A phenotypic risk score for predicting mortality in sickle cell disease.
A phenotypic risk score for predicting mortality in sickle cell disease.
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一种用于预测镰状细胞病死亡率的表型风险评分
DOI：
10.1111/bjh.17342
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发表时间：
2021-03
影响因子：
6.5
通讯作者：
Thein, Swee Lay
中科院分区：
文献类型：
--
作者：
Sachdev, Vandana;Tian, Xin;Gu, Yuan;Nichols, James;Sidenko, Stanislav;Li, Wen;Beri, Andrea;Layne, W. Austin;Allen, Darlene;Wu, Colin O.;Thein, Swee Lay
镰状细胞病（SCD）患者的风险评估仍然具有挑战性，因为它取决于个体医生整合多种检测结果的经验和能力。我们旨在提供一种结合临床、实验室和影像学数据的新风险评分。在一个包含600名成年SCD患者的前瞻性队列中，我们评估了70个基线协变量与全因死亡率的关系。使用随机生存森林和正则化Cox回归机器学习（ML）方法来选择主要预测因子。开发了多变量模型和风险评分，并进行了内部验证。在中位随访4.3年期间，记录了131例死亡。使用9个独立的死亡预测因子开发了多变量模型：三尖瓣反流速度、估计的右心房压力、二尖瓣E峰速度、左心室间隔厚度、体重指数、血尿素氮、碱性磷酸酶、心率和年龄。我们的预后风险评分表现优异，校正偏倚后的C统计量为0.763。我们的模型将患者分为4组，其4年死亡率显著不同（分别为3%、11%、35%和75%）。利用SCD患者容易获得的变量，我们应用ML技术开发并验证了一种反映心肺、肾和肝终末器官损害总和的死亡风险评分方法。
ClinicalTrials.gov标识符：NCT#00011648
Risk assessment for patients with sickle cell disease (SCD) remains challenging as it depends on an individual physician’s experience and ability to integrate a variety of test results. We aimed to provide a new risk score that combines clinical, laboratory, and imaging data. In a prospective cohort of 600 adult patients with SCD, we assessed the relationship of 70 baseline covariates to all-cause mortality. Random survival forest and regularised Cox regression machine learning (ML) methods were used to select top predictors. Multivariable models and a risk score were developed and internally validated. Over a median follow-up of 4·3 years, 131 deaths were recorded. Multivariable models were developed using nine independent predictors of mortality: tricuspid regurgitant velocity, estimated right atrial pressure, mitral E velocity, left ventricular septal thickness, body mass index, blood urea nitrogen, alkaline phosphatase, heart rate and age. Our prognostic risk score had superior performance with a bias-corrected C-statistic of 0·763. Our model stratified patients into four groups with significantly different 4-year mortality rates (3%, 11%, 35% and 75% respectively). Using readily available variables from patients with SCD, we applied ML techniques to develop and validate a mortality risk scoring method that reflects the summation of cardiopulmonary, renal and liver end-organ damage. ClinicalTrials.gov Identifier: NCT#00011648.
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影响因子：
20.1
作者：
Ambale-Venkatesh B;Yang X;Wu CO;Liu K;Hundley WG;McClelland R;Gomes AS;Folsom AR;Shea S;Guallar E;Bluemke DA;Lima JAC
通讯作者：
Lima JAC
DOI：
10.1056/nejmoa1414799
发表时间：
2015-07-02
期刊：
The New England journal of medicine
影响因子：
--
作者：
Yoshizato T;Dumitriu B;Hosokawa K;Makishima H;Yoshida K;Townsley D;Sato-Otsubo A;Sato Y;Liu D;Suzuki H;Wu CO;Shiraishi Y;Clemente MJ;Kataoka K;Shiozawa Y;Okuno Y;Chiba K;Tanaka H;Nagata Y;Katagiri T;Kon A;Sanada M;Scheinberg P;Miyano S;Maciejewski JP;Nakao S;Young NS;Ogawa S
通讯作者：
Ogawa S
影响因子：
5.1
作者：
Theocharidou, Eleni;Suddle, Abid R.
通讯作者：
Suddle, Abid R.
影响因子：
9.3
作者：
Sachdev, Vandana;Rosing, Douglas R.;Lay, Swee
通讯作者：
Lay, Swee
DOI：
10.14740/jocmr3137w
发表时间：
2017-10-01
期刊：
Journal of clinical medicine research
影响因子：
--
作者：
Ballas, Samir K
通讯作者：
Ballas, Samir K