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1. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Author

Vaid, Akhil, Somani, Sulaiman, Russak, Adam J, De Freitas, Jessica K, Chaudhry, Fayzan F, Paranjpe, Ishan, Johnson, Kipp W, Lee, Samuel J, Miotto, Riccardo, Richter, Felix, Zhao, Shan, Beckmann, Noam D, Naik, Nidhi, Kia, Arash, Timsina, Prem, Lala, Anuradha, Paranjpe, Manish, Golden, Eddye, Danieletto, Matteo, Singh, Manbir, Meyer, Dara, O'Reilly, Paul F, Huckins, Laura, Kovatch, Patricia, Finkelstein, Joseph, Freeman, Robert M., Argulian, Edgar, Kasarskis, Andrew, Percha, Bethany, Aberg, Judith A, Bagiella, Emilia, Horowitz, Carol R, Murphy, Barbara, Nestler, Eric J, Schadt, Eric E, Cho, Judy H, Cordon-Cardo, Carlos, Fuster, Valentin, Charney, Dennis S, Reich, David L, Bottinger, Erwin P, Levin, Matthew A, Narula, Jagat, Fayad, Zahi A, Just, Allan C, Charney, Alexander W, Nadkarni, Girish N, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundCOVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. ObjectiveThe aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. MethodsWe used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. ResultsUpon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. ConclusionsWe externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Published
2020
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2. Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models

Author

Vaid, Akhil, Lampert, Joshua, Lee, Juhee, Sawant, Ashwin, Apakama, Donald, Sakhuja, Ankit, Soroush, Ali, Bick, Sarah, Abbott, Ethan, Gomez, Hernando, Hadley, Michael, Lee, Denise, Landi, Isotta, Duong, Son Q, Bussola, Nicole, Nabeel, Ismail, Muehlstedt, Silke, Freeman, Robert, Kovatch, Patricia, Carr, Brendan, Wang, Fei, Glicksberg, Benjamin, Argulian, Edgar, Lerakis, Stamatios, Khera, Rohan, Reich, David L., Kraft, Monica, Charney, Alexander, and Nadkarni, Girish

Subjects
Computer Science - Artificial Intelligence
Abstract

Generative Large Language Models (LLMs) hold significant promise in healthcare, demonstrating capabilities such as passing medical licensing exams and providing clinical knowledge. However, their current use as information retrieval tools is limited by challenges like data staleness, resource demands, and occasional generation of incorrect information. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center, using real-world clinical cases across multiple specialties. Both proprietary and open-source LLMs were evaluated, with Retrieval Augmented Generation (RAG) enhancing contextual relevance. Proprietary models, particularly GPT-4, generally outperformed open-source models, showing improved guideline adherence and more accurate responses with RAG. The manual evaluation by expert clinicians was crucial in validating models' outputs, underscoring the importance of human oversight in LLM operation. Further, the study emphasizes Natural Language Programming (NLP) as the appropriate paradigm for modifying model behavior, allowing for precise adjustments through tailored prompts and real-world interactions. This approach highlights the potential of LLMs to significantly enhance and supplement clinical decision-making, while also emphasizing the value of continuous expert involvement and the flexibility of NLP to ensure their reliability and effectiveness in healthcare settings., Comment: Figures: 5, Tables: 3

Published
2024

3. An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets

Author

Gulamali, Faris F., Sawant, Ashwin S., Liharska, Lora, Horowitz, Carol R., Chan, Lili, Kovatch, Patricia H., Hofer, Ira, Singh, Karandeep, Richardson, Lynne D., Mensah, Emmanuel, Charney, Alexander W, Reich, David L., Hu, Jianying, and Nadkarni, Girish N.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The adoption of diagnosis and prognostic algorithms in healthcare has led to concerns about the perpetuation of bias against disadvantaged groups of individuals. Deep learning methods to detect and mitigate bias have revolved around modifying models, optimization strategies, and threshold calibration with varying levels of success. Here, we generate a data-centric, model-agnostic, task-agnostic approach to evaluate dataset bias by investigating the relationship between how easily different groups are learned at small sample sizes (AEquity). We then apply a systematic analysis of AEq values across subpopulations to identify and mitigate manifestations of racial bias in two known cases in healthcare - Chest X-rays diagnosis with deep convolutional neural networks and healthcare utilization prediction with multivariate logistic regression. AEq is a novel and broadly applicable metric that can be applied to advance equity by diagnosing and remediating bias in healthcare datasets.

Published
2023

4. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Author

Gulamali, Faris, Jayaraman, Pushkala, Sawant, Ashwin S., Desman, Jacob, Fox, Benjamin, Chang, Annette, Soong, Brian Y., Arivazagan, Naveen, Reynolds, Alexandra S., Duong, Son Q., Vaid, Akhil, Kovatch, Patricia, Freeman, Robert, Hofer, Ira S., Sakhuja, Ankit, Dangayach, Neha S., Reich, David S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2024
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6. Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images

Author

Campanella, Gabriele, Kwan, Ricky, Fluder, Eugene, Zeng, Jennifer, Stock, Aryeh, Veremis, Brandon, Polydorides, Alexandros D., Hedvat, Cyrus, Schoenfeld, Adam, Vanderbilt, Chad, Kovatch, Patricia, Cordon-Cardo, Carlos, and Fuchs, Thomas J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks. While this training paradigm is well suited for the medical domain where annotations are scarce, large-scale pre-training in the medical domain, and in particular pathology, has not been extensively studied. Previous work in self-supervised learning in pathology has leveraged smaller datasets for both pre-training and evaluating downstream performance. The aim of this project is to train the largest academic foundation model and benchmark the most prominent self-supervised learning algorithms by pre-training and evaluating downstream performance on large clinical pathology datasets. We collected the largest pathology dataset to date, consisting of over 3 billion images from over 423 thousand microscopy slides. We compared pre-training of visual transformer models using the masked autoencoder (MAE) and DINO algorithms. We evaluated performance on six clinically relevant tasks from three anatomic sites and two institutions: breast cancer detection, inflammatory bowel disease detection, breast cancer estrogen receptor prediction, lung adenocarcinoma EGFR mutation prediction, and lung cancer immunotherapy response prediction. Our results demonstrate that pre-training on pathology data is beneficial for downstream performance compared to pre-training on natural images. Additionally, the DINO algorithm achieved better generalization performance across all tasks tested. The presented results signify a phase change in computational pathology research, paving the way into a new era of more performant models based on large-scale, parallel pre-training at the billion-image scale.

Published
2023

8. Returning integrated genomic risk and clinical recommendations: The eMERGE study

Author

Gordon, Adam, Sobowale, Agboade, Allworth, Aimee, Patel, Akshar, DiVietro, Alanna, Strong, Alanna, Sherafati, Alborz, Sherfati, Alborz, Bick, Alex, Miller, Alexandra, Chandel, Alka, Rosenthal, Alyssa, Khera, Amit, Kontorovich, Amy, Beck, Andrew, Beck, Andy, Espinoza, Angelica, Lewis, Anna, Prince, Anya, Khan, Atlas, Iverson, Ayuko, Khales, Bahram Namjou, Benoit, Barbara, Hernan, Becca, Kallman, Ben, Kerman, Ben, Shoemaker, Ben, Satterfield, Benjamin, Devine, Beth, Etheridge, Bethany, Goff, Blake, Freimuth, Bob, Grundmeier, Bob, Collier, Brenae, Mutai, Brenda, Harnett, Brett, Chang, Brian, Piening, Brian, Davis, Brittney, Korf, Bruce, Patterson, Candace, Demetriou, Carmen, Ta, Casey, Hammack, Catherine, Nelson, Catrina, Gascoigne, Caytie, Dorn, Chad, Moretz, Chad, Kachulis, Chris, Hoell, Christie, Cowles, Christine, Lange, Christoph, Weng, Chunhua, Prows, Cindy, Brokamp, Cole, Liu, Cong, Scherr, Courtney, Gonzalez, Crystal, Ramirez, Cynthia, Shimbo, Daichi, Roden, Dan, Schaid, Daniel, Kaufman, Dave, Crosslin, David, Kochan, David, Veenstra, David, Singh, Davinder, Karavite, Dean, Abrams, Debbie, Absher, Devin, Edwards, Digna Velez, Haverfield, Eden, Morales, Eduardo, Esplin, Edward, Malolepsza, Edyta, Alipour, Ehsan, Kenny, Eimear, Rosenthal, Elisabeth, Duvall, Eliza, McNally, Elizabeth, Bhoj, Elizabeth, Cohn, Elizabeth, Hibler, Elizabeth, Karlson, Elizabeth, Clayton, Ellen, Chesnut, Emily, DeFranco, Emily, Gallagher, Emily, Soper, Emily, Perez, Emma, Cash, Erin, Berner, Eta, Wang, Fei, Wehbe, Firas, Ricci, Francisco, Mentch, Frank, Shaibi, Gabriel, Jarvik, Gail, Hahn, George, Hripcsak, George, Wiesner, Georgia, Belbin, Gillian, Davogustto, Gio, Nadkarni, Girish, Qiu, Haijun, Hakonarson, Hakon, Bangash, Hana, Beasley, Hannah, Liu, Hao, Aungst, Heide, Tiwari, Hemant, Duckham, Hillary, Thomas, Hope, Kullo, Iftikhar, Holm, Ingrid, Allen, Isabelle, Ionita-Laza, Iuliana, Hellwege, Jacklyn, Petrzelka, Jacob, Odgis, Jacqueline, Narula, Jahnavi, Petrzelka, Jake, Patel, Jalpa, Cimino, James, Meigs, James, Snyder, James, Olson, Janet, Zahner, Janet, Pennington, Jeff, Pacheco, Jen, Pacheco, Jennifer Allen, Morse, Jennifer, Corsmo, Jeremy, Thayer, Jeritt, Cimino, Jim, Chen, Jingheng, Fournier, Jocelyn, Jackson, Jodell, Glessner, Joe, Pacyna, Joel, Smith, Johanna, Connolly, John, Lynch, John, Shelley, John, Mosley, Jonathan, Nestor, Jordan, Smoller, Jordan, Alsip, Jorge, Kannry, Joseph, Sutton, Joseph, Peterson, Josh, Smith, Joshua, Galasso, Julia, Smith, Julia, Wynn, Julia, Gundelach, Justin, Starren, Justin, Choi, Karmel, Mittendorf, Kate, Anderson, Katherine, Bonini, Katherine, Leppig, Kathleen, Muenzen, Kathleen, Larkin, Katie, Stuttgen, Kelsey, Wiley, Ken, Nguyen, Kenny, Dufendach, Kevin, Atkins, Kiley, Sawicki, Konrad, Norland, Kristjan, Kiryluk, Krzysztof, Beskow, Laura, Rasmussen-Torvik, Laura, Kottyan, Leah, Hsu, Li, Tian, Lifeng, Mahanta, Lisa, Martin, Lisa, Wang, Lisa, Gomez, Lizbeth, Thompson, Lorenzo, Orlando, Lori, Richter, Lucas, Rasmussen, Luke, Petukhova, Lynn, Seabolt, Lynn, O’Brien, Madison, Harden, Maegan, Fullerton, Malia, Harr, Margaret, Beasley, Mark, Guindo, Marta, Horike, Martha, Horike-Pyne, Martha, Abdalla, Marwah, Hamed, Marwan, Terry, Mary Beth, Maradik, Mary, Wyatt, Matt, Davis, Matthew, Lebo, Matthew, Smith, Maureen, Rosario, Maya del, Sabatello, Maya, Behr, Meckenzie, Roy-Puckelwartz, Meg, Habrat, Mel, Myers, Melanie, Yetisgen, Meliha, Iris, Merve, DaSilva, Michael, Preuss, Michael, McGowan, Michelle, Shi, Mingjian, Perera, Minoli, Thomas, Minta, Elkind, Mitch, Abbass, Mohammad, Saadatagah, Mohammad, Hess, Molly, Maradik, Molly, Vaitinadin, Nataraja “RJ”, Vaitinadin, Nataraja, Muthu, Naveen, Netherly, Neil, Lennon, Niall, Shang, Ning, Limdi, Nita, Forrest, Noah, Romero, Noheli, Robinson, Nora, Abul-Husn, Noura, Elsekaily, Omar, Dikilitas, Ozan, Kovatch, Patricia, Davis, Patrick, Appelbaum, Paul, Francaviglia, Paul, O’Reilly, Paul, Chandler, Paulette, Caraballo, Pedro, Tarczy-Hornoch, Peter, Shum, Pierre, Marathe, Priya, Murali, Priyanka, Feng, Qiping, Wells, Quinn, Atchley, Rachel, Narla, Radhika, Barton, Rene, Sterling, Rene, Chisholm, Rex, Green, Richard, Sharp, Richard, Peters, Riki, Kukafka, Rita, Rowley, Robb, Freimuth, Robert, Green, Robert, Winter, Robert, Mueller, Roger, Loos, Ruth, Irvin, Ryan, Suckiel, Sabrina, Hussain, Sajjad, Sharba, Samer, Aronson, Sandy, Jones, Sarah, Knerr, Sarah, Nigbur, Scott, Weiss, Scott, Mooney, Sean, Terek, Shannon, Aufox, Sharon, Nirenberg, Sharon, Murphy, Shawn, O’Byrne, Sheila, Wang (Sam) Choi, Shing, Aguilar, Sienna, Bland, S.T., Rodrigues, Stefanie, Ledbetter, Stephanie, Rutledge, Stephanie, Booth, Stuart James, Xian, Su, Trinidad, Susan Brown, Bakken, Suzanne, Schmidlen, Tara, Rakhra-Burris, Tejinder, Manolio, Teri, Mersha, Tesfaye, Walunas, Theresa, Chandereng, Thevaa, May, Thomas, Ge, Tian, Edwards, Todd, Kaszemacher, Tom, Hernandez, Valentina, Willis, Valerie, Desai, Vemi, Desai, Vimi, Lorenzi, Virginia, Gainer, Vivian, Wei, Wei-Qi, Chung, Wendy, Su, Wu-Chen, Chang, Xiao, Zhao, Yiqing, Luo, Yuan, Shen, Yufeng, Linder, Jodell E., Bland, Harris T., Caraballo, Pedro J., Chisholm, Rex L., Clayton, Ellen Wright, Crosslin, David R., Esplin, Edward D., Forman, Sophie, Freimuth, Robert R., Gordon, Adam S., Harden, Maegan V., Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth W., Labrecque, Sofia, Lennon, Niall J., Limdi, Nita A., Mittendorf, Kathleen F., Murphy, Shawn N., Prows, Cynthia A., Rasmussen, Luke V., Sawicki, Konrad Teodor, Velez Edwards, Digna R., Abul-Husn, Noura S., Below, Jennifer E., Berner, Eta S., Booth, James, Chung, Wendy K., Cimino, James J., Fullerton, Stephanie M., Guiducci, Candace, Habrat, Melissa L., Hain, Heather, Hoell, Christin, Irvin, Marguerite R., Kachulis, Christopher, Kenny, Eimear E., Kullo, Iftikhar J., Manolio, Teri A., McNally, Elizabeth M., Mooney, Sean D., Namjou, Bahram, Perez, Emma F., Puckelwartz, Megan J., Roden, Dan M., Rosenthal, Elisabeth A., Saadatagah, Seyedmohammad, Schaid, Dan J., Schultz, Baergen, Shaibi, Gabriel Q., Sharp, Richard R., Shirts, Brian, Smith, Maureen E., Smoller, Jordan W., Suckiel, Sabrina A., Tiwari, Hemant K., Trinidad, Susan B., Wells, Quinn S., Wiesner, Georgia L., and Peterson, Josh F.

Published
2023
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11. GUÍA: a digital platform to facilitate result disclosure in genetic counseling

Author

Suckiel, Sabrina A., Odgis, Jaqueline A., Gallagher, Katie M., Rodriguez, Jessica E., Watnick, Dana, Bertier, Gabrielle, Sebastin, Monisha, Yelton, Nicole, Maria, Estefany, Lopez, Jessenia, Ramos, Michelle, Kelly, Nicole, Teitelman, Nehama, Beren, Faygel, Kaszemacher, Tom, Davis, Kojo, Laguerre, Irma, Richardson, Lynne D., Diaz, George A., Pearson, Nathaniel M., Ellis, Stephen B., Stolte, Christian, Robinson, Mimsie, Kovatch, Patricia, Horowitz, Carol R., Gelb, Bruce D., Greally, John M., Bauman, Laurie J., Zinberg, Randi E., Abul-Husn, Noura S., Wasserstein, Melissa P., and Kenny, Eimear E.

Published
2021
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12. Generative Large Language Models are autonomous practitioners of evidence-based medicine

Author

Vaid, Akhil, Lampert, Joshua, Lee, Juhee, Sawant, Ashwin, Apakama, Donald, Sakhuja, Ankit, Soroush, Ali, Lee, Denise, Landi, Isotta, Bussola, Nicole, Nabeel, Ismail, Freeman, Robbie, Kovatch, Patricia, Carr, Brendan, Glicksberg, Benjamin, Argulian, Edgar, Lerakis, Stamatios, Kraft, Monica, Charney, Alexander, Nadkarni, Girish, Vaid, Akhil, Lampert, Joshua, Lee, Juhee, Sawant, Ashwin, Apakama, Donald, Sakhuja, Ankit, Soroush, Ali, Lee, Denise, Landi, Isotta, Bussola, Nicole, Nabeel, Ismail, Freeman, Robbie, Kovatch, Patricia, Carr, Brendan, Glicksberg, Benjamin, Argulian, Edgar, Lerakis, Stamatios, Kraft, Monica, Charney, Alexander, and Nadkarni, Girish

Abstract

Background: Evidence-based medicine (EBM) is fundamental to modern clinical practice, requiring clinicians to continually update their knowledge and apply the best clinical evidence in patient care. The practice of EBM faces challenges due to rapid advancements in medical research, leading to information overload for clinicians. The integration of artificial intelligence (AI), specifically Generative Large Language Models (LLMs), offers a promising solution towards managing this complexity. Methods: This study involved the curation of real-world clinical cases across various specialties, converting them into .json files for analysis. LLMs, including proprietary models like ChatGPT 3.5 and 4, Gemini Pro, and open-source models like LLaMA v2 and Mixtral-8x7B, were employed. These models were equipped with tools to retrieve information from case files and make clinical decisions similar to how clinicians must operate in the real world. Model performance was evaluated based on correctness of final answer, judicious use of tools, conformity to guidelines, and resistance to hallucinations. Results: GPT-4 was most capable of autonomous operation in a clinical setting, being generally more effective in ordering relevant investigations and conforming to clinical guidelines. Limitations were observed in terms of model ability to handle complex guidelines and diagnostic nuances. Retrieval Augmented Generation made recommendations more tailored to patients and healthcare systems. Conclusions: LLMs can be made to function as autonomous practitioners of evidence-based medicine. Their ability to utilize tooling can be harnessed to interact with the infrastructure of a real-world healthcare system and perform the tasks of patient management in a guideline directed manner. Prompt engineering may help to further enhance this potential and transform healthcare for the clinician and the patient., Comment: Word count: 4548 words, Figures: 4, Tables: 4

Published
2024

13. An inflammatory cytokine signature predicts COVID-19 severity and survival

Author

Del Valle, Diane Marie, Kim-Schulze, Seunghee, Huang, Hsin-Hui, Beckmann, Noam D., Nirenberg, Sharon, Wang, Bo, Lavin, Yonit, Swartz, Talia H., Madduri, Deepu, Stock, Aryeh, Marron, Thomas U., Xie, Hui, Patel, Manishkumar, Tuballes, Kevin, Van Oekelen, Oliver, Rahman, Adeeb, Kovatch, Patricia, Aberg, Judith A., Schadt, Eric, Jagannath, Sundar, Mazumdar, Madhu, Charney, Alexander W., Firpo-Betancourt, Adolfo, Mendu, Damodara Rao, Jhang, Jeffrey, Reich, David, Sigel, Keith, Cordon-Cardo, Carlos, Feldmann, Marc, Parekh, Samir, Merad, Miriam, and Gnjatic, Sacha

Published
2020
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16. Correction to: The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children

Author

Odgis, Jacqueline A., Gallagher, Katie M., Suckiel, Sabrina A., Donohue, Katherine E., Ramos, Michelle A., Kelly, Nicole R., Bertier, Gabrielle, Blackburn, Christina, Brown, Kaitlyn, Fielding, Lena, Lopez, Jessenia, Aguiniga, Karla Lopez, Maria, Estefany, Rodriguez, Jessica E., Sebastin, Monisha, Teitelman, Nehama, Watnick, Dana, Yelton, Nicole M., Abhyankar, Avinash, Abul-Husn, Noura S., Baum, Aaron, Bauman, Laurie J., Beal, Jules C., Bloom, Toby, Cunningham-Rundles, Charlotte, Diaz, George A., Dolan, Siobhan, Ferket, Bart S., Jobanputra, Vaidehi, Kovatch, Patricia, McDonald, Thomas V., McGoldrick, Patricia E., Rhodes, Rosamond, Rinke, Michael L., Robinson, Mimsie, Rubinstein, Arye, Shulman, Lisa H., Stolte, Christian, Wolf, Steven M., Yozawitz, Elissa, Zinberg, Randi E., Greally, John M., Gelb, Bruce D., Horowitz, Carol R., Wasserstein, Melissa P., and Kenny, Eimear E.

Published
2021
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18. Returning integrated genomic risk and clinical recommendations: The eMERGE study

Author

Linder, Jodell E., primary, Allworth, Aimee, additional, Bland, Sarah T., additional, Caraballo, Pedro J., additional, Chisholm, Rex L., additional, Clayton, Ellen Wright, additional, Crosslin, David R., additional, Dikilitas, Ozan, additional, DiVietro, Alanna, additional, Esplin, Edward D., additional, Forman, Sophie, additional, Freimuth, Robert R., additional, Gordon, Adam S., additional, Green, Richard, additional, Harden, Maegan V., additional, Holm, Ingrid A., additional, Jarvik, Gail P., additional, Karlson, Elizabeth W., additional, Labrecque, Sofia, additional, Lennon, Niall J., additional, Limdi, Nita A., additional, Mittendorf, Kathleen F., additional, Murphy, Shawn N., additional, Orlando, Lori, additional, Prows, Cynthia A., additional, Rasmussen, Luke V., additional, Rasmussen-Torvik, Laura, additional, Rowley, Robb, additional, Sawicki, Konrad Teodor, additional, Schmidlen, Tara, additional, Terek, Shannon, additional, Veenstra, David, additional, Velez Edwards, Digna R., additional, Absher, Devin, additional, Abul-Husn, Noura S., additional, Alsip, Jorge, additional, Bangash, Hana, additional, Beasley, Mark, additional, Below, Jennifer E., additional, Berner, Eta S., additional, Booth, James, additional, Chung, Wendy K., additional, Cimino, James J., additional, Connolly, John, additional, Davis, Patrick, additional, Devine, Beth, additional, Fullerton, Stephanie M., additional, Guiducci, Candace, additional, Habrat, Melissa L., additional, Hain, Heather, additional, Hakonarson, Hakon, additional, Harr, Margaret, additional, Haverfield, Eden, additional, Hernandez, Valentina, additional, Hoell, Christin, additional, Horike-Pyne, Martha, additional, Hripcsak, George, additional, Irvin, Marguerite R., additional, Kachulis, Christopher, additional, Karavite, Dean, additional, Kenny, Eimear E., additional, Khan, Atlas, additional, Kiryluk, Krzysztof, additional, Korf, Bruce, additional, Kottyan, Leah, additional, Kullo, Iftikhar J., additional, Larkin, Katie, additional, Liu, Cong, additional, Malolepsza, Edyta, additional, Manolio, Teri A., additional, May, Thomas, additional, McNally, Elizabeth M., additional, Mentch, Frank, additional, Miller, Alexandra, additional, Mooney, Sean D., additional, Murali, Priyanka, additional, Mutai, Brenda, additional, Muthu, Naveen, additional, Namjou, Bahram, additional, Perez, Emma F., additional, Puckelwartz, Megan J., additional, Rakhra-Burris, Tejinder, additional, Roden, Dan M., additional, Rosenthal, Elisabeth A., additional, Saadatagah, Seyedmohammad, additional, Sabatello, Maya, additional, Schaid, Dan J., additional, Schultz, Baergen, additional, Seabolt, Lynn, additional, Shaibi, Gabriel Q., additional, Sharp, Richard R., additional, Shirts, Brian, additional, Smith, Maureen E., additional, Smoller, Jordan W., additional, Sterling, Rene, additional, Suckiel, Sabrina A., additional, Thayer, Jeritt, additional, Tiwari, Hemant K., additional, Trinidad, Susan B., additional, Walunas, Theresa, additional, Wei, Wei-Qi, additional, Wells, Quinn S., additional, Weng, Chunhua, additional, Wiesner, Georgia L., additional, Wiley, Ken, additional, Peterson, Josh F., additional, Gordon, Adam, additional, Sobowale, Agboade, additional, Patel, Akshar, additional, Strong, Alanna, additional, Sherafati, Alborz, additional, Sherfati, Alborz, additional, Bick, Alex, additional, Chandel, Alka, additional, Rosenthal, Alyssa, additional, Khera, Amit, additional, Kontorovich, Amy, additional, Beck, Andrew, additional, Beck, Andy, additional, Espinoza, Angelica, additional, Lewis, Anna, additional, Prince, Anya, additional, Iverson, Ayuko, additional, Khales, Bahram Namjou, additional, Benoit, Barbara, additional, Hernan, Becca, additional, Kallman, Ben, additional, Kerman, Ben, additional, Shoemaker, Ben, additional, Satterfield, Benjamin, additional, Etheridge, Bethany, additional, Goff, Blake, additional, Freimuth, Bob, additional, Grundmeier, Bob, additional, Collier, Brenae, additional, Harnett, Brett, additional, Chang, Brian, additional, Piening, Brian, additional, Davis, Brittney, additional, Patterson, Candace, additional, Demetriou, Carmen, additional, Ta, Casey, additional, Hammack, Catherine, additional, Nelson, Catrina, additional, Gascoigne, Caytie, additional, Dorn, Chad, additional, Moretz, Chad, additional, Kachulis, Chris, additional, Hoell, Christie, additional, Cowles, Christine, additional, Lange, Christoph, additional, Prows, Cindy, additional, Brokamp, Cole, additional, Scherr, Courtney, additional, Gonzalez, Crystal, additional, Ramirez, Cynthia, additional, Shimbo, Daichi, additional, Roden, Dan, additional, Schaid, Daniel, additional, Kaufman, Dave, additional, Crosslin, David, additional, Kochan, David, additional, Singh, Davinder, additional, Abrams, Debbie, additional, Edwards, Digna Velez, additional, Morales, Eduardo, additional, Esplin, Edward, additional, Alipour, Ehsan, additional, Kenny, Eimear, additional, Rosenthal, Elisabeth, additional, Duvall, Eliza, additional, McNally, Elizabeth, additional, Bhoj, Elizabeth, additional, Cohn, Elizabeth, additional, Hibler, Elizabeth, additional, Karlson, Elizabeth, additional, Clayton, Ellen, additional, Chesnut, Emily, additional, DeFranco, Emily, additional, Gallagher, Emily, additional, Soper, Emily, additional, Perez, Emma, additional, Cash, Erin, additional, Berner, Eta, additional, Wang, Fei, additional, Wehbe, Firas, additional, Ricci, Francisco, additional, Shaibi, Gabriel, additional, Jarvik, Gail, additional, Hahn, George, additional, Wiesner, Georgia, additional, Belbin, Gillian, additional, Davogustto, Gio, additional, Nadkarni, Girish, additional, Qiu, Haijun, additional, Beasley, Hannah, additional, Liu, Hao, additional, Aungst, Heide, additional, Tiwari, Hemant, additional, Duckham, Hillary, additional, Thomas, Hope, additional, Kullo, Iftikhar, additional, Holm, Ingrid, additional, Allen, Isabelle, additional, Ionita-Laza, Iuliana, additional, Hellwege, Jacklyn, additional, Petrzelka, Jacob, additional, Odgis, Jacqueline, additional, Narula, Jahnavi, additional, Petrzelka, Jake, additional, Patel, Jalpa, additional, Cimino, James, additional, Meigs, James, additional, Snyder, James, additional, Olson, Janet, additional, Zahner, Janet, additional, Pennington, Jeff, additional, Pacheco, Jen, additional, Pacheco, Jennifer Allen, additional, Morse, Jennifer, additional, Corsmo, Jeremy, additional, Cimino, Jim, additional, Chen, Jingheng, additional, Fournier, Jocelyn, additional, Jackson, Jodell, additional, Glessner, Joe, additional, Pacyna, Joel, additional, Smith, Johanna, additional, Lynch, John, additional, Shelley, John, additional, Mosley, Jonathan, additional, Nestor, Jordan, additional, Smoller, Jordan, additional, Kannry, Joseph, additional, Sutton, Joseph, additional, Peterson, Josh, additional, Smith, Joshua, additional, Galasso, Julia, additional, Smith, Julia, additional, Wynn, Julia, additional, Gundelach, Justin, additional, Starren, Justin, additional, Choi, Karmel, additional, Mittendorf, Kate, additional, Anderson, Katherine, additional, Bonini, Katherine, additional, Leppig, Kathleen, additional, Muenzen, Kathleen, additional, Stuttgen, Kelsey, additional, Nguyen, Kenny, additional, Dufendach, Kevin, additional, Atkins, Kiley, additional, Sawicki, Konrad, additional, Norland, Kristjan, additional, Beskow, Laura, additional, Hsu, Li, additional, Tian, Lifeng, additional, Mahanta, Lisa, additional, Martin, Lisa, additional, Wang, Lisa, additional, Gomez, Lizbeth, additional, Thompson, Lorenzo, additional, Richter, Lucas, additional, Rasmussen, Luke, additional, Petukhova, Lynn, additional, O’Brien, Madison, additional, Harden, Maegan, additional, Fullerton, Malia, additional, Guindo, Marta, additional, Horike, Martha, additional, Abdalla, Marwah, additional, Hamed, Marwan, additional, Terry, Mary Beth, additional, Maradik, Mary, additional, Wyatt, Matt, additional, Davis, Matthew, additional, Lebo, Matthew, additional, Smith, Maureen, additional, Rosario, Maya del, additional, Behr, Meckenzie, additional, Roy-Puckelwartz, Meg, additional, Habrat, Mel, additional, Myers, Melanie, additional, Yetisgen, Meliha, additional, Iris, Merve, additional, DaSilva, Michael, additional, Preuss, Michael, additional, McGowan, Michelle, additional, Shi, Mingjian, additional, Perera, Minoli, additional, Thomas, Minta, additional, Elkind, Mitch, additional, Abbass, Mohammad, additional, Saadatagah, Mohammad, additional, Hess, Molly, additional, Maradik, Molly, additional, Vaitinadin, Nataraja “RJ”, additional, Vaitinadin, Nataraja, additional, Netherly, Neil, additional, Lennon, Niall, additional, Shang, Ning, additional, Limdi, Nita, additional, Forrest, Noah, additional, Romero, Noheli, additional, Robinson, Nora, additional, Abul-Husn, Noura, additional, Elsekaily, Omar, additional, Kovatch, Patricia, additional, Appelbaum, Paul, additional, Francaviglia, Paul, additional, O’Reilly, Paul, additional, Chandler, Paulette, additional, Caraballo, Pedro, additional, Tarczy-Hornoch, Peter, additional, Shum, Pierre, additional, Marathe, Priya, additional, Feng, Qiping, additional, Wells, Quinn, additional, Atchley, Rachel, additional, Narla, Radhika, additional, Barton, Rene, additional, Chisholm, Rex, additional, Sharp, Richard, additional, Peters, Riki, additional, Kukafka, Rita, additional, Freimuth, Robert, additional, Green, Robert, additional, Winter, Robert, additional, Mueller, Roger, additional, Loos, Ruth, additional, Irvin, Ryan, additional, Suckiel, Sabrina, additional, Hussain, Sajjad, additional, Sharba, Samer, additional, Aronson, Sandy, additional, Jones, Sarah, additional, Knerr, Sarah, additional, Nigbur, Scott, additional, Weiss, Scott, additional, Mooney, Sean, additional, Aufox, Sharon, additional, Nirenberg, Sharon, additional, Murphy, Shawn, additional, O’Byrne, Sheila, additional, Wang (Sam) Choi, Shing, additional, Aguilar, Sienna, additional, Bland, S.T., additional, Rodrigues, Stefanie, additional, Ledbetter, Stephanie, additional, Rutledge, Stephanie, additional, Booth, Stuart James, additional, Xian, Su, additional, Trinidad, Susan Brown, additional, Bakken, Suzanne, additional, Manolio, Teri, additional, Mersha, Tesfaye, additional, Chandereng, Thevaa, additional, Ge, Tian, additional, Edwards, Todd, additional, Kaszemacher, Tom, additional, Willis, Valerie, additional, Desai, Vemi, additional, Desai, Vimi, additional, Lorenzi, Virginia, additional, Gainer, Vivian, additional, Chung, Wendy, additional, Su, Wu-Chen, additional, Chang, Xiao, additional, Zhao, Yiqing, additional, Luo, Yuan, additional, and Shen, Yufeng, additional

Published
2023
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19. Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

Author

Shameer, Khader, Perez-Rodriguez, M. Mercedes, Bachar, Roy, Li, Li, Johnson, Amy, Johnson, Kipp W., Glicksberg, Benjamin S., Smith, Milo R., Readhead, Ben, Scarpa, Joseph, Jebakaran, Jebakumar, Kovatch, Patricia, Lim, Sabina, Goodman, Wayne, Reich, David L., Kasarskis, Andrew, Tatonetti, Nicholas P., and Dudley, Joel T.

Published
2018
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21. Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis

Author

Vaid, Akhil, primary, Jiang, Joy J., additional, Sawant, Ashwin, additional, Singh, Karandeep, additional, Kovatch, Patricia, additional, Charney, Alexander W., additional, Charytan, David M., additional, Divers, Jasmin, additional, Glicksberg, Benjamin S., additional, Chan, Lili, additional, and Nadkarni, Girish N., additional

Published
2022
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22. Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit

Author

Oh, Wonsuk, Jayaraman, Pushkala, Sawant, Ashwin S, Chan, Lili, Levin, Matthew A, Charney, Alexander W, Kovatch, Patricia, Glicksberg, Benjamin S, and Nadkarni, Girish N

Subjects
Intensive Care Units, Respiratory Distress Syndrome, SARS-CoV-2, COVID-19, Cluster Analysis, Humans, Research and Applications
Abstract

OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes. MATERIALS AND METHODS: We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward’s minimum variance linkage. RESULTS: We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation. CONCLUSION: We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches.

Published
2021

23. Functional Effects of Cardiomyocyte Injury in COVID-19

Author

Siddiq, Mustafa M., primary, Chan, Angel T., additional, Miorin, Lisa, additional, Yadaw, Arjun S., additional, Beaumont, Kristin G., additional, Kehrer, Thomas, additional, Cupic, Anastasija, additional, White, Kris M., additional, Tolentino, Rosa E., additional, Hu, Bin, additional, Stern, Alan D., additional, Tavassoly, Iman, additional, Hansen, Jens, additional, Sebra, Robert, additional, Martinez, Pedro, additional, Prabha, Som, additional, Dubois, Nicole, additional, Schaniel, Christoph, additional, Iyengar-Kapuganti, Rupa, additional, Kukar, Nina, additional, Giustino, Gennaro, additional, Sud, Karan, additional, Nirenberg, Sharon, additional, Kovatch, Patricia, additional, Albrecht, Randy A., additional, Goldfarb, Joseph, additional, Croft, Lori, additional, McLaughlin, Maryann A., additional, Argulian, Edgar, additional, Lerakis, Stamatios, additional, Narula, Jagat, additional, García-Sastre, Adolfo, additional, and Iyengar, Ravi, additional

Published
2022
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24. Additional file 1 of The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children

Author

Odgis, Jacqueline A., Gallagher, Katie M., Suckiel, Sabrina A., Donohue, Katherine E., Ramos, Michelle A., Kelly, Nicole R., Bertier, Gabrielle, Blackburn, Christina, Brown, Kaitlyn, Fielding, Lena, Jessenia Lopez, Aguiniga, Karla Lopez, Estefany Maria, Rodriguez, Jessica E., Sebastin, Monisha, Nehama Teitelman, Watnick, Dana, Yelton, Nicole M., Abhyankar, Avinash, Abul-Husn, Noura S., Baum, Aaron, Bauman, Laurie J., Beal, Jules C., Bloom, Toby, Cunningham-Rundles, Charlotte, Diaz, George A., Dolan, Siobhan, Ferket, Bart S., Vaidehi Jobanputra, Kovatch, Patricia, McDonald, Thomas V., McGoldrick, Patricia E., Rhodes, Rosamond, Rinke, Michael L., Mimsie Robinson, Rubinstein, Arye, Shulman, Lisa H., Stolte, Christian, Wolf, Steven M., Yozawitz, Elissa, Zinberg, Randi E., Greally, John M., Gelb, Bruce D., Horowitz, Carol R., Wasserstein, Melissa P., and Kenny, Eimear E.

Abstract

Additional file 1. SPIRIT checklist.

Published
2021
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25. Multiethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes.

Author

Tomi Jun, Divij Mathew, Sharma, Navya, Nirenberg, Sharon, Hsin-Hui Huang, Kovatch, Patricia, Wherry, Edward John, and Kuan-lin Huang

Subjects
HOSPITAL mortality, COVID-19, ELECTRONIC health records, RACE, BLACK people, ETHNICITY, HISPANIC Americans
Abstract

Background: Disparate COVID-19 outcomes have been observed between Hispanic, non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood. Methods: This was a retrospective study utilizing electronic medical record data from five hospitals within a single academic health system based in New York City. Multivariable logistic regression models were used to identify demographic, clinical, and lab values associated with in-hospital mortality. Results: A total of 3,086 adult patients with self-reported race/ethnicity information presenting to the emergency department and hospitalized with COVID-19 up to April 13, 2020, were included in this study. While older age (multivariable odds ratio (OR) 1.06, 95% CI 1.05-1.07) and baseline hypoxia (multivariable OR 2.71, 95% CI 2.17-3.36) were associated with increased mortality overall and across all races/ethnicities, non-Hispanic Black (median age 67, interquartile range (IQR) 58-76) and Hispanic (median age 63, IQR 50-74) patients were younger and had different comorbidity profiles as compared to non-Hispanic White patients (median age 73, IQR 62-84; p < 0.05 for both comparisons). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between the non-Hispanic Black population and interleukin-1-beta (interaction p-value 0.04). Conclusions: This analysis of a multiethnic cohort highlights the need for inclusion and consideration of diverse populations in ongoing COVID-19 trials targeting inflammatory cytokines. [ABSTRACT FROM AUTHOR]

Published
2022
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26. The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children

Author

Odgis, Jacqueline A., primary, Gallagher, Katie M., additional, Suckiel, Sabrina A., additional, Donohue, Katherine E., additional, Ramos, Michelle A., additional, Kelly, Nicole R., additional, Bertier, Gabrielle, additional, Blackburn, Christina, additional, Brown, Kaitlyn, additional, Fielding, Lena, additional, Lopez, Jessenia, additional, Aguiniga, Karla Lopez, additional, Maria, Estefany, additional, Rodriguez, Jessica E., additional, Sebastin, Monisha, additional, Teitelman, Nehama, additional, Watnick, Dana, additional, Yelton, Nicole M., additional, Abhyankar, Avinash, additional, Abul-Husn, Noura S., additional, Baum, Aaron, additional, Bauman, Laurie J., additional, Beal, Jules C., additional, Bloom, Toby, additional, Cunningham-Rundles, Charlotte, additional, Diaz, George A., additional, Dolan, Siobhan, additional, Ferket, Bart S., additional, Jobanputra, Vaidehi, additional, Kovatch, Patricia, additional, McDonald, Thomas V., additional, McGoldrick, Patricia E., additional, Rhodes, Rosamond, additional, Rinke, Michael L., additional, Robinson, Mimsie, additional, Rubinstein, Arye, additional, Shulman, Lisa H., additional, Stolte, Christian, additional, Wolf, Steven M., additional, Yozawitz, Elissa, additional, Zinberg, Randi E., additional, Greally, John M., additional, Gelb, Bruce D., additional, Horowitz, Carol R., additional, Wasserstein, Melissa P., additional, and Kenny, Eimear E., additional

Published
2021
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27. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City

Author

Paranjpe, Ishan, primary, Russak, Adam J, additional, De Freitas, Jessica K, additional, Lala, Anuradha, additional, Miotto, Riccardo, additional, Vaid, Akhil, additional, Johnson, Kipp W, additional, Danieletto, Matteo, additional, Golden, Eddye, additional, Meyer, Dara, additional, Singh, Manbir, additional, Somani, Sulaiman, additional, Kapoor, Arjun, additional, O'Hagan, Ross, additional, Manna, Sayan, additional, Nangia, Udit, additional, Jaladanki, Suraj K, additional, O’Reilly, Paul, additional, Huckins, Laura M, additional, Glowe, Patricia, additional, Kia, Arash, additional, Timsina, Prem, additional, Freeman, Robert M, additional, Levin, Matthew A, additional, Jhang, Jeffrey, additional, Firpo, Adolfo, additional, Kovatch, Patricia, additional, Finkelstein, Joseph, additional, Aberg, Judith A, additional, Bagiella, Emilia, additional, Horowitz, Carol R, additional, Murphy, Barbara, additional, Fayad, Zahi A, additional, Narula, Jagat, additional, Nestler, Eric J, additional, Fuster, V, additional, Cordon-Cardo, Carlos, additional, Charney, Dennis, additional, Reich, David L, additional, Just, Allan, additional, Bottinger, Erwin P, additional, Charney, Alexander W, additional, Glicksberg, Benjamin S, additional, and Nadkarni, Girish N, additional

Published
2020
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28. Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records

Author

Wang, Zichen, primary, Zheutlin, Amanda, additional, Kao, Yu-Han, additional, Ayers, Kristin, additional, Gross, Susan, additional, Kovatch, Patricia, additional, Nirenberg, Sharon, additional, Charney, Alexander, additional, Nadkarni, Girish, additional, De Freitas, Jessica K, additional, O’Reilly, Paul, additional, Just, Allan, additional, Horowitz, Carol, additional, Martin, Glenn, additional, Branch, Andrea, additional, Glicksberg, Benjamin S, additional, Charney, Dennis, additional, Reich, David, additional, Oh, William K, additional, Schadt, Eric, additional, Chen, Rong, additional, and Li, Li, additional

Published
2020
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29. Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit.

Author

Oh, Wonsuk, Jayaraman, Pushkala, Sawant, Ashwin S, Chan, Lili, Levin, Matthew A, Charney, Alexander W, Kovatch, Patricia, Glicksberg, Benjamin S, and Nadkarni, Girish N

Abstract

Objective: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes.Materials and Methods: We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage.Results: We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation.Conclusion: We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches. [ABSTRACT FROM AUTHOR]

Published
2022
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30. Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19

Author

Vaid, Akhil, Chan, Lili, Chaudhary, Kumardeep, Jaladanki, Suraj K., Paranjpe, Ishan, Russak, Adam, Kia, Arash, Timsina, Prem, Levin, Matthew A., He, John Cijiang, Böttinger, Erwin P., Charney, Alexander W., Fayad, Zahi A., Coca, Steven G., Glicksberg, Benjamin S., Nadkarni, Girish N., Charney, Alex, Just, Allan C., Glicksberg, Benjamin, Nadkarni, Girish, Huckins, Laura, O’Reilly, Paul, Miotto, Riccardo, Fayad, Zahi, Russak, Adam J., Rahman, Adeeb, Vaid, Akhil, Le Dobbyn, Amanda, Leader, Andrew, Moscati, Arden, Kapoor, Arjun, Chang, Christie, Bellaire, Christopher, Carrion, Daniel, Chaudhry, Fayzan, Richter, Felix, Soultanidis, Georgios, Paranjpe, Ishan, Nabeel, Ismail, De Freitas, Jessica, Xu, Jiayi, Rush, Johnathan, Johnson, Kipp, Vemuri, Krishna, Chaudhary, Kumardeep, Lepow, Lauren, Cotter, Liam, Liharska, Lora, Pereanez, Marco, Bicak, Mesude, DeFelice, Nicholas, Naik, Nidhi, Beckmann, Noam, Nadukuru, Rajiv, O’Hagan, Ross, Zhao, Shan, Somani, Sulaiman, Van Vleck, Tielman T., Mutetwa, Tinaye, Wanyan, Tingyi, Fauveau, Valentin, Yang, Yang, Lavin, Yonit, Lanksy, Alona, Atreja, Ashish, Del Valle, Diane, Meyer, Dara, Golden, Eddye, Fasihuddin, Farah, Hsun Wen, Huei, Rogers, Jason, Lilly Gutierrez, Jennifer, Walker, Laura, Singh, Manbir, Danieletto, Matteo, Nieves, Melissa A., Zweig, Micol, Pyzik, Renata, Fayad, Rima, Glowe, Patricia, Calorossi, Sharlene, Kaur, Sparshdeep, Ascolillo, Steven, Roa, Yovanna, Lala-Trindade, Anuradha, Coca, Steven G., Percha, Bethany, Sigel, Keith, Polak, Paz, Hirten, Robert, Swartz, Talia, Do, Ron, Loos, Ruth J. F., Charney, Dennis, Nestler, Eric, Murphy, Barbara, Reich, David, Böttinger, Erwin, Chatani, Kumar, Martin, Glenn, Nestler, Eric, Kovatch, Patricia, Finkelstein, Joseph, Murphy, Barbara, Buxbaum, Joseph, Cho, Judy, Kasarskis, Andrew, Horowitz, Carol, Cordon-Cardo, Carlos, Sohn, Monica, Martin, Glenn, Garcia-Sastre, Adolfo, Bagiella, Emilia, Krammer, Florian, Aberg, Judith, Narula, Jagat, Wright, Robert, Lium, Erik, Wright, Rosalind, Gelijns, Annetine, Fuster, Valentin, and Merad, Miriam

Published
2021
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31. PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT.

Author

SHAMEER, KHADER, JOHNSON, KIPP W., YAHI, ALEXANDRE, MIOTTO, RICCARDO, LI LI, RICKS, DORAN, JEBAKARAN, JEBAKUMAR, KOVATCH, PATRICIA, SENGUPTA, PARTHO P., GELIJNS, ANNETINE, MOSKOVITZ, ALAN, DARROW, BRUCE, REICH, DAVID L., KASARSKIS, ANDREW, TATONETTI, NICHOLAS P., PINNEY, SEAN, and DUDLEY, JOEL T.

Subjects
ELECTRONIC health records, HOSPITAL costs, PATIENT readmissions, MEDICAL technology, CARDIAC arrest
Published
2016

32. PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT.

Author

Shameer K, Johnson KW, Yahi A, Miotto R, Li LI, Ricks D, Jebakaran J, Kovatch P, Sengupta PP, Gelijns S, Moskovitz A, Darrow B, David DL, Kasarskis A, Tatonetti NP, Pinney S, and Dudley JT

Subjects
Algorithms, Bayes Theorem, Cohort Studies, Computational Biology, Heart Failure therapy, Humans, Models, Statistical, New York City, Electronic Health Records statistics & numerical data, Machine Learning, Patient Readmission statistics & numerical data
Abstract

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6-0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such datadriven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

Published
2017
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