Browsing by Author "Vallabhajosyula, Saraschandra"

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    ItemOpen Access
    2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines
    (Circulation) Thompson, Annemarie; Fleischmann, Kirsten E; Smilowitz, Nathaniel R; de Las Fuentes, Lisa; Mukherjee, Debabrata; Aggarwal, Niti R; Ahmad, Faraz S; Allen, Robert B; Altin, S Elissa; Auerbach, Andrew; Berger, Jeffrey S; Chow, Benjamin; Dakik, Habib A; Eisenstein, Eric L; Gerhard-Herman, Marie; Ghadimi, Kamrouz; Kachulis, Bessie; Leclerc, Jacinthe; Lee, Christopher S; Macaulay, Tracy E; Mates, Gail; Merli, Geno J; Parwani, Purvi; Poole, Jeanne E; Rich, Michael W; Ruetzler, Kurt; Stain, Steven C; Sweitzer, BobbieJean; Talbot, Amy W; Vallabhajosyula, Saraschandra; Whittle, John; Williams, Kim Allan
    Aim: The “2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery” provides recommendations to guide clinicians in the perioperative cardiovascular evaluation and management of adult patients undergoing noncardiac surgery. Methods: A comprehensive literature search was conducted from August 2022 to March 2023 to identify clinical studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Structure: Recommendations from the “2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery” have been updated with new evidence consolidated to guide clinicians; clinicians should be advised this guideline supersedes the previously published 2014 guideline. In addition, evidence-based management strategies, including pharmacological therapies, perioperative monitoring, and devices, for cardiovascular disease and associated medical conditions, have been developed.
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    Impact of ANCA-Associated Vasculitis on Outcomes of Hospitalizations for Goodpasture's Syndrome in the United States: Nationwide Inpatient Sample 2003-2014.
    (Medicina (Kaunas, Lithuania), 2020-03) Thongprayoon, Charat; Kaewput, Wisit; Boonpheng, Boonphiphop; Ungprasert, Patompong; Bathini, Tarun; Srivali, Narat; Vallabhajosyula, Saraschandra; Castaneda, Jorge L; Monga, Divya; Kanduri, Swetha R; Medaura, Juan; Cheungpasitporn, Wisit
    Background and objectives: Goodpasture's syndrome (GS) is a rare, life-threatening autoimmune disease. Although the coexistence of anti-neutrophil cytoplasmic antibody (ANCA) with Goodpasture's syndrome has been recognized, the impacts of ANCA vasculitis on mortality and resource utilization among patients with GS are unclear. Materials and Methods: We used the National Inpatient Sample to identify hospitalized patients with a principal diagnosis of GS from 2003 to 2014 in the database. The predictor of interest was the presence of ANCA-associated vasculitis. We tested the differences concerning in-hospital treatment and outcomes between GS patients with and without ANCA-associated vasculitis using logistic regression analysis with adjustment for other clinical characteristics. Results: A total of 964 patients were primarily admitted to hospital for GS. Of these, 84 (8.7%) had a concurrent diagnosis of ANCA-associated vasculitis. Hemoptysis was more prevalent in GS patients with ANCA-associated vasculitis. During hospitalization, GS patients with ANCA-associated required non-significantly more mechanical ventilation and non-invasive ventilation support, but non-significantly less renal replacement therapy and plasmapheresis than those with GS alone. There was no significant difference in in-hospital outcomes, including organ failure and mortality, between GS patients with and without ANCA-associated vasculitis. Conclusions: Our study demonstrated no significant differences between resource utilization and in-hospital mortality among hospitalized patients with coexistence of ANCA vasculitis and GS, compared to those with GS alone.
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    Inpatient Burden and Mortality of Goodpasture's Syndrome in the United States: Nationwide Inpatient Sample 2003-2014.
    (Journal of clinical medicine, 2020-02) Kaewput, Wisit; Thongprayoon, Charat; Boonpheng, Boonphiphop; Ungprasert, Patompong; Bathini, Tarun; Chewcharat, Api; Srivali, Narat; Vallabhajosyula, Saraschandra; Cheungpasitporn, Wisit
    Background: Goodpasture's syndrome is a rare, life-threatening, small vessel vasculitis. Given its rarity, data on its inpatient burden and resource utilization are lacking. We conducted this study aiming to assess inpatient prevalence, mortality, and resource utilization of Goodpasture's syndrome in the United States. Methods: The 2003-2014 National Inpatient Sample was used to identify patients with a principal diagnosis of Goodpasture's syndrome. The inpatient prevalence, clinical characteristics, in-hospital treatment, end-organ failure, mortality, length of hospital stay, and hospitalization cost were studied. Multivariable logistic regression was performed to identify independent factors associated with in-hospital mortality. Results: A total of 964 patients were admitted in hospital with Goodpasture's syndrome as the principal diagnosis, accounting for an overall inpatient prevalence of Goodpasture's syndrome among hospitalized patients in the United States of 10.3 cases per 1,000,000 admissions. The mean age of patients was 54 ± 21 years, and 47% were female; 52% required renal replacement therapy, whereas 39% received plasmapheresis during hospitalization. Furthermore, 78% had end-organ failure, with renal failure and respiratory failure being the two most common end-organ failures. The in-hospital mortality rate was 7.7 per 100 admissions. The factors associated with increased in-hospital mortality were age older than 70 years, sepsis, the development of respiratory failure, circulatory failure, renal failure, and liver failure, whereas the factors associated with decreased in-hospital mortality were more recent year of hospitalization and the use of therapeutic plasmapheresis. The median length of hospital stay was 10 days. The median hospitalization cost was $75,831. Conclusion: The inpatient prevalence of Goodpasture's syndrome in the United States is 10.3 cases per 1,000,000 admissions. Hospitalization of patients with Goodpasture's syndrome was associated with high hospital inpatient utilization and costs.
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    Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.
    (Journal of personalized medicine, 2021-11) Pattharanitima, Pattharawin; Thongprayoon, Charat; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; Kaewput, Wisit; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; O'Corragain, Oisin A; Mao, Michael A; Garovic, Vesna D; Qureshi, Fawad; Dillon, John J; Cheungpasitporn, Wisit
    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. 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. 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. 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.
    (Journal of clinical medicine, 2021-10) Pattharanitima, Pattharawin; Thongprayoon, Charat; Kaewput, Wisit; Qureshi, Fawad; Qureshi, Fahad; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; O'Corragain, Oisin A; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; Guru, Pramod K; Mao, Michael A; Garovic, Vesna D; Dillon, John J; Cheungpasitporn, Wisit
    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. We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum 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 forward 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. 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, respectively. 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). The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.