Tacrolimús versus ciclosporina como inmunosupresión primaria para los receptores de trasplante pulmonar
Resumen
Antecedentes
El trasplante pulmonar es un tratamiento bien aceptado en los pacientes con enfermedades pulmonares más terminales. Aunque el tacrolimús y la ciclosporina se usan como agentes inmunosupresores primarios en los receptores de trasplante pulmonar, no está claro cuál de estos fármacos es mejor para reducir el rechazo y la mortalidad sin causar efectos adversos.
Objetivos
Evaluar los efectos beneficiosos y perjudiciales del tacrolimús versus la ciclosporina para la inmunosupresión primaria en los receptores de trasplante pulmonar.
Métodos de búsqueda
Se realizaron búsquedas en el Registro Cochrane Central de Ensayos Controlados (Cochrane Renal Group) hasta el 10 de abril de 2013 a través del contacto con el Trials Search Co‐ordinator usando términos de búsqueda relevantes para esta revisión. También se buscó en Science Citation Index Expanded y en Transplant Library hasta el 20 de abril de 2013.
Criterios de selección
Se incluyeron todos los ensayos controlados aleatorios (ECA) que compararon cualquier dosis y duración de la administración de tacrolimús versus ciclosporina como tratamiento inmunosupresor primario en los receptores de trasplante pulmonar. Los criterios de selección exigían que todos los pacientes incluidos recibieran el mismo tratamiento inmunosupresor adicional en cada estudio.
Obtención y análisis de los datos
Tres autores extrajeron los datos. Para los datos dicotómicos, se usó el cociente de riesgos (CR), y para los datos continuos, la diferencia de medias (DM), cada uno con intervalos de confianza (IC) del 95%. Los componentes metodológicos de los estudios incluidos se usaron para evaluar el riesgo de los errores sistemáticos (sesgo). Se utilizó el análisis secuencial del ensayo para evaluar el riesgo de los errores aleatorios (influencia del azar).
Resultados principales
Se incluyeron tres estudios con un total de 413 pacientes adultos que compararon el tacrolimús con la microemulsión o la solución oral de ciclosporina. Se consideró que todos los estudios presentaban un alto riesgo de sesgo. El tacrolimús fue significativamente superior a la ciclosporina con respecto a la incidencia del síndrome de bronquiolitis obliterante (CR 0,46; IC del 95%: 0,29 a 0,74), la puntuación de bronquitis linfocítica (DM ‐0,60; IC del 95%: ‐1,04 a ‐0,16), el retiro del tratamiento (CR 0,27; IC del 95%: 0,16 a 0,46) y la hipertensión arterial (CR 0,67; IC del 95%: 0,50 a 0,89). Sin embargo, el resultado para la hipertensión arterial no se confirmó cuando se analizó mediante un modelo de efectos aleatorios (CR 0,54; IC del 95%: 0,17 a 1,73). Además, el análisis secuencial de ensayos halló que ninguno de los metanálisis alcanzó los tamaños de información necesarios y las curvas z acumulativas no cruzaron los límites de monitorización secuencial del ensayo. La diabetes mellitus ocurrió con mayor frecuencia en los pacientes del grupo con tacrolimús en comparación con los del grupo con ciclosporina cuando se aplicó el modelo de efectos fijos (CR 4,24; IC del 95%: 1,58 a 11,40), pero no se encontró ninguna diferencia cuando se usó el modelo de efectos aleatorios para el análisis (CR 4,43; IC del 95%: 0,75 a 26,05). Nuevamente, el análisis secuencial de ensayos halló que no se alcanzó el umbral de información necesario y la curva z acumulativa no cruzó el límite de monitorización secuencial del ensayo. No se observó ninguna diferencia significativa entre los grupos de tratamiento con respecto a la mortalidad (CR 1,06; IC del 95%: 0,75 a 1,49), la incidencia del rechazo agudo (CR 0,89; IC del 95%: 0,77 a 1,03), los números de infecciones 100 días‐paciente (DM ‐0,15; IC del 95%: ‐0,30 a 0,00), el cáncer (CR 0,21; IC del 95%: 0,04 a 1,16), la disfunción renal (CR 1,41; IC del 95%: 0,93 a 2,14), la insuficiencia renal (CR 1,57; IC del 95%: 0,28 a 8,94), la neurotoxicidad (CR 7,06; IC del 95%: 0,37 a 135,19) y la hiperlipidemia (CR 0,60; IC del 95%: 0,30 a 1,20). El análisis secuencial de ensayos demostró que no se llegó a los umbrales de información necesarios para ninguna de estas medidas de resultado.
Conclusiones de los autores
El tacrolimús es superior a la ciclosporina con respecto al síndrome de bronquiolitis obliterante, la bronquitis linfocítica, el retiro del tratamiento y la hipertensión arterial, pero es inferior con respecto al desarrollo de diabetes. No se observó ninguna diferencia en la mortalidad y el rechazo agudo entre los pacientes que recibieron tacrolimús y ciclosporina. Hubo pocos estudios que compararon el tacrolimús y la ciclosporina después del trasplante pulmonar, y el número de pacientes y eventos en los estudios incluidos fue limitado. Además, se consideró que los estudios tenían un alto riesgo de sesgo. En consecuencia, se necesitan más ECA para evaluar los resultados de la presente revisión. Tales estudios deben realizarse con bajo riesgo de errores sistemáticos (sesgo) y de errores aleatorios (intervención del azar).
Resumen en términos sencillos
¿Qué fármaco para prevenir el rechazo del trasplante pulmonar proporciona mejores resultados con menor cantidad de efectos adversos?
Casi todos los pacientes que reciben un trasplante pulmonar son tratados con tacrolimús o ciclosporina para prevenir el rechazo del órgano trasplantado. Ambos fármacos tienen efectos adversos graves y frecuentes. Se comparó el uso de tacrolimús con ciclosporina para prevenir el rechazo del órgano después del trasplante pulmonar para evaluar qué fármaco funcionaba mejor y tenía menor cantidad de efectos adversos.
Se identificaron tres ensayos controlados aleatorios que compararon el tacrolimús con la ciclosporina en los pacientes que recibieron trasplantes pulmonares. No se encontró ninguna diferencia en la supervivencia o el rechazo pulmonar con los fármacos en los receptores de trasplante pulmonar. El tacrolimús resultó mejor para prevenir las enfermedades pulmonares inflamatorias y de las vías respiratorias graves (síndrome de bronquiolitis obliterante y bronquitis linfocítica) y la hipertensión arterial. La diabetes ocurrió más a menudo en los pacientes que se trataron con tacrolimús en comparación con los que recibieron ciclosporina. Más pacientes que recibieron tacrolimús continuaron con el tratamiento en comparación con los que se trataron con ciclosporina.
Sin embargo, estas observaciones deben verse con precaución porque los tres ensayos fueron imperfectos en cuanto al diseño, la manera en que se realizaron y los errores aleatorios. Se hallaron escasos ensayos que compararon el tacrolimús y la ciclosporina después del trasplante pulmonar, y el número de pacientes y eventos fue bajo.
Se necesitan estudios con mejor diseño para informar las decisiones clínicas a los pacientes que reciben trasplantes pulmonares.
Authors' conclusions
Summary of findings
| Tacrolimus compared to cyclosporin for lung transplant recipients | ||||||
| Patient or population: Lung transplant recipients | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | No of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Cyclosporin | Tacrolimus | |||||
| Mortality | Study population | RR 1.06 | 413 (3) | ⊕⊕⊝⊝ | ||
| 226 per 1000 | 240 per 1000 | |||||
| Moderate | ||||||
| 196 per 1000 | 208 per 1000 | |||||
| Acute rejection | Study population | RR 0.89 | 323 (2) | ⊕⊕⊝⊝ | ||
| 728 per 1000 | 648 per 1000 | |||||
| Moderate | ||||||
| 700 per 1000 | 623 per 1000 | |||||
| Bronchiolitis obliterans syndrome | Study population | RR 0.46 | 413 (3) | ⊕⊕⊕⊝ | ||
| 231 per 1000 | 106 per 1000 | |||||
| Moderate | ||||||
| 208 per 1000 | 96 per 1000 | |||||
| Infection | The mean infection in the intervention groups was | 74 (1) | ⊕⊕⊝⊝ | |||
| Treatment withdrawal | 260 per 1000 | 70 per 1000 | RR 0.27 | 413 (3) | ⊕⊕⊝⊝ | |
| Arterial hypertension | Study population | RR 0.67 | 164 (2) | ⊕⊕⊝⊝ | ||
| 614 per 1000 | 412 per 1000 | |||||
| Moderate | ||||||
| 599 per 1000 | 401 per 1000 | |||||
| Diabetes mellitus | Study population | RR 4.24 | 164 (2) | ⊕⊕⊝⊝ | ||
| 48 per 1000 | 204 per 1000 | |||||
| Moderate | ||||||
| 44 per 1000 | 187 per 1000 | |||||
| *The basis for the assumed risk (e.g. the median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). | ||||||
| GRADE Working Group grades of evidence | ||||||
| ¹All included studies were at high risk of bias as assessed using the Cochrane risk of bias tool | ||||||
Background
Description of the condition
Since the early 1980s, lung transplantation has enjoyed increasing success to become a well‐accepted treatment for many people with end‐stage lung diseases. Worldwide, more than 30,000 lung transplantations have been reported to the International Society for Heart and Lung Transplantation (Christie 2009). Currently, more than 2700 lung transplantations are reported annually worldwide, with one year survival of over 80%, and five year survival of 60% (Aurora 2009; Christie 2009).
However, achieving long‐term survival after lung transplantation remains challenging, due mainly to the occurrence of bronchiolitis obliterans syndrome. Bronchiolitis obliterans syndrome and late graft failure are responsible for more than 40% of deaths beyond the first year of transplantation (Christie 2009). Bronchiolitis obliterans syndrome, also called obliterative bronchiolitis or constrictive bronchiolitis, is a non‐reversible obstructive lung disease in which the bronchioles are affected and narrowed by inflammation and fibrosis. It has become clear that acute rejection and lymphocytic bronchitis are primary risk factors for the development of the bronchiolitis obliterans syndrome in lung transplant recipients (Hollmen 2008). Lung transplant recipients are also at high risk of developing co‐morbidities that contribute to limiting long‐term survival. These include hypertension (85%, five years after transplantation), kidney dysfunction (36%, five years after transplantation), hyperlipidaemia (55%, 5 years after transplantation), and diabetes mellitus (37%, five years after transplantation)(Christie 2009).
Description of the intervention
To prevent rejection and reduce cardiovascular risk factors, thereby increasing long‐term survival, it is essential to find the best immunosuppressive treatment strategy. Maintenance immunosuppressive therapy in lung transplantation often involves three types of drugs directed against the T‐cell activation and proliferation cascade: antiproliferative agents (mycophenolate mofetil or azathioprine), steroids (prednisolone), and calcineurin inhibitors (Hopkins 2008). Two calcineurin inhibitors, cyclosporin and tacrolimus, are currently used as immunosuppression therapy in lung transplant recipients, and have been essential in reducing the frequency of acute rejection and improving early survival (Iversen 2009a).
Cyclosporin is a lipophilic cyclic undecapeptide with one unique amino acid in its structure. It was originally derived from the filamentous fungus Tolypocladium inflatum (Kapturczak 2004). Cyclosporin was discovered in 1971, and in 1983 the US Food and Drug Administration approved the drug for the prevention and treatment of transplant rejection (Kapturczak 2004). To address the intra‐ and inter‐individual differences in absorption and oral bioavailability of the original oil‐based formulation of cyclosporin (Sandimmune), a new microemulsion formula of cyclosporin (Neoral) was introduced in the 1990s (Cantarovich 2004; Dunn 2001; Kahan 2004; Lee 1998). A modified cyclosporin formulation (Gengraf), which has a better absorption profile than the original oil‐based formulation, is currently available (Hachem 2007).
Tacrolimus (Prograf) is a macrolide derived from the fungus Streptomyces tsukubaensis, and was developed as an alternative to cyclosporin. Tacrolimus was discovered in the early 1980s, and since 1989 has been used to prevent and treat liver transplantation rejection (Kapturczak 2004; Starzl 1989). Use of tacrolimus subsequently expanded rapidly into transplantation management of other organs (Kapturczak 2004).
Both cyclosporin and tacrolimus inhibit phosphatase calcineurin action. Calcineurin regulates the transport of nuclear factor of activated T‐cells, which is a transcription factor that regulates lymphokine gene transcription. Cyclosporin and tacrolimus exert their cellular effects on the action of calcineurin through different cytoplasmic receptors, as cyclosporin binds to cyclophilins and tacrolimus binds to FK‐binding proteins. Differences between cyclosporin and tacrolimus with regard to adverse effects, safety, and tolerability have been observed (Jiang 1999), but the toxicodynamic molecular mechanism of both drugs is still largely unknown and the involvement of calcineurin inhibition in calcineurin inhibitor toxicity is unclear.
Both cyclosporin and tacrolimus are known to be nephrotoxic (Bechstein 2000; Demirjian 2009). Tacrolimus has been associated with more new‐onset diabetes mellitus and neurotoxic reactions, but with less hypertension and hypercholesterolaemia compared with cyclosporin (Flechner 2008; Heisel 2004; McAlister 2006; Moore 2001; Penninga 2010a; Pham 1996; Vincenti 2007; White 2005). Differences between tacrolimus and cyclosporin in preventing rejection have also been reported (McAlister 2006; Penninga 2010a; Webster 2005a; Webster 2005b).
How the intervention might work
Randomised controlled trials (RCT) and meta‐analyses of such studies comparing tacrolimus versus cyclosporin in other solid organ recipients have shown differences in graft survival and incidence of acute rejection in kidney, liver, and heart transplant recipients (McAlister 2006; Penninga 2010a; Webster 2005a; Webster 2005b). Differences in morbidities such as diabetes, hyperlipidaemia, and hypertension have also been observed between these treatment groups (McAlister 2006; Penninga 2010a; Webster 2005a; Webster 2005b).
Why it is important to do this review
The International Society for Heart and Lung Transplantation has reported that at both one and five years after lung transplantation tacrolimus is currently the most frequently used calcineurin inhibitor (Christie 2009). However, there is no consensus to date on the choice of tacrolimus versus cyclosporin for primary immunosuppressive therapy in lung transplant recipients (Iversen 2009a). RCTs comparing tacrolimus versus cyclosporin in lung transplant recipients have shown conflicting results and optimal immunosuppressive maintenance therapy in this population continues to be discussed (Reichenspurner 2005). Clear evidence is essential to find the optimal and most effective immunosuppressive treatment strategy in lung transplant recipients, and to improve long‐term survival. It is of utmost importance for lung transplant recipients to identify which of the two drugs works best regarding survival and rejection, is best tolerated, and is associated with fewest adverse effects. A published meta‐analysis has reported fewer rejection episodes, but more new‐onset diabetes mellitus associated with tacrolimus compared with cyclosporin (Fan 2009). Fewer treatment withdrawals were also observed in patients treated with tacrolimus compared with cyclosporin (Fan 2009).
Objectives
This review aimed to assess the benefits and harms of tacrolimus versus cyclosporin for primary immunosuppressive treatment in lung transplantation recipients.
Methods
Criteria for considering studies for this review
Types of studies
All RCTs assessing tacrolimus versus cyclosporin for lung transplant patients, irrespective of blinding, publication status, or language were considered. Quasi‐RCTs and cohort studies identified from the searches were considered only for reporting of harms.
Types of participants
Adult and paediatric patients after first‐time single or double lung transplantation were included.
Types of interventions
Studies comparing any dose and duration of administration of tacrolimus versus cyclosporin as primary immunosuppression in lung transplant recipients. We required that all included patients received the same additional immunosuppressive therapy within each study.
Types of outcome measures
Primary outcomes
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Mortality
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Acute rejection
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Bronchiolitis obliterans syndrome
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Infection or sepsis.
Secondary outcomes
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Quality of life
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Any adverse event. Serious adverse events were defined as any untoward medical occurrence that was life threatening, resulted in death, or persistent or significant disability, or any medical event, which might have jeopardised the patient, or required intervention to prevent it (ICH‐GCP 2002). All other adverse events (any medical occurrence not necessarily having a causal relationship with the treatment, but did however cause a dose reduction or discontinuation of the treatment) were considered as non‐serious.
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Withdrawals
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Lymphocytic bronchitis
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Pneumonia (viral, bacterial, and fungal)
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Cytomegalovirus infection
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Cancer
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Neurotoxic reaction
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Kidney failure
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Arterial hypertension
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Diabetes mellitus
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Hyperlipidaemia
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Hirsutism
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Gingival hyperplasia
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Serum creatinine
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Total cholesterol.
All outcomes were assessed at latest follow‐up.
Search methods for identification of studies
Electronic searches
We searched the Cochrane Renal Group's Specialised Register to 10 April 2013 through contact with the Trials Search Co‐ordinator using search terms relevant to this review.
The Cochrane Renal Group’s Specialised Register contains studies identified from:
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Quarterly searches of the Cochrane Central Register of Controlled Trials CENTRAL
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Weekly searches of MEDLINE OVID SP
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Handsearching of renal‐related journals and the proceedings of major renal conferences
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Searching of the current year of EMBASE OVID SP
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Weekly current awareness alerts for selected renal journals
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Searches of the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.
Studies contained in the Specialised Register are identified through search strategies for CENTRAL, MEDLINE, and EMBASE based on the scope of the Cochrane Renal Group. Details of these strategies as well as a list of handsearched journals, conference proceedings and current awareness alerts are available in the Specialised Register section of information about the Cochrane Renal Group.
We also searched the following online resources:
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Science Citation Index Expanded (1945 to 20 April 2013)(Royle 2003).
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Transplant Library (1970 to 20 April 2013).
Appendix 1 presents search terms used in strategies for this review.
Searching other resources
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Reference lists of respiratory and transplantation textbooks, review articles, and relevant studies.
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Letters seeking information about unpublished or incomplete studies to investigators known to be involved in previous studies on the topic.
Data collection and analysis
Selection of studies
The search strategy described was used to obtain titles and abstracts of studies that might be relevant to the review. Two authors independently assessed study eligibility. Excluded studies were listed with the reason for exclusion. Disagreement was solved by discussion or in consultation with a third author. Study authors were contacted if information about methodology or data was unclear or missing (Thompson 2002).
Data extraction and management
Data extraction was carried out independently by three authors using standard data extraction forms. Studies reported in non‐English language journals were planned to be translated before assessment. Where more than one publication of a study existed, reports were grouped together and only the publication with the most complete data were included (Moher 2009). Where relevant outcomes were only published in earlier versions these data were also used. Any discrepancy between published versions was highlighted. Any further information required from the original authors was requested by written correspondence and any relevant information obtained in this manner was included in the review. Disagreements were resolved by consultation with all authors. From each study we extracted the following information: first author, country of origin, study design, inclusion and exclusion criteria, number of participants, patients characteristics, single or double lung transplantation, study drugs: dose, trough levels, type of cyclosporin formulation (oil‐based capsules or micro‐emulsion capsules, or oral solution), administration, additional immunosuppressive therapy, follow‐up period, primary and secondary outcomes, adverse events, and patients lost for follow‐up.
Assessment of risk of bias in included studies
The following items were assessed using the risk of bias assessment tool (Higgins 2011)(see Appendix 2).
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Was there adequate sequence generation (selection bias)?
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Was allocation adequately concealed (selection bias)?
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Was knowledge of the allocated interventions adequately prevented during the study (detection bias)?
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Participants and personnel
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Outcome assessors
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Were incomplete outcome data adequately addressed (attrition bias)?
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Are reports of the study free of suggestion of selective outcome reporting (reporting bias)?
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Was the study apparently free of other problems that could put it at a risk of bias?
Studies with adequate generation of the allocation sequence, adequate allocation concealment, adequate blinding, adequate outcome data reporting, no selective outcome reporting, and without other interests were considered as studies with low risk of bias (high methodological quality)(Higgins 2011; Kjaergard 2001; Moher 1998; Schulz 1995). Studies with one or more unclear or inadequate quality components were considered as studies with high risk of bias (low methodological quality)(Higgins 2011; Kjaergard 2001; Moher 1998; Schulz 1995). High inter‐rater agreement between blinded and unblinded assessments as well as between two independent assessors has been found previously (Gluud 2006; Kjaergard 2001). As tacrolimus and cyclosporin are usually dosed on trough levels (or currently two hours post‐dose levels for cyclosporin) studies are expected to be open label and without adequate blinding, which might increase the risk of bias.
Measures of treatment effect
For dichotomous outcomes results were expressed as risk ratio (RR) with 95% confidence intervals (CI). Where continuous scales of measurement are used to assess the effects of treatment the mean difference (MD) was used, or the standardised mean difference (SMD) if different scales have been used.
Dealing with missing data
When data were missing we took the following steps.
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We contacted the original investigators to request missing data.
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We analysed the missing data assuming that data are missing at random.
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For incomplete data, we performed sensitivity analyses to assess how sensitive results are to reasonable changes in the assumptions that are made.
Assessment of heterogeneity
Heterogeneity was analysed using a Chi² test on N‐1 degrees of freedom, with an alpha of 0.05 used for statistical significance and with the I² test (Higgins 2002; Higgins 2003; Higgins 2011).
Assessment of reporting biases
We planned to construct funnel plots to explore bias (Egger 1997; Macaskill 2001) if more than 10 studies were identified for inclusion. Asymmetry in funnel plot of study size was planned to be used to assess this bias. We planned to perform linear regression approach to determine the funnel plot asymmetry (Egger 1997). However, because only three studies met the review's inclusion criteria, funnel plots were not created nor proposed analyses conducted.
Data synthesis
The analyses were performed using Review Manager 5 and trial sequential analysis (TSA 2011; TSA Manual 2011; Wetterslev 2008). Data were analysed using both fixed‐effect and random‐effects models. Where discrepancies occurred between models, both results were reported; otherwise only results from the fixed‐effect model were reported. Data were analysed according to the intention‐to‐treat principle and presented as RR and risk difference (RD) with 95% confidence intervals.
Subgroup analysis and investigation of heterogeneity
Subgroup analyses were planned to be performed for:
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Studies with low risk of bias compared with studies with high risk of bias.
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Tacrolimus versus oil‐based cyclosporin compared with tacrolimus versus microemulsion cyclosporin studies. This subgroup analysis was planned because differences in absorption and oral bioavailability of the two formulae have been described (Cantarovich 2004; Kahan 2004; Lee 1998; Penninga 2010a).
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Adult compared with paediatric studies. This was planned because immunological differences in paediatric patients might be expected (Aurora 2009; Christie 2009).
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Single compared with double lung transplant patients. Sub group analysis was planned because we anticipated differences between these populations (Christie 2009).
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Tacrolimus versus two hours post‐dose monitoring of cyclosporin (C2‐monitoring) compared with tacrolimus versus cyclosporin dosing based on trough levels (C0‐monitoring). This was planned to investigate reports of better outcomes for two hours post‐dose monitoring of cyclosporin (Iversen 2009a).
Tests for subgroup differences were performed to evaluate differences between estimates.
Sensitivity analysis
Zero‐event studies
RevMan 5 was not designed to analyse studies with no events in both intervention groups when meta‐analyses are performed as RR or odds ratio (OR). Exclusion of zero event studies seemed to us to be unjustified and unreasonable, and potentially created risk of inflating the magnitude of the pooled treatment effects (Keus 2009). Therefore, we also performed a random‐effects meta‐analysis with empirical continuity correction of 0.01 in zero event studies.
Trial sequential analysis
Because cumulative meta‐analyses can produce random errors resulting from sparse data and repetitive testing on accumulating data, we conducted trial sequential analysis (Thorlund 2011; TSA Manual 2011; Wetterslev 2008). We calculated the required number of participants needed in a meta‐analysis to detect or reject a certain intervention effect (the 'information size') to minimise random errors (Wetterslev 2008; Wetterslev 2009). Required information size calculation can account for heterogeneity present in meta‐analyses. In our meta‐analysis, information size was based on the observed proportion with the focus outcome in the cyclosporin group, and on the assumption of a plausible relative risk reduction of 20%, or on the relative risk reduction observed in the included studies determined to be at low risk of bias (Wetterslev 2008). The underlying assumption of trial sequential analysis was that significance testing may be performed each time a new study was added to the cumulative meta‐analysis. We added studies according to the year of publication, but if more than one study was published in a year, studies were added alphabetically according to the last name of the first author. Trial sequential monitoring boundaries were constructed on the basis of the required information size and risk for type I (5%) and type II (20%) errors (TSA 2011; Wetterslev 2008). The purpose of these boundaries was to determine the statistical inference that could be drawn about the cumulative meta‐analysis that had not reached the required information size; if a trial sequential monitoring boundary is crossed before the required information size is reached in a cumulative meta‐analysis, firm evidence may have been established and further studies may be superfluous. On the other hand, if boundaries are not surpassed, it is probably necessary to continue doing studies to detect or reject a certain intervention effect. We applied type I errors of 5%, type II errors of 20% as defaults and adjusted information size for heterogeneity unless otherwise stated (TSA Manual 2011; Wetterslev 2008).
Results
Description of studies
Results of the search
Our search identified 86 references. After exclusion of duplicates and references that did not match inclusion criteria, we identified three RCTs reported in 11 publications (three peer‐reviewed journal articles and eight conference abstracts) for analysis (Figure 1).
Study flow diagram
Included studies
We included three studies with a total of 413 adult participants. Of these, 205 were randomised to tacrolimus and 208 to cyclosporin (Hachem 2007; Treede 2012; Zuckermann 2003). Hachem 2007 was a single‐centre study; Zuckermann 2003 was conducted at two centres; and Treede 2012 was a multicentre study.
Reported mean age of the total study population ranged from 44 to 50 years (Hachem 2007; Zuckermann 2003). Mean age of participants in the single‐ and dual‐centre studies was similar (Hachem 2007; Zuckermann 2003).
All included studies reported on the number of single versus double lung transplant recipients. Most recipients were reported to have undergone double lung transplantations (62%, 74% and 93% respectively in Zuckermann 2003; Treede 2012; and Hachem 2007). Transplantation type was similar between the tacrolimus and cyclosporin treatment groups within each study.
Treede 2012 and Zuckermann 2003 (323 participants) compared tacrolimus with the new formula microemulsion cyclosporin, and Hachem 2007 (90 participants) compared tacrolimus with microemulsion (Neoral) or modified cyclosporin formulation (Gengraf).
All patients in the three included studies were treated with steroids as well as the antiproliferative agents azathioprine (Hachem 2007) and mycophenolate mofetil (Treede 2012; Zuckermann 2003). Induction therapy immunosuppressive agents administered were basiliximab (20 mg on day 0 and day 4) and rat anti‐thymocyte globulin during the first three postoperative days in Hachem 2007 and Zuckermann 2003 respectively. Treede 2012 did not administer induction therapy.
Follow‐up ranged from 2.2 years (Hachem 2007) to 3 years (Treede 2012; Zuckermann 2003).
Excluded studies
Following appraisal of full‐text articles six studies were excluded (Bhorade 2002; Fung 1994; Keenan 1995; Kesten 1997; Kur 1999; Schwaiblmair 2000). Of these, five studies did not compare tacrolimus versus cyclosporin in a randomised setting. In one study randomisation was performed in an alternate fashion (Keenan 1995). This study design was classified as quasi‐randomised, and following our protocol inclusion criteria, the study was excluded from analysis for benefits. However, we carefully analysed the study of Keenan for harms, and the nature and incidence of harms in this study was not different from the included RCTs, Reasons for exclusions are described in Characteristics of excluded studies.
Risk of bias in included studies
Overall, study methodology reporting was inadequate in the included studies (Figure 2). All three studies were considered to be at high risk of bias because one or more of the components assessed were either at high risk of bias or unclear due to incomplete reporting.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study
Allocation
Generation of the allocation sequence was reported in all three studies. The allocation sequence generation method was considered to be unclear in Zuckermann 2003. Hachem 2007 and Treede 2012, which both used computer‐generated allocation sequencing, were assessed to be at low risk of bias.
Allocation concealment methods were not reported in Hachem 2007 or Zuckermann 2003 and were considered to be unclear in terms of risk of allocation bias. Treede 2012 used a telephone‐based centralised method and was considered to be at low risk of bias regarding allocation concealment.
Blinding
All included studies indicated that they were open‐label and were considered to be at high risk of bias regarding blinding of patients and study personnel.
Hachem 2007 reported that pathologists who examined transbronchial lung biopsy specimens were blinded to study drug assignment, but the impact of blinding outcome assessors to other outcome measures was not reported, and this study was therefore considered to carry an unclear risk of bias regarding blinding of outcome assessors. Treede 2012 and Zuckermann 2003 were considered to be at high risk of bias regarding blinding of outcome assessors.
Incomplete outcome data
The included studies reported on incomplete outcome data and were not found to be at risk of attrition bias.
Selective reporting
Although we did not have access to study protocols, all three included studies reported on the most relevant clinical outcome measures. Therefore, the included studies were considered to be at low risk of selective outcome reporting bias.
Other potential sources of bias
Hachem 2007 and Treede 2012 were industry‐sponsored studies and considered to be at high risk of bias. No other potential risks of bias were found in Zuckermann 2003.
Effects of interventions
Mortality
All three studies reported mortality. Overall, no significant difference in mortality was found between tacrolimus (49/205; 24%) and cyclosporin (47/208; 23%)(Analysis 1.1 (3 studies, 413 participants): RR 1.06, 95% CI 0.75 to 1.49).
Acute rejection
Treede 2012 and Zuckermann 2003 reported acute rejection (defined as the number of patients who experienced at least one episode of rejection, and determined by clinical criteria or transbronchial lung biopsy). No significant difference was found between participants treated with tacrolimus (104/161; 65%) and cyclosporin (118/162; 73%)(Analysis 1.2 (2 studies, 322 participants): RR 0.89, 95% CI 0.77 to 1.03).
Zuckermann 2003 reported the number of acute rejection episodes/100‐patient days. No significant difference in acute rejection episodes was found when tacrolimus (0.22 ± 0.3) was compared with cyclosporin (0.32 ± 0.42)(Analysis 1.3 (1 study 74 participants): MD ‐0.10, 95% CI ‐0.27 to 0.07).
Bronchiolitis obliterans syndrome
All three studies reported the numbers of participants diagnosed with bronchiolitis obliterans syndrome, and all defined bronchiolitis obliterans according to the guidelines of the International Society for Heart and Lung Transplantation (Cooper 1993).
There were statistically significant fewer numbers of participants treated with tacrolimus (22/205; 11%) who developed bronchiolitis obliterans syndrome compared with those treated with cyclosporin (48/208; 23%)(Analysis 1.4 (3 studies, 413 participants): RR 0.46, 95% CI 0.29 to 0.74). This statistically significant effect persisted when the random‐effects model was applied (RR 0.47, 95% CI 0.29 to 0.75).
Infection or sepsis
Zuckermann 2003 reported infection as the total overall number of infections/100 patient‐days. No statistically significant difference in infections/100 patient‐days was observed among participants treated with tacrolimus (0.55 ± 0.31) compared with cyclosporin (0.7 ± 0.36)(Analysis 1.5 (1 study, 149 participants): MD ‐0.15, 95% CI ‐0.30 to 0.00).
Quality of life
None of the included studies reported quality of life.
Any adverse event
Treede 2012 reported on adverse effects (not including acute rejection, infection or bronchiolitis obliterans). It was reported that 6/124 (5%) participants in the tacrolimus group experienced an adverse event leading to treatment withdrawal (3 cytopenia; 3 neurotoxicity) compared with 11/125 (9%) participants in the cyclosporin group (3 cytopenia; 4 nephrotoxicity; 4 side effects)(Analysis 1.6 (1 study, 249 participants): RR 0.55, 95% CI 0.21 to 1.44).
Treatment withdrawals
All three studies reported on treatment withdrawals. Treatment withdrawals were statistically less frequent in the tacrolimus group (14/205; 7%) compared with the cyclosporin group (54/208; 26%)(Analysis 1.7 (3 studies 413 participants): RR 0.27, 95% CI 0.16 to 0.46). This effect persisted when the random‐effects model was applied (RR 0.27, 95% CI 0.16 to 0.46) Withdrawals were attributed to recurrent rejection episodes, occurrence of bronchiolitis obliterans, or adverse effects, or where participants often switched between drugs.
Lymphocytic bronchitis
Hachem 2007 reported lymphocytic bronchitis. The cumulative lymphocytic bronchitis B score in the study was defined as the sum of all B scores for each subject, excluding B scores in the setting of a confirmed bacterial or viral respiratory tract infection (Hachem 2007; Husain 1999) and the lymphocytic bronchitis score was significantly lower in patients treated with tacrolimus (0.7 ± 0.9) compared with cyclosporin (1.3 ± 1.2)(Analysis 1.8 (1 study, 90 participants): MD ‐0.60, 95% CI ‐1.04 to ‐0.16).
Pneumonia (viral, bacterial, fungal)
None of the included studies reported on pneumonia.
Cytomegalovirus infection
None of the included studies reported on cytomegalovirus infection.
Cancer
Hachem 2007 and Zuckermann 2003 reported cancer. No significant difference was seen in the numbers of participants diagnosed with cancer between tacrolimus (1/81; 1%) and cyclosporin (7/83; 8%)(Analysis 1.9 (2 studies, 164 participants): RR 0.21, 95% CI 0.04 to 1.16).
Kidney function
Hachem 2007 reported kidney failure, defined as the number of participants who required chronic haemodialysis. Kidney failure was not significantly different between tacrolimus (3/44; 7%) and cyclosporin (2/46; 4%)(Analysis 1.10 (1 study, 90 participants): RR 1.57, 95% CI 0.28 to 8.94).
Kidney dysfunction was reported in all three studies and was defined as creatinine over 2.0 mg/dL (Hachem 2007), creatinine over 2.5 mg/dL (Zuckermann 2003), or a persistent increase in creatinine over 2 mg/dL or dialysis dependency (Treede 2012). No statistically significant difference in kidney dysfunction was seen between the tacrolimus (43/205; 21%) and cyclosporin (31/208; 15%)(Analysis 1.11 (3 studies, 413 participants): RR 1.41, 95% CI 0.93 to 2.14).
Zuckermann 2003 reported mean serum creatinine. Serum creatinine (mg/dL) was not significantly different in the tacrolimus group (1.6 ± 0.7 mg/dL) compared with the cyclosporin group (1.5 ± 0.4 mg/dL)(Analysis 1.12 (1 study, 74 participants): MD 0.10 mg/dL, 95% CI ‐0.16 to 0.36).
Arterial hypertension
Hachem 2007 and Zuckermann 2003 reported arterial hypertension. Zuckermann 2003 defined arterial hypertension as the requirement for antihypertensive treatment; the condition was not defined by Hachem 2007. When analysed using a fixed‐effect model, arterial hypertension was found to be significantly less common in participants treated with tacrolimus (33/81; 41%) compared with cyclosporin (51/83; 61%)(Analysis 1.13 (2 studies, 164 participants): RR 0.67, 95% CI 0.50 to 0.89). No significant difference was seen when the random‐effects model was applied (RR 0.54, 95% CI 0.17 to 1.73).
New‐onset diabetes mellitus
Hachem 2007 and Zuckermann 2003 reported new‐onset diabetes mellitus. When analysed using a fixed‐effect model, diabetes occurred more frequently in the tacrolimus group (18/81; 22%) compared with the cyclosporin group (4/83; 5%)(Analysis 1.14 (2 studies, 164 participants): RR 4.24, 95% CI 1.58 to 11.40). No significant difference was seen when the random‐effects model was applied (RR 4.43, 95% CI 0.75 to 26.05).
Hyperlipidaemia and total cholesterol
Zuckermann 2003 reported the number of patients treated for hyperlipidaemia. No significant difference between tacrolimus (9/37; 24%) versus cyclosporin‐treated patients 15/37; 41%) was seen (Analysis 1.15 (1 study, 74 participants): RR 0.60, 95% CI 0.30 to 1.20).
None of the included studies reported total cholesterol concentrations.
Neurotoxic reaction
Treede 2012 reported neurotoxicity. No significant difference was seen in the numbers of patients among whom treatment was withdrawn between the tacrolimus (3/124; 2%) cyclosporin (0/125; 0%) groups (Analysis 1.16 (1 study, 249 participants): RR 7.06, 95% CI 0.37 to 135.19).
Hirsutism and gingival hyperplasia
None of the included studies reported on hirsutism or gingival hyperplasia.
Zero event correction
Hachem 2007 reported the outcome 'treatment withdrawal' with zero events in both intervention groups. Therefore, we also performed a random‐effects meta‐analysis with empirical continuity correction of 0.01 in this zero event study. After zero event correction for treatment withdrawal, no significant difference remained with regard to treatment withdrawals for tacrolimus and cyclosporin (RR 0.25, 95% CI 0.02 to 2.50; P = 0.24).
Trial sequential analysis
Trial sequential analysis was performed for all outcome measures (TSA 2011). The required information size for the primary outcome measures was 2436 patients for mortality (Figure 3), 2436 patients for bronchiolitis obliterans syndrome (Figure 4), and 332 patients for acute rejection (Figure 5). Hence, the required information size was almost reached to reject a 20% relative risk reduction in acute rejection. However, the cumulative Z‐curve reached the area of futility, indicating that we could reject a 20% relative risk reduction in acute rejection. Trial sequential analysis showed that the required information size to accept or reject a 15% relative risk reduction in acute rejection is 575 patients and assuming such a difference, neither the area of futility nor the required information size were reached (Figure 6).
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on mortality based on three studies (n = 413). The required information size of 2436 patients was calculated based on an observed mortality of 23% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 20 % in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on bronchiolitis obliterans syndrome based on three studies (n = 413). The required information size of 2436 patients was calculated based on an observed bronchiolitis obliterans syndrome incidence of 23% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 20 % in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on acute rejection based on two studies (n = 323). The required information size of 332 patients was calculated based on an observed acute rejection incidence of 73% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 20 % in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on acute rejection based on two studies (n = 323). The required information size of 332 patients was calculated based on an observed acute rejection incidence of 73% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 15% in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
The required information size was not achieved for mortality and bronchiolitis obliterans syndrome, and none of the studies' sequential monitoring boundaries were broken by the cumulative Z‐curves. Trial sequential analysis was performed for secondary outcome measures, but neither the required information size, nor any of the areas of futility, was reached.
Subgroup analyses
We were unable to perform subgroup analyses of studies assessed to be at low risk of bias compared to those at high risk of bias. All included studies in this review were assessed to be at high risk of bias. Similarly, we were unable to perform subgroup analyses comparing adult and paediatric studies because all participants in the included studies were adults. None of the studies reported separately on results for recipients of single lung transplants compared with double lung transplants, and hence, were also unable to perform this subgroup analysis.
Furthermore, we were unable to perform subgroup analysis of C2‐monitoring (2 hours post‐dose monitoring) of cyclosporin compared with cyclosporin dosing based on of trough levels (C0): all included studies applied cyclosporin dosing based on trough levels (C0).
We were unable to perform subgroup analysis on tacrolimus versus oil‐based cyclosporin compared with tacrolimus versus microemulsion cyclosporin studies: the included studies used microemulsion or oral solution cyclosporin only.
Discussion
Summary of main results
We identified three studies with a total of 413 adult participants that assessed the effects of tacrolimus versus cyclosporin for lung transplant recipients. All studies were assessed to be at high risk of bias.
Our systematic review generated a number of potentially important findings.
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Tacrolimus may be significantly superior to cyclosporin regarding the incidence of bronchiolitis obliterans syndrome, lymphocytic bronchitis, treatment withdrawal, and arterial hypertension. The finding for arterial hypertension was not confirmed when the random‐effects model was applied in the meta‐analysis.
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We found that cyclosporin may be superior to tacrolimus regarding new‐onset diabetes mellitus when assessed using the fixed‐effect model, but this was not confirmed when the random‐effects model was applied.
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No difference between the treatment groups was seen in mortality, incidence of acute rejection, infections/100 patient‐days, cancer, kidney failure, serum creatinine, hyperlipidaemia, and neurotoxic reactions.
Trial sequential analysis indicated that the required information size was not reached in any of the meta‐analyses. Moreover, the cumulative Z‐curve did not cross the alpha spending monitoring boundaries in any of the meta‐analyses with statistically significant findings according to the traditional level of P < 0.05.
Likewise, the cumulative Z‐curve did not cross the beta spending monitoring boundaries of futility in the meta‐analyses that did not achieve statistically significant findings according to the traditional level of P < 0.05. Acute rejection based on a relative risk reduction of 20% was reached in the area of futility, but not when a relative risk reduction of 15% was assumed. Accordingly, we were unable to exclude random errors in any of these comparisons. Further research is needed.
Overall completeness and applicability of evidence
This systematic review examined evidence from three RCTs that investigated the use of tacrolimus versus cyclosporin in lung transplant recipients. We were unable to obtain relevant data regarding all of our nominated outcome measures because of insufficient or absent reporting in the included studies.
All included studies reported on three of our primary outcome measures: mortality, acute rejection and bronchiolitis obliterans syndrome; only one study reported on infection. None of the studies reported on our secondary outcomes: quality of life, pneumonia, cytomegalovirus infection, gingival hyperplasia, hirsutism, and total cholesterol concentration.
It has been shown that two hours post‐dose cyclosporin monitoring (C2‐monitoring) in solid organ transplant recipients is the most accurate single time‐point predictor of one to four hour abbreviated area under the curve (Iversen 2009b). Furthermore, C2‐monitoring has been found to be a sensitive predictor of acute cellular rejection in lung transplant recipients (Iversen 2009b). Two included studies applied C2‐monitoring in all studies may have improved outcomes in cyclosporin group participants.
In this review, a significant difference in treatment withdrawals was seen between the tacrolimus and cyclosporin groups. However, this difference may reflect common current practice that lung transplant recipients who develop bronchiolitis obliterans syndrome or experience recurrent episodes of acute rejection have their baseline immunosuppression therapy changed; it is not necessarily due to cyclosporin being less well tolerated or it being more toxic than tacrolimus (Snell 2007).
Quality of the evidence
We conducted this review according to the processes described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). The quality and quantity of available evidence limited our findings and interpretations. The three studies assessed for this review included relatively few participants. Hence, risk of random errors is a potential explanation for our findings as suggested by our trial sequential analyses. It was also possible that participants in the included studies may not be representative of the general patient population. Of the three included studies, one was performed in the 1990s, and may not accurately reflect current practice regarding treatment and management of lung transplant patients (Zuckermann 2003).
Study follow‐up duration was up to three years, and therefore, there is no evidence on long‐term effects of tacrolimus and cyclosporin treatment on our outcome measures. Long‐term effects would be particularly relevant for outcome measures such as mortality, bronchiolitis obliterans syndrome, infection, and cancer.
We explored the presence of statistical heterogeneity using the Chi² test and assessed heterogeneity using the I² test (Higgins 2003). The Chi² test is low powered in situations where meta‐analyses are conducted on studies with small sample sizes, or are few in number, as in this review. This means that while a statistically significant result may indicate a problem with heterogeneity, a non‐significant result should not be taken as evidence of no heterogeneity. To reflect our concerns about heterogeneity, we investigated both fixed‐effect and random‐effects models to provide more conservative estimates of effect. We found low heterogeneity (I² = 0%) for our primary outcome measures of mortality, acute rejection and bronchiolitis obliterans syndrome. No differences were seen between fixed‐effect and random‐effects model analyses for any of the primary outcome measures. We observed moderate heterogeneity regarding a secondary outcome measure: arterial hypertension (I² = 84%). A significant treatment difference was observed for this outcome when the fixed‐effect model was applied, but this disappeared when the random‐effects model was applied.
Some outcomes of the included studies in our meta‐analysis include few patients and few events, and thus have wide confidence intervals around the estimate of effect, which influenced the precision of our results.
Potential biases in the review process
We conducted a comprehensive literature search for this review: inclusion and exclusion criteria were specified; data were extracted in triplicate; and data analysis was conducted according to the review's protocol (Penninga 2010b).
Risk of bias is known to impact on the estimated intervention effect; studies assessed at high risk of bias tend to overestimate beneficial intervention effects (Kjaergard 2001; Moher 1998; Schulz 1995). Of the three include studies, two reported adequate generation of allocation sequencing; none reported adequate allocation concealment or blinded patients and personnel; one study reported blinding of the pathologist who examined transbronchial biopsies for rejection; two adequately addressed incomplete outcome data; all three reported on clinically relevant and reasonably expected outcome measures; and one appeared to be free of other components that could put the study at risk of bias. Accordingly, all studies were considered to be at high risk of bias. Therefore, it may be possible that the estimated intervention effect for all significant beneficial effects could be attributable to systematic errors.
The risk of random error is higher when data are sourced from small evidence bases (or sample sizes for individual studies). Hence, the evidence base needs to be sufficiently large to reduce the risk of random error and increase the change of observing a true intervention effect (Wetterslev 2008). Accordingly, we also analysed data using trial sequential analysis. Trial sequential analysis is a statistical method that controls for random error caused by sparse data and formal or informal repetitive testing of accumulating data (TSA Manual 2011). Trial sequential analysis provides information regarding the required evidence base and enables assessment of the risks of random errors when the required volume of evidence has not been reached. Furthermore, when no significant difference exists trial sequential analysis provides an area of futility, which may help in determining the need for further studies (TSA Manual 2011).
Trial sequential analysis of the outcome measures in this review showed that we could not exclude the possibility that beneficial effects found in the meta‐analyses were caused by random errors.
Agreements and disagreements with other studies or reviews
A meta‐analysis regarding tacrolimus versus cyclosporin in lung transplant recipients has been published (Fan 2009), and the results are slightly different from our systematic review. This might be explained by the following differences: we found one additional RCT that compared tacrolimus with cyclosporin, and we analysed data as intention‐to treat when possible (Treede 2012). Furthermore, we excluded one quasi‐RCT (Keenan 1995) from the analyses of which was included in the Fan 2009 meta‐analysis. We carefully analysed the study of Keenan (Keenan 1995) for harms, and the nature and incidence of harms in this study was not different from the included RCTs, Furthermore we performed all the meta‐analyses including data from the study from Keenan 1995, and all statistically significant outcomes remained statistically significant, and all statistically non‐significant outcomes remained statistically non‐significant.
Observational data from the International Society of Heart and Lung Transplantation (ISHLT) registry showed that the percentage of recipients with reported acute rejection was highest in cyclosporin‐based regimens and lowest in tacrolimus‐based regimens (Christie 2009). In our review, we found a slight reduction in the number of patients affected by acute rejection in the tacrolimus group compared with the cyclosporin group, but this was not statistically significant (RR 0.88, 95% CI 0.76 to 1.03).
Traditionally, clinicians administering immunosuppressive treatment for lung transplantation acquire substantial experience from knowledge regarding other types of organ transplantation. A meta‐analysis comparing tacrolimus versus cyclosporin in 3813 liver transplant recipients found tacrolimus to be superior to cyclosporin in improving patient survival, graft survival, and preventing acute rejection; however, tacrolimus was significantly more diabetogenic than cyclosporin (McAlister 2006). A meta‐analysis comparing tacrolimus versus cyclosporin in 4102 kidney transplant recipients found tacrolimus to be superior to cyclosporin in improving graft survival and preventing acute rejection after kidney transplantation; however, tacrolimus was associated with increased post‐transplant diabetes, neurological, and gastrointestinal adverse effects (Webster 2005a; Webster 2005b). A systematic review with meta‐analysis comparing tacrolimus versus cyclosporin in 952 heart transplant recipients found tacrolimus to be superior to both formulations of cyclosporin with regard to hypertension, hyperlipidaemia, gingival hyperplasia, and hirsutism (Penninga 2010a). Tacrolimus was also found to be superior to microemulsion cyclosporin in heart transplant recipients with regard to mortality, biopsy‐proven acute rejection, hyperlipidaemia, hirsutism, and gingival hyperplasia (Penninga 2010a). Follow‐up in the randomised studies included in the meta‐analyses was between three months and five years among kidney transplant recipients (Webster 2005a; Webster 2005b); between three months and four years in liver transplant recipients (McAlister 2006), and six months to five years in heart transplant recipients (Penninga 2010a). Follow‐up in the randomised studies on lung transplant recipients in this review ranged from 2.2 to 3 years.
Our analysis did not find similar reductions in mortality or graft survival for lung transplant recipients treated with tacrolimus compared with cyclosporin as seen in liver, kidney and a subgroup of heart transplant recipients (McAlister 2006; Penninga 2010a; Webster 2005a; Webster 2005b). Likewise, our analysis did not identify a similar reduction in numbers of lung transplant recipients affected by acute rejection who were treated with tacrolimus compared with cyclosporin as was observed in liver, kidney and a subgroup of heart transplant recipients (McAlister 2006; Penninga 2010a; Webster 2005a; Webster 2005b).
Tacrolimus was found to be more diabetogenic than cyclosporin among kidney and liver transplant recipients (McAlister 2006; Webster 2005a; Webster 2005b), and this agrees in part with our findings for this review. We found tacrolimus to be inferior to cyclosporin with regard to diabetes when analysed using a fixed‐effect model, but no statistically significant difference was observed when a random‐effects model was applied. This review showed that tacrolimus in lung transplant recipients seems to be superior to cyclosporin with regard to hypertension (when applying the fixed‐effect model was applied), which is similar to findings among heart transplant recipients (Penninga 2010a).
In heart transplant recipients, treatment differences were observed between tacrolimus versus oil‐based cyclosporin compared to tacrolimus versus microemulsion or oral cyclosporin formulations (Penninga 2010a). In this review, all included studies investigated microemulsion cyclosporin, and microemulsion or oral solution cyclosporin. Hence, we were unable to perform subgroup analyses on tacrolimus versus oil‐based cyclosporin compared to tacrolimus versus microemulsion or oral cyclosporin formulations.
We found that a reduction in bronchiolitis obliterans syndrome seemed to occur in patients treated with tacrolimus compared with cyclosporin. This appears to be a significant finding because the ISHLT registry has identified the bronchiolitis obliterans syndrome and non‐cytomegalovirus infections as the most common causes of death more than one year after lung transplantation (Christie 2009).
Study flow diagram
Risk of bias summary: review authors' judgements about each risk of bias item for each included study
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on mortality based on three studies (n = 413). The required information size of 2436 patients was calculated based on an observed mortality of 23% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 20 % in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on bronchiolitis obliterans syndrome based on three studies (n = 413). The required information size of 2436 patients was calculated based on an observed bronchiolitis obliterans syndrome incidence of 23% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 20 % in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on acute rejection based on two studies (n = 323). The required information size of 332 patients was calculated based on an observed acute rejection incidence of 73% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 20 % in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Trial sequential analysis of the effect of tacrolimus versus cyclosporin on acute rejection based on two studies (n = 323). The required information size of 332 patients was calculated based on an observed acute rejection incidence of 73% in the cyclosporin group in the meta‐analysis (Pc); a relative risk reduction (RRR) of 15% in the tacrolimus group; a type I error (α) of 5%; a type II error (β) of 20%, and the observed heterogeneity in the meta‐analysis (I² = 0%)
Comparison 1 Tacrolimus versus cyclosporin, Outcome 1 Mortality.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 2 Acute rejection.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 3 Acute rejection (episodes/100 patient‐days).
Comparison 1 Tacrolimus versus cyclosporin, Outcome 4 Bronchiolitis obliterans syndrome.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 5 Infection or sepsis (episodes/100 patient‐days).
Comparison 1 Tacrolimus versus cyclosporin, Outcome 6 Adverse events.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 7 Treatment withdrawal.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 8 Lymphocytic bronchitis score.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 9 Cancer.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 10 Kidney failure.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 11 Kidney dysfunction.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 12 Creatinine.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 13 Arterial hypertension.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 14 New‐onset diabetes mellitus.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 15 Hyperlipidaemia.
Comparison 1 Tacrolimus versus cyclosporin, Outcome 16 Neurotoxicity.
| Tacrolimus compared to cyclosporin for lung transplant recipients | ||||||
| Patient or population: Lung transplant recipients | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | No of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Cyclosporin | Tacrolimus | |||||
| Mortality | Study population | RR 1.06 | 413 (3) | ⊕⊕⊝⊝ | ||
| 226 per 1000 | 240 per 1000 | |||||
| Moderate | ||||||
| 196 per 1000 | 208 per 1000 | |||||
| Acute rejection | Study population | RR 0.89 | 323 (2) | ⊕⊕⊝⊝ | ||
| 728 per 1000 | 648 per 1000 | |||||
| Moderate | ||||||
| 700 per 1000 | 623 per 1000 | |||||
| Bronchiolitis obliterans syndrome | Study population | RR 0.46 | 413 (3) | ⊕⊕⊕⊝ | ||
| 231 per 1000 | 106 per 1000 | |||||
| Moderate | ||||||
| 208 per 1000 | 96 per 1000 | |||||
| Infection | The mean infection in the intervention groups was | 74 (1) | ⊕⊕⊝⊝ | |||
| Treatment withdrawal | 260 per 1000 | 70 per 1000 | RR 0.27 | 413 (3) | ⊕⊕⊝⊝ | |
| Arterial hypertension | Study population | RR 0.67 | 164 (2) | ⊕⊕⊝⊝ | ||
| 614 per 1000 | 412 per 1000 | |||||
| Moderate | ||||||
| 599 per 1000 | 401 per 1000 | |||||
| Diabetes mellitus | Study population | RR 4.24 | 164 (2) | ⊕⊕⊝⊝ | ||
| 48 per 1000 | 204 per 1000 | |||||
| Moderate | ||||||
| 44 per 1000 | 187 per 1000 | |||||
| *The basis for the assumed risk (e.g. the median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). | ||||||
| GRADE Working Group grades of evidence | ||||||
| ¹All included studies were at high risk of bias as assessed using the Cochrane risk of bias tool | ||||||
| Outcome or subgroup title | No. of studies | No. of participants | Statistical method | Effect size |
| 1 Mortality Show forest plot | 3 | 413 | Risk Ratio (M‐H, Fixed, 95% CI) | 1.06 [0.75, 1.49] |
| 2 Acute rejection Show forest plot | 2 | 323 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.89 [0.77, 1.03] |
| 3 Acute rejection (episodes/100 patient‐days) Show forest plot | 1 | Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 4 Bronchiolitis obliterans syndrome Show forest plot | 3 | 413 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.46 [0.29, 0.74] |
| 5 Infection or sepsis (episodes/100 patient‐days) Show forest plot | 1 | Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 6 Adverse events Show forest plot | 1 | Risk Ratio (M‐H, Fixed, 95% CI) | Totals not selected | |
| 7 Treatment withdrawal Show forest plot | 3 | 413 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.27 [0.16, 0.46] |
| 8 Lymphocytic bronchitis score Show forest plot | 1 | Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 9 Cancer Show forest plot | 2 | 164 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.21 [0.04, 1.16] |
| 10 Kidney failure Show forest plot | 1 | Risk Ratio (M‐H, Fixed, 95% CI) | Totals not selected | |
| 11 Kidney dysfunction Show forest plot | 3 | 413 | Risk Ratio (M‐H, Fixed, 95% CI) | 1.41 [0.93, 2.14] |
| 12 Creatinine Show forest plot | 1 | Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 13 Arterial hypertension Show forest plot | 2 | 164 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.67 [0.50, 0.89] |
| 14 New‐onset diabetes mellitus Show forest plot | 2 | 164 | Risk Ratio (M‐H, Random, 95% CI) | 4.43 [0.75, 26.05] |
| 15 Hyperlipidaemia Show forest plot | 1 | Risk Ratio (M‐H, Fixed, 95% CI) | Totals not selected | |
| 16 Neurotoxicity Show forest plot | 1 | Risk Ratio (M‐H, Fixed, 95% CI) | Totals not selected | |
