@article{TaalStPourcainThieringetal.2012,
  author    = {Taal, H. Rob and St Pourcain, Beate and Thiering, Elisabeth and Das, Shikta and Mook-Kanamori, Dennis O. and Warrington, Nicole M. and Kaakinen, Marika and Kreiner-Moller, Eskil and Bradfield, Jonathan P. and Freathy, Rachel M. and Geller, Frank and Guxens, Monica and Cousminer, Diana L. and Kerkhof, Marjan and Timpson, Nicholas J. and Ikram, M. Arfan and Beilin, Lawrence J. and Bonnelykke, Klaus and Buxton, Jessica L. and Charoen, Pimphen and Chawes, Bo Lund Krogsgaard and Eriksson, Johan and Evans, David M. and Hofman, Albert and Kemp, John P. and Kim, Cecilia E. and Klopp, Norman and Lahti, Jari and Lye, Stephen J. and McMahon, George and Mentch, Frank D. and Mueller-Nurasyid, Martina and O'Reilly, Paul F. and Prokopenko, Inga and Rivadeneira, Fernando and Steegers, Eric A. P. and Sunyer, Jordi and Tiesler, Carla and Yaghootkar, Hanieh and Breteler, Monique M. B. and Debette, Stephanie and Fornage, Myriam and Gudnason, Vilmundur and Launer, Lenore J. and van der Lugt, Aad and Mosley, Thomas H. and Seshadri, Sudha and Smith, Albert V. and Vernooij, Meike W. and Blakemore, Alexandra I. F. and Chiavacci, Rosetta M. and Feenstra, Bjarke and Fernandez-Banet, Julio and Grant, Struan F. A. and Hartikainen, Anna-Liisa and van der Heijden, Albert J. and Iniguez, Carmen and Lathrop, Mark and McArdle, Wendy L. and Molgaard, Anne and Newnham, John P. and Palmer, Lyle J. and Palotie, Aarno and Pouta, Annneli and Ring, Susan M. and Sovio, Ulla and Standl, Marie and Uitterlinden, Andre G. and Wichmann, H-Erich and Vissing, Nadja Hawwa and DeCarli, Charles and van Duijn, Cornelia M. and McCarthy, Mark I. and Koppelman, Gerard H. and Estivill, Xavier and Hattersley, Andrew T. and Melbye, Mads and Bisgaard, Hans and Pennell, Craig E. and Widen, Elisabeth and Hakonarson, Hakon and Smith, George Davey and Heinrich, Joachim and Jarvelin, Marjo-Riitta and Jaddoe, Vincent W. V. and Adair, Linda S. and Ang, Wei and Atalay, Mustafa and van Beijsterveldt, Toos and Bergen, Nienke and Benke, Kelly and Berry, Diane J. and Bradfield, Jonathan P. and Charoen, Pimphen and Coin, Lachlan and Cousminer, Diana L. and Das, Shikta and Davis, Oliver S. P. and Elliott, Paul and Evans, David M. and Feenstra, Bjarke and Flexeder, Claudia and Frayling, Tim and Freathy, Rachel M. and Gaillard, Romy and Geller, Frank and Groen-Blokhuis, Maria and Goh, Liang-Kee and Guxens, Monica and Haworth, Claire M. A. and Hadley, Dexter and Hebebrand, Johannes and Hinney, Anke and Hirschhorn, Joel N. and Holloway, John W. and Holst, Claus and Hottenga, Jouke Jan and Horikoshi, Momoko and Huikari, Ville and Hypponen, Elina and Iniguez, Carmen and Kaakinen, Marika and Kilpelainen, Tuomas O. and Kirin, Mirna and Kowgier, Matthew and Lakka, Hanna-Maaria and Lange, Leslie A. and Lawlor, Debbie A. and Lehtimaki, Terho and Lewin, Alex and Lindgren, Cecilia and Lindi, Virpi and Maggi, Reedik and Marsh, Julie and Middeldorp, Christel and Millwood, Iona and Mook-Kanamori, Dennis O. and Murray, Jeffrey C. and Nivard, Michel and Nohr, Ellen Aagaard and Ntalla, Ioanna and Oken, Emily and O'Reilly, Paul F. and Palmer, Lyle J. and Panoutsopoulou, Kalliope and Pararajasingham, Jennifer and Prokopenko, Inga and Rodriguez, Alina and Salem, Rany M. and Sebert, Sylvain and Siitonen, Niina and Sovio, Ulla and St Pourcain, Beate and Strachan, David P. and Sunyer, Jordi and Taal, H. Rob and Teo, Yik-Ying and Thiering, Elisabeth and Tiesler, Carla and Uitterlinden, Andre G. and Valcarcel, Beatriz and Warrington, Nicole M. and White, Scott and Willemsen, Gonneke and Yaghootkar, Hanieh and Zeggini, Eleftheria and Boomsma, Dorret I. and Cooper, Cyrus and Estivill, Xavier and Gillman, Matthew and Grant, Struan F. A. and Hakonarson, Hakon and Hattersley, Andrew T. and Heinrich, Joachim and Hocher, Berthold and Jaddoe, Vincent W. V. and Jarvelin, Marjo-Riitta and Lakka, Timo A. and McCarthy, Mark I. and Melbye, Mads and Mohlke, Karen L. and Dedoussis, George V. and Ong, Ken K. and Pearson, Ewan R. and Pennell, Craig E. and Price, Thomas S. and Power, Chris and Raitakari, Olli T. and Saw, Seang-Mei and Scherag, Andre and Simell, Olli and Sorensen, Thorkild I. A. and Timpson, Nicholas J. and Widen, Elisabeth and Wilson, James F. and Ang, Wei and van Beijsterveldt, Toos and Bergen, Nienke and Benke, Kelly and Berry, Diane J. and Bradfield, Jonathan P. and Charoen, Pimphen and Coin, Lachlan and Cousminer, Diana L. and Das, Shikta and Elliott, Paul and Evans, David M. and Frayling, Tim and Freathy, Rachel M. and Gaillard, Romy and Groen-Blokhuis, Maria and Guxens, Monica and Hadley, Dexter and Hottenga, Jouke Jan and Huikari, Ville and Hypponen, Elina and Kaakinen, Marika and Kowgier, Matthew and Lawlor, Debbie A. and Lewin, Alex and Lindgren, Cecilia and Marsh, Julie and Middeldorp, Christel and Millwood, Iona and Mook-Kanamori, Dennis O. and Nivard, Michel and O'Reilly, Paul F. and Palmer, Lyle J. and Prokopenko, Inga and Rodriguez, Alina and Sebert, Sylvain and Sovio, Ulla and St Pourcain, Beate and Standl, Marie and Strachan, David P. and Sunyer, Jordi and Taal, H. Rob and Thiering, Elisabeth and Tiesler, Carla and Uitterlinden, Andre G. and Valcarcel, Beatriz and Warrington, Nicole M. and White, Scott and Willemsen, Gonneke and Yaghootkar, Hanieh and Boomsma, Dorret I. and Estivill, Xavier and Grant, Struan F. A. and Hakonarson, Hakon and Hattersley, Andrew T. and Heinrich, Joachim and Jaddoe, Vincent W. V. and Jarvelin, Marjo-Riitta and McCarthy, Mark I. and Pennell, Craig E. and Power, Chris and Timpson, Nicholas J. and Widen, Elisabeth and Ikram, M. Arfan and Fornage, Myriam and Smith, Albert V. and Seshadri, Sudha and Schmidt, Reinhold and Debette, Stephanie and Vrooman, Henri A. and Sigurdsson, Sigurdur and Ropele, Stefan and Coker, Laura H. and Longstreth, W. T. and Niessen, Wiro J. and DeStefano, Anita L. and Beiser, Alexa and Zijdenbos, Alex P. and Struchalin, Maksim and Jack, Clifford R. and Nalls, Mike A. and Au, Rhoda and Hofman, Albert and Gudnason, Haukur and van der Lugt, Aad and Harris, Tamara B. and Meeks, William M. and Vernooij, Meike W. and van Buchem, Mark A. and Catellier, Diane and Gudnason, Vilmundur and Windham, B. Gwen and Wolf, Philip A. and van Duijn, Cornelia M. and Mosley, Thomas H. and Schmidt, Helena and Launer, Lenore J. and Breteler, Monique M. B. and DeCarli, Charles},
  title     = {Common variants at 12q15 and 12q24 are associated with infant head circumference},
  series = {Nature genetics},
  volume    = {44},
  journal   = {Nature genetics},
  number    = {5},
  publisher = {Nature Publ. Group},
  address   = {New York},
  organization    = {Cohorts Heart Aging Res Genetic Ep, Early Genetics Lifecourse Epidemio, Early Growth Genetics EGG Consorti},
  issn      = {1061-4036},
  doi       = {10.1038/ng.2238},
  pages     = {532 -- +},
  year      = {2012},
  abstract  = {To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 x 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 x 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height(1), their effects on infant head circumference were largely independent of height (P = 3.8 x 10(-7) for rs7980687 and P = 1.3 x 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 x 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume(2), Parkinson's disease and other neurodegenerative diseases(3-5), indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.},
  language  = {en}
}
@article{VaidSomaniRussaketal.2020,
  author    = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.},
  title     = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation},
  series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  volume    = {22},
  journal   = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  number    = {11},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/24018},
  pages     = {19},
  year      = {2020},
  abstract  = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.},
  language  = {en}
}