Browsing by Subject "nephrology"
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Item Open Access 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, WisitLactic 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.Item Open Access 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, WisitLactic 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.Item Open Access Medical student attitudes toward kidney physiology and nephrology: a qualitative study.(Ren Fail, 2016-11) roberts, JK; Sparks, MA; lehrich, RInterest in nephrology among trainees is waning in the USA. Early perceptions and attitudes to subject matter can be linked to the quality of pre-clinical curricula. We wanted to explore these attitudes in the setting of modern curriculum redesign. We utilized Q methodology to understand first-year medical student attitudes after an innovative kidney physiology curriculum redesign that focuses on blending multiple learning methods. First-year medical students were invited to take a Q sort survey at the conclusion of a kidney physiology course. Students prioritized statements related to their understanding of kidney physiology, learning preferences, preferred course characteristics, perceived clinical relevance of kidney physiology, and interest in nephrology as a career. Factor analysis was performed to identify different student viewpoints. At the conclusion of our modified course, all students (n = 108) were invited to take the survey and 44 (41%) Q sorts were returned. Two dominant viewpoints were defined according to interest in nephrology. The Potentials are students who understand kidney physiology, perceive kidney physiology as clinically relevant, attend class sessions, utilize videos, and are willing to shadow a nephrologist. The Uninterested are students who are less satisfied with their kidney physiology knowledge, prefer to study alone with a textbook, avoid lectures, and are not interested in learning about nephrology. In an updated renal physiology course, students that use multiple learning methods also have favorable attitudes toward learning kidney physiology. Thus, modern curriculum changes that accommodate a variety of learning styles may promote positive attitudes toward nephrology.Item Open Access Systematic review of safety and efficacy of COVID-19 vaccines in patients with kidney disease.(Kidney international reports, 2021-02-09) Glenn, Dorey A; Hegde, Anisha; Kotzen, Elizabeth; Walter, Emmanuel B; Kshirsagar, Abhijit V; Falk, Ronald; Mottl, Amy