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Ying LD, Chao GF, Canner J, Graetz E, Ghiassi S, Schwartz JS, Zolfaghari EJ, Schneider EB, Gibbs KE. The Degree of Preoperative Hypoalbuminemia Is Associated with Risk of Postoperative Complications in Metabolic and Bariatric Surgery Patients. Obes Surg 2024; 34:51-70. [PMID: 37994997 DOI: 10.1007/s11695-023-06944-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/26/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND The incidence and impact of hypoalbuminemia in bariatric surgery patients is poorly characterized. We describe its distribution in laparoscopic sleeve gastrectomy (VSG) and Roux-en-Y gastric bypass (RYGB) patients undergoing primary or revision surgeries and assess its impact on postoperative complications. METHODS The Metabolic and Bariatric Surgery Quality Improvement Program Database (2015 to 2021) was analyzed. Hypoalbuminemia was defined as Severe (< 3 g/dL), Moderate (3 ≤ 3.5 g/dL), Mild (3.5 ≤ 4 g/dL), or Normal (≥ 4 g/dL). Multivariable logistic regression was performed to calculate odds ratios of postoperative complications compared to those with Normal albumin after controlling for procedure, age, gender, race, body mass index, functional status, American Society of Anesthesia class, and operative length. RESULTS A total of 817,310 patients undergoing Primary surgery and 69,938 patients undergoing Revision/Conversion ("Revision") surgery were analyzed. The prevalence of hypoalbuminemia was as follows (Primary, Revision): Severe, 0.3%, 0.6%; Moderate, 5.2%, 6.5%; Mild, 28.3%, 31.4%; Normal, 66.2%, 61.4%. Primary and Revision patients with hypoalbuminemia had a significantly higher prevalence (p < 0.01) of several co-morbidities, including hypertension and insulin-dependent diabetes. Any degree of hypoalbuminemia increased the odds ratio of several complications in Primary and Revision patients, including readmission, intervention, and reoperation. In Primary patients, all levels of hypoalbuminemia also increased the odds ratio of unplanned intubation, intensive care unit admission, and venous thromboembolism requiring therapy. CONCLUSION Over 30% of patients present with hypoalbuminemia. Even mild hypoalbuminemia was associated with an increased rate of several complications including readmission, intervention, and reoperation. Ensuring nutritional optimization, especially prior to revision surgery, may improve outcomes in this challenging population.
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Affiliation(s)
- Lee D Ying
- Department of Surgery, Yale New Haven Hospital, New Haven, CT, USA.
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Morris R, Karam BS, Zolfaghari EJ, Chen B, Kirsh T, Tourani R, Milia DJ, Napolitano L, de Moya M, Conterato M, Aliferis C, Ma S, Tignanelli C. Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage. PREHOSP EMERG CARE 2022; 26:556-565. [PMID: 34313534 DOI: 10.1080/10903127.2021.1958961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/29/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.
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Ingraham NE, King S, Proper J, Siegel L, Zolfaghari EJ, Murray TA, Vakayil V, Sheka A, Feng R, Guzman G, Roy SS, Muddappa D, Usher MG, Chipman JG, Tignanelli CJ, Pendleton KM. Morbidity and Mortality Trends of Pancreatitis: An Observational Study. Surg Infect (Larchmt) 2021; 22:1021-1030. [PMID: 34129395 DOI: 10.1089/sur.2020.473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background: Pancreatitis accounts for more than $2.5 billion of healthcare costs and remains the most common gastrointestinal (GI) admission. Few contemporary studies have assessed temporal trends of incidence, complications, management, and outcomes for acute pancreatitis in hospitalized patients at the national level. Methods: We used data from one of the largest hospital-based databases available in the United States, the Healthcare Cost and Utilization Project's (HCUP) State Inpatient Database, from 10 states between 2008 and 2015. We included patients with a diagnosis of acute pancreatitis (ICD-9 CM 577.0). Patient- and hospital-level data were used to estimate incidence and inpatient mortality rates. Results: From 80,736,256 hospitalizations, 929,914 (1.15%) cases of acute pancreatitis were identified, 186,226 (20.2%) of which were caused by gallbladder disease). The median age was 53 years (interquartile range [IQR], 41-67) and 50.8% were men. In-hospital mortality was 2.5% and crude mortality rates declined from 2.9% to 2.0% over the study period. Admission year remained significant after adjusting for patient demographics and comorbidities (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.89-0.90; p < 0.001). Gallbladder disease was associated with decreased odds of mortality (OR, 0.60; 95% CI, 0.57-0.62). Median length of stay was four days (IQR, 2-7) and decreased over time. The rates of surgical and endoscopic interventions were highest in 2011 (peak incidence of 16.1% and 9.5%, respectively) and have been decreasing since. Surgical providers were, on average, more likely than medical providers to perform surgery in both those with and without gallbladder disease etiology (gallbladder disease OR, 7.11; 95% CI, 5.46-9.25; non-gallbladder disease OR, 20.50; 95% CI, 16.81-25.01), endoscopy (gallbladder disease OR, 1.22; 95% CI, 0.87-1.72; non-gallbladder disease OR, 1.60; 95% CI, 1.18-2.16), or both (gallbladder disease OR, 7.00; 95% CI, 5.22-9.37; non-gallbladder disease OR, 8.85; 95% CI, 5.61-13.96). Conclusions: The incidence of pancreatitis, from 2008 to 2015, has increased whereas inpatient mortality (i.e., case fatality) has decreased. Understanding temporal trends in outcomes and management along with provider, hospital, and regional variation can better identify areas for future research and collaboration in managing these patients.
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Affiliation(s)
- Nicholas E Ingraham
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Samantha King
- University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Jennifer Proper
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Lianne Siegel
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Thomas A Murray
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Victor Vakayil
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Adam Sheka
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ruoying Feng
- Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Gabriel Guzman
- Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Samit Sunny Roy
- University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Dhannanjay Muddappa
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael G Usher
- Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jeffrey G Chipman
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christopher J Tignanelli
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Surgery, North Memorial Health Hospital, Robbinsdale, Minnesota, USA.,Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Kathryn M Pendleton
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota, USA
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Lusczek ER, Ingraham NE, Karam BS, Proper J, Siegel L, Helgeson ES, Lotfi-Emran S, Zolfaghari EJ, Jones E, Usher MG, Chipman JG, Dudley RA, Benson B, Melton GB, Charles A, Lupei MI, Tignanelli CJ. Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles. PLoS One 2021; 16:e0248956. [PMID: 33788884 PMCID: PMC8011766 DOI: 10.1371/journal.pone.0248956] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 03/09/2021] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. METHODS This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. RESULTS The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III. CONCLUSION We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
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Affiliation(s)
- Elizabeth R. Lusczek
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America
| | - Nicholas E. Ingraham
- Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America
| | - Basil S. Karam
- Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, United States of America
| | - Jennifer Proper
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Lianne Siegel
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Erika S. Helgeson
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Sahar Lotfi-Emran
- Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America
| | - Emily J. Zolfaghari
- University of Minnesota Medical School, Minneapolis, MN, United States of America
| | - Emma Jones
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America
| | - Michael G. Usher
- Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, United States of America
| | - Jeffrey G. Chipman
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America
| | - R. Adams Dudley
- Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States of America
| | - Bradley Benson
- Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, United States of America
| | - Genevieve B. Melton
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States of America
| | - Anthony Charles
- Department of Surgery, University of North Carolina, Chapel Hill, NC, United States of America
- School of Public Health, University of North Carolina, Chapel Hill, NC, United States of America
| | - Monica I. Lupei
- Department of Anesthesiology, University of Minnesota, Minneapolis, MN, United States of America
| | - Christopher J. Tignanelli
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States of America
- Department of Surgery, North Memorial Health Hospital, Robbinsdale, MN, United States of America
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Karam BS, Morris RS, Bramante CT, Puskarich M, Zolfaghari EJ, Lotfi-Emran S, Ingraham NE, Charles A, Odde DJ, Tignanelli CJ. mTOR inhibition in COVID-19: A commentary and review of efficacy in RNA viruses. J Med Virol 2020; 93:1843-1846. [PMID: 33314219 DOI: 10.1002/jmv.26728] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 02/06/2023]
Abstract
In this commentary, we shed light on the role of the mammalian target of rapamycin (mTOR) pathway in viral infections. The mTOR pathway has been demonstrated to be modulated in numerous RNA viruses. Frequently, inhibiting mTOR results in suppression of virus growth and replication. Recent evidence points towards modulation of mTOR in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We discuss the current literature on mTOR in SARS-CoV-2 and highlight evidence in support of a role for mTOR inhibitors in the treatment of coronavirus disease 2019.
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Affiliation(s)
- Basil S Karam
- Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Rachel S Morris
- Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Carolyn T Bramante
- Division of General Internal Medicine, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael Puskarich
- Department of Emergency Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Sahar Lotfi-Emran
- Division of Rheumatology, Department of Medicine, Minneapolis, Minnesota, USA
| | - Nicholas E Ingraham
- Division of Pulmonary and Critical Care, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Anthony Charles
- Department of Surgery, University of North Carolina, Chapel Hill, North Carolina, USA.,School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - David J Odde
- Department of Biomedical Engineering, University of Minnesota, Minnesota, USA
| | - Christopher J Tignanelli
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.,Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA
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Lusczek ER, Ingraham NE, Karam B, Proper J, Siegel L, Helgeson E, Lotfi-Emran S, Zolfaghari EJ, Jones E, Usher M, Chipman J, Dudley RA, Benson B, Melton GB, Charles A, Lupei MI, Tignanelli CJ. Characterizing COVID-19 Clinical Phenotypes and Associated Comorbidities and Complication Profiles. medRxiv 2020. [PMID: 32995813 DOI: 10.1101/2020.09.12.20193391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes. OBJECTIVE Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. DESIGN, SETTINGS, AND PARTICIPANTS Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020. METHODS Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality. RESULTS The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III. CONCLUSION In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.
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