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5. Treatment of patients hospitalized for COVID-19 with remdesivir is associated with lower likelihood of 30-day readmission: a retrospective observational study.

Author

Mozaffari, Essy, Chandak, Aastha, Gottlieb, Robert, Chima-Melton, Chidinma, Kalil, Andre, Sarda, Vishnudas, Der-Torossian, Celine, Oppelt, Thomas, Berry, Mark, and Amin, Alpesh

Subjects
COVID-19, post-discharge outcomes, readmission, remdesivir, Adult, Humans, COVID-19, Patient Readmission, COVID-19 Drug Treatment, Hospitalization, Retrospective Studies, Adenosine Monophosphate, Alanine
Abstract

Aim: This observational study investigated the association between remdesivir treatment during hospitalization for COVID-19 and 30-day COVID-19-related and all-cause readmission across different variants time periods. Patients & methods: Hospitalization records for adult patients discharged from a COVID-19 hospitalization between 1 May 2020 to 30 April 2022 were extracted from the US PINC AI Healthcare Database. Likelihood of 30-day readmission was compared among remdesivir-treated and nonremdesivir-treated patients using multivariable logistic regression models adjusted for age, corticosteroid treatment, Charlson comorbidity index and intensive care unit stay during the COVID-19 hospitalization. Analyses were stratified by maximum supplemental oxygen requirement and variant time period (pre-Delta, Delta and Omicron). Results: Of the 440,601 patients discharged alive after a COVID-19 hospitalization, 248,785 (56.5%) patients received remdesivir. Overall, remdesivir patients had a 30-day COVID-19-related readmission rate of 3.0% and all-cause readmission rate of 6.3% compared with 5.4% and 9.1%, respectively, for patients who did not receive remdesivir during their COVID-19 hospitalization. After adjusting for demographics and clinical characteristics, remdesivir treatment was associated with significantly lower odds of 30-day COVID-19-related readmission (odds ratio 0.60 [95% confidence interval: 0.58-0.62]), and all-cause readmission (0.73 [0.72-0.75]). Significantly lower odds of 30-day readmission in remdesivir-treated patients was observed across all variant time periods. Conclusion: Treating patients hospitalized for COVID-19 with remdesivir is associated with a statistically significant reduction in 30-day COVID-19-related and all-cause readmission across variant time periods. These findings indicate that the clinical benefit of remdesivir may extend beyond the COVID-19 hospitalization.

Published
2024

9. Remdesivir Reduced Mortality in Immunocompromised Patients Hospitalized for COVID-19 Across Variant Waves: Findings From Routine Clinical Practice.

Author

Mozaffari, Essy, Chandak, Aastha, Gottlieb, Robert, Read, Stephanie, Jiang, Heng, Chiang, Mel, Lee, EunYoung, Gupta, Rikisha, Berry, Mark, Kalil, Andre, and Chima-Melton, Chidinma

Subjects
COVID-19, comparative effectiveness research, immunocompromised, mortality, remdesivir, Humans, COVID-19, COVID-19 Drug Treatment, Immunocompromised Host, Inpatients, Antiviral Agents
Abstract

BACKGROUND: Immunocompromised patients are at high risk of severe coronavirus disease 2019 (COVID-19) and death, yet treatment strategies for immunocompromised patients hospitalized for COVID-19 reflect variations in clinical practice. In this comparative effectiveness study, we investigated the effect of remdesivir treatment on inpatient mortality among immunocompromised patients hospitalized for COVID-19 across all variants of concern (VOC) periods. METHODS: Data for immunocompromised patients hospitalized for COVID-19 between December 2020 and April 2022 were extracted from the US PINC AITM Healthcare Database. Patients who received remdesivir within 2 days of hospitalization were matched 1:1 using propensity score matching to patients who did not receive remdesivir. Additional matching criteria included admission month, age group, and hospital. Cox proportional hazards models were used to examine the effect of remdesivir on risk of 14- and 28-day mortality during VOC periods. RESULTS: A total of 19 184 remdesivir patients were matched to 11 213 non-remdesivir patients. Overall, 11.1% and 17.7% of remdesivir patients died within 14 and 28 days, respectively, compared with 15.4% and 22.4% of non-remdesivir patients. Remdesivir was associated with a reduction in mortality at 14 (hazard ratio [HR], 0.70; 95% confidence interval, .62-.78) and 28 days (HR, 0.75; 95% CI, .68-.83). The survival benefit remained significant during the pre-Delta, Delta, and Omicron periods. CONCLUSIONS: Prompt initiation of remdesivir in immunocompromised patients hospitalized for COVID-19 is associated with significant survival benefit across all variant waves. These findings provide much-needed evidence relating to the effectiveness of a foundational treatment for hospitalized COVID-19 patients among a high-risk population.

Published
2023

11. Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users

Author

Diehl, Peter Udo, Zilly, Hannes, Sattler, Felix, Singer, Yosef, Kepp, Kevin, Berry, Mark, Hasemann, Henning, Zippel, Marlene, Kaya, Müge, Meyer-Rachner, Paul, Pudszuhn, Annett, Hofmann, Veit M., Vormann, Matthias, and Sprengel, Elias

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech understanding in noise, improve by 1.6 dB SRT. Ours is the first denoising system that is implemented on a mobile device, streamed directly to users' hearing aids using only a single channel as audio input while improving user satisfaction on all tested aspects, including speech intelligibility. This includes overall preference of the denoised and streamed signal over the hearing aid, thereby accepting the higher latency for the significant improvement in speech understanding.

Published
2023

13. Remdesivir Is Associated With Reduced Mortality in COVID-19 Patients Requiring Supplemental Oxygen Including Invasive Mechanical Ventilation Across SARS-CoV-2 Variants.

Author

Mozaffari, Essy, Chandak, Aastha, Gottlieb, Robert, Chima-Melton, Chidinma, Read, Stephanie, Lee, EunYoung, Der-Torossian, Celine, Gupta, Rikisha, Berry, Mark, Hollemeersch, Stijn, and Kalil, Andre

Subjects
COVID-19, comparative effectiveness research, hospitalization, mortality, remdesivir
Abstract

BACKGROUND: This comparative effectiveness study investigated the effect of remdesivir on in-hospital mortality among patients hospitalized for coronavirus disease 2019 (COVID-19) requiring supplemental oxygen including low-flow oxygen (LFO), high-flow oxygen/noninvasive ventilation (HFO/NIV), or invasive mechanical ventilation/extracorporeal membrane oxygenation (IMV/ECMO) across variant of concern (VOC) periods. METHODS: Patients hospitalized for COVID-19 between December 2020 and April 2022 and administered remdesivir upon admission were 1:1 propensity score matched to patients not administered remdesivir during their COVID-19 hospitalization. Analyses were stratified by supplemental oxygen requirement upon admission and VOC period. Cox proportional hazards models were used to derive adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for 14- and 28-day mortality. RESULTS: Patients treated with remdesivir (67 582 LFO, 34 857 HFO/NIV, and 4164 IMV/ECMO) were matched to non-remdesivir patients. Unadjusted mortality rates were significantly lower for remdesivir-treated patients at 14 days (LFO: 6.4% vs. 8.8%; HFO/NIV: 16.8% vs. 19.4%; IMV/ECMO: 27.8% vs. 35.3%) and 28 days (LFO: 9.8% vs. 12.3%; HFO/NIV: 25.8% vs. 28.3%; IMV/ECMO: 41.4% vs. 50.6%). After adjustment, remdesivir treatment was associated with a statistically significant reduction in in-hospital mortality at 14 days (LFO: aHR, 0.72; 95% CI, 0.66-0.79; HFO/NIV: aHR, 0.83; 95% CI, 0.77-0.89; IMV/ECMO: aHR, 0.73; 95% CI, 0.65-0.82) and 28 days (LFO: aHR, 0.79; 95% CI, 0.73-0.85; HFO/NIV: aHR, 0.88; 95% CI, 0.82-0.93; IMV/ECMO: aHR, 0.74; 95% CI, 0.67-0.82) compared with non-remdesivir treatment. Lower risk of mortality among remdesivir-treated patients was observed across VOC periods. CONCLUSIONS: Remdesivir treatment is associated with significantly reduced mortality among patients hospitalized for COVID-19 requiring supplemental oxygen upon admission, including those requiring HFO/NIV or IMV/ECMO with severe or critical disease, across VOC periods.

Published
2023

22. Restoring speech intelligibility for hearing aid users with deep learning

Author

Diehl, Peter Udo, Singer, Yosef, Zilly, Hannes, Schönfeld, Uwe, Meyer-Rachner, Paul, Berry, Mark, Sprekeler, Henning, Sprengel, Elias, Pudszuhn, Annett, and Hofmann, Veit M.

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Quantitative Biology - Neurons and Cognition
Abstract

Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural architecture search, using a novel deep learning-based metric for speech intelligibility. The network achieves state-of-the-art denoising on a range of human-graded assessments, generalizes across different noise categories and - in contrast to classic beamforming approaches - operates on a single microphone. The system runs in real time on a laptop, suggesting that large-scale deployment on hearing aid chips could be achieved within a few years. Deep learning-based denoising therefore holds the potential to improve the quality of life of millions of hearing impaired people soon.

Published
2022

41. The impact of perioperative stroke and delirium on outcomes after surgical aortic valve replacement

Author

Miller, Marissa A., Taddei-Peters, Wendy C., Jeffries, Neal O., Buxton, Dennis, Geller, Nancy L., Gordon, David, Burke, Catherine, Lee, Albert, Smith, Tyrone, Moy, Claudia S., Gombos, Ilana Kogan, Weisel, Richard, Gardner, Timothy J., O'Gara, Patrick T., Rose, Eric A., Gelijns, Annetine C., Parides, Michael K., Ascheim, Deborah D., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, Shah, Kinjal, Overbey, Jessica R., Pan, Stephanie, Chang, Helena, Chase, Melissa, Goldfarb, Seth, Gupta, Lopa, Kirkwood, Katherine, Dobrev, Edlira, Levitan, Ron, O'Sullivan, Karen, Santos, Milerva, Ye, Xia, Mack, Michael, Winkle, Rachelle, Boswell, Haley, Fenlon, Amanda, Johnson, Melissa, Jones, Jessica, Kolb, Megan, Lam, Sarah, Miranda, Lucy, Ward, Jackie, Whitman, Renessa, Zingler, Brittany, Ryan, William, Smith, Robert L., Grayburn, Paul, Nosnik, Pedro, Gillinov, A. Marc, Blackstone, Eugene H., Moazami, Nader, Starling, Randall C., Barzilai, Benico, Grimm, Richard A., Soltesz, Edward G., Katzan, Irene, Strippy, Brian, Smith, Shoi, Garcia, Michelle, Alice bowman, Mary, Geither, Carrie, Wang, Robert, Argenziano, Michael, Borger, Michael, Takayama, Hiroo, Leon, Martin B., Goldsmith, Lyn, Schwartz, Allan, Sookraj, Nadia, McCright-Gill, Talaya, Sreekanth, Sowmya, McCullough, Jock N., Iribarne, Alexander, DeSimone, Joseph P., DiScipio, Anthony W., Stokes, Henry, Ivany, Amanda St., Petty, Gaylin, Smith, Peter K., Alexander, John H., Milano, Carmelo A., Glower, Donald D., Huber, Joel, Morganlander, Joel, Mathew, Joseph P., Welsh, Stacey, Casalinova, Sarah, Johnson, Victoria, Lane, Kathleen, Smith, Derek, Tipton, Greg, Berry, Mark F., Williams, Judson B., Englum, Brian, Hartwig, Matthew, Thourani, Vinod H., Guyton, Robert, Lattouf, Omar, Chen, Edward, Vega, J. David, Baer, Jefferson, Nguyen, Duc, Halkos, Michael, Baio, Kim, Prince, Tamara, Cook, Natascha, Neill, Alexis A., Voisine, Pierre, Senechal, Mario, Dagenais, François, Laforce, Robert, Jr., O'Connor, Kim, Dussault, Gladys, Caouette, Manon, Tremblay, Hugo, Gagne, Nathalie, Dumont, Julie, Landry, Patricia, Groh, Mark A., Trichon, Benjamin H., Binns, Oliver A., Ely, Stephen W., Johnson, Alan M., Hansen, Todd H., Short, John G., Taylor, Reid D., Mangusan, Ralph, Nanney, Tracy, Aubart, Holly, Cross, Kristin, McPeters, Leslie, Riggsbee, Christina, Rixey, Lucy, Michler, Robert E., DeRose, Joseph J., Jr., Goldstein, Daniel J., Bello, Ricardo A., Taub, Cynthia, Spevack, Daniel, Kirchoff, Kathryn, Meli, Rebecca, Garcia, Juan, Goldenberg, Jon, Kealy, Lauren, Perrault, Louis P., Bouchard, Denis, Tanguay, Jean François, O'Meara, Eileen, Lacharité, Jonathan, Robichaud, Sophie, Horvath, Keith A., Corcoran, Philip C., Siegenthaler, Michael P., Murphy, Mandy, Iraola, Margaret, Greenberg, Ann, Kumkumian, Greg, Milner, Mark, Nadareishvili, Zurab, Whitson, Bryan A., Hasan, Ayesha, McDavid, Asia, Fadorsen, Denise, Ouzounian, Maral, Yau, Terry, Farkouh, Michael, Woo, Anna, Cusimano, Robert James, David, Tirone, Feindel, Christopher, Fumakia, Nishit, Christie, Shakira, Mullen, John C., Bissonauth, Asvina, Hripko, Alexandra, Gammie, James S., Noor, Zahid, Mackowick, Kristen, Deasey, Stephanie, Al-Suqi, Manal, Collins, Julia, Acker, Michael A., Messé, Steven, Kirkpatrick, James, Mayer, Mary Lou, McDonald, Caitlin, Fok, Holley, Maffei, Breanna, Cresse, Stephen, Gepty, Christine, Bowdish, Michael, Starnes, Vaughn A., Shavalle, David, Heck, Christi, Hackmann, Amy, Baker, Craig, Fleischman, Fernando, Cunningham, Mark, Lozano, Edward, Hernandez, Michelle, Ailawadi, Gorav, Kron, Irving L., Johnston, Karen, Ghanta, Ravi K., Dent, John M., Kern, John, Yarboro, Leora, Ragosta, Michael, Annex, Brian, Bergin, Jim, Burks, Sandra, Cosner, Mike, Green, China, Loya, Samantha, Kim, Hye Ryun, Bull, David A., Desvigne-Nickens, Patrice, Dixon, Dennis O., Gottesman, Rebecca, Haigney, Mark, Holubkov, Richard, Iadecola, Constantino, Jacobs, Alice, Meslin, Eric M., Murkin, John M., Spertus, John A., Sellke, Frank, McDonald, Cheryl L., Canty, John, Dickert, Neal, Ikonomidis, John S., Kim, KyungMann, Williams, David O., Yancy, Clyde W., Chaturvedi, Seemant, Chimowitz, Marc, Fang, James C., Richenbacher, Wayne, Rao, Vivek, Furie, Karen L., Miller, Rachel, Cook, Jennifer, D'Alessandro, David, Han, Frederick, Pinney, Sean, Walsh, Mary N., Greer, David, Ishida, Koto, Stapf, Christian, Hung, Judy, Zeng, Xin, Hung, David, Satitthummanid, Sudarat, Billelo, Michel, Davatzikos, Christos, Erus, Guray, Karpf, Lauren, Desiderio, Lisa, Browndyke, Jeffrey N., James, Michael L., Toulgoat-Dubois, Yanne, Brassard, Rachele, Virmanu, Renu, Romero, Maria E., Braumann, Ryan, Messé, Steven R., Mack, Michael J., Southerland, Andrew M., Moy, Claudia Scala, and Bowdish, Michael E.

Published
2024
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