Donation after circulatory death and liver transplantation: a cohort study

Passamonti, Serena Maria; Cannavó, Antonino; Panigada, Mauro; Trunzo, Valentina; Bottazzi, Andrea; Longobardi, Antonio; Buonocore, Ruggero; Fiorattini, Andrea; Torelli, Rosanna; Piccolo, Giuseppe Lo; Feo, Tullia Maria De

doi:10.1111/tri.13919

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Most importantly, multicollinearity issues are not mentioned, which is a phenomenon that undermines the statistical significance of an independent variable, increases the standard deviation of variables, as well as the inverse direction of coefficients [30][31][32]. Logistical regression is generally popular in the application of epidemiology [33][34][35][36]; most research investigated models without eliminating multicollinearity among variables. PCA is one of the effective ways to reduce dimensionality and minimize multicollinearity.…”

Section: Pca-la Analysis Proceduresmentioning

confidence: 99%

Predict Health Care Accessibility for Texas Medicaid Gap

Zhang

2021

Healthcare

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Medicaid is a unique approach in ensuring the below poverty population obtains free insurance coverage under federal and state provisions in the United States. Twelve states without expanded Medicaid caused two million people who were under the poverty line into health insecurity. Principal Component-based logistical regression (PCA-LA) is used to consider health status (HS) as a dependent variable and fourteen social-economic indexes as independent variables. Four composite components incorporated health conditions (i.e., “no regular source of care” (NRC), “last check-up more than a year ago” (LCT)), demographic impacts (i.e., four categorized adults (AS)), education (ED), and marital status (MS). Compared to the unadjusted LA, direct adjusted LA, and PCA-unadjusted LA three methods, the PCA-LA approach exhibited objective and reasonable outcomes in presenting an odd ratio (OR). They included that health condition is positively significant to HS due to beyond one OR, and negatively significant to ED, AS, and MS. This paper provided quantitative evidence for the Medicaid gap in Texas to extend Medicaid, exposed healthcare geographical inequity, offered a sight for the Centers for Disease Control and Prevention (CDC) to improve the Medicaid program and make political justice for the Medicaid gap.

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Section: Pca-la Analysis Proceduresmentioning

confidence: 99%

Predict Health Care Accessibility for Texas Medicaid Gap

Zhang

2021

Healthcare

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“…It is well recognized that DCD livers expose the recipient to increased risk from the inevitably longer donor warm ischemia time (dWIT). Aside from primary nonfunction [4], the most feared complication, and one of the main reasons for graft loss, is ischemic cholangiopathy (IC), defined as the appearance of intrahepatic non-anastomotic biliary strictures (NAS), which occurs in 10%-50% of cases [5][6][7][8][9]. The increasing use of normothermic preservation machines (NMP) might significantly modify these complication rates [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Biliary Complications Rates After Brain Death, Donation After Circulatory Death, and Living-Donor Liver Transplantation: A Single-Center Cohort Study

Meier

Kelly²,

Braun³

et al. 2022

Transpl Int

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Donation-after-circulatory-death (DCD), donation-after-brain-death (DBD), and living-donation (LD) are the three possible options for liver transplantation (LT), each with unique benefits and complication rates. We aimed to compare DCD-, DBD-, and LD-LT-specific graft survival and biliary complications (BC). We collected data on 138 DCD-, 3,027 DBD- and 318 LD-LTs adult recipients from a single center and analyzed patient/graft survival. BC (leak and anastomotic/non-anastomotic stricture (AS/NAS)) were analyzed in a subset of 414 patients. One-/five-year graft survival were 88.6%/70.0% for DCD-LT, 92.6%/79.9% for DBD-LT, and, 91.7%/82.9% for LD-LT. DCD-LTs had a 1.7-/1.3-fold adjusted risk of losing their graft compared to DBD-LT and LD-LT, respectively (p < 0.010/0.403). Bile leaks were present in 10.1% (DCD-LTs), 7.2% (DBD-LTs), and 36.2% (LD-LTs) (ORs, DBD/LD vs. DCD: 0.7/4.2, p = 0.402/<0.001). AS developed in 28.3% DCD-LTs, 18.1% DBD-LTs, and 43.5% LD-LTs (ORs, DBD/LD vs. DCD: 0.5/1.8, p = 0.018/0.006). NAS was present in 15.2% DCD-LTs, 1.4% DBDs-LT, and 4.3% LD-LTs (ORs, DBD/LD vs. DCD: 0.1/0.3, p = 0.001/0.005). LTs w/o BC had better liver graft survival compared to any other groups with BC. DCD-LT and LD-LT had excellent graft survival despite significantly higher BC rates compared to DBD-LT. DCD-LT represents a valid alternative whose importance should increase further with machine/perfusion systems.

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“…Principal Component Analysis (PCA) is one of the effective ways to reduce dimensionality and minimize multicollinearity. Currently published articles of logistical regression based on PCA are focused on genome−wide association studies [16] and diseases research such as gestational diabetes mellitus [17] and nephropathy [18], which is hard to see in medical gap research. PCA−based LA health access analysis of the medical gap aims to compare comprehensive social−economic impacts between unadjusted conditions and adjusted conditions.…”

Section: Introductionmentioning

confidence: 99%

Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

Zhang¹,

Wu²

2021

Preprint

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Background12 states without expanded Medicaid caused 2 million people who were under the poverty line across the U.S to be in Medicaid limbo and not eligible for subsidized health plans on the Affordable Care Act insurance exchanges. In order to amplify geographic equity, this paper aims to explore the health access for Medicaid gaps in Texas. MethodsPrincipal Component-based logistical regression algorithms (PCA-LA) is provided data visualization and comparison in between unadjusted and adjusted Medicaid programs. Initially, Principal Component Analysis (PCA) eliminated well-known multiplicity problems between explanatory variables in the application of epidemiology. Optimized the traditional logistical Regression (LR), the PCA-LA method, is considered health status (HS) as a dependent variable with 0 (“poor” health) and 1 (“good” health), fourteen social-economic indexes as independent variables. ResultsAfter Principal Component Analysis (PCA), four composite components incorporated health conditions (i.e., “no regular source of care” (NRC), “Last check up more than a year ago” (LCT)), demographic impacts (i.e., four categorized adults (AS)), education (ED), and marital status (MS). Compared to the unadjusted LA, direct adjusted LA, and PCA-unadjusted LA three methods, the PCA-LA approach exhibited objective and reasonable outcomes in presenting an Odd Ratio (OR). They included that health condition is positively significant to HS due to beyond 1 OR, and negatively significant to ED, AS, and MS due to less than 1 OR. ConclusionsThis paper provided quantitative evidence for the Medicaid gap in Texas to extend Medicaid, exposed healthcare geographical inequity, offered a sight for the Centers for Disease Control and Prevention (CDC) to raise researchable direction of the Medicaid program and make a timely scientific judgment of Texas healthcare accessibility.

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Donation after circulatory death and liver transplantation: a cohort study

Cited by 3 publications

References 25 publications

Predict Health Care Accessibility for Texas Medicaid Gap

Predict Health Care Accessibility for Texas Medicaid Gap

Comparison of Biliary Complications Rates After Brain Death, Donation After Circulatory Death, and Living-Donor Liver Transplantation: A Single-Center Cohort Study

Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

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