Development of a Severity Classification System for Sickle Cell Disease

References

• Quinn CT. Minireview: clinical severity in sickle cell disease: the challenges of definition and prognostication. Exp Biol Med. 2016;241(7):679–688. doi:10.1177/1535370216640385
   PubMed Web of Science ®Google Scholar
• Miller ST, Sleeper LA, Pegelow CH, et al. Prediction of adverse outcomes in children with sickle cell disease. N Engl J Med. 2000;342(2):83–89. doi:10.1056/NEJM200001133420203
   PubMed Web of Science ®Google Scholar
• Day SW. Development and evaluation of a sickle cell disease assessment instrument. Pediatr Nurs. 2004;30(6).
   Google Scholar
• Sebastiani P, Nolan VG, Baldwin CT, et al. A network model to predict the risk of death in sickle cell disease. Blood. 2007;110(7):2727–2735. doi:10.1182/blood-2007-04-084921
   PubMed Web of Science ®Google Scholar
• van den Tweel XW, van der Lee JH, Heijboer H, Peters M, Fijnvandraat K. Development and validation of a pediatric severity index for sickle cell patients. Am J Hematol. 2010;85(10):746–751. doi:10.1002/ajh.21846
   PubMed Web of Science ®Google Scholar
• Hunt SA, Abraham WT, Chin MH, et al. ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in the adult: a report of the American college of cardiology/American heart association task force on practice guidelines (writing committee to update the 2001 guidelines for the evaluation and management of heart failure): developed in collaboration with the American college of chest physicians and the international society for heart and lung transplantation: endorsed by the heart rhythm society. Circulation. 2005;112(12):e154–235. doi:10.1161/CIRCULATIONAHA.105.167586
   PubMed Web of Science ®Google Scholar
• Lim FY, Yap J, Gao F, Teo LL, Lam CSP, Yeo KK. Correlation of the New York Heart Association classification and the cardiopulmonary exercise test: a systematic review. Int J Cardiol. 2018;263:88–93. doi:10.1016/j.ijcard.2018.04.021
   Web of Science ®Google Scholar
• Fink A, Kosecoff J, Chassin M, Brook RH. Consensus methods: characteristics and guidelines for use. Am J Public Health. 1984;74(9):979–983. doi:10.2105/ajph.74.9.979
   PubMed Web of Science ®Google Scholar
• Campbell SM, Braspenning J, Hutchinson A, Marshall M. Research methods used in developing and applying quality indicators in primary care. Qual Saf Health Care. 2002;11(4):358–364. doi:10.1136/qhc.11.4.358
   PubMed Web of Science ®Google Scholar
• Fitch K, ed. The Rand/UCLA Appropriateness Method User’s Manual. Rand; 2001.
   Google Scholar
• National Institutes of Health, National Heart, Lung, and Blood Institute. Evidence-based management of sickle cell disease: expert panel report. 2014:161.
   Google Scholar
• Meier ER, Fasano RM, Levett PR. A systematic review of the literature for severity predictors in children with sickle cell anemia. Blood Cells Mol Dis. 2017;65:86–94. doi:10.1016/j.bcmd.2017.01.014
   PubMed Web of Science ®Google Scholar
• Maitra P, Caughey M, Robinson L, et al. Risk factors for mortality in adult patients with sickle cell disease: a meta-analysis of studies in North America and Europe. Haematologica. 2017;102(4):626–636. doi:10.3324/haematol.2016.153791
   PubMed Web of Science ®Google Scholar
• Dampier C, Palermo TM, Darbari DS, Hassell K, Smith W, Zempsky W. AAPT diagnostic criteria for chronic sickle cell disease pain. J Pain. 2017;18(5):490–498. doi:10.1016/j.jpain.2016.12.016
   PubMed Web of Science ®Google Scholar
• Ballas SK, Lusardi M. Hospital readmission for adult acute sickle cell painful episodes: frequency, etiology, and prognostic significance. Am J Hematol. 2005;79(1):17–25. doi:10.1002/ajh.20336
   PubMed Web of Science ®Google Scholar
• Ballas SK, Lieff S, Benjamin LJ, et al. Definitions of the phenotypic manifestations of sickle cell disease. Am J Hematol. 2010;85:6–13. doi:10.1002/ajh.21550
   PubMed Web of Science ®Google Scholar
• van Beers EJ, van Tuijn CFJ, Mac Gillavry MR, et al. Sickle cell disease-related organ damage occurs irrespective of pain rate: implications for clinical practice. Haematologica. 2008;93(5):757–760. doi:10.3324/haematol.12152
   PubMed Web of Science ®Google Scholar
• Sogutlu A, Levenson JL, McClish DK, Rosef SD, Smith WR. Somatic symptom burden in adults with sickle cell disease predicts pain, depression, anxiety, health care utilization, and quality of life: the PiSCES project. Psychosomatics. 2011;52(3):272–279. doi:10.1016/j.psym.2011.01.010
   PubMed Web of Science ®Google Scholar
• Karafin MS, Singavi A, Hussain J, et al. Predictive factors of daily opioid use and quality of life in adults with sickle cell disease. Hematology. 2018;23(10):856–863. doi:10.1080/10245332.2018.1479997
   PubMed Web of Science ®Google Scholar
• Platt OS, Brambilla DJ, Rosse WF, et al. Mortality in sickle cell disease. Life expectancy and risk factors for early death. N Engl J Med. 1994;330(23):1639–1644. doi:10.1056/NEJM199406093302303
   PubMed Web of Science ®Google Scholar
• Lanzkron S, Little J, Field J, et al. Increased acute care utilization in a prospective cohort of adults with sickle cell disease. Blood Adv. 2018;2(18):2412–2417. doi:10.1182/bloodadvances.2018018382
   PubMed Web of Science ®Google Scholar
• Doust JA, Bell KJL, Glasziou PP. Potential consequences of changing disease classifications. JAMA. 2020;323(10):921–922. doi:10.1001/jama.2019.22373
   Web of Science ®Google Scholar
• Yap J, Lim FY, Gao F, Teo LL, Lam CSP, Yeo KK. Correlation of the New York heart association classification and the 6-minute walk distance: a systematic review: correlation of NYHA and 6MWD. Clin Cardiol. 2015;38(10):621–628. doi:10.1002/clc.22468
   Web of Science ®Google Scholar
• Aalbers J, O’Brien KK, Chan W-S, et al. Predicting streptococcal pharyngitis in adults in primary care: a systematic review of the diagnostic accuracy of symptoms and signs and validation of the centor score. BMC Med. 2011;9(1):67. doi:10.1186/1741-7015-9-67
   Web of Science ®Google Scholar
• Boulkedid R, Abdoul H, Loustau M, Sibony O, Alberti C. Using and reporting the delphi method for selecting healthcare quality indicators: a systematic review. PLoS One. 2011;6(6):e20476. doi:10.1371/journal.pone.0020476
   PubMed Web of Science ®Google Scholar
• Kravitz RL, Laouri M, Kahan JP, et al. Validity of criteria used for detecting underuse of coronary revascularization. JAMA. 1995;274(8):632–638. doi:10.1001/jama.1995.03530080048040
   PubMed Web of Science ®Google Scholar
• Shekelle PG, Kahan JP, Bernstein SJ, Leape LL, Kamberg CJ, Park RE. The reproducibility of a method to identify the overuse and underuse of medical procedures. N Engl J Med. 1998;338(26):1888–1895. doi:10.1056/NEJM199806253382607
   PubMed Web of Science ®Google Scholar