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New prediction tool-LIST-with improved prediction accuracy for 30-day readmission rates in patients with head and neck cancer after major cancer surgery.
Oral Oncology ( IF 4.8 ) Pub Date : 2020-05-24 , DOI: 10.1016/j.oraloncology.2020.104772
Chun-Hao Yin , Bor-Hwang Kang , Wen-Shan Liu , Li-Fei Pan , Hsiu-Min Chen , Ching-Chih Lee

We hope to establish a new readmission prediction score for patients with head and neck cancer (HNC) after major cancer surgery. A retrospective cohort study was conducted from the clinical and cancer registry data at Kaohsiung Veterans General Hospital. We included the data of patients with newly diagnosed HNC who underwent surgical treatment between Nov 2010 and Dec 2017. Multivariate logistic regression was performed to determine independent factors for 30-day readmission rate and establish a new prediction score. We compared the discriminatory ability of our new prediction score, HOSPTIAL score, and LACE index using linear trend chi-square test, the Akaike information criterion (AIC), and c-statistic. The 487 patients with HNC who underwent major surgery were discharged from the medical center. Of these patients, 40 (8.2%) were readmitted to the same hospital within 30 days. Our prediction score, namely LIST (representing leukocytosis, Charlson comorbidity index score of > 0, length of stay of top 33% for the total population, and advanced tumor stage) was derived through multivariate logistic regression. Compared with the HOSPITAL score and LACE index, LIST had a higher linear trend chi-square value (27.8 vs 4.3 and 6.3), higher prediction accuracy (0.743 vs 0.586 and 0.589), and lower AIC value (251 vs 274 and 272). The LIST can estimate 30-day readmission rates in patients with HNC. More intensive discharge planning and transition of care along with patient education can be applied to this high-risk group before discharge.



中文翻译:

新的预测工具LIST-具​​有改善的重大癌症手术后头颈癌患者30天再入院率的预测准确性。

我们希望为重大癌症手术后的头颈癌(HNC)患者建立新的再入院预测评分。根据高雄荣民总医院的临床和癌症登记数据进行了一项回顾性队列研究。我们纳入了2010年11月至2017年12月之间接受手术治疗的新诊断HNC患者的数据。进行了多因素logistic回归分析以确定30天再入院率的独立因素并建立新的预测评分。我们使用线性趋势卡方检验,Akaike信息标准(AIC)和c统计量,比较了新预测得分,HOSPTIAL得分和LACE指数的判别能力。接受大手术的487例HNC患者已从医疗中心出院。在这些患者中,有40(8。2%)在30天内再次进入同一家医院。我们的预测得分是LIST(代表白细胞增多,Charlson合并症指数得分> 0,在总人群中居前33%的住院时间以及晚期肿瘤分期)是通过多因素logistic回归得出的。与医院得分和LACE指数相比,LIST的线性趋势卡方值更高(27.8 vs 4.3和6.3),更高的预测准确性(0.743 vs 0.586和0.589)和更低的AIC值(251 vs 274和272)。LIST可以估计HNC患者30天的再入院率。在出院前,可以对这个高风险人群进行更深入的出院计划和护理过渡以及患者教育。通过多因素logistic回归得出总住院时间的前33%(肿瘤晚期)。与医院得分和LACE指数相比,LIST的线性趋势卡方值更高(27.8 vs 4.3和6.3),更高的预测准确性(0.743 vs 0.586和0.589)和更低的AIC值(251 vs 274和272)。LIST可以估计HNC患者30天的再入院率。在出院前,可以对这个高风险人群进行更深入的出院计划和护理过渡以及患者教育。通过多因素logistic回归得出总住院时间的前33%(肿瘤晚期)。与医院得分和LACE指数相比,LIST的线性趋势卡方值更高(27.8 vs 4.3和6.3),更高的预测准确性(0.743 vs 0.586和0.589)和更低的AIC值(251 vs 274和272)。LIST可以估计HNC患者30天的再入院率。在出院前,可以对这个高风险人群进行更深入的出院计划和护理过渡以及患者教育。586和0.589),以及更低的AIC值(251和274和272)。LIST可以估计HNC患者30天的再入院率。在出院前,可以对这个高风险人群进行更深入的出院计划和护理过渡以及患者教育。586和0.589),以及更低的AIC值(251和274和272)。LIST可以估计HNC患者30天的再入院率。出院前,可以对这个高风险人群进行更深入的出院计划和护理过渡以及患者教育。

更新日期:2020-05-24
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