TY  - JOUR
A1  - Taal, H. Rob
A1  - St Pourcain, Beate
A1  - Thiering, Elisabeth
A1  - Das, Shikta
A1  - Mook-Kanamori, Dennis O.
A1  - Warrington, Nicole M.
A1  - Kaakinen, Marika
A1  - Kreiner-Moller, Eskil
A1  - Bradfield, Jonathan P.
A1  - Freathy, Rachel M.
A1  - Geller, Frank
A1  - Guxens, Monica
A1  - Cousminer, Diana L.
A1  - Kerkhof, Marjan
A1  - Timpson, Nicholas J.
A1  - Ikram, M. Arfan
A1  - Beilin, Lawrence J.
A1  - Bonnelykke, Klaus
A1  - Buxton, Jessica L.
A1  - Charoen, Pimphen
A1  - Chawes, Bo Lund Krogsgaard
A1  - Eriksson, Johan
A1  - Evans, David M.
A1  - Hofman, Albert
A1  - Kemp, John P.
A1  - Kim, Cecilia E.
A1  - Klopp, Norman
A1  - Lahti, Jari
A1  - Lye, Stephen J.
A1  - McMahon, George
A1  - Mentch, Frank D.
A1  - Mueller-Nurasyid, Martina
A1  - O'Reilly, Paul F.
A1  - Prokopenko, Inga
A1  - Rivadeneira, Fernando
A1  - Steegers, Eric A. P.
A1  - Sunyer, Jordi
A1  - Tiesler, Carla
A1  - Yaghootkar, Hanieh
A1  - Breteler, Monique M. B.
A1  - Debette, Stephanie
A1  - Fornage, Myriam
A1  - Gudnason, Vilmundur
A1  - Launer, Lenore J.
A1  - van der Lugt, Aad
A1  - Mosley, Thomas H.
A1  - Seshadri, Sudha
A1  - Smith, Albert V.
A1  - Vernooij, Meike W.
A1  - Blakemore, Alexandra I. F.
A1  - Chiavacci, Rosetta M.
A1  - Feenstra, Bjarke
A1  - Fernandez-Banet, Julio
A1  - Grant, Struan F. A.
A1  - Hartikainen, Anna-Liisa
A1  - van der Heijden, Albert J.
A1  - Iniguez, Carmen
A1  - Lathrop, Mark
A1  - McArdle, Wendy L.
A1  - Molgaard, Anne
A1  - Newnham, John P.
A1  - Palmer, Lyle J.
A1  - Palotie, Aarno
A1  - Pouta, Annneli
A1  - Ring, Susan M.
A1  - Sovio, Ulla
A1  - Standl, Marie
A1  - Uitterlinden, Andre G.
A1  - Wichmann, H-Erich
A1  - Vissing, Nadja Hawwa
A1  - DeCarli, Charles
A1  - van Duijn, Cornelia M.
A1  - McCarthy, Mark I.
A1  - Koppelman, Gerard H.
A1  - Estivill, Xavier
A1  - Hattersley, Andrew T.
A1  - Melbye, Mads
A1  - Bisgaard, Hans
A1  - Pennell, Craig E.
A1  - Widen, Elisabeth
A1  - Hakonarson, Hakon
A1  - Smith, George Davey
A1  - Heinrich, Joachim
A1  - Jarvelin, Marjo-Riitta
A1  - Jaddoe, Vincent W. V.
A1  - Adair, Linda S.
A1  - Ang, Wei
A1  - Atalay, Mustafa
A1  - van Beijsterveldt, Toos
A1  - Bergen, Nienke
A1  - Benke, Kelly
A1  - Berry, Diane J.
A1  - Bradfield, Jonathan P.
A1  - Charoen, Pimphen
A1  - Coin, Lachlan
A1  - Cousminer, Diana L.
A1  - Das, Shikta
A1  - Davis, Oliver S. P.
A1  - Elliott, Paul
A1  - Evans, David M.
A1  - Feenstra, Bjarke
A1  - Flexeder, Claudia
A1  - Frayling, Tim
A1  - Freathy, Rachel M.
A1  - Gaillard, Romy
A1  - Geller, Frank
A1  - Groen-Blokhuis, Maria
A1  - Goh, Liang-Kee
A1  - Guxens, Monica
A1  - Haworth, Claire M. A.
A1  - Hadley, Dexter
A1  - Hebebrand, Johannes
A1  - Hinney, Anke
A1  - Hirschhorn, Joel N.
A1  - Holloway, John W.
A1  - Holst, Claus
A1  - Hottenga, Jouke Jan
A1  - Horikoshi, Momoko
A1  - Huikari, Ville
A1  - Hypponen, Elina
A1  - Iniguez, Carmen
A1  - Kaakinen, Marika
A1  - Kilpelainen, Tuomas O.
A1  - Kirin, Mirna
A1  - Kowgier, Matthew
A1  - Lakka, Hanna-Maaria
A1  - Lange, Leslie A.
A1  - Lawlor, Debbie A.
A1  - Lehtimaki, Terho
A1  - Lewin, Alex
A1  - Lindgren, Cecilia
A1  - Lindi, Virpi
A1  - Maggi, Reedik
A1  - Marsh, Julie
A1  - Middeldorp, Christel
A1  - Millwood, Iona
A1  - Mook-Kanamori, Dennis O.
A1  - Murray, Jeffrey C.
A1  - Nivard, Michel
A1  - Nohr, Ellen Aagaard
A1  - Ntalla, Ioanna
A1  - Oken, Emily
A1  - O'Reilly, Paul F.
A1  - Palmer, Lyle J.
A1  - Panoutsopoulou, Kalliope
A1  - Pararajasingham, Jennifer
A1  - Prokopenko, Inga
A1  - Rodriguez, Alina
A1  - Salem, Rany M.
A1  - Sebert, Sylvain
A1  - Siitonen, Niina
A1  - Sovio, Ulla
A1  - St Pourcain, Beate
A1  - Strachan, David P.
A1  - Sunyer, Jordi
A1  - Taal, H. Rob
A1  - Teo, Yik-Ying
A1  - Thiering, Elisabeth
A1  - Tiesler, Carla
A1  - Uitterlinden, Andre G.
A1  - Valcarcel, Beatriz
A1  - Warrington, Nicole M.
A1  - White, Scott
A1  - Willemsen, Gonneke
A1  - Yaghootkar, Hanieh
A1  - Zeggini, Eleftheria
A1  - Boomsma, Dorret I.
A1  - Cooper, Cyrus
A1  - Estivill, Xavier
A1  - Gillman, Matthew
A1  - Grant, Struan F. A.
A1  - Hakonarson, Hakon
A1  - Hattersley, Andrew T.
A1  - Heinrich, Joachim
A1  - Hocher, Berthold
A1  - Jaddoe, Vincent W. V.
A1  - Jarvelin, Marjo-Riitta
A1  - Lakka, Timo A.
A1  - McCarthy, Mark I.
A1  - Melbye, Mads
A1  - Mohlke, Karen L.
A1  - Dedoussis, George V.
A1  - Ong, Ken K.
A1  - Pearson, Ewan R.
A1  - Pennell, Craig E.
A1  - Price, Thomas S.
A1  - Power, Chris
A1  - Raitakari, Olli T.
A1  - Saw, Seang-Mei
A1  - Scherag, Andre
A1  - Simell, Olli
A1  - Sorensen, Thorkild I. A.
A1  - Timpson, Nicholas J.
A1  - Widen, Elisabeth
A1  - Wilson, James F.
A1  - Ang, Wei
A1  - van Beijsterveldt, Toos
A1  - Bergen, Nienke
A1  - Benke, Kelly
A1  - Berry, Diane J.
A1  - Bradfield, Jonathan P.
A1  - Charoen, Pimphen
A1  - Coin, Lachlan
A1  - Cousminer, Diana L.
A1  - Das, Shikta
A1  - Elliott, Paul
A1  - Evans, David M.
A1  - Frayling, Tim
A1  - Freathy, Rachel M.
A1  - Gaillard, Romy
A1  - Groen-Blokhuis, Maria
A1  - Guxens, Monica
A1  - Hadley, Dexter
A1  - Hottenga, Jouke Jan
A1  - Huikari, Ville
A1  - Hypponen, Elina
A1  - Kaakinen, Marika
A1  - Kowgier, Matthew
A1  - Lawlor, Debbie A.
A1  - Lewin, Alex
A1  - Lindgren, Cecilia
A1  - Marsh, Julie
A1  - Middeldorp, Christel
A1  - Millwood, Iona
A1  - Mook-Kanamori, Dennis O.
A1  - Nivard, Michel
A1  - O'Reilly, Paul F.
A1  - Palmer, Lyle J.
A1  - Prokopenko, Inga
A1  - Rodriguez, Alina
A1  - Sebert, Sylvain
A1  - Sovio, Ulla
A1  - St Pourcain, Beate
A1  - Standl, Marie
A1  - Strachan, David P.
A1  - Sunyer, Jordi
A1  - Taal, H. Rob
A1  - Thiering, Elisabeth
A1  - Tiesler, Carla
A1  - Uitterlinden, Andre G.
A1  - Valcarcel, Beatriz
A1  - Warrington, Nicole M.
A1  - White, Scott
A1  - Willemsen, Gonneke
A1  - Yaghootkar, Hanieh
A1  - Boomsma, Dorret I.
A1  - Estivill, Xavier
A1  - Grant, Struan F. A.
A1  - Hakonarson, Hakon
A1  - Hattersley, Andrew T.
A1  - Heinrich, Joachim
A1  - Jaddoe, Vincent W. V.
A1  - Jarvelin, Marjo-Riitta
A1  - McCarthy, Mark I.
A1  - Pennell, Craig E.
A1  - Power, Chris
A1  - Timpson, Nicholas J.
A1  - Widen, Elisabeth
A1  - Ikram, M. Arfan
A1  - Fornage, Myriam
A1  - Smith, Albert V.
A1  - Seshadri, Sudha
A1  - Schmidt, Reinhold
A1  - Debette, Stephanie
A1  - Vrooman, Henri A.
A1  - Sigurdsson, Sigurdur
A1  - Ropele, Stefan
A1  - Coker, Laura H.
A1  - Longstreth, W. T.
A1  - Niessen, Wiro J.
A1  - DeStefano, Anita L.
A1  - Beiser, Alexa
A1  - Zijdenbos, Alex P.
A1  - Struchalin, Maksim
A1  - Jack, Clifford R.
A1  - Nalls, Mike A.
A1  - Au, Rhoda
A1  - Hofman, Albert
A1  - Gudnason, Haukur
A1  - van der Lugt, Aad
A1  - Harris, Tamara B.
A1  - Meeks, William M.
A1  - Vernooij, Meike W.
A1  - van Buchem, Mark A.
A1  - Catellier, Diane
A1  - Gudnason, Vilmundur
A1  - Windham, B. Gwen
A1  - Wolf, Philip A.
A1  - van Duijn, Cornelia M.
A1  - Mosley, Thomas H.
A1  - Schmidt, Helena
A1  - Launer, Lenore J.
A1  - Breteler, Monique M. B.
A1  - DeCarli, Charles
T1  - Common variants at 12q15 and 12q24 are associated with infant head circumference
JF  - Nature genetics
N2  - 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.
Y1  - 2012
U6  - https://doi.org/10.1038/ng.2238
SN  - 1061-4036
VL  - 44
IS  - 5
SP  - 532
EP  - +
PB  - Nature Publ. Group
CY  - New York
ER  - 
TY  - JOUR
A1  - Vaid, Akhil
A1  - Somani, Sulaiman
A1  - Russak, Adam J.
A1  - De Freitas, Jessica K.
A1  - Chaudhry, Fayzan F.
A1  - Paranjpe, Ishan
A1  - Johnson, Kipp W.
A1  - Lee, Samuel J.
A1  - Miotto, Riccardo
A1  - Richter, Felix
A1  - Zhao, Shan
A1  - Beckmann, Noam D.
A1  - Naik, Nidhi
A1  - Kia, Arash
A1  - Timsina, Prem
A1  - Lala, Anuradha
A1  - Paranjpe, Manish
A1  - Golden, Eddye
A1  - Danieletto, Matteo
A1  - Singh, Manbir
A1  - Meyer, Dara
A1  - O'Reilly, Paul F.
A1  - Huckins, Laura
A1  - Kovatch, Patricia
A1  - Finkelstein, Joseph
A1  - Freeman, Robert M.
A1  - Argulian, Edgar
A1  - Kasarskis, Andrew
A1  - Percha, Bethany
A1  - Aberg, Judith A.
A1  - Bagiella, Emilia
A1  - Horowitz, Carol R.
A1  - Murphy, Barbara
A1  - Nestler, Eric J.
A1  - Schadt, Eric E.
A1  - Cho, Judy H.
A1  - Cordon-Cardo, Carlos
A1  - Fuster, Valentin
A1  - Charney, Dennis S.
A1  - Reich, David L.
A1  - Böttinger, Erwin
A1  - Levin, Matthew A.
A1  - Narula, Jagat
A1  - Fayad, Zahi A.
A1  - Just, Allan C.
A1  - Charney, Alexander W.
A1  - Nadkarni, Girish N.
A1  - Glicksberg, Benjamin S.
T1  - Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation
JF  - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR
N2  - 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.
KW  - machine learning
KW  - COVID-19
KW  - electronic health record
KW  - TRIPOD
KW  - clinical
KW  - informatics
KW  - prediction
KW  - mortality
KW  - EHR
KW  - cohort
KW  - hospital
KW  - performance
Y1  - 2020
U6  - https://doi.org/10.2196/24018
SN  - 1439-4456
SN  - 1438-8871
VL  - 22
IS  - 11
PB  - Healthcare World
CY  - Richmond, Va.
ER  -