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| 1 | Model combining pre-transplant tumor biomarkers and tumor size shows more utility in predicting hepatocellular carcinoma recurrence and survival than the BALAD models显示文摘AIM To assess the performance of BALAD, BALAD-2 and their component biomarkers in predicting outcome of hepatocellular carcinoma(HCC) patients after liver transplant.METHODS BALAD score and BALAD-2 class are derived from bilirubin, albumin, alpha-fetoprotein(AFP), Lens culinaris agglutinin-reactive AFP(AFP-L3), and des-gammacarboxyprothrombin(DCP). Pre-transplant AFP, AFP-L3 and DCP were measured in 113 patients transplanted for HCC from 2000 to 2008. Hazard ratios(HR) for recurrence and death were calculated. Univariate and multivariate regression analyses were conducted. C-statistics were used to compare biomarker-based to predictive models. RESULTS During a median follow-up of 12.2 years, 38 patients recurred and 87 died. The HRs for recurrence in patients with elevated AFP, AFP-L3, and DCP defined by BALAD cut-off values were 2.42(1.18-5.00), 1.86(0.98-3.52), and 2.83(1.42-5.61), respectively. For BALAD, the HRs for recurrence and death per unit increased score were 1.48(1.15-1.91) and 1.59(1.28-1.97). For BALAD-2, the HRs for recurrence and death per unit increased class were 1.45(1.06-1.98) and 1.38(1.09-1.76). For recurrence prediction, the combination of three biomarkers had the highest c-statistic of 0.66 vs. 0.64, 0.61, 0.53, and 0.53 for BALAD, BALAD-2, Milan, and UCSF, respectively. Similarly, for death prediction, the combination of three biomarkers had the highest c-statistic of 0.66 vs 0.65,0.61, 0.52, and 0.50 for BALAD, BALAD-2, Milan, and UCSF. A new model combining biomarkers with tumor size at the time of transplant(S-LAD) demonstrated the highest predictive capability with c-statistics of 0.71 and 0.69 for recurrence and death. CONCLUSION BALAD and BALAD-2 are valid in transplant HCC patients, but less predictive than the three biomarkers in combination or the three biomarkers in combination with maximal tumor diameter(S-LAD). | Nicha Wongjarupong Gabriela M Negron-Ocasio Roongruedee Chaiteerakij Benyam D Addissie Essa A Mohamed Kristin C Mara William S Harmsen J Paul Theobald Brian E Peters Joseph G Balsanek Melissa M Ward Nasra H Giama Sudhakar K Venkatesh Denise M Harnois Michael R Charlton Hiroyuki Yamada Alicia Algeciras-Schimnich Melissa R Snyder Terry M Therneau Lewis R Roberts | 2018 | World Journal of Gastroenterology2018，24，12： | 5 |
| 2 | Efficacy of boceprevir, an NS3 protease inhibitor, in combination with peginterferon alfa-2b and ribavirin in treatment-naive patients with genotype 1 hepatitis C infection (SPRINT-1): an open-label, randomised, multicentre phase 2 trial显示文摘 | Paul Y Kwo Eric J Lawitz Jonathan McCone Eugene R Schiff John M Vierling David Pound Mitchell N Davis Joseph S Galati Stuart C Gordon Natarajan Ravendhran Lorenzo Rossaro Frank H Anderson Ira M Jacobson Raymond Rubin Kenneth Koury Lisa D Pedicone Clifford | 2010 | The Lancet2010，，9742： | 3 |
| 3 | 一种房颤风险评分系统的建立(Framingham心脏研究):基于社区的队列研究显示文摘背景房颤导致了发病率和病死率的显著上升。本研究旨在建立一种预测个体罹患房颤绝对风险的风险评分系统,并提供研究人员评价新危险因素的流程。方法作者评估了Framingham心脏研究中于1968年6月至1987年9月间进行了8044次检测的4764例参与者(55%为女性,年龄45~95岁)。此后,参与者被随访至房颤首发,随访期最长达10年。多变量Coxi回归确认出1(1年内罹患房颤的临床危险因素。次级分析纳入了常规超声心动图检测指标(5152例4参与者,7156次检测)对房颤风险进行再分层评估,并评价超声检测指标能否提高风险预测能力。结果4764例参与者中的457例4(10%)罹患房颤。年龄、性别、体重指数、收缩压、降压治疗、PR间期、有临床意义的心脏杂音及心力衰竭与房颤相关,并被纳入了风险评分模型(除体重指数P=0.08外,其余均为P〈0.05),模型的C统计量为0.78(95%CI0.76~0.80)。10年房颤风险随年龄变化:年龄〈65岁的人群中53例(1%)风险高于15%,而〉65岁的人群中为783例(27%)。为提高预测能力而纳入超声检测指标仅使模型C统计量略微增高,由0.78(95%C10.75~0.80)增至0.79(95%CI0.77~0.82:P=0.005)。超声心动图检测指标并不能改善风险再分层评估(P=0.18)。结论基于社区医疗中易得的临床因素建立的风险评分系统,有助于确认社区个体罹患房颤的风险,评估技术或标志物能否改善风险预测,以及针对高危个体采取预防措施。 | Renate B Schnabel Lisa M Sullivan Daniel Levy Michael J Pencina Joseph M Massaro Ralph B D'Agostino Sr Christopher Newton-Cheh Jennifer F Yamamoto Jared W Magnani Thomas M Tadros William B Kannel Thomas J Wang Patrick T Ellinor Philip A Wolf Ramachanclran S Vasan Emelia J Benjamin 黄刚(译) | 2009 | 世界临床医学2009，，9： | 2 |
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