Developing a resiliency model for survival without major morbidity in preterm infants.
Developing a resiliency model for survival without major morbidity in preterm infants.
复制标题
DOI：
10.1038/s41372-022-01521-3
复制
发表时间：
2023-04
影响因子：
2.9
通讯作者：
Rogers, Elizabeth E.
中科院分区：
文献类型：
--
作者：
Steurer, Martina A.;Ryckman, Kelli K.;Baer, Rebecca J.;Costello, Jean;Oltman, Scott P.;McCulloch, Charles E.;Jelliffe-Pawlowski, Laura L.;Rogers, Elizabeth E.
使用机器学习开发并验证弹性评分，以预测妊娠<32周的早产儿的存活率和无新生儿发病率的存活率。使用孕产妇，围产期和新生儿变量的模型开发使用LASSO方法在人口为基础的加州行政数据集。结局为存活和无严重新生儿发病率的存活。在推导和来自三级护理中心的外部数据集中评估了歧视。内部验证数据集中的区分度极好，生存率的c-统计量为0.895（95% CI 0.882-0.908），无严重新生儿发病率的生存率的c-统计量为0.867（95% CI 0.857-0.877）。外部验证数据集中的区分度仍然很高（c-统计量分别为0.817，CI 0.741-0.893和0.804，CI 0.770-0.837）。我们成功地预测了存活率和无重大发病率的早产儿出生在<32周。此分数可用于调整管理数据集的多个变量。
Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning. Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center. Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882–0.908) for survival and 0.867 (95% CI 0.857–0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741–0.893 and 0.804, CI 0.770–0.837, respectively). Our successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets.
登录
查看更多内容
影响因子：
64.8
作者：
Mallick, Swapan;Li, Heng;Lipson, Mark;Mathieson, Iain;Gymrek, Melissa;Racimo, Fernando;Zhao, Mengyao;Chennagiri, Niru;Nordenfelt, Susanne;Tandon, Arti;Skoglund, Pontus;Lazaridis, Iosif;Sankararaman, Sriram;Fu, Qiaomei;Rohland, Nadin;Renaud, Gabriel;Erlich, Yaniv;Willems, Thomas;Gallo, Carla;Spence, Jeffrey P.;Song, Yun S.;Poletti, Giovanni;Balloux, Francois;van Driem, George;de Knijff, Peter;Romero, Irene Gallego;Jha, Aashish R.;Behar, Doron M.;Bravi, Claudio M.;Capelli, Cristian;Hervig, Tor;Moreno-Estrada, Andres;Posukh, Olga L.;Balanovska, Elena;Balanovsky, Oleg;Karachanak-Yankova, Sena;Sahakyan, Hovhannes;Toncheva, Draga;Yepiskoposyan, Levon;Tyler-Smith, Chris;Xue, Yali;Abdullah, M. Syafiq;Ruiz-Linares, Andres;Beall, Cynthia M.;Di Rienzo, Anna;Jeong, Choongwon;Starikovskaya, Elena B.;Metspalu, Ene;Parik, Juri;Villems, Richard;Henn, Brenna M.;Hodoglugil, Ugur;Mahley, Robert;Sajantila, Antti;Stamatoyannopoulos, George;Wee, Joseph T. S.;Khusainova, Rita;Khusnutdinova, Elza;Litvinov, Sergey;Ayodo, George;Comas, David;Hammer, Michael F.;Kivisild, Toomas;Klitz, William;Winkler, Cheryl A.;Labuda, Damian;Bamshad, Michael;Jorde, Lynn B.;Tishkoff, Sarah A.;Watkins, W. Scott;Metspalu, Mait;Dryomov, Stanislav;Sukernik, Rem;Singh, Lalji;Thangaraj, Kumarasamy;Paeaebo, Svante;Kelso, Janet;Patterson, Nick;Reich, David
通讯作者：
Reich, David
DOI：
10.14366/usg.16045
发表时间：
2018-01
期刊：
Ultrasonography (Seoul, Korea)
影响因子：
--
作者：
Kim SM;Kim Y;Jeong K;Jeong H;Kim J
通讯作者：
Kim J
影响因子：
8
作者：
Lemmon, Monica E.;Huffstetler, Hanna;Brandon, Debra
通讯作者：
Brandon, Debra
影响因子：
9.8
作者：
Jelliffe-Pawlowski, Laura L.;Norton, Mary E.;Currier, Robert J.
通讯作者：
Currier, Robert J.
影响因子：
3.6
作者：
Haumont, Dominique;Modi, Neena;Aelvoet, Willem
通讯作者：
Aelvoet, Willem