K. Risk prediction for early in-hospital mortality following heart transplantation in the United States. Circ Heart Fail. 2012;5:259–66.

    Article  PubMed  Google Scholar 

  44. Feng KY, Henricksen EJ, Wayda B, Moayedi Y, Lee R, Han J, et al. Impact of diabetes mellitus on clinical outcomes after heart transplantation. Clin Transpl. 2021;35:e14460.

    Article  CAS  Google Scholar 

  45. Organización Nacional de Trasplantes. Actividad de donación y trasplante cardiaco España 2022. Available at (Accessed on 31 May 2023).

  46. González-Vílchez F, Gómez-Bueno M, Almenar-Bonet L, Crespo-Leiro MG, Del Arizón JM, Blázquez-Bermejo Z, et al. Spanish heart transplant registry. 33rd official report of the heart failure association of the Spanish Society of Cardiology. Rev Esp Cardiol (Engl Ed). 2022;1885–5857.

  47. Voruganti D, Shantha G, Dugyala S, Pothineni NVK, Agnihotri K, Bozorgnia B, et al. Trends and Outcomes of Atrial Fibrillation-Flutter Hospitalizations among Heart Transplant recipients (from the National Inpatient Sample). Am J Cardiol. 2020;125:87–91.

    Article  PubMed  Google Scholar 

  48. Minhas AMK, Mahmood Shah SM, Shahid I, Siddiqi TJ, Arshad MS, Jain V, et al. Utilization of Implantable Cardioverter-Defibrillators in patients with Heart Transplant (from National Inpatient Sample database). Am J Cardiol. 2022;175:65–71.

    Article  PubMed  Google Scholar 

  49. Minhas AMK, Fudim M, Garan AR, Davis JD, Fonarow GC, Antoine SM, et al. Socioeconomic status and in-hospital outcomes for patients undergoing heart transplantation or ventricular assist device implantation. Clin Transpl. 2023;e15093.

  50. Vallabhaneni S, Singh A, Meera SJ, Shirani J. Cardiac transplantation for hypertrophic cardiomyopathy in the United States 2003–2011. Int J Crit Illn Inj Sci. 2020;10:129–33.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


Not aplicable.


This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Universidad Complutense de Madrid in the line Excellence Programme for university teaching staff, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). And by Universidad Complutense de Madrid. Grupo de Investigación en Epidemiología de las Enfermedades Crónicas de Alta Prevalencia en España (970970). And by the FIS (Fondo de Investigaciones Sanitarias—Health Research Fund, Instituto de Salud Carlos III) and co-financed by the European Union through the Fondo Europeo de Desarrollo Regional (FEDER, “Una manera de hacer Europa”): grant no. PI20/00118.

Author information

Authors and Affiliations



ALdA, RJG and NCC contributed to the study design and interpretation of the results. VHB, JdMD, JMdMY, JLB, DCA, ROP and JJZL contributed to the collection, analysis, or interpretation of data. ALdA, RJG and NCC prepared the manuscript. VHB, JdMD, JMdMY, JLB, DCA, ROP and JJZL critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of the work, ensuring integrity and accuracy. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rodrigo Jiménez-García.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lopez-de-Andres, A., Jiménez-García, R., Hernández-Barrera, V. et al. Temporal trends and outcomes of heart transplantation in Spain (2002–2021): propensity score matching analysis to compare patients with and without type 2 diabetes. Cardiovasc Diabetol 22, 266 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: