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31 Machine Learning Algorithm to Predict Duration to Full Time Care after Alzheimer's Disease Diagnosis
- Jessica H Helphrey, Jayme M Palka, Jake Rossmango, Hudaisa Fatima, Michael Conley, Anthony Longoria, Jennifer Sawyer, Jeffrey Schaffert, Anne Carlew, Munro Cullum, Laura Lacritz, John Hart, Hsueh-Sheng Chiang, Trung Nguyen, Alka Khera, Christian LoBue
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, p. 241
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Objective:
Patients and their families often ask clinicians to estimate when full-time care (FTC) will be needed after Alzheimer's Disease (AD) is diagnosed. Although a few algorithms predictive algorithms for duration to FTC have been created, these have not been widely adopted for clinical use due to questions regarding precision from limited sample sizes and lack of an easy, user friendly prediction model. Our objective was to develop a clinically relevant, data-driven predictive model using machine learning to estimate time to FTC in AD based on information gathered from a) clinical interview alone, and b) clinical interview plus neuropsychological data.
Participants and Methods:The National Alzheimer's Coordinating Center dataset was used to examine 3,809 participants (M age at AD diagnosis = 76.05, SD = 9.76; 47.10% male; 87.20% Caucasian) with AD dementia who were aged >50 years, had no history of stroke, and not dependent on others for basic activities of daily living at time of diagnosis based on qualitative self or informant report. To develop a predictive model for time until FTC, supervised machine learning algorithms (e.g., gradient descent, gradient boosting) were implemented. In Model 1, 29 variables captured at the time of AD diagnosis and often gathered in a clinical interview, including sociodemographic factors, psychiatric conditions, medical history, and MMSE, were included. In Model 2, additional neuropsychological variables assessing episodic memory, language, attention, executive function, and processing speed were added. To train and test the algorithm(s), data were split into a 70:30 ratio. Prediction optimization was examined via cross validation using 1000 bootstrapped samples. Model evaluation included assessment of confusion matrices and calculation of accuracy and precision.
Results:The average time to requiring FTC after AD diagnosis was 3.32 years (Range = 0.53-14.57 years). For the clinical interview only model (Model 1), younger age of onset, use of cholinesterase inhibitor medication, incontinence, and apathy were among the clinical variables that significantly predicted duration to FTC, with the largest effects shown for living alone, a positive family history of dementia, and lower MMSE score. In Model 2, the clinical predictors remained significant, and lower Boston Naming Test and Digit-Symbol Coding scores showed the largest effects in predicting duration to FTC among the neuropsychological measures. Final prediction models were further tested using five randomly selected cases. The average estimated time to FTC using the clinical interview model was within an average of 5.2 months of the recorded event and within an average of 5.8 months for the model with neuropsychological data.
Conclusions:Predicting when individuals diagnosed with AD will need FTC is important as the transition often carries significant financial costs related to caregiving. Duration to FTC was predicted by clinical and neuropsychological variables that are easily obtained during standard dementia evaluations. Implementation of the model for prediction of FTC in cases showed encouraging prognostic accuracy. The two models show promise as a first step towards creation of a user friendly prediction calculator that could help clinicians better counsel patients on when FTC after AD diagnosis may occur, though the development of separate models for use in more diverse populations will be essential.
Post-operative blood loss is higher among African American neonates undergoing open-heart surgery with cardiopulmonary bypass for CHD
- Vyas M. Kartha, Mohamed Rehman, Anh Thy H. Nguyen, Ernest Amankwah, Erica M.S. Sibinga, Neil A. Goldenberg, Jeffrey P. Jacobs
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- Journal:
- Cardiology in the Young / Volume 30 / Issue 1 / January 2020
- Published online by Cambridge University Press:
- 06 December 2019, pp. 74-81
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Background:
Neonates are at high risk of bleeding after open-heart surgery. We sought to determine pre-operative and intra-operative risk factors for increased bleeding after neonatal open-heart surgery with cardiopulmonary bypass.
Methods:We conducted a retrospective cohort study of neonates (0–30 days old) who underwent open-heart surgery with cardiopulmonary bypass from January, 2009, to March, 2013. Cardiac diagnosis; demographic and surgical data; and blood products, haemostatic agents, and anti-thrombotic agents administered before, during, and within 24 hours after surgery were abstracted from the electronic health record and anaesthesia records. The outcome of interest was chest tube output (in ml/kg body weight) within 24 hours. Relationships between chest tube output and putative associated factors were evaluated by unadjusted and adjusted linear regression.
Results:The cohort consisted of 107 neonates, of whom 79% had a Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) Mortality Category of 4 or 5. Median chest tube output was 37 ml/kg (range 9–655 ml/kg). Age, African-American race, and longer durations of surgery and cardiopulmonary bypass each had statistically significant associations with increased chest tube output in unadjusted analyses. In multivariable analysis, African-American race retained an independent, statistically significant association with increased chest tube output; the geometric mean of chest tube output among African-American neonates was 71% higher than that of Caucasians (95% confidence interval, 29–125%; p = 0.001).
Conclusion:Among neonates with CHD undergoing open-heart surgery with cardiopulmonary bypass, African-American race is independently associated with greater chest tube output over the first 24 hours post-operatively.
Contributors
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- By Cecil S. Ash, Paul Barach, Ulrike Buehner, M. Ross Bullock, Leonardo Canale, Henry G. Chou, Jeffrey A. Claridge, John J. Como, Armagan Dagal, Martin Dauber, James S. Davis, Shalini Dhir, François Donati, Roman Dudaryk, Richard P. Dutton, Talmage D. Egan, Yashar Eshraghi, John R. Fisgus, Jeff Gadsden, Sugantha Ganapathy, Mark A. Gerhardt, Inderjit Gill, Joseph F. Golob, Glenn P. Gravlee, Marcello Guglielmi, Jana Hambley, Peter Hebbard, Elena J. Holak, Khadil Hosein, Ken Johnson, Matthew A. Joy, George W. Kanellakos, Olga Kaslow, Arthur M. Lam, Vanetta Levesque, Jessica Anne Lovich-Sapola, M. Jocelyn Loy, Peter F. Mahoney, Donn Marciniak, Maureen McCunn, Craig C. McFarland, Maroun J. Mhanna, Timothy Moore, Cynthia Nguyen, Maxim Novikov, E. Orestes O’Brien, Ketan P. Parekh, Claire L. Park, Michael J. A. Parr, Elie Rizkala, Steven Roth, Alistair Royse, Colin Royse, Kasia Petelenz Rubin, David Ryan, Claire Sandstrom, Carl I. Schulman, Rishad Shaikh, Ranjita Sharma, Jeffrey H. Silverstein, Peter Slinger, Charles E. Smith, Christopher Smith, Paul Soeding, Rakesh V. Sondekoppam, P. David Soran, Eldar Søreide, Elizabeth A. Steele, Kristian Strand, Dennis M. Super, Kutaiba Tabbaa, Nicholas T. Tarmey, Joshua M. Tobin, Kalpana Tyagaraj, Heather A. Vallier, Sandra Werner, Earl Willis Weyers, William C. Wilson, Shoji Yokobori, Charles J. Yowler
- Edited by Charles E. Smith
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- Book:
- Trauma Anesthesia
- Published online:
- 05 April 2015
- Print publication:
- 09 April 2015, pp vii-x
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- By Linda S. Aglio, Cyrus Ahmadi Yazdi, Syed Irfan Qasim Ali, Caryn Barnet, Jessica Bauerle, Felicity Billings, Evan Blaney, Beverly Chang, Christopher Chen, Zinaida Chepurny, Hyung Sun Choi, Allison Clark, Lauren J. Cornella, Lisa Crossley, Michael D’Ambra, Galina Davidyuk, Whitney de Luna, Manisha S. Desai, Sukumar P. Desai, Kelly G. Elterman, Michaela K. Farber, Iuliu Fat, Jaida Fitzgerald, Devon Flaherty, John A. Fox, Gyorgy Frendl, Rejean Gareau, Joseph M. Garfield, Andrea Girnius, Laverne D. Gugino, J. Tasker Gundy, Carly C. Guthrie, Lisa M. Hammond, M. Tariq Hanifi, James Hardy, Philip M. Hartigan, Thomas Hickey, Richard Hsu, Mohab Ibrahim, David Janfaza, Yuka Kiyota, Suzanne Klainer, Benjamin Kloesel, Hanjo Ko, Bhavani Kodali, Vesela Kovacheva, J. Matthew Kynes, Robert W. Lekowski, Joyce Lo, Jeffrey Lu, Alvaro A. Macias, Zahra M. Malik, Erich N. Marks, Brendan McGinn, Jonathan R. Meserve, Annette Mizuguchi, Srdjan S. Nedeljkovic, Ju-Mei Ng, Michael Nguyen, Olutoyin Okanlawon, Jennifer Oliver, Krishna Parekh, Jessica Patterson, Christian Peccora, Pete Pelletier, Sujatha Pentakota, James H. Philip, Marc Philip T. Pimentel, Timothy D. Quinn, Elizabeth M. Rickerson, Susan L. Sager, Julia Serber, Shaheen Shaikh, Stanton Shernan, David Silver, Alissa Sodickson, Pingping Song, George P. Topulos, Agnieszka Trzcinka, Richard D. Urman, Rosemary Uzomba, Joshua Vacanti, Assia Valovska, Michael Vaninetti, Scott W. Vaughan, Kamen Vlassakov, Christopher Voscopoulos, Emily L. Wang, Laura Westfall, Zhiling Xiong, Stephanie Yacoubian, Dongdong Yao, Martin Zammert, Maksim Zayaruzny, Jose Luis Zeballos, Natthasorn Zinboonyahgoon, Jie Zhou
- Edited by Linda S. Aglio, Robert W. Lekowski, Richard D. Urman
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- Book:
- Essential Clinical Anesthesia Review
- Published online:
- 05 February 2015
- Print publication:
- 08 January 2015, pp xi-xvi
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- By Aakash Agarwala, Linda S. Aglio, Rae M. Allain, Paul D. Allen, Houman Amirfarzan, Yasodananda Kumar Areti, Amit Asopa, Edwin G. Avery, Patricia R. Bachiller, Angela M. Bader, Rana Badr, Sibinka Bajic, David J. Baker, Sheila R. Barnett, Rena Beckerly, Lorenzo Berra, Walter Bethune, Sascha S. Beutler, Tarun Bhalla, Edward A. Bittner, Jonathan D. Bloom, Alina V. Bodas, Lina M. Bolanos-Diaz, Ruma R. Bose, Jan Boublik, John P. Broadnax, Jason C. Brookman, Meredith R. Brooks, Roland Brusseau, Ethan O. Bryson, Linda A. Bulich, Kenji Butterfield, William R. Camann, Denise M. Chan, Theresa S. Chang, Jonathan E. Charnin, Mark Chrostowski, Fred Cobey, Adam B. Collins, Mercedes A. Concepcion, Christopher W. Connor, Bronwyn Cooper, Jeffrey B. Cooper, Martha Cordoba-Amorocho, Stephen B. Corn, Darin J. Correll, Gregory J. Crosby, Lisa J. Crossley, Deborah J. Culley, Tomas Cvrk, Michael N. D'Ambra, Michael Decker, Daniel F. Dedrick, Mark Dershwitz, Francis X. Dillon, Pradeep Dinakar, Alimorad G. Djalali, D. John Doyle, Lambertus Drop, Ian F. Dunn, Theodore E. Dushane, Sunil Eappen, Thomas Edrich, Jesse M. Ehrenfeld, Jason M. Erlich, Lucinda L. Everett, Elliott S. Farber, Khaldoun Faris, Eddy M. Feliz, Massimo Ferrigno, Richard S. Field, Michael G. Fitzsimons, Hugh L. Flanagan Jr., Vladimir Formanek, Amanda A. Fox, John A. Fox, Gyorgy Frendl, Tanja S. Frey, Samuel M. Galvagno Jr., Edward R. Garcia, Jonathan D. Gates, Cosmin Gauran, Brian J. Gelfand, Simon Gelman, Alexander C. Gerhart, Peter Gerner, Omid Ghalambor, Christopher J. Gilligan, Christian D. Gonzalez, Noah E. Gordon, William B. Gormley, Thomas J. Graetz, Wendy L. Gross, Amit Gupta, James P. Hardy, Seetharaman Hariharan, Miriam Harnett, Philip M. Hartigan, Joaquim M. Havens, Bishr Haydar, Stephen O. Heard, James L. Helstrom, David L. Hepner, McCallum R. Hoyt, Robert N. Jamison, Karinne Jervis, Stephanie B. Jones, Swaminathan Karthik, Richard M. Kaufman, Shubjeet Kaur, Lee A. Kearse Jr., John C. Keel, Scott D. Kelley, Albert H. Kim, Amy L. Kim, Grace Y. Kim, Robert J. Klickovich, Robert M. Knapp, Bhavani S. Kodali, Rahul Koka, Alina Lazar, Laura H. Leduc, Stanley Leeson, Lisa R. Leffert, Scott A. LeGrand, Patricio Leyton, J. Lance Lichtor, John Lin, Alvaro A. Macias, Karan Madan, Sohail K. Mahboobi, Devi Mahendran, Christine Mai, Sayeed Malek, S. Rao Mallampati, Thomas J. Mancuso, Ramon Martin, Matthew C. Martinez, J. A. Jeevendra Martyn, Kai Matthes, Tommaso Mauri, Mary Ellen McCann, Shannon S. McKenna, Dennis J. McNicholl, Abdel-Kader Mehio, Thor C. Milland, Tonya L. K. Miller, John D. Mitchell, K. Annette Mizuguchi, Naila Moghul, David R. Moss, Ross J. Musumeci, Naveen Nathan, Ju-Mei Ng, Liem C. Nguyen, Ervant Nishanian, Martina Nowak, Ala Nozari, Michael Nurok, Arti Ori, Rafael A. Ortega, Amy J. Ortman, David Oxman, Arvind Palanisamy, Carlo Pancaro, Lisbeth Lopez Pappas, Benjamin Parish, Samuel Park, Deborah S. Pederson, Beverly K. Philip, James H. Philip, Silvia Pivi, Stephen D. Pratt, Douglas E. Raines, Stephen L. Ratcliff, James P. Rathmell, J. Taylor Reed, Elizabeth M. Rickerson, Selwyn O. Rogers Jr., Thomas M. Romanelli, William H. Rosenblatt, Carl E. Rosow, Edgar L. Ross, J. Victor Ryckman, Mônica M. Sá Rêgo, Nicholas Sadovnikoff, Warren S. Sandberg, Annette Y. Schure, B. Scott Segal, Navil F. Sethna, Swapneel K. Shah, Shaheen F. Shaikh, Fred E. Shapiro, Torin D. Shear, Prem S. Shekar, Stanton K. Shernan, Naomi Shimizu, Douglas C. Shook, Kamal K. Sikka, Pankaj K. Sikka, David A. Silver, Jeffrey H. Silverstein, Emily A. Singer, Ken Solt, Spiro G. Spanakis, Wolfgang Steudel, Matthias Stopfkuchen-Evans, Michael P. Storey, Gary R. Strichartz, Balachundhar Subramaniam, Wariya Sukhupragarn, John Summers, Shine Sun, Eswar Sundar, Sugantha Sundar, Neelakantan Sunder, Faraz Syed, Usha B. Tedrow, Nelson L. Thaemert, George P. Topulos, Lawrence C. Tsen, Richard D. Urman, Charles A. Vacanti, Francis X. Vacanti, Joshua C. Vacanti, Assia Valovska, Ivan T. Valovski, Mary Ann Vann, Susan Vassallo, Anasuya Vasudevan, Kamen V. Vlassakov, Gian Paolo Volpato, Essi M. Vulli, J. Matthias Walz, Jingping Wang, James F. Watkins, Maxwell Weinmann, Sharon L. Wetherall, Mallory Williams, Sarah H. Wiser, Zhiling Xiong, Warren M. Zapol, Jie Zhou
- Edited by Charles Vacanti, Scott Segal, Pankaj Sikka, Richard Urman
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- Book:
- Essential Clinical Anesthesia
- Published online:
- 05 January 2012
- Print publication:
- 11 July 2011, pp xv-xxviii
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Tape Casting Technique for Fabrication of Graded-Density Impactors for Tailored Dynamic Compression
- L. Peter Martin, Jeffrey H. Nguyen, Jeremy R. Patterson, Daniel Orlikowski, Palakkal P. Asoka-Kumar, Neil C. Holmes
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- Journal:
- MRS Online Proceedings Library Archive / Volume 987 / 2006
- Published online by Cambridge University Press:
- 26 February 2011, 0987-PP01-09
- Print publication:
- 2006
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Recently, the use of graded density impactors for dynamic compression experiments has received increasing interest. These gas gun experiments have demonstrated complex loading paths which can last microseconds, and may be capable of bridging the timescales of existing static and dynamic compression experiments. A tape casting technique has been developed for fabrication of the impactors. In the technique, a series of tapes were prepared in the Mg-Cu system with compositions ranging from 100% Mg to 100% Cu. The tapes were characterized for their layer thickness, density, and sound wave velocity. Impactors were fabricated by punching individual layers from the tapes, stacking and laminating them, removing the organic matrix, and hot-pressing the laminated structure. The density profile is determined by the order in which the tapes are stacked in the laminate and is therefore highly flexible. The resultant thickness and average density of the impactors is consistent with the data for the individual layers. Impactors were characterized for uniformity by ultrasonic C-scan and white light interferometry. Dynamic compression experiments were performed on a two-stage helium gas gun using the graded density impactors. Results will be presented and shown to agree well with hydrocode modeling.
The Stability and Oxidation Resistance of Iron- and Cobalt-Based Magnetic Nanoparticle Fluids Fabricated by Inert-Gas Condensation
- Nguyen H. Hai, Raymond Lemoine, Shaina Remboldt, Michelle A. Strand, Steve Wignall, Jeffrey E. Shield, Diandra Leslie-Pelecky
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- Journal:
- MRS Online Proceedings Library Archive / Volume 845 / 2004
- Published online by Cambridge University Press:
- 01 February 2011, AA5.44
- Print publication:
- 2004
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Magnetic nanoparticle fluids have numerous biomedical applications, including magnetic imaging, drug delivery, and hyperthermia treatment for cancer. Ideal magnetic nanoparticle fluids have well-separated, biocompatible nanoparticles with a small size distribution that form a stable colloid. We have combined inert-gas condensation, which produces nanoparticles with low polydispersity, with deposition directly into a surfactant-laden fluid to prevent agglomeration. Iron, cobalt, and iron-nitride nanoparticle fluids fabricated using inert-gas condensation have with mean particle sizes from 5–50 nm and remain stable over long periods of time. Iron and cobalt nanoparticles oxidize on exposure to air, with oxidation rates dependent on surfactant type and concentration. Iron-nitride fluids are more oxidation and corrosion resistant, while retaining the same high degree of colloidal stability. Magnetic properties vary depending on the nanoparticle size and material, but can be varied from superparamagnetic to ferromagnetic with coercivities on the order of 1000 Oe. In addition to future biomedical applications, inertgas condensation into fluids offers the opportunity to study interparticle interactions over a broad range of intrinsic materials parameters and interparticle separations.
High Pressure - High Temperature Synthesis of Low-Compressibility Cubic C3N4
- Jeffrey H. Nguyen, Wendel A. Caldwell, Laura Robin Benedetti, Michael B. Kruger, Raymond Jeanloz
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- Journal:
- MRS Online Proceedings Library Archive / Volume 499 / 1997
- Published online by Cambridge University Press:
- 10 February 2011, 303
- Print publication:
- 1997
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We report the synthesis of a crystalline carbon-nitrogen compound having a zero-pressure bulk modulus of ∼ 249 GPa. Its crystal-structural and compressional properties are in good accord with those predicted theoretically by Teter and Hemley for a cubic C3N4 phase. Experimentally, this phase is synthesized at pressures and temperatures above 18 GPa and 2000 K. It decomposes upon pressure release below 14 GPa, and may offer a route to synthesizing other incompressible carbon nitride phases.