Autores: Luiz Carlos Ferreira da Silva, Roseana de Almeida Freitas, Manoel Pacheco de Andrade Jr,
Marta Rabello Piva, Paulo Ricardo Saquete Martins-Filho, Thiago de Santana Santos.
Periódico: Journal of Cranio-Maxillo-Facial Surgery (fator de impacto: 0.822). Status:
publicado.
Abstract: To prevent rejection of kidney transplants, patients must be kept in
immunosuppressive therapy for a long time, which includes the use of drugs such as
cyclosporine, azathioprine, cyclophosphamide, and prednisone. The action of these drugs
reduces the general immune response of transplant patients and thus increases their
susceptibility to infections. Moreover, these drugs increase the potential of developing lesions.
Therefore, oral hygiene in kidney transplant recipients contributes to maintenance of the
transplanted organ and its function. Thus, an investigation of oral lesions could be counted as a
notable work. The aim of this study was to investigate oral lesions in a group of 21 kidney
transplant patients under immunosuppressive therapy attended during a 1-year period in the
Nephrology Department of the Federal University of Sergipe, Brazil. Data related to sex, age,
etiology of renal disease, types of renal transplant, time elapsed after transplantation,
immunosuppressive treatment, use of concomitant agents, and presence of oral lesions were
obtained. All patients received a kidney transplant from a living donor, and the mean
posttransplantation follow-up time was 31.6 months; 71.5% used triple immunosuppressive
therapy with cyclosporine A, azathioprine, and prednisone. Ten patients were also treated with
calcium-channel blockers. Of the 21 transplant patients, 17 (81%) presented oral lesions.
Gingival overgrowth was the most common alteration, followed by candidiasis and superficial
ulcers. One case of spindle cell carcinoma of the lower lip was observed. Oral cavity can harbor
a variety of manifestations related to renal transplantation under immunosuppressive therapy.
4 Referências
1
Pascual M, Theruvath T, Kawai T, Tolkoff-Rubin N, Cosimi AB. Strategies to
improve long-term outcomes after renal transplantation. N Engl J
Med 2002, 346:580-590.
2
Matesanz R, Mahillo B, Alvarez M, Carmona M. Global observatory and
database on donation and transplantation: world overview on transplantation
activities. Transplant Proc 2009; 41: 2297–301.
3
Ariyawardana SP, Hay KD. Oral manifestations and dental management of
immunocompromised patients. N Z Dent J 1999; 95: 89–97.
4
Piselli P, Serraino D, Segoloni GP, et al.Risk of de novo cancers after
transplantation: results from a cohort of 7217 kidney transplant recipients, Italy
1997-2009. Eur J Cancer 2013; 49: 336–44.
5
Villeneuve PJ, Schaubel DE, Fenton SS, Shepherd F a, Jiang Y, Mao Y. Cancer
incidence among Canadian kidney transplant recipients. Am J Transplant 2007;
7: 941–8.
6
Birkeland SA, Storm HH. Cancer risk in patients on dialysis and after renal
transplantation. Lancet 2000; 355: 1886–7.
7
Grulich AE, Leeuwen MT Van, Falster MO, Vajdic CM. Incidence of cancers in
people with HIV / AIDS compared with immunosuppressed transplant
recipients : a meta-analysis. Lancet 2007; 370: 59–67.
8
Vajdic CM, van Leeuwen MT. Cancer incidence and risk factors after solid organ
transplantation. Int J Cancer 2009; 125: 1747–54.
9
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for
systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 2010;
8: 336–41.
10
Stroup DF. Meta-analysis of Observational Studies in Epidemiology: A Proposal
for Reporting. Meta-analysis of Observational Studies in Epidemiology
(MOOSE) Group. Jama 2000; 283: 2008.
11
Nobre MRC, Bernardo WM, Jatene FB. [Evidence based clinical practice. Part 1-
-well structured clinical questions]. Rev Assoc Med Bras; 49: 445–9.
12
Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO
framework to improve searching PubMed for clinical questions. BMC Med
Inform Decis Mak 2007; 7: 16.
13
Haynes RB, Wilczynski NL, McKibbonK KA, Walker CJ, Sinclair JC.
Developing optimal search strategies for detecting clinically sound studies in
MEDLINE. J Am Med Inf Assoc 1994; 1: 447–58.
14
World Health Organization. International classification of diseases and related
health problems, 10th Revis. Geneva, World Health Organization, 1992.
15
Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O, Peacock R.
Storylines of research in diffusion of innovation: a meta-narrative approach to
systematic review. Soc Sci Med 2005; 61: 417–30.
16
Wilczynski NL, Haynes RB. Developing optimal search strategies for detecting
clinically sound prognostic studies in MEDLINE: an analytic survey. BMC Med
2004; 2: 23.
17
Wilczynski NL, Haynes RB. Optimal search strategies for detecting clinical
sound prognostic studies in EMBASE: an analytic survey. J Am Med Informatics
Assoc 2005; 12: 481–5.
18
Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP.
The Strengthening the Reporting of Observational Studies in Epidemiology
(STROBE) statement: guidelines for reporting observational studies. Lancet
2007; 370: 1453–7.
19
Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of
Observational Studies in Epidemiology (STROBE): explanation and elaboration.
PLoS Med 2007; 4: e297.
20
Da Costa BR, Cevallos M, Altman DG, Rutjes AWS, Egger M. Uses and misuses
of the STROBE statement: bibliographic study. BMJ Open 2011; 1: e000048.
21
Wells GA, Shea B, O‟Connell D, Peterson J, Welch V, Losos M TP. The
Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised
studies in meta-analyses. Ottawa, Canada.
Available.http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm
(accessed 20 Apr2013).
22
Walker E, Hernandez A V, Kattan MW. Meta-analysis: Its strengths and
limitations. Cleve Clin J Med 2008; 75: 431–9.
23
Cochran WG. The Combination of Estimates from Different Experiments.
Biometrics 1954; 10: 101.
24
Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat
Med 2002; 21: 1539–58.
25
Borenstein M, Hedges L V., Higgins JPT, Rothstein HR. A basic introduction to
fixed-effect and random-effects models for meta-analysis. Res Synth Methods
2010; 1: 97–111.
26
Rothstein HR, Sutton AJ, Borenstein M, editors. Publication Bias in Meta-
Analysis. Chichester, UK, John Wiley & Sons, Ltd, 2005
27
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a
simple , graphical test. 1997; 315.
28
Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing
and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455–63.
29
Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines
No documento
Transplante renal e risco de câncer de cabeça e pescoço : revisão sistemática e meta-análise
(páginas 91-95)