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6. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew J., Nho, Kwangsik, Risacher, Shannon L., Kastenmüller, Gabi, Arnold, Matthias, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jr., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John C., Perrin, Richard J., Shaw, Leslie M., Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K., Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormos, Adrienne, Montine, Tom, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Sloan, Brittany, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C., Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I., Nir, Talia M., Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, Crawford, Karen, Yushkevich, Paul A., Das, Sandhitsu, Koeppe, Robert A., Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Franklin, Erin, Bernhardt, Haley, Taylor-Reinwald, Lisa, Korecka, Magdalena, Figurski, Michal, Neu, Scott, Apostolova, Liana G., Shen, Li, Foroud, Tatiana M., Nudelman, Kelly, Faber, Kelley, Wilmes, Kristi, Thal, Leon, Silbert, Lisa C., Lind, Betty, Crissey, Rachel, Kaye, Jeffrey A., Carter, Raina, Dolen, Sara, Quinn, Joseph, Schneider, Lon S., Pawluczyk, Sonia, Becerra, Mauricio, Teodoro, Liberty, Dagerman, Karen, Spann, Bryan M., Vanderswag, Helen, Ziolkowski, Jaimie, Heidebrink, Judith L., Zbizek-Nulph, Lisa, Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Villanueva-Meyer, Javier, Pavlik, Valory, Pacini, Nathaniel, Lamb, Ashley, Kass, Joseph S., Doody, Rachelle S., Shibley, Victoria, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Mintz, Akiva, Ances, Beau, Winkfield, David, Carroll, Maria, Stobbs-Cucchi, Georgia, Oliver, Angela, Creech, Mary L., Mintun, Mark A., Schneider, Stacy, Geldmacher, David, Love, Marissa Natelson, Griffith, Randall, Clark, David, Brockington, John, Marson, Daniel, Grossman, Hillel, Goldstein, Martin A., Greenberg, Jonathan, Mitsis, Effie, Shah, Raj C., Lamar, Melissa, Samuels, Patricia, Duara, Ranjan, Greig-Custo, Maria T., Rodriguez, Rosemarie, Albert, Marilyn, Onyike, Chiadi, Farrington, Leonie, Rudow, Scott, Brichko, Rottislav, Kielb, Stephanie, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin, Sadowski, Martin, Wisniewski, Thomas, Shulman, Melanie, Faustin, Arline, Rao, Julia, Castro, Karen M., Ulysse, Anaztasia, Chen, Shannon, Sheikh, Mohammed O., Singleton-Garvin, Jamika, Petrella, JeffreyR., James, Olga, Wong, Terence Z., Borges-Neto, Salvador, Karlawish, Jason H., Wolk, David A., Vaishnavi, Sanjeev, Clark, Christopher M., Arnold, Steven E., Smith, Charles D., Jicha, Gregory A., Raslau, Flavius D., Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Martin, Kim, Kowalski, Nancy, Keltz, Melanie, Goldstein, Bonnie S., Makino, Kelly M., Ismail, M. Saleem, Brand, Connie, Thai, Gaby, Pierce, Aimee, Yanez, Beatriz, Sosa, Elizabeth, Witbracht, Megan, Kelley, Brendan, Nguyen, Trung, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan I., Lah, James J., Hajjar, Ihab, Cellar, Janet S., Burns, Jeffrey M., Swerdlow, Russell H., Brooks, William M., Silverman, Daniel H.S., Kremen, Sarah, Apostolova, Liana, Tingus, Kathleen, Lu, Po H., Bartzokis, George, Woo, Ellen, Teng, Edmond, Graff-Radford, Neill R., Parfitt, Francine, Poki-Walker, Kim, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Brosch, Jared R., Herring, Scott, van, Christopher H., Mecca, Adam P., Good, Susan P., MacAvoy, Martha G., Carson, Richard E., Varma, Pradeep, Chertkow, Howard, Vaitekunis, Susan, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Heyn, Chris (Chinthaka), Robin Hsiung, Ging-Yuek, Kim, Ellen, Mudge, Benita, Sossi, Vesna, Feldman, Howard, Assaly, Michele, Finger, Elizabeth, Pasternak, Stephen, Rachinsky, Irina, Kertesz, Andrew, Drost, Dick, Rogers, John, Grant, Ian, Muse, Brittanie, Rogalski, Emily, Robson, Jordan, Mesulam, M.-Marsel, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Rosen, Howard J., Miller, Bruce L., Perry, David, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, MCCann, Kelly, Poe, Jessica, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad A., Yesavage, Jerome, Taylor, Joy L., Chao, Steven, Coleman, Jaila, White, Jessica D., Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Belden, Christine M., Atri, Alireza, Clark, Kelly A., Zamrini, Edward, Sabbagh, Marwan, Killiany, Ronald, Stern, Robert, Mez, Jesse, Kowall, Neil, Budson, Andrew E., Obisesan, Thomas O., Ntekim, Oyonumo E., Wolday, Saba, Khan, Javed I., Nwulia, Evaristus, Nadarajah, Sheeba, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Maillard, Pauline, Olichney, John, Carmichael, Owen, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Borrie, Michael, Lee, T.-Y., Bartha, Dr Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Perrin, Allison, Burke, Anna, Scharre, Douglas W., Kataki, Maria, Tarawneh, Rawan, Hart, David, Zimmerman, Earl A., Celmins, Dzintra, Miller, Delwyn D., BolesPonto, Laura L., Smith, Karen Ekstam, Koleva, Hristina, Shim, Hyungsub, Nam, Ki Won, Schultz, Susan K., Williamson, Jeff D., Craft, Suzanne, Cleveland, Jo, Yang, Mia, Sink, Kaycee M., Ott, Brian R., Drake, Jonathan, Tremont, Geoffrey, Daiello, Lori A., Drake, Jonathan D., Ritter, Aaron, Bernick, Charles, Munic, Donna, O'Connelll, Abigail, Mintzer, Jacobo, Wiliams, Arthur, Masdeu, Joseph, Shi, Jiong, Garcia, Angelica, Newhouse, Paul, Potkin, Steven, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Kittur, Smita, Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Flashman, Laura A., Seltzer, Marc, Hynes, Mary L., Santulli, Robert B., Relkin, Norman, Chiang, Gloria, Lee, Athena, Lin, Michael, Ravdin, Lisa, Petersen, Ron, Neylan, Thomas, Grafman, Jordan, Danowski, Sarah, Nguyen-Barrera, Catherine, Hayes, Jacqueline, Finley, Shannon, Bernstein, Matthew, Senjem, Matt, Foster, Norm, Kim, Sungeun, Sood, Ajay, Blanchard, Kimberly S., Fleischman, Debra, Arfanakis, Konstantinos, Varon, Daniel, Greig, Maria T., Petrella, Jeffrey R., Goldstein, Bonnie, Martin, Kimberly S., Reist, Christopher, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Rosen, Howard, Marshall, Gad, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Mackin, Scott, Jimenez-Maggiora, Gustavo, Drake, Erin, Donohue, Mike, Nelson, Craig, Bickford, David, Butters, Meryl, Zmuda, Michelle, Reyes, Denise, Faber, Kelley M., Nudelman, Kelly N., Au, Yiu Ho, Scherer, Kelly, Catalinotto, Daniel, Stark, Samuel, Ong, Elise, Fernandez, Dariella, Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Meikle, Peter J., and Giles, Corey

Published
2023
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12. The impact of genomic biomarkers on a clinical risk prediction model for upgrading/upstaging among men with favorable-risk prostate cancer.

Author

Braun, Avery E., Chan, June M., Neuhaus, John, Cowan, Janet E., Kenfield, Stacey A., Van Blarigan, Erin L., Tenggara, Imelda, Broering, Jeanette M., Simko, Jeffry P., Carroll, Peter R., and Cooperberg, Matthew R.

Subjects
PROSTATE cancer, PROSTATE cancer patients, PREDICTION models, RECEIVER operating characteristic curves, BIOMARKERS, PROSTATE-specific antigen
Abstract

Background: The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision-making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model. Methods: Participants had favorable-risk PCa (cT1-2, prostate-specific antigen [PSA] =15 ng/mL, and Gleason grade group 1 [GG1]/low-volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction. Results: The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73-9.10); PSA (OR, 1.10; 95% CI, 1.01-1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01-1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97-0.99; per mL), and age (OR, 1.05; 95% CI, 1.02-1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01-1.06; p < .01) and AUC 0.72, which indicates a comparable performance to the prediction model. Conclusions: GCs did not substantially improve a clinical prediction model for UG/US, a short-term and imperfect surrogate for clinically relevant disease outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Participant completion of longitudinal assessments in an online cognitive aging registry: The role of medical conditions.

Author

Ashford, Miriam T., Jin, Chengshi, Neuhaus, John, Diaz, Adam, Aaronson, Anna, Tank, Rachana, Eichenbaum, Joseph, Camacho, Monica R., Fockler, Juliet, Ulbricht, Aaron, Flenniken, Derek, Truran, Diana, Mackin, Robert Scott, Weiner, Michael W., Mindt, Monica Rivera, and Nosheny, Rachel L.

Subjects
COGNITIVE aging, OLDER people, MEDICAL registries, ALZHEIMER'S disease, SUBSTANCE abuse
Abstract

INTRODUCTION: This study aimed to understand whether older adults' longitudinal completion of assessments in an online Alzheimer's disease and related dementias (ADRD)–related registry is influenced by self‐reported medical conditions. METHODS: Brain Health Registry (BHR) is an online cognitive aging and ADRD‐related research registry that includes longitudinal health and cognitive assessments. Using logistic regressions, we examined associations between longitudinal registry completion outcomes and self‐reported (1) number of medical conditions and (2) eight defined medical condition groups (cardiovascular, metabolic, immune system, ADRD, current psychiatric, substance use/abuse, acquired, other specified conditions) in adults aged 55+ (N = 23,888). Longitudinal registry completion outcomes were assessed by the completion of the BHR initial questionnaire (first questionnaire participants see at each visit) at least twice and completion of a cognitive assessment (Cogstate Brief Battery) at least twice. Models included ethnocultural identity, education, age, and subjective memory concern as covariates. RESULTS: We found that the likelihood of longitudinally completing the initial questionnaire was negatively associated with reporting a diagnosis of ADRD and current psychiatric conditions but was positively associated with reporting substance use/abuse and acquired medical conditions. The likelihood of longitudinally completing the cognitive assessment task was negatively associated with number of reported medical conditions, as well as with reporting cardiovascular conditions, ADRD, and current psychiatric conditions. Previously identified associations between ethnocultural identity and longitudinal assessment completion in BHR remained after accounting for the presence of medical conditions. DISCUSSION: This post hoc analysis provides novel, initial evidence that older adults' completion of longitudinal assessments in an online registry is associated with the number and types of participant‐reported medical conditions. Our findings can inform future efforts to make online studies with longitudinal health and cognitive assessments more usable for older adults with medical conditions. The results need to be interpreted with caution due to selection biases, and the under‐inclusion of minoritized communities. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Probing the correlation of neuronal loss, neurofibrillary tangles, and cell death markers across the Alzheimer's disease Braak stages: a quantitative study in humans

Author

Theofilas, Panos, Ehrenberg, Alexander J., Nguy, Austin, Thackrey, Julia M., Dunlop, Sara, Mejia, Maria B., Alho, Ana T., Paraizo Leite, Renata Elaine, Rodriguez, Roberta Diehl, Suemoto, Claudia K., Nascimento, Camila F., Chin, Marcus, Medina-Cleghorn, Daniel, Cuervo, Ana Maria, Arkin, Michelle, Seeley, William W., Miller, Bruce L., Nitrini, Ricardo, Pasqualucci, Carlos Augusto, Filho, Wilson Jacob, Rueb, Udo, Neuhaus, John, Heinsen, Helmut, and Grinberg, Lea T.

Published
2018
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20. Placebo Adherence and Its Association with Morbidity and Mortality in the Studies of Left Ventricular Dysfunction

Author

Avins, Andrew L., Pressman, Alice, Ackerson, Lynn, Rudd, Peter, Neuhaus, John, and Vittinghoff, Eric

Subjects
Medicine & Public Health, Internal Medicine, placebo, health outcome
Abstract

A provocative finding from several double-blind clinical trials has been the association between greater adherence to placebo study medication and better health outcomes. We used data from the Studies of Left Ventricular Dysfunction (SOLVD) Treatment Trial (SOLVD-TT) and the SOLVD Prevention Trial (SOLVD-PT) to examine whether such associations could be validated and to examine several sources of bias and potential confounding.Survival analytic methods were used to estimate the association between placebo adherence and several health outcomes, employing a number of modeling techniques to test for the existence of alternative explanations for the association. Higher adherence was defined as having taken ≥75% of prescribed study medication.Higher placebo adherence was associated with improved overall survival in both SOLVD-TT and SOLVD-PT [hazard ratio (HR) = 0.52, 95% confidence interval (CI): 0.35 to 0.79 and HR = 0.52, 95%CI: 0.38 to 0.71, respectively]. Associations were similar for fatal or non-fatal cardiovascular or coronary heart disease events. Adjustment for both modifiable and non-modifiable cardiac risk factors (including age, gender, diabetes, blood pressure, smoking, weight, alcohol use, and levels of education) had minimal effect on the strength of the association. Little evidence of bias was found as an explanation for this relationship.In these two trials, better adherence to placebo was associated with markedly superior health outcomes, including total in-study mortality and incident cardiovascular events. No important confounders were identified. These data suggest there may exist strong but unrecognized determinants of health outcomes for which placebo adherence is a marker.

Published
2010

24. Locus coeruleus volume and cell population changes during Alzheimer's disease progression: A stereological study in human postmortem brains with potential implication for early-stage biomarker discovery

Author

Theofilas, Panos, Ehrenberg, Alexander J., Dunlop, Sara, Di Lorenzo Alho, Ana T., Nguy, Austin, Leite, Renata Elaine Paraizo, Rodriguez, Roberta Diehl, Mejia, Maria B., Suemoto, Claudia K., Ferretti-Rebustini, Renata Eloah De Lucena, Polichiso, Livia, Nascimento, Camila F., Seeley, William W., Nitrini, Ricardo, Pasqualucci, Carlos Augusto, Jacob Filho, Wilson, Rueb, Udo, Neuhaus, John, Heinsen, Helmut, and Grinberg, Lea T.

Published
2017
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27. Correction: Racial and ethnic disparities in outcomes through 1 year of life in infants born prematurely: a population based study in California

Author

Karvonen, Kayla L., Baer, Rebecca J., Rogers, Elizabeth E., Steurer, Martina A., Ryckman, Kelli K., Feuer, Sky K., Anderson, James G., Franck, Linda S., Gano, Dawn, Petersen, Mark A., Oltman, Scott P., Chambers, Brittany D., Neuhaus, John, Rand, Larry, Jelliffe-Pawlowski, Laura L., and Pantell, Matthew S.

Published
2021
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39. Performance of the CANTAB ‐ Paired Associates Learning task in an unsupervised online setting: Results from the Brain Health Registry.

Author

Ashford, Miriam T., Aaronson, Anna, Kwang, Winnie, Eichenbaum, Joseph, Gummadi, Shilpa L, Jin, Chengshi, Cashdollar, Nathan, Thorp, Emily, Wragg, Elizabeth, Zavitz, Kenton, Cormack, Francesca K, Banh, Timothy, Neuhaus, John, Ulbricht, Aaron, Camacho, Monica R, Fockler, Juliet, Flenniken, Derek, Truran‐Sacrey, Diana, Mackin, Scott R, and Weiner, Michael S. W.

Abstract

Background: The aim of this study was to evaluate the feasibility and usability of collecting data from an unsupervised online version of the Paired Associates Learning (PAL) task from the Cambridge Neuropsychological Test Automated Battery (CANTAB™) in adults enrolled in the Brain Health Registry (BHR). Method: BHR is an online cognitive aging registry for referral and longitudinal unsupervised assessment. The PAL assesses visual associative learning and memory and was administered in BHR between 03/2021‐07/2022. PAL variables included in this analysis were 'Memory Score' (patterns correctly identified at first attempt for each difficulty level) and 'Total Errors', adjusted for level reached. We analyzed PAL task completion rate and characteristics of PAL completers and used linear regressions to assess whether cross‐sectional PAL Memory Score and PAL Total Errors were associated with participant data (age, gender, education, self‐ and study partner‐reported cognitive complaints (Everyday Cognition scale (ECog)), self‐reported memory concern, and self‐reported depressive symptom severity (PHQ‐9), self‐report of Mild Cognitive Impairment (MCI)). Result: As of July 2022, 14,520 participants aged 18+ years have completed PAL at least once. Participants were on average 66.3 years old (SD = 11.3), 72.4% female gender, 92.8% self‐identified as White, and had a mean of 16.6 years of education (SD = 2.27). BHR received 374 requests for study support regarding PAL, of which 36% (135) were related to device compatibility. Mean PAL scores were similar to those of previous, supervised studies (Figure 1). Worse PAL performance was significantly associated with being older, female gender, having less education, more self‐ and study partner‐reported ECog, self‐reported memory concern, more depressive symptoms, and self‐report of MCI (Table 1). Conclusion: Our findings provide evidence for the feasibility of collecting PAL data in an unsupervised online setting and construct validity of cross‐sectional BHR PAL. In terms of usability, there was a minimal need for support. Future studies will investigate the predictive value of BHR PAL in identifying adults with self‐reported MCI, longitudinal BHR PAL performance and associations with in‐clinic assessments and Alzheimer's disease biomarkers. Our sample lacked diversity. Therefore, a future direction is to increase accessibility of PAL to ethnoculturally and socioeconomically diverse communities. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Effects of Delayed Surgical Intervention Following Emergency Department Presentation on Stone Surgery Complexity.

Author

Bayne, David, Maru, Johsias, Srirangapatanam, Sudarshan, Hicks, Cameron, Neuhaus, John, Scales, Charles, Chi, Thomas, and Stoller, Marshall

Subjects
TREATMENT delay (Medicine), HOSPITAL emergency services, HEALTH services accessibility, LASER lithotripsy, KIDNEY stones, KIDNEY surgery, PERCUTANEOUS nephrolithotomy
Abstract

Introduction and Objective: Prior literature had demonstrated increased stone burden and higher rates of staged surgery in individuals of lower socioeconomic status (SES). Low SES individuals are more likely to experience delays in definitive stone surgery after initial presentation to the emergency department (ED) for kidney stones. This study aims to investigate the relationship between delays in definitive kidney stone surgery and the subsequent need for percutaneous nephrolithotomy (PNL) and/or staged surgical procedures using a statewide data set. Methods: This retrospective cohort study gathered longitudinal data from 2009 to 2018 using the California Department of Health Care Access and Information data set. Patient demographic characteristics, comorbidities, diagnosis/procedure codes, and distance were analyzed. Complex stone surgery was defined as initial PNL and/or undergoing more than one procedure within 365 days of initial intervention. Results: A total of 1,816,093 billing encounters from 947,798 patients were screened, resulting in 44,835 patients with ED visits for kidney stones followed by a urologic stone procedure. Multivariable analysis revealed that relative to patients who underwent surgery within 1 month of initial ED visit for stone disease, patients were at increased odds of undergoing complex surgery if waiting ≥6 months (odds ratio [OR] 1.18, p = 0.022), ≥1 year (OR 1.29, p < 0.001), and ≥3 years (OR 1.43, p < 0.001). Conclusions: Delays in definitive stone surgery after initial ED encounter for stone disease were associated with increased likelihood of undergoing a complex stone treatment. [ABSTRACT FROM AUTHOR]

Published
2023
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42. We Shall Not Weary, We Shall Not Rest.

Author

Neuhaus, John

Abstract

The article offers information about the pro-life movement that began in the twentieth century laid the foundation for the pro-life movement of the twenty-first century. It mentions about the "Roe v. Wade, " a landmark decision of the U.S. Supreme Court in which the Court ruled that the Constitution of the United States generally protects a pregnant individual's liberty to have an abortion.

Published
2023

47. Memantine in patients with frontotemporal lobar degeneration: a multicentre, randomised, double-blind, placebo-controlled trial

Author

Boxer, Adam L, Knopman, David S, Kaufer, Daniel I, Grossman, Murray, Onyike, Chiadi, Graf-Radford, Neill, Mendez, Mario, Kerwin, Diana, Lerner, Alan, Wu, Chuang-Kuo, Koestler, Mary, Shapira, Jill, Sullivan, Kathryn, Klepac, Kristen, Lipowski, Kristine, Ullah, Jerin, Fields, Scott, Kramer, Joel H, Merrilees, Jennifer, Neuhaus, John, Mesulam, M Marsel, and Miller, Bruce L

Published
2013
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49. Improving Predictions When Interest Focuses on Extreme Random Effects.

Author

McCulloch, Charles E. and Neuhaus, John M.

Subjects
*FIXED effects model, *EXTREME value theory, *INDEPENDENT variables, *WALKING speed, *STATISTICAL models, *FORECASTING
Abstract

Statistical models that generate predicted random effects are widely used to evaluate the performance of and rank patients, physicians, hospitals and health plans from longitudinal and clustered data. Predicted random effects have been proven to outperform treating clusters as fixed effects (essentially a categorical predictor variable) and using standard regression models, on average. These predicted random effects are often used to identify extreme or outlying values, such as poorly performing hospitals or patients with rapid declines in their health. When interest focuses on the extremes rather than performance on average, there has been no systematic investigation of best approaches. We develop novel methods for prediction of extreme values, evaluate their performance, and illustrate their application using data from the Osteoarthritis Initiative to predict walking speed in older adults. The new methods substantially outperform standard random and fixed-effects approaches for extreme values. [ABSTRACT FROM AUTHOR]

Published
2023
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