Browsing by Author "Temple University Hospital"

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    Efficacy and safety of anticoagulation for atrial fibrillation in patients with cirrhosis: A systematic review and meta-analysis
    (2019-04-01) Ronpichai Chokesuwattanaskul; Charat Thongprayoon; Tarun Bathini; Aldo Torres-Ortiz; Oisin A. O'Corragain; Kanramon Watthanasuntorn; Ploypin Lertjitbanjong; Konika Sharma; Somchai Preechawat; Patompong Ungprasert; Paul T. Kröner; Karn Wijarnpreecha; Wisit Cheungpasitporn; King Chulalongkorn Memorial Hospital, Faculty of Medicine Chulalongkorn University; Temple University Hospital; Faculty of Medicine, Siriraj Hospital, Mahidol University; University of Arizona; Mayo Clinic; University of Mississippi Medical Center; Mayo Clinic in Jacksonville, Florida; Bassett Medical Center
    © 2018 Editrice Gastroenterologica Italiana S.r.l. Objective: The atrial fibrillation-related stroke is clearly prevented by anticoagulation treatment, however, management of anticoagulation for AF in patients with cirrhosis represents a challenge due to bleeding concerns. To address this issue, a systematic review and meta-analysis of the literature was performed. Methods: A literature search for studies reporting the incidence of AF in patients with cirrhosis was conducted using MEDLINE, EMBASE and Cochrane Database, from inception through July 2018. Results: 7 cohort studies including 19,798 patients with AF and cirrhosis were identified. The use of anticoagulation (%) among included studies ranged from 8.3% to 53.9%. Anticoagulation use for AF in patients with cirrhosis was significantly associated with a reduced risk of stroke, with a pooled HR of 0.58 (95%CI: 0.35–0.96). When compared with no anticoagulation, the use of anticoagulation was not significantly associated with a higher risk of bleeding, with a pooled HR of 1.45 (95%CI: 0.96–2.17). Compared to warfarin, the use of direct oral anticoagulants (DOACs) was associated with a lower risk of bleeding among AF patients with cirrhosis. Conclusion: Our study demonstrates that anticoagulation use for AF in patients with cirrhosis is associated with a reduced risk of stroke, without increasing significantly the risk of bleeding, when compared to those without anticoagulation.
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    Epidemiology of atrial fibrillation in patients with cirrhosis and clinical significance: A meta-analysis
    (2019-04-01) Ronpichai Chokesuwattanaskul; Charat Thongprayoon; Tarun Bathini; Oisin A. O'Corragain; Konika Sharma; Somchai Preechawat; Karn Wijarnpreecha; Paul T. Kröner; Patompong Ungprasert; Wisit Cheungpasitporn; Chulalongkorn University; Temple University Hospital; Faculty of Medicine, Siriraj Hospital, Mahidol University; University of Arizona; Mayo Clinic; University of Mississippi Medical Center; Mayo Clinic in Jacksonville, Florida; Bassett Medical Center
    Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved. Objective The epidemiology of atrial fibrillation (AF) in patients with cirrhosis and its clinical significance remain unclear. This study aimed (i) to investigate the pooled prevalence and/or incidence of AF in patients with cirrhosis and (ii) to assess the mortality risk of AF in patients with cirrhosis. Patients and methods A literature search for studies that reported incidence of AF in patients with cirrhosis was carried out using Medline, Embase, and Cochrane Database from inception through July 2018. Pooled incidence with 95% confidence interval (CI) was calculated using a random-effect model. The protocol for this meta-analysis is registered with PROSPERO (International Prospective Register of Systematic Reviews; no. CRD42018102664). Results Seven cohort studies including 385 866 patients with cirrhosis were identified. The pooled estimated prevalence of AF in patients with cirrhosis was 5.0% (95% CI: 2.8-8.6%). When studies that solely assessed patients undergoing transplant evaluation or on transplant waiting list were excluded, the pooled estimated prevalence of AF in patients with cirrhosis was 7.4% (95% CI: 3.5-15.2%). There was a significant association between AF and increased mortality risk in cirrhotic patients with a pooled odds ratio of 1.44 (95% CI: 1.36-1.53). Conclusion The overall estimated prevalence of AF among patients with cirrhosis is 5.0%. Our study demonstrates a statistically significant increased mortality risk in cirrhotic patients with AF.
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    Machine learning consensus clustering approach for patients with lactic acidosis in intensive care units
    (2021-11-01) Pattharawin Pattharanitima; Charat Thongprayoon; Tananchai Petnak; Narat Srivali; Guido Gembillo; Wisit Kaewput; Supavit Chesdachai; Saraschandra Vallabhajosyula; Oisin A. O’corragain; Michael A. Mao; Vesna D. Garovic; Fawad Qureshi; John J. Dillon; Wisit Cheungpasitporn; Ramathibodi Hospital; Wake Forest University School of Medicine; St. Agnes Hospital; Temple University Hospital; Faculty of Medicine, Thammasat University; Università degli Studi di Messina; Phramongkutklao College of Medicine; Mayo Clinic; Mayo Clinic in Jacksonville, Florida
    Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.
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    Machine learning prediction models for mortality in intensive care unit patients with lactic acidosis
    (2021-11-01) Pattharawin Pattharanitima; Charat Thongprayoon; Wisit Kaewput; Fawad Qureshi; Fahad Qureshi; Tananchai Petnak; Narat Srivali; Guido Gembillo; Oisin A. O’corragain; Supavit Chesdachai; Saraschandra Vallabhajosyula; Pramod K. Guru; Michael A. Mao; Vesna D. Garovic; John J. Dillon; Wisit Cheungpasitporn; Ramathibodi Hospital; Wake Forest University School of Medicine; St. Agnes Hospital; Temple University Hospital; UMKC School of Medicine; Faculty of Medicine, Thammasat University; Università degli Studi di Messina; Phramongkutklao College of Medicine; Mayo Clinic; Mayo Clinic in Jacksonville, Florida
    Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (se-rum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with for-ward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respec-tively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.