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189 results on '"McQuillan, B"'

7. Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation

Author

Lu, J., Hutchens, R., Hung, J., Bennamoun, M., McQuillan, B., Briffa, T., Sohel, F., Murray, K., Stewart, J., Chow, B., Sanfilippo, F., Dwivedi, G., Lu, J., Hutchens, R., Hung, J., Bennamoun, M., McQuillan, B., Briffa, T., Sohel, F., Murray, K., Stewart, J., Chow, B., Sanfilippo, F., and Dwivedi, G.

Abstract

Background Appropriate anticoagulant therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Multilabel machine learning (ML) techniques may improve predictive performance and support decision-making for anticoagulant therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. Methods This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The outcomes were ischemic stroke (167), major bleeding (430) admissions, all-cause death (1912) and event-free survival (7387). Discrimination and calibration of ML models were compared with clinical risk scores by area under the curve (AUC). Risk stratification was assessed using net reclassification index (NRI). Results Multilabel gradient boosting classifier chain provided the best AUCs for stroke (0.685 95% CI 0.676, 0.694), major bleeding (0.709 95% CI 0.703, 0.716) and death (0.765 95% CI 0.763, 0.768) compared to multi-layer neural networks and classifier chain using support vector machine. It provided modest performance improvement for stroke compared to AUC of CHA2DS2-VASc (0.652, NRI = 3.2%, p-value = 0.1), but significantly improved major bleeding prediction compared to AUC of HAS-BLED (0.522, NRI = 22.8%, p-value < 0.05). It also achieved greater discriminant power for death compared with AUC of CHA2DS2-VASc (0.606, p-value < 0.05). ML models identified additional risk features such as hemoglobin level, renal function. Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.

Published
2022

8. Lawson Criterion for Ignition Exceeded in an Inertial Fusion Experiment

Author

Abu-Shawareb, H, Acree, R, Adams, P, Adams, J, Addis, B, Aden, R, Adrian, P, Afeyan, BB, Aggleton, M, Aghaian, L, Aguirre, A, Aikens, D, Akre, J, Albert, F, Albrecht, M, Albright, BJ, Albritton, J, Alcala, J, Alday, C, Alessi, DA, Alexander, N, Alfonso, J, Alfonso, N, Alger, E, Ali, SJ, Ali, ZA, Alley, WE, Amala, P, Amendt, PA, Amick, P, Ammula, S, Amorin, C, Ampleford, DJ, Anderson, RW, Anklam, T, Antipa, N, Appelbe, B, Aracne-Ruddle, C, Araya, E, Arend, M, Arnold, P, Arnold, T, Asay, J, Atherton, LJ, Atkinson, D, Atkinson, R, Auerbach, JM, Austin, B, Auyang, L, Awwal, AS, Ayers, J, Ayers, S, Ayers, T, Azevedo, S, Bachmann, B, Back, CA, Bae, J, Bailey, DS, Bailey, J, Baisden, T, Baker, KL, Baldis, H, Barber, D, Barberis, M, Barker, D, Barnes, A, Barnes, CW, Barrios, MA, Barty, C, Bass, I, Batha, SH, Baxamusa, SH, Bazan, G, Beagle, JK, Beale, R, Beck, BR, Beck, JB, Bedzyk, M, Beeler, RG, Behrendt, W, Belk, L, Bell, P, Belyaev, M, Benage, JF, Bennett, G, Benedetti, LR, Benedict, LX, Berger, R, Bernat, T, Bernstein, LA, Berry, B, Bertolini, L, Besenbruch, G, Betcher, J, Bettenhausen, R, Betti, R, Bezzerides, B, Bhandarkar, SD, Bickel, R, Biener, J, Biesiada, T, Bigelow, K, Bigelow-Granillo, J, Bigman, V, Bionta, RM, Birge, NW, Bitter, M, Black, AC, Bleile, R, Bleuel, DL, Bliss, E, Blue, B, Boehly, T, Boehm, K, Boley, CD, Bonanno, R, Bond, EJ, Bond, T, Bonino, MJ, Borden, M, Bourgade, J-L, Bousquet, J, Bowers, J, Bowers, M, Boyd, R, Bozek, A, Bradley, DK, Bradley, KS, Bradley, PA, Bradley, L, Brannon, L, Brantley, PS, Braun, D, Braun, T, Brienza-Larsen, K, Briggs, TM, Britten, J, Brooks, ED, Browning, D, Bruhn, MW, Brunner, TA, Bruns, H, Brunton, G, Bryant, B, Buczek, T, Bude, J, Buitano, L, Burkhart, S, Burmark, J, Burnham, A, Burr, R, Busby, LE, Butlin, B, Cabeltis, R, Cable, M, Cabot, WH, Cagadas, B, Caggiano, J, Cahayag, R, Caldwell, SE, Calkins, S, Callahan, DA, Calleja-Aguirre, J, Camara, L, Camp, D, Campbell, EM, Campbell, JH, Carey, B, Carey, R, Carlisle, K, Carlson, L, Carman, L, Carmichael, J, Carpenter, A, Carr, C, Carrera, JA, Casavant, D, Casey, A, Casey, DT, Castillo, A, Castillo, E, Castor, JI, Castro, C, Caughey, W, Cavitt, R, Celeste, J, Celliers, PM, Cerjan, C, Chandler, G, Chang, B, Chang, C, Chang, J, Chang, L, Chapman, R, Chapman, T, Chase, L, Chen, H, Chen, K, Chen, L-Y, Cheng, B, Chittenden, J, Choate, C, Chou, J, Chrien, RE, Chrisp, M, Christensen, K, Christensen, M, Christopherson, AR, Chung, M, Church, JA, Clark, A, Clark, DS, Clark, K, Clark, R, Claus, L, Cline, B, Cline, JA, Cobble, JA, Cochrane, K, Cohen, B, Cohen, S, Collette, MR, Collins, G, Collins, LA, Collins, TJB, Conder, A, Conrad, B, Conyers, M, Cook, AW, Cook, D, Cook, R, Cooley, JC, Cooper, G, Cope, T, Copeland, SR, Coppari, F, Cortez, J, Cox, J, Crandall, DH, Crane, J, Craxton, RS, Cray, M, Crilly, A, Crippen, JW, Cross, D, Cuneo, M, Cuotts, G, Czajka, CE, Czechowicz, D, Daly, T, Danforth, P, Darbee, R, Darlington, B, Datte, P, Dauffy, L, Davalos, G, Davidovits, S, Davis, P, Davis, J, Dawson, S, Day, RD, Day, TH, Dayton, M, Deck, C, Decker, C, Deeney, C, DeFriend, KA, Deis, G, Delamater, ND, Delettrez, JA, Demaret, R, Demos, S, Dempsey, SM, Desjardin, R, Desjardins, T, Desjarlais, MP, Dewald, EL, DeYoreo, J, Diaz, S, Dimonte, G, Dittrich, TR, Divol, L, Dixit, SN, Dixon, J, Dodd, ES, Dolan, D, Donovan, A, Donovan, M, Döppner, T, Dorrer, C, Dorsano, N, Douglas, MR, Dow, D, Downie, J, Downing, E, Dozieres, M, Draggoo, V, Drake, D, Drake, RP, Drake, T, Dreifuerst, G, DuBois, DF, DuBois, PF, Dunham, G, Dylla-Spears, R, Dymoke-Bradshaw, AKL, Dzenitis, B, Ebbers, C, Eckart, M, Eddinger, S, Eder, D, Edgell, D, Edwards, MJ, Efthimion, P, Eggert, JH, Ehrlich, B, Ehrmann, P, Elhadj, S, Ellerbee, C, Elliott, NS, Ellison, CL, Elsner, F, Emerich, M, Engelhorn, K, England, T, English, E, Epperson, P, Epstein, R, Erbert, G, Erickson, MA, Erskine, DJ, Erlandson, A, Espinosa, RJ, Estes, C, Estabrook, KG, Evans, S, Fabyan, A, Fair, J, Fallejo, R, Farmer, N, Farmer, WA, Farrell, M, Fatherley, VE, Fedorov, M, Feigenbaum, E, Feit, M, Ferguson, W, Fernandez, JC, Fernandez-Panella, A, Fess, S, Field, JE, Filip, CV, Fincke, JR, Finn, T, Finnegan, SM, Finucane, RG, Fischer, M, Fisher, A, Fisher, J, Fishler, B, Fittinghoff, D, Fitzsimmons, P, Flegel, M, Flippo, KA, Florio, J, Folta, J, Folta, P, Foreman, LR, Forrest, C, Forsman, A, Fooks, J, Foord, M, Fortner, R, Fournier, K, Fratanduono, DE, Frazier, N, Frazier, T, Frederick, C, Freeman, MS, Frenje, J, Frey, D, Frieders, G, Friedrich, S, Froula, DH, Fry, J, Fuller, T, Gaffney, J, Gales, S, Le Galloudec, B, Le Galloudec, KK, Gambhir, A, Gao, L, Garbett, WJ, Garcia, A, Gates, C, Gaut, E, Gauthier, P, Gavin, Z, Gaylord, J, Geissel, M, Génin, F, Georgeson, J, Geppert-Kleinrath, H, Geppert-Kleinrath, V, Gharibyan, N, Gibson, J, Gibson, C, Giraldez, E, Glebov, V, Glendinning, SG, Glenn, S, Glenzer, SH, Goade, S, Gobby, PL, Goldman, SR, Golick, B, Gomez, M, Goncharov, V, Goodin, D, Grabowski, P, Grafil, E, Graham, P, Grandy, J, Grasz, E, Graziani, F, Greenman, G, Greenough, JA, Greenwood, A, Gregori, G, Green, T, Griego, JR, Grim, GP, Grondalski, J, Gross, S, Guckian, J, Guler, N, Gunney, B, Guss, G, Haan, S, Hackbarth, J, Hackel, L, Hackel, R, Haefner, C, Hagmann, C, Hahn, KD, Hahn, S, Haid, BJ, Haines, BM, Hall, BM, Hall, C, Hall, GN, Hamamoto, M, Hamel, S, Hamilton, CE, Hammel, BA, Hammer, JH, Hampton, G, Hamza, A, Handler, A, Hansen, S, Hanson, D, Haque, R, Harding, D, Harding, E, Hares, JD, Harris, DB, Harte, JA, Hartouni, EP, Hatarik, R, Hatchett, S, Hauer, AA, Havre, M, Hawley, R, Hayes, J, Hayes, S, Hayes-Sterbenz, A, Haynam, CA, Haynes, DA, Headley, D, Heal, A, Heebner, JE, Heerey, S, Heestand, GM, Heeter, R, Hein, N, Heinbockel, C, Hendricks, C, Henesian, M, Heninger, J, Henrikson, J, Henry, EA, Herbold, EB, Hermann, MR, Hermes, G, Hernandez, JE, Hernandez, VJ, Herrmann, MC, Herrmann, HW, Herrera, OD, Hewett, D, Hibbard, R, Hicks, DG, Hill, D, Hill, K, Hilsabeck, T, Hinkel, DE, Ho, DD, Ho, VK, Hoffer, JK, Hoffman, NM, Hohenberger, M, Hohensee, M, Hoke, W, Holdener, D, Holdener, F, Holder, JP, Holko, B, Holunga, D, Holzrichter, JF, Honig, J, Hoover, D, Hopkins, D, Berzak Hopkins, L, Hoppe, M, Hoppe, ML, Horner, J, Hornung, R, Horsfield, CJ, Horvath, J, Hotaling, D, House, R, Howell, L, Hsing, WW, Hu, SX, Huang, H, Huckins, J, Hui, H, Humbird, KD, Hund, J, Hunt, J, Hurricane, OA, Hutton, M, Huynh, KH-K, Inandan, L, Iglesias, C, Igumenshchev, IV, Izumi, N, Jackson, M, Jackson, J, Jacobs, SD, James, G, Jancaitis, K, Jarboe, J, Jarrott, LC, Jasion, D, Jaquez, J, Jeet, J, Jenei, AE, Jensen, J, Jimenez, J, Jimenez, R, Jobe, D, Johal, Z, Johns, HM, Johnson, D, Johnson, MA, Gatu Johnson, M, Johnson, RJ, Johnson, S, Johnson, SA, Johnson, T, Jones, K, Jones, O, Jones, M, Jorge, R, Jorgenson, HJ, Julian, M, Jun, BI, Jungquist, R, Kaae, J, Kabadi, N, Kaczala, D, Kalantar, D, Kangas, K, Karasiev, VV, Karasik, M, Karpenko, V, Kasarky, A, Kasper, K, Kauffman, R, Kaufman, MI, Keane, C, Keaty, L, Kegelmeyer, L, Keiter, PA, Kellett, PA, Kellogg, J, Kelly, JH, Kemic, S, Kemp, AJ, Kemp, GE, Kerbel, GD, Kershaw, D, Kerr, SM, Kessler, TJ, Key, MH, Khan, SF, Khater, H, Kiikka, C, Kilkenny, J, Kim, Y, Kim, Y-J, Kimko, J, Kimmel, M, Kindel, JM, King, J, Kirkwood, RK, Klaus, L, Klem, D, Kline, JL, Klingmann, J, Kluth, G, Knapp, P, Knauer, J, Knipping, J, Knudson, M, Kobs, D, Koch, J, Kohut, T, Kong, C, Koning, JM, Koning, P, Konior, S, Kornblum, H, Kot, LB, Kozioziemski, B, Kozlowski, M, Kozlowski, PM, Krammen, J, Krasheninnikova, NS, Kraus, B, Krauser, W, Kress, JD, Kritcher, AL, Krieger, E, Kroll, JJ, Kruer, WL, Kruse, MKG, Kucheyev, S, Kumbera, M, Kumpan, S, Kunimune, J, Kustowski, B, Kwan, TJT, Kyrala, GA, Laffite, S, Lafon, M, LaFortune, K, Lahmann, B, Lairson, B, Landen, OL, Langenbrunner, J, Lagin, L, Land, T, Lane, M, Laney, D, Langdon, AB, Langer, SH, Langro, A, Lanier, NE, Lanier, TE, Larson, D, Lasinski, BF, Lassle, D, LaTray, D, Lau, G, Lau, N, Laumann, C, Laurence, A, Laurence, TA, Lawson, J, Le, HP, Leach, RR, Leal, L, Leatherland, A, LeChien, K, Lechleiter, B, Lee, A, Lee, M, Lee, T, Leeper, RJ, Lefebvre, E, Leidinger, J-P, LeMire, B, Lemke, RW, Lemos, NC, Le Pape, S, Lerche, R, Lerner, S, Letts, S, Levedahl, K, Lewis, T, Li, CK, Li, H, Li, J, Liao, W, Liao, ZM, Liedahl, D, Liebman, J, Lindford, G, Lindman, EL, Lindl, JD, Loey, H, London, RA, Long, F, Loomis, EN, Lopez, FE, Lopez, H, Losbanos, E, Loucks, S, Lowe-Webb, R, Lundgren, E, Ludwigsen, AP, Luo, R, Lusk, J, Lyons, R, Ma, T, Macallop, Y, MacDonald, MJ, MacGowan, BJ, Mack, JM, Mackinnon, AJ, MacLaren, SA, MacPhee, AG, Magelssen, GR, Magoon, J, Malone, RM, Malsbury, T, Managan, R, Mancini, R, Manes, K, Maney, D, Manha, D, Mannion, OM, Manuel, AM, Mapoles, E, Mara, G, Marcotte, T, Marin, E, Marinak, MM, Mariscal, C, Mariscal, DA, Mariscal, EF, Marley, EV, Marozas, JA, Marquez, R, Marshall, CD, Marshall, FJ, Marshall, M, Marshall, S, Marticorena, J, Martinez, D, Maslennikov, I, Mason, D, Mason, RJ, Masse, L, Massey, W, Masson-Laborde, P-E, Masters, ND, Mathisen, D, Mathison, E, Matone, J, Matthews, MJ, Mattoon, C, Mattsson, TR, Matzen, K, Mauche, CW, Mauldin, M, McAbee, T, McBurney, M, Mccarville, T, McCrory, RL, McEvoy, AM, McGuffey, C, Mcinnis, M, McKenty, P, McKinley, MS, McLeod, JB, McPherson, A, Mcquillan, B, Meamber, M, Meaney, KD, Meezan, NB, Meissner, R, Mehlhorn, TA, Mehta, NC, Menapace, J, Merrill, FE, Merritt, BT, Merritt, EC, Meyerhofer, DD, Mezyk, S, Mich, RJ, Michel, PA, Milam, D, Miller, C, Miller, D, Miller, DS, Miller, E, Miller, EK, Miller, J, Miller, M, Miller, PE, Miller, T, Miller, W, Miller-Kamm, V, Millot, M, Milovich, JL, Minner, P, Miquel, J-L, Mitchell, S, Molvig, K, Montesanti, RC, Montgomery, DS, Monticelli, M, Montoya, A, Moody, JD, Moore, AS, Moore, E, Moran, M, Moreno, JC, Moreno, K, Morgan, BE, Morrow, T, Morton, JW, Moses, E, Moy, K, Muir, R, Murillo, MS, Murray, JE, Murray, JR, Munro, DH, Murphy, TJ, Munteanu, FM, Nafziger, J, Nagayama, T, Nagel, SR, Nast, R, Negres, RA, Nelson, A, Nelson, D, Nelson, J, Nelson, S, Nemethy, S, Neumayer, P, Newman, K, Newton, M, Nguyen, H, Di Nicola, J-MG, Di Nicola, P, Niemann, C, Nikroo, A, Nilson, PM, Nobile, A, Noorai, V, Nora, R, Norton, M, Nostrand, M, Note, V, Novell, S, Nowak, PF, Nunez, A, Nyholm, RA, O'Brien, M, Oceguera, A, Oertel, JA, Okui, J, Olejniczak, B, Oliveira, J, Olsen, P, Olson, B, Olson, K, Olson, RE, Opachich, YP, Orsi, N, Orth, CD, Owen, M, Padalino, S, Padilla, E, Paguio, R, Paguio, S, Paisner, J, Pajoom, S, Pak, A, Palaniyappan, S, Palma, K, Pannell, T, Papp, F, Paras, D, Parham, T, Park, H-S, Pasternak, A, Patankar, S, Patel, MV, Patel, PK, Patterson, R, Patterson, S, Paul, B, Paul, M, Pauli, E, Pearce, OT, Pearcy, J, Pedrotti, B, Peer, A, Pelz, LJ, Penetrante, B, Penner, J, Perez, A, Perkins, LJ, Pernice, E, Perry, TS, Person, S, Petersen, D, Petersen, T, Peterson, DL, Peterson, EB, Peterson, JE, Peterson, JL, Peterson, K, Peterson, RR, Petrasso, RD, Philippe, F, Phipps, TJ, Piceno, E, Ping, Y, Pickworth, L, Pino, J, Plummer, R, Pollack, GD, Pollaine, SM, Pollock, BB, Ponce, D, Ponce, J, Pontelandolfo, J, Porter, JL, Post, J, Poujade, O, Powell, C, Powell, H, Power, G, Pozulp, M, Prantil, M, Prasad, M, Pratuch, S, Price, S, Primdahl, K, Prisbrey, S, Procassini, R, Pruyne, A, Pudliner, B, Qiu, SR, Quan, K, Quinn, M, Quintenz, J, Radha, PB, Rainer, F, Ralph, JE, Raman, KS, Raman, R, Rambo, P, Rana, S, Randewich, A, Rardin, D, Ratledge, M, Ravelo, N, Ravizza, F, Rayce, M, Raymond, A, Raymond, B, Reed, B, Reed, C, Regan, S, Reichelt, B, Reis, V, Reisdorf, S, Rekow, V, Remington, BA, Rendon, A, Requieron, W, Rever, M, Reynolds, H, Reynolds, J, Rhodes, J, Rhodes, M, Richardson, MC, Rice, B, Rice, NG, Rieben, R, Rigatti, A, Riggs, S, Rinderknecht, HG, Ring, K, Riordan, B, Riquier, R, Rivers, C, Roberts, D, Roberts, V, Robertson, G, Robey, HF, Robles, J, Rocha, P, Rochau, G, Rodriguez, J, Rodriguez, S, Rosen, M, Rosenberg, M, Ross, G, Ross, JS, Ross, P, Rouse, J, Rovang, D, Rubenchik, AM, Rubery, MS, Ruiz, CL, Rushford, M, Russ, B, Rygg, JR, Ryujin, BS, Sacks, RA, Sacks, RF, Saito, K, Salmon, T, Salmonson, JD, Sanchez, J, Samuelson, S, Sanchez, M, Sangster, C, Saroyan, A, Sater, J, Satsangi, A, Sauers, S, Saunders, R, Sauppe, JP, Sawicki, R, Sayre, D, Scanlan, M, Schaffers, K, Schappert, GT, Schiaffino, S, Schlossberg, DJ, Schmidt, DW, Schmitt, MJ, Schneider, DHG, Schneider, MB, Schneider, R, Schoff, M, Schollmeier, M, Schölmerich, M, Schroeder, CR, Schrauth, SE, Scott, HA, Scott, I, Scott, JM, Scott, RHH, Scullard, CR, Sedillo, T, Seguin, FH, Seka, W, Senecal, J, Sepke, SM, Seppala, L, Sequoia, K, Severyn, J, Sevier, JM, Sewell, N, Seznec, S, Shah, RC, Shamlian, J, Shaughnessy, D, Shaw, M, Shaw, R, Shearer, C, Shelton, R, Shen, N, Sherlock, MW, Shestakov, AI, Shi, EL, Shin, SJ, Shingleton, N, Shmayda, W, Shor, M, Shoup, M, Shuldberg, C, Siegel, L, Silva, FJ, Simakov, AN, Sims, BT, Sinars, D, Singh, P, Sio, H, Skulina, K, Skupsky, S, Slutz, S, Sluyter, M, Smalyuk, VA, Smauley, D, Smeltser, RM, Smith, C, Smith, I, Smith, J, Smith, L, Smith, R, Sohn, R, Sommer, S, Sorce, C, Sorem, M, Soures, JM, Spaeth, ML, Spears, BK, Speas, S, Speck, D, Speck, R, Spears, J, Spinka, T, Springer, PT, Stadermann, M, Stahl, B, Stahoviak, J, Stanton, LG, Steele, R, Steele, W, Steinman, D, Stemke, R, Stephens, R, Sterbenz, S, Sterne, P, Stevens, D, Stevers, J, Still, CB, Stoeckl, C, Stoeffl, W, Stolken, JS, Stolz, C, Storm, E, Stone, G, Stoupin, S, Stout, E, Stowers, I, Strauser, R, Streckart, H, Streit, J, Strozzi, DJ, Suratwala, T, Sutcliffe, G, Suter, LJ, Sutton, SB, Svidzinski, V, Swadling, G, Sweet, W, Szoke, A, Tabak, M, Takagi, M, Tambazidis, A, Tang, V, Taranowski, M, Taylor, LA, Telford, S, Theobald, W, Thi, M, Thomas, A, Thomas, CA, Thomas, I, Thomas, R, Thompson, IJ, Thongstisubskul, A, Thorsness, CB, Tietbohl, G, Tipton, RE, Tobin, M, Tomlin, N, Tommasini, R, Toreja, AJ, Torres, J, Town, RPJ, Townsend, S, Trenholme, J, Trivelpiece, A, Trosseille, C, Truax, H, Trummer, D, Trummer, S, Truong, T, Tubbs, D, Tubman, ER, Tunnell, T, Turnbull, D, Turner, RE, Ulitsky, M, Upadhye, R, Vaher, JL, VanArsdall, P, VanBlarcom, D, Vandenboomgaerde, M, VanQuinlan, R, Van Wonterghem, BM, Varnum, WS, Velikovich, AL, Vella, A, Verdon, CP, Vermillion, B, Vernon, S, Vesey, R, Vickers, J, Vignes, RM, Visosky, M, Vocke, J, Volegov, PL, Vonhof, S, Von Rotz, R, Vu, HX, Vu, M, Wall, D, Wall, J, Wallace, R, Wallin, B, Walmer, D, Walsh, CA, Walters, CF, Waltz, C, Wan, A, Wang, A, Wang, Y, Wark, JS, Warner, BE, Watson, J, Watt, RG, Watts, P, Weaver, J, Weaver, RP, Weaver, S, Weber, CR, Weber, P, Weber, SV, Wegner, P, Welday, B, Welser-Sherrill, L, Weiss, K, Widmann, K, Wheeler, GF, Whistler, W, White, RK, Whitley, HD, Whitman, P, Wickett, ME, Widmayer, C, Wiedwald, J, Wilcox, R, Wilcox, S, Wild, C, Wilde, BH, Wilde, CH, Wilhelmsen, K, Wilke, MD, Wilkens, H, Wilkins, P, Wilks, SC, Williams, EA, Williams, GJ, Williams, W, Williams, WH, Wilson, DC, Wilson, B, Wilson, E, Wilson, R, Winters, S, Wisoff, J, Wittman, M, Wolfe, J, Wong, A, Wong, KW, Wong, L, Wong, N, Wood, R, Woodhouse, D, Woodruff, J, Woods, DT, Woods, S, Woodworth, BN, Wooten, E, Wootton, A, Work, K, Workman, JB, Wright, J, Wu, M, Wuest, C, Wysocki, FJ, Xu, H, Yamaguchi, M, Yang, B, Yang, ST, Yatabe, J, Yeamans, CB, Yee, BC, Yi, SA, Yin, L, Young, B, Young, CS, Young, CV, Young, P, Youngblood, K, Zacharias, R, Zagaris, G, Zaitseva, N, Zaka, F, Ze, F, Zeiger, B, Zika, M, Zimmerman, GB, Zobrist, T, Zuegel, JD, Zylstra, AB, Indirect Drive ICF Collaboration, Collaboration, Indirect Drive ICF, AWE Plc, Lawrence Livermore National Laboratory, and U.S Department of Energy

Subjects
General Physics, 02 Physical Sciences, General Physics and Astronomy, Indirect Drive ICF Collaboration, 01 Mathematical Sciences, 09 Engineering
Abstract

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion.

Published
2022

10. Association of hypertension with mortality in patients hospitalised with COVID-19

Author

Bhatia, KS, Sritharan, HP, Ciofani, J, Chia, J, Allahwala, UK, Chui, K, Nour, D, Vasanthakumar, S, Khandadai, D, Jayadeva, P, Bhagwandeen, R, Brieger, D, Choong, C, Delaney, A, Dwivedi, G, Harris, B, Hillis, G, Hudson, B, Javorski, G, Jepson, N, Kanagaratnam, L, Kotsiou, G, Lee, A, Lo, ST, MacIsaac, AI, McQuillan, B, Ranasinghe, I, Walton, A, Weaver, J, Wilson, W, Yong, ASC, Zhu, J, Van Gaal, W, Kritharides, L, Chow, CK, Bhindi, R, Bhatia, KS, Sritharan, HP, Ciofani, J, Chia, J, Allahwala, UK, Chui, K, Nour, D, Vasanthakumar, S, Khandadai, D, Jayadeva, P, Bhagwandeen, R, Brieger, D, Choong, C, Delaney, A, Dwivedi, G, Harris, B, Hillis, G, Hudson, B, Javorski, G, Jepson, N, Kanagaratnam, L, Kotsiou, G, Lee, A, Lo, ST, MacIsaac, AI, McQuillan, B, Ranasinghe, I, Walton, A, Weaver, J, Wilson, W, Yong, ASC, Zhu, J, Van Gaal, W, Kritharides, L, Chow, CK, and Bhindi, R

Abstract

OBJECTIVE: To assess whether hypertension is an independent risk factor for mortality among patients hospitalised with COVID-19, and to evaluate the impact of ACE inhibitor and angiotensin receptor blocker (ARB) use on mortality in patients with a background of hypertension. METHOD: This observational cohort study included all index hospitalisations with laboratory-proven COVID-19 aged ≥18 years across 21 Australian hospitals. Patients with suspected, but not laboratory-proven COVID-19, were excluded. Registry data were analysed for in-hospital mortality in patients with comorbidities including hypertension, and baseline treatment with ACE inhibitors or ARBs. RESULTS: 546 consecutive patients (62.9±19.8 years old, 51.8% male) hospitalised with COVID-19 were enrolled. In the multivariable model, significant predictors of mortality were age (adjusted OR (aOR) 1.09, 95% CI 1.07 to 1.12, p<0.001), heart failure or cardiomyopathy (aOR 2.71, 95% CI 1.13 to 6.53, p=0.026), chronic kidney disease (aOR 2.33, 95% CI 1.02 to 5.32, p=0.044) and chronic obstructive pulmonary disease (aOR 2.27, 95% CI 1.06 to 4.85, p=0.035). Hypertension was the most prevalent comorbidity (49.5%) but was not independently associated with increased mortality (aOR 0.92, 95% CI 0.48 to 1.77, p=0.81). Among patients with hypertension, ACE inhibitor (aOR 1.37, 95% CI 0.61 to 3.08, p=0.61) and ARB (aOR 0.64, 95% CI 0.27 to 1.49, p=0.30) use was not associated with mortality. CONCLUSIONS: In patients hospitalised with COVID-19, pre-existing hypertension was the most prevalent comorbidity but was not independently associated with mortality. Similarly, the baseline use of ACE inhibitors or ARBs had no independent association with in-hospital mortality.

Published
2021

16. 230 Machine learning models for predicting ischemic stroke and major bleeding risk in patients with atrial fibrillation

Author

Lu, J., Dwivedi, G., Sanfilippo, F., Bennamoun, M., Hung, J., Briffa, T., Sohel, F., Hutchens, R., Stewart, J., Chow, B., McQuillan, B., Lu, J., Dwivedi, G., Sanfilippo, F., Bennamoun, M., Hung, J., Briffa, T., Sohel, F., Hutchens, R., Stewart, J., Chow, B., and McQuillan, B.

Abstract

Background Risk scores such as CHA2DS2-VASc and HAS-BLED are used to assess stroke and bleeding risk respectively and choose appropriate antithrombotic therapy in patients with atrial fibrillation (AF). The application of ML models may improve risk prediction and identification of potential risk factors. Objective To investigate the usefulness of ML methods in estimating one-year risk of ischemic stroke and major bleeding in patients after hospitalisation with AF. Methods We identified adults with a history of non-valvular AF or atrial flutter who were admitted to a tertiary or secondary hospital in Perth, Western Australia from 2009 to 2016 using linked clinical and administrative data. Based on all the available risk factors in the data including individual risk factors in the scores, we built ML models and compared their predictive performance [Area under the receiver operating characteristic curve (AUC)] with the standard risk scores. Results There were 9,634 patients in the study cohort with a mean age of 77 years and 46% were female. 2407 patients died (n=1636) or were readmitted for ischemic stroke (n=157) and major bleeding (n=614) within one year after the first admission. All-cause death was treated as a competing risk. Gradient Boosting Machine identified nonconventional risk factors and achieved the best prediction (ischemic stroke: AUC 0.67 vs 0.64 for CHA2DS2-VASc; major bleeding: AUC 0.66 vs 0.53 for HAS-BLED). Conclusion ML models can identify nonconventional risk factors and also outperform commonly used risk scores for predicting ischemic stroke and major bleeding in patients with AF.

Published
2020

31. Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data

Author

Wensley, F, Gao, P, Burgess, S, Kaptoge, S, Di Angelantonio, E, Shah, T, Engert, JC, Clarke, R, Davey-Smith, G, Nordestgaard, BG, Saleheen, D, Samani, NJ, Sandhu, M, Anand, S, Pepys, MB, Smeeth, L, Whittaker, J, Casas, JP, Thompson, SG, Hingorani, AD, Danesh, J, Eiriksdottir, G, Harris, TB, Launer, LJ, Gudnason, V, Folsom, AR, Andrews, G, Ballantyne, CM, Hall, AS, Braund, PS, Balmforth, AJ, Whincup, PH, Morris, R, Lawlor, DA, Lowe, GDO, Timpson, N, Ebrahim, S, Ben-Shlomo, Y, Tybjaerg-Hansen, A, Zacho, J, Brown, M, Ricketts, SL, Ashford, S, Lange, L, Reiner, A, Cushman, M, Tracy, R, Wu, C, Ge, J, Zou, Y, Sun, A, Hung, J, McQuillan, B, Thompson, P, Beilby, J, Warrington, N, Palmer, LJ, Wanner, C, Drechsler, C, Hoffmann, MM, Fowkes, FGR, Tzoulaki, I, Kumari, M, Miller, M, Marmot, M, Onland-Moret, C, van der Schouw, YT, Boer, JM, Wijmenga, C, Khaw, K-T, Vasan, RS, Schnabel, RB, Yamamoto, JF, Benjamin, EJ, Schunkert, H, Erdmann, J, Koenig, IR, Hengstenberg, C, Chiodini, B, Franzosi, MG, Pietri, S, Gori, F, Rudock, M, Liu, Y, Lohman, K, Humphries, SE, Hamsten, A, Norman, PE, Hankey, GJ, Jamrozik, K, Rimm, EB, Pai, JK, Psaty, BM, Heckbert, SR, Bis, JC, Yusuf, S, Xie, C, Collins, R, Bennett, D, Kooner, J, Chambers, J, Elliott, P, Maerz, W, Kleber, ME, Boehm, BO, Winkelmann, BR, Melander, O, Berglund, G, Koenig, W, Thorand, B, Baumert, J, Peters, A, Manson, J, Cooper, JA, Talmud, PJ, Ladenvall, P, Johansson, L, Jansson, J-H, Hallmans, G, Reilly, MP, Qu, L, Li, M, Rader, DJ, Watkins, H, Hopewell, J, Frossard, P, Sattar, N, Robertson, M, Shepherd, J, Schaefer, E, Hofman, A, Witteman, JCM, Kardys, I, Dehghan, A, de Faire, U, Bennet, A, Gigante, B, Leander, K, Peters, B, Maitland-van der Zee, AH, de Boer, A, Klungel, O, Greenland, P, Dai, J, Liu, S, Brunner, E, Kivimaki, M, O'Reilly, D, Ford, I, Packard, CJ, Dis, CRPCH, CIHDS, CC, CUPID, C, Medical Research Council (MRC), University of Groningen, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Pulmonology, and Coronel Institute of Occupational Health

Subjects
Male, Coronary Disease, Bioinformatics, Gene Frequency, MARKERS, Polymorphism (computer science), Risk Factors, Myocardial infarction, Prospective Studies, Prospective cohort study, General Environmental Science, Genetics, RISK, biology, General Engineering, Mendelian Randomization Analysis, General Medicine, Middle Aged, C-Reactive Protein, 1117 Public Health And Health Services, CARDIOVASCULAR-DISEASE, Meta-analysis, symbols, Female, Life Sciences & Biomedicine, Ischaemic Heart Disease, Polymorphism, Single Nucleotide, Molecular Genetics, symbols.namesake, Medicine, General & Internal, INFLAMMATION, Drugs: Cardiovascular System, General & Internal Medicine, medicine, INSTRUMENTAL VARIABLES, Humans, C Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC), Allele frequency, METAANALYSIS, POLYMORPHISMS, Science & Technology, business.industry, Research, C-reactive protein, medicine.disease, GENE, MYOCARDIAL-INFARCTION, ATHEROSCLEROSIS, Mendelian inheritance, biology.protein, General Earth and Planetary Sciences, business
Abstract

Objective To use genetic variants as unconfounded proxies of C reactive protein concentration to study its causal role in coronary heart disease. Design Mendelian randomisation meta-analysis of individual participant data from 47 epidemiological studies in 15 countries. Participants 194 418 participants, including 46 557 patients with prevalent or incident coronary heart disease. Information was available on four CRP gene tagging single nucleotide polymorphisms (rs3093077, rs1205, rs1130864, rs1800947), concentration of C reactive protein, and levels of other risk factors. Main outcome measures Risk ratios for coronary heart disease associated with genetically raised C reactive protein versus risk ratios with equivalent differences in C reactive protein concentration itself, adjusted for conventional risk factors and variability in risk factor levels within individuals. Results CRP variants were each associated with up to 30% per allele difference in concentration of C reactive protein (P

Published
2011

32. Higher ferritin levels, but not serum iron or transferrin saturation, are associated with Type 2 diabetes mellitus in adult men and women free of genetic haemochromatosis

Author

Yeap, B., Divitini, M., Gunton, J., Olynyk, John, Beilby, J., McQuillan, B., Hung, J., Knuiman, M., Yeap, B., Divitini, M., Gunton, J., Olynyk, John, Beilby, J., McQuillan, B., Hung, J., and Knuiman, M.

Abstract

Context - Iron overload predisposes to diabetes and higher ferritin levels have been associated with diabetes. However, it is unclear whether ferritin reflects differences in iron-related parameters between diabetic and nondiabetic persons. We examined associations of serum ferritin, iron and transferrin saturation with Type 2 diabetes in adults without genetic predisposition to iron overload. Design, participants and measurements - Cross-sectional analysis of community-dwelling men and women aged 17–97 years from the Busselton Health Survey, Western Australia. Men and women carrying genotypes associated with haemochromatosis (C282Y/C282Y or C282Y/H63D) were excluded. Serum ferritin, iron and transferrin saturation were assayed. Results - There were 1834 men (122 with diabetes, 6·6%) and 2351 women (141 with diabetes, 6%). In men, higher serum ferritin was associated with diabetes after adjusting for age, smoking, alcohol, cardiovascular history, body mass index (BMI), waist, blood pressure, lipids, C-reactive protein (CRP), adiponectin, alanine transaminase (ALT) and gamma-glutamyl transpeptidase (GGT) [odds ratio (OR): 1·29 per 1 unit increase log ferritin, 95% confidence interval (CI) = 1·01–1·65, P = 0·043]. In women, higher serum ferritin was associated with diabetes [fully adjusted OR: 1·31 per 1 unit increase log ferritin, 95% CI = 1·04–1·63, P = 0·020; 1·84 for tertile (T) 3 vs T1, 95% CI = 1·09–3·11]. Neither iron levels nor transferrin saturation were associated with diabetes risk in men or women. Higher ferritin was not associated with insulin resistance in nondiabetic adults. Conclusions - In adults, higher ferritin levels are independently associated with prevalent diabetes while iron and transferrin saturation are not. Ferritin is a robust biomarker for diabetes risk, but further investigation is needed to clarify whether this relationship is mediated via iron metabolism.

Published
2015

36. Higher ferritin levels, but not serum iron or transferrin saturation, are associated with Type 2 diabetes mellitus in adult men and women free of genetic haemochromatosis

Author

Yeap, B.B., Divitini, M.L., Gunton, J.E., Olynyk, J.K., Beilby, J.P., McQuillan, B., Hung, J., Knuiman, M.W., Yeap, B.B., Divitini, M.L., Gunton, J.E., Olynyk, J.K., Beilby, J.P., McQuillan, B., Hung, J., and Knuiman, M.W.

Abstract

Context Iron overload predisposes to diabetes and higher ferritin levels have been associated with diabetes. However, it is unclear whether ferritin reflects differences in iron-related parameters between diabetic and nondiabetic persons. We examined associations of serum ferritin, iron and transferrin saturation with Type 2 diabetes in adults without genetic predisposition to iron overload. Design, participants and measurements Cross-sectional analysis of community-dwelling men and women aged 17–97 years from the Busselton Health Survey, Western Australia. Men and women carrying genotypes associated with haemochromatosis (C282Y/C282Y or C282Y/H63D) were excluded. Serum ferritin, iron and transferrin saturation were assayed. Results There were 1834 men (122 with diabetes, 6•6%) and 2351 women (141 with diabetes, 6%). In men, higher serum ferritin was associated with diabetes after adjusting for age, smoking, alcohol, cardiovascular history, body mass index (BMI), waist, blood pressure, lipids, C-reactive protein (CRP), adiponectin, alanine transaminase (ALT) and gamma-glutamyl transpeptidase (GGT) [odds ratio (OR): 1•29 per 1 unit increase log ferritin, 95% confidence interval (CI) = 1•01–1•65, P = 0•043]. In women, higher serum ferritin was associated with diabetes [fully adjusted OR: 1•31 per 1 unit increase log ferritin, 95% CI = 1•04–1•63, P = 0•020; 1•84 for tertile (T) 3 vs T1, 95% CI = 1•09–3•11]. Neither iron levels nor transferrin saturation were associated with diabetes risk in men or women. Higher ferritin was not associated with insulin resistance in nondiabetic adults. Conclusions In adults, higher ferritin levels are independently associated with prevalent diabetes while iron and transferrin saturation are not. Ferritin is a robust biomarker for diabetes risk, but further investigation is needed to clarify whether this relationship is mediated via iron metabolism.

Published
2014

37. Association between C reactive protein and coronary heart disease : mendelian randomisation analysis based on individual participant data

Author

Wensley, Frances, Gao, Pei, Burgess, Stephen, Kaptoge, Stephen, Di Angelantonio, Emanuele, Shah, Tina, Engert, James C., Clarke, Robert, Davey-Smith, George, Nordestgaard, Borge G., Saleheen, Danish, Samani, Nilesh J., Sandhu, Manjinder, Anand, Sonia, Pepys, Mark B., Smeeth, Liam, Whittaker, John, Casas, Juan Pablo, Thompson, Simon G., Hingorani, Aroon D., Danesh, John, Eiriksdottir, G., Harris, T. B., Launer, L. J., Gudnason, V., Folsom, A. R., Andrews, G., Ballantyne, C. M., Samani, N. J., Hall, A. S., Braund, P. S., Balmforth, A. J., Whincup, P. H., Morris, R., Lawlor, D. A., Lowe, G. D. O., Timpson, N., Ebrahim, S., Ben-Shlomo, Y., Davey-Smith, G., Nordestgaard, B. G., Tybjaerg-Hansen, A., Zacho, J., Brown, M., Sandhu, M., Ricketts, S. L., Ashford, S., Lange, L., Reiner, A., Cushman, M., Tracy, R., Wu, C., Ge, J., Zou, Y., Sun, A., Hung, J., McQuillan, B., Thompson, P., Beilby, J., Warrington, N., Palmer, L. J., Wanner, C., Drechsler, C., Hoffmann, M. M., Fowkes, F. G. R., Tzoulaki, I., Kumari, M., Miller, M., Marmot, M., Onland-Moret, C., van der Schouw, Y. T., Boer, J. M., Wijmenga, C., Khaw, K-T, Vasan, R. S., Schnabel, R. B., Yamamoto, J. F., Benjamin, E. J., Schunkert, H., Erdmann, J., Koenig, I. R., Hengstenberg, C., Chiodini, B., Franzosi, M. G., Pietri, S., Gori, F., Rudock, M., Liu, Y., Lohman, K., Humphries, S. E., Hamsten, A., Norman, P. E., Hankey, G. J., Jamrozik, K., Rimm, E. B., Pai, J. K., Psaty, B. M., Heckbert, S. R., Bis, J. C., Yusuf, S., Anand, S., Engert, J. C., Xie, C., Collins, R., Clarke, R., Bennett, D., Kooner, J., Chambers, J., Elliott, P., Maerz, W., Kleber, M. E., Boehm, B. O., Winkelmann, B. R., Melander, O., Berglund, G., Koenig, W., Thorand, B., Baumert, J., Peters, A., Manson, J., Cooper, J. A., Talmud, P. J., Ladenvall, P., Johansson, L., Jansson, Jan-Håkan, Hallmans, Göran, Reilly, M. P., Qu, L., Li, M., Rader, D. J., Watkins, H., Hopewell, J., Saleheen, D., Danesh, J., Frossard, P., Sattar, N., Robertson, M., Shepherd, J., Schaefer, E., Hofman, A., Witteman, J. C. M., Kardys, I., Dehghan, A., de Faire, U., Bennet, A., Gigante, B., Leander, K., Peters, B., Maitland-van der Zee, A. H., de Boer, A., Klungel, O., Greenland, P., Dai, J., Liu, S., Brunner, E., Kivimaki, M., O'Reilly, D., Ford, I., Packard, C. J., Wensley, Frances, Gao, Pei, Burgess, Stephen, Kaptoge, Stephen, Di Angelantonio, Emanuele, Shah, Tina, Engert, James C., Clarke, Robert, Davey-Smith, George, Nordestgaard, Borge G., Saleheen, Danish, Samani, Nilesh J., Sandhu, Manjinder, Anand, Sonia, Pepys, Mark B., Smeeth, Liam, Whittaker, John, Casas, Juan Pablo, Thompson, Simon G., Hingorani, Aroon D., Danesh, John, Eiriksdottir, G., Harris, T. B., Launer, L. J., Gudnason, V., Folsom, A. R., Andrews, G., Ballantyne, C. M., Samani, N. J., Hall, A. S., Braund, P. S., Balmforth, A. J., Whincup, P. H., Morris, R., Lawlor, D. A., Lowe, G. D. O., Timpson, N., Ebrahim, S., Ben-Shlomo, Y., Davey-Smith, G., Nordestgaard, B. G., Tybjaerg-Hansen, A., Zacho, J., Brown, M., Sandhu, M., Ricketts, S. L., Ashford, S., Lange, L., Reiner, A., Cushman, M., Tracy, R., Wu, C., Ge, J., Zou, Y., Sun, A., Hung, J., McQuillan, B., Thompson, P., Beilby, J., Warrington, N., Palmer, L. J., Wanner, C., Drechsler, C., Hoffmann, M. M., Fowkes, F. G. R., Tzoulaki, I., Kumari, M., Miller, M., Marmot, M., Onland-Moret, C., van der Schouw, Y. T., Boer, J. M., Wijmenga, C., Khaw, K-T, Vasan, R. S., Schnabel, R. B., Yamamoto, J. F., Benjamin, E. J., Schunkert, H., Erdmann, J., Koenig, I. R., Hengstenberg, C., Chiodini, B., Franzosi, M. G., Pietri, S., Gori, F., Rudock, M., Liu, Y., Lohman, K., Humphries, S. E., Hamsten, A., Norman, P. E., Hankey, G. J., Jamrozik, K., Rimm, E. B., Pai, J. K., Psaty, B. M., Heckbert, S. R., Bis, J. C., Yusuf, S., Anand, S., Engert, J. C., Xie, C., Collins, R., Clarke, R., Bennett, D., Kooner, J., Chambers, J., Elliott, P., Maerz, W., Kleber, M. E., Boehm, B. O., Winkelmann, B. R., Melander, O., Berglund, G., Koenig, W., Thorand, B., Baumert, J., Peters, A., Manson, J., Cooper, J. A., Talmud, P. J., Ladenvall, P., Johansson, L., Jansson, Jan-Håkan, Hallmans, Göran, Reilly, M. P., Qu, L., Li, M., Rader, D. J., Watkins, H., Hopewell, J., Saleheen, D., Danesh, J., Frossard, P., Sattar, N., Robertson, M., Shepherd, J., Schaefer, E., Hofman, A., Witteman, J. C. M., Kardys, I., Dehghan, A., de Faire, U., Bennet, A., Gigante, B., Leander, K., Peters, B., Maitland-van der Zee, A. H., de Boer, A., Klungel, O., Greenland, P., Dai, J., Liu, S., Brunner, E., Kivimaki, M., O'Reilly, D., Ford, I., and Packard, C. J.

Abstract

Objective To use genetic variants as unconfounded proxies of C reactive protein concentration to study its causal role in coronary heart disease. Design Mendelian randomisation meta-analysis of individual participant data from 47 epidemiological studies in 15 countries. Participants 194 418 participants, including 46 557 patients with prevalent or incident coronary heart disease. Information was available on four CRP gene tagging single nucleotide polymorphisms (rs3093077, rs1205, rs1130864, rs1800947), concentration of C reactive protein, and levels of other risk factors. Main outcome measures Risk ratios for coronary heart disease associated with genetically raised C reactive protein versus risk ratios with equivalent differences in C reactive protein concentration itself, adjusted for conventional risk factors and variability in risk factor levels within individuals. Results CRP variants were each associated with up to 30% per allele difference in concentration of C reactive protein (P<10(-34)) and were unrelated to other risk factors. Risk ratios for coronary heart disease per additional copy of an allele associated with raised C reactive protein were 0.93 (95% confidence interval 0.87 to 1.00) for rs3093077; 1.00 (0.98 to 1.02) for rs1205; 0.98 (0.96 to 1.00) for rs1130864; and 0.99 (0.94 to 1.03) for rs1800947. In a combined analysis, the risk ratio for coronary heart disease was 1.00 (0.90 to 1.13) per 1 SD higher genetically raised natural log (ln) concentration of C reactive protein. The genetic findings were discordant with the risk ratio observed for coronary heart disease of 1.33 (1.23 to 1.43) per 1 SD higher circulating ln concentration of C reactive protein in prospective studies (P=0.001 for difference). Conclusion Human genetic data indicate that C reactive protein concentration itself is unlikely to be even a modest causal factor in coronary heart disease.

Published
2011
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45. Developing a commercial production process for 500 000 targets per day: A key challenge for inertial fusion energy

Author

Goodin, D. T., primary, Alexander, N. B., additional, Besenbruch, G. E., additional, Bozek, A. S., additional, Brown, L. C., additional, Carlson, L. C., additional, Flint, G. W., additional, Goodman, P., additional, Kilkenny, J. D., additional, Maksaereekul, W., additional, McQuillan, B. W., additional, Nikroo, A., additional, Paguio, R. R., additional, Petzoldt, R. W., additional, Raffray, R., additional, Schroen, D. G., additional, Sheliak, J. D., additional, Spalding, J., additional, Streit, J. E., additional, Tillack, M. S., additional, and Vermillion, B. A., additional

Published
2006
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