Development and Validation of Nomogram for Predicting the Risk of Community-acquired Pneumonia after Kidney Transplantation of Deceased Donors
DOI:
https://doi.org/10.71321/kbfm1398Keywords:
deceased donor; kidney transplantation; community-acquired pneumonia; nomogram; risk predictorAbstract
Background: Community-acquired pneumonia (CAP) is one of the main complications associated with kidney transplantation recipient (KTR). In order to retrospectively analyze KT cases from deceased donors (DD), and construct a nomogram that could effectively assess the risk of CAP.
Methods: We employed logistic regression and the least absolute shrinkage and selection operator (LASSO) to identify predictors from 238 cases collected at Department of Urology, The First Affiliated Hospital of Anhui Medical University, between January 1, 2018, and May 31, 2023. The dataset comprised 6 demographic and 18 clinical indicators, which were used for training and validation. A nomogram was constructed using these predictors, and its effectiveness was evaluated through receiver operating characteristic (ROC) analysis, calibration curves, and clinical decision analysis. Internal validation further confirmed the model's predictive accuracy.
Results: The predictive factors screened in terms of demographic data included recipient/donor age and body mass index. The clinical data screened eight predictors to obtain ‘RiskScore’. The Area under Curve value for the nomogram constructed using the aforementioned predictors was recorded to be 0.779. The calibration curve showed that the model exhibited better predictive performance. In particular, DCA showed that in cases where the probability for the prediction of CAP was 0.13–0.61, the clinical intervention was recommended. The internally verified data established a better predictive ability of the model.
Conclusion: The study constructed an effective and concise prediction model based on clinical data, providing an important reference baseline for the prevention of CAP in KTR.
References
[1] Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. (1999). Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med, 341(23), 1725-1730. https://doi.org/10.1056/nejm199912023412303
[2] Merion RM, Ashby VB, Wolfe RA, Distant DA, Hulbert-Shearon TE, Metzger RA, et al. (2005). Deceased-donor characteristics and the survival benefit of kidney transplantation. Jama, 294(21), 2726-2733. https://doi.org/10.1001/jama.294.21.2726
[3] Hernandez-Fuentes MP, & Lechler RI. (2005). Chronic graft loss. Immunological and non-immunological factors. Contrib Nephrol, 146, 54-64. https://doi.org/10.1159/000082065
[4] Zhang X, Lyu J, Yu X, Wang L, Peng W, Chen J, et al. (2020). Comparison of Graft Outcome Between Donation After Circulatory Death and Living-Donor Kidney Transplantation. Transplant Proc, 52(1), 111-118. https://doi.org/10.1016/j.transproceed.2019.10.001
[5] Kinnunen S, Karhapää P, Juutilainen A, Finne P, & Helanterä I. (2018). Secular Trends in Infection-Related Mortality after Kidney Transplantation. Clin J Am Soc Nephrol, 13(5), 755-762. https://doi.org/10.2215/cjn.11511017
[6] Stratta RJ, Rohr MS, Sundberg AK, Farney AC, Hartmann EL, Moore PS, et al. (2006). Intermediate-term outcomes with expanded criteria deceased donors in kidney transplantation: a spectrum or specter of quality? Ann Surg, 243(5), 594-601; discussion 601-593. https://doi.org/10.1097/01.sla.0000216302.43776.1a
[7] Chen G, Zhang Z, Gu J, Qiu J, Wang C, Kung R, et al. (2010). Incidence and risk factors for pulmonary mycosis in kidney transplantation. Transplant Proc, 42(10), 4094-4098. https://doi.org/10.1016/j.transproceed.2010.10.010
[8] Flabouris K, Chadban S, Ladhani M, Cervelli M, & Clayton P. (2019). Body mass index, weight-adjusted immunosuppression and the risk of acute rejection and infection after kidney transplantation: a cohort study. Nephrol Dial Transplant, 34(12), 2132-2143. https://doi.org/10.1093/ndt/gfz095
[9] Qu JM, & Cao B. (2016). [Guidelines for the diagnosis and treatment of adult community acquired pneumonia in China (2016 Edition)]. Zhonghua Jie He He Hu Xi Za Zhi, 39(4), 241-242. https://doi.org/10.3760/cma.j.issn.1001-0939.2016.04.001
[10] Mandell LA, Wunderink RG, Anzueto A, Bartlett JG, Campbell GD, Dean NC, et al. (2007). Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis, 44 Suppl 2(Suppl 2), S27-72. https://doi.org/10.1086/511159
[11] Saran R, Robinson B, Abbott KC, Agodoa LY, Albertus P, Ayanian J, et al. (2017). US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis, 69(3 Suppl 1), A7-a8. https://doi.org/10.1053/j.ajkd.2016.12.004
[12] Friedman J, Hastie T, & Tibshirani R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw, 33(1), 1-22.
[13] Sauerbrei W, Royston P, & Binder H. (2007). Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med, 26(30), 5512-5528. https://doi.org/10.1002/sim.3148
[14] Kidd AC, McGettrick M, Tsim S, Halligan DL, Bylesjo M, & Blyth KG. (2018). Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors. BMJ Open Respir Res, 5(1), e000240. https://doi.org/10.1136/bmjresp-2017-000240
[15] Kramer AA, & Zimmerman JE. (2007). Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited. Crit Care Med, 35(9), 2052-2056. https://doi.org/10.1097/01.Ccm.0000275267.64078.B0
[16] Vickers AJ, Cronin AM, Elkin EB, & Gonen M. (2008). Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak, 8, 53. https://doi.org/10.1186/1472-6947-8-53
[17] Purnell TS, Auguste P, Crews DC, Lamprea-Montealegre J, Olufade T, Greer R, et al. (2013). Comparison of life participation activities among adults treated by hemodialysis, peritoneal dialysis, and kidney transplantation: a systematic review. Am J Kidney Dis, 62(5), 953-973. https://doi.org/10.1053/j.ajkd.2013.03.022
[18] Silva SB, Caulliraux HM, Araújo CA, & Rocha E. (2016). Cost comparison of kidney transplant versus dialysis in Brazil. Cad Saude Publica, 32(6). https://doi.org/10.1590/0102-311x00013515
[19] Mao N, Yin P, Li Q, Wang Q, Liu M, Ma H, et al. (2020). Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol, 30(12), 6732-6739. https://doi.org/10.1007/s00330-020-07016-z
[20] Schlott F, Steubl D, Hoffmann D, Matevossian E, Lutz J, Heemann U, et al. (2017). Primary Cytomegalovirus Infection in Seronegative Kidney Transplant Patients Is Associated with Protracted Cold Ischemic Time of Seropositive Donor Organs. PLoS One, 12(1), e0171035. https://doi.org/10.1371/journal.pone.0171035
[21] Sucher R, Wagner T, Köhler H, Sucher E, Quice H, Recknagel S, et al. (2022). Hyperspectral Imaging (HSI) of Human Kidney Allografts. Ann Surg, 276(1), e48-e55. https://doi.org/10.1097/sla.0000000000004429
[22] Adani GL, Pravisani R, Tulissi P, Isola M, Calini G, Terrosu G, et al. (2021). Hypothermic machine perfusion can safely prolong cold ischemia time in deceased donor kidney transplantation. A retrospective analysis on postoperative morbidity and graft function. Artif Organs, 45(5), 516-523. https://doi.org/10.1111/aor.13858
[23] Chapal M, Le Borgne F, Legendre C, Kreis H, Mourad G, Garrigue V, et al. (2014). A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors. Kidney Int, 86(6), 1130-1139. https://doi.org/10.1038/ki.2014.188
[24] Sutherland AI, JN IJ, Forsythe JL, & Dor FJ. (2016). Kidney and liver transplantation in the elderly. Br J Surg, 103(2), e62-72. https://doi.org/10.1002/bjs.10064
[25] Meier-Kriesche HU, Arndorfer JA, & Kaplan B. (2002). The impact of body mass index on renal transplant outcomes: a significant independent risk factor for graft failure and patient death. Transplantation, 73(1), 70-74. https://doi.org/10.1097/00007890-200201150-00013
[26] Ladhani M, Lade S, Alexander SI, Baur LA, Clayton PA, McDonald S, et al. (2017). Obesity in pediatric kidney transplant recipients and the risks of acute rejection, graft loss and death. Pediatr Nephrol, 32(8), 1443-1450. https://doi.org/10.1007/s00467-017-3636-1
[27] Sarnak MJ, & Jaber BL. (2001). Pulmonary infectious mortality among patients with end-stage renal disease. Chest, 120(6), 1883-1887. https://doi.org/10.1378/chest.120.6.1883
[28] Elias M, Pievani D, Randoux C, Louis K, Denis B, Delion A, et al. (2020). COVID-19 Infection in Kidney Transplant Recipients: Disease Incidence and Clinical Outcomes. J Am Soc Nephrol, 31(10), 2413-2423. https://doi.org/10.1681/asn.2020050639
[29] Pan J, & Liao G. (2021). Development and Validation of Nomogram for Predicting Delayed Graft Function After Kidney Transplantation of Deceased Donor. Int J Gen Med, 14, 9103-9115. https://doi.org/10.2147/ijgm.S331854
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