Browsing by Author "Mao, Michael A"

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    Acute Kidney Injury after Lung Transplantation: A Systematic Review and Meta-Analysis.
    (Journal of clinical medicine, 2019-10) Lertjitbanjong, Ploypin; Thongprayoon, Charat; Cheungpasitporn, Wisit; O'Corragain, Oisín A; Srivali, Narat; Bathini, Tarun; Watthanasuntorn, Kanramon; Aeddula, Narothama Reddy; Salim, Sohail Abdul; Ungprasert, Patompong; Gillaspie, Erin A; Wijarnpreecha, Karn; Mao, Michael A; Kaewput, Wisit
    Lung transplantation has been increasingly performed worldwide and is considered an effective therapy for patients with various causes of end-stage lung diseases. We performed a systematic review to assess the incidence and impact of acute kidney injury (AKI) and severe AKI requiring renal replacement therapy (RRT) in patients after lung transplantation. A literature search was conducted utilizing Ovid MEDLINE, EMBASE, and Cochrane Database from inception through June 2019. We included studies that evaluated the incidence of AKI, severe AKI requiring RRT, and mortality risk of AKI among patients after lung transplantation. Pooled incidence and odds ratios (ORs) with 95% confidence interval (CI) were obtained using random-effects meta-analysis. The protocol for this meta-analysis is registered with PROSPERO (International Prospective Register of Systematic Reviews; no. CRD42019134095). A total of 26 cohort studies with a total of 40,592 patients after lung transplantation were enrolled. Overall, the pooled estimated incidence rates of AKI (by standard AKI definitions) and severe AKI requiring RRT following lung transplantation were 52.5% (95% CI: 45.8-59.1%) and 9.3% (95% CI: 7.6-11.4%). Meta-regression analysis demonstrated that the year of study did not significantly affect the incidence of AKI (p = 0.22) and severe AKI requiring RRT (p = 0.68). The pooled ORs of in-hospital mortality in patients after lung transplantation with AKI and severe AKI requiring RRT were 2.75 (95% CI, 1.18-6.41) and 10.89 (95% CI, 5.03-23.58). At five years, the pooled ORs of mortality among patients after lung transplantation with AKI and severe AKI requiring RRT were 1.47 (95% CI, 1.11-1.94) and 4.79 (95% CI, 3.58-6.40), respectively. The overall estimated incidence rates of AKI and severe AKI requiring RRT in patients after lung transplantation are 52.5% and 9.3%, respectively. Despite advances in therapy, the incidence of AKI in patients after lung transplantation does not seem to have decreased. In addition, AKI after lung transplantation is significantly associated with reduced short-term and long-term survival.
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    Incidence and Impact of Acute Kidney Injury in Patients Receiving Extracorporeal Membrane Oxygenation: A Meta-Analysis.
    (Journal of clinical medicine, 2019-07) Thongprayoon, Charat; Cheungpasitporn, Wisit; Lertjitbanjong, Ploypin; Aeddula, Narothama Reddy; Bathini, Tarun; Watthanasuntorn, Kanramon; Srivali, Narat; Mao, Michael A; Kashani, Kianoush
    Although acute kidney injury (AKI) is a frequent complication in patients receiving extracorporeal membrane oxygenation (ECMO), the incidence and impact of AKI on mortality among patients on ECMO remain unclear. We conducted this systematic review to summarize the incidence and impact of AKI on mortality risk among adult patients on ECMO. A literature search was performed using EMBASE, Ovid MEDLINE, and Cochrane Databases from inception until March 2019 to identify studies assessing the incidence of AKI (using a standard AKI definition), severe AKI requiring renal replacement therapy (RRT), and the impact of AKI among adult patients on ECMO. Effect estimates from the individual studies were obtained and combined utilizing random-effects, generic inverse variance method of DerSimonian-Laird. The protocol for this systematic review is registered with PROSPERO (no. CRD42018103527). 41 cohort studies with a total of 10,282 adult patients receiving ECMO were enrolled. Overall, the pooled estimated incidence of AKI and severe AKI requiring RRT were 62.8% (95%CI: 52.1%-72.4%) and 44.9% (95%CI: 40.8%-49.0%), respectively. Meta-regression showed that the year of study did not significantly affect the incidence of AKI (p = 0.67) or AKI requiring RRT (p = 0.83). The pooled odds ratio (OR) of hospital mortality among patients receiving ECMO with AKI on RRT was 3.73 (95% CI, 2.87-4.85). When the analysis was limited to studies with confounder-adjusted analysis, increased hospital mortality remained significant among patients receiving ECMO with AKI requiring RRT with pooled OR of 3.32 (95% CI, 2.21-4.99). There was no publication bias as evaluated by the funnel plot and Egger's regression asymmetry test with p = 0.62 and p = 0.17 for the incidence of AKI and severe AKI requiring RRT, respectively. Among patients receiving ECMO, the incidence rates of AKI and severe AKI requiring RRT are high, which has not changed over time. Patients who develop AKI requiring RRT while on ECMO carry 3.7-fold higher hospital mortality.
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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.
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    Periodic limb movements during sleep and risk of hypertension: A systematic review.
    (Sleep medicine, 2023-02) Srivali, Narat; Thongprayoon, Charat; Tangpanithandee, Supawit; Krisanapan, Pajaree; Mao, Michael A; Zinchuk, Andrey; Koo, Brain B; Cheungpasitporn, Wisit

    Background

    Several studies suggest an association between periodic limb movements during sleep (PLMS) and hypertension; however, a systematic evaluation of this relationship is lacking.

    Methods

    We conducted a systematic review and meta-analysis of observational studies that reported odds ratio, relative risk, hazard ratio, or standardized incidence ratio, comparing the risk of hypertension in persons with PLMS (defined by the level of periodic limb movements per hour of sleep depended on individual studies) versus those without PLMS. After assessing heterogeneity and bias, the pooled risk ratio and 95% confidence intervals (CIs) were determined using a random-effect, generic inverse variance method of DerSimonian and Laird.

    Results

    Out of 572 potentially relevant articles, six eligible studies were included in the data analysis. Studies (6 cross-sectional) included 8949 participants. The statistical heterogeneity of this study was insignificant, with an I2 of 0%. A funnel plot and Egger's regression asymmetry test showed no publication bias with P-value ≥0.05. The pooled risk ratio of hypertension in patients with PLMS was 1.26 (95% CI, 1.12-1.41).

    Conclusions

    Our analysis demonstrates an increased hypertension risk among patients with PLMS. Prospective or interventional studies are needed to confirm this association.