Pearson correlation coefficients between waist circumference and the other variables in hemodialysis patients (n = 204).

<p>Pearson correlation coefficients between waist circumference and the other variables in hemodialysis patients (n = 204).</p

Logistic regression of multiple factors associated with abdominal obesity in hemodialysis patients (n = 204).

<p>Model 1: By using multiple logistic foreward regression analysis, all covariates were used for analysis. Model 2: By using multiple logistic foreward regression analysis, all covariates were used for analysis, except body mass index. CI, confidence interval.</p

Differences of clinical and biochemical parameters in hemodialysis patients between abdominal obesity and non-abdominal obesity.

<p>Differences of clinical and biochemical parameters in hemodialysis patients between abdominal obesity and non-abdominal obesity.</p

Logistic regression of multiple factors associated with PAD in hemodialysis patients (n = 204).

<p>AO, abdominal obesity; CI, confidence interval.</p

Protein-energy wasting significantly increases healthcare utilization and costs among patients with chronic kidney disease: a propensity-score matched cohort study

<p><b>Background:</b> Disease-related malnutrition is highly prevalent, and has prognostic implications for patients with chronic kidney disease (CKD); however, few studies have investigated the impact of malnutrition, or protein-energy wasting (PEW), on healthcare utilization and medical expenditure among CKD patients.</p> <p><b>Methods:</b> Using claim data from the National Health Insurance in Taiwan, this study identified patients with CKD between 2009–2013 and categorized them into those with mild, moderate, or severe CKD. Cases with PEW after CKD was diagnosed were propensity-score matched with controls in a 1:4 ratio. Healthcare resource utilization metrics were compared, including outpatient and emergency department visits, frequency and duration of hospitalization, and the cumulative costs associated with different CKD severity.</p> <p><b>Results:</b> From among 347,501 CKD patients, eligible cohorts of 66,872 with mild CKD (49.2%), 27,122 with moderate CKD (19.9%), and 42,013 with severe CKD (30.9%) were selected. Malnourished CKD patients had significantly higher rates of hospitalization (<i>p</i> < .001 for all severities) and re-admission (<i>p</i> = .015 for mild CKD, <i>p</i> = .002 for severe CKD) than non-malnourished controls. Cumulative medical costs for outpatient and emergency visits, and hospitalization, were significantly higher among all malnourished CKD patients than non-malnourished ones (<i>p</i> < .001); total medical costs were also higher among malnourished patients with mild (62.9%), moderate (59.6%), or severe (43.6%) CKD compared to non-malnourished patients (<i>p</i> < .001).</p> <p><b>Conclusions:</b> In a nationally-representative cohort, CKD patients with PEW had significantly more healthcare resource utilization and higher aggregate medical costs than those without, across the spectrum of CKD: preventing PEW in CKD patients should receive high priority if we would like to reduce medical costs.</p

Additional file 4: Table S3. of Monitoring of serum lactate level during cardiopulmonary resuscitation in adult in-hospital cardiac arrest

Features of cardiac arrest events stratified by lactate level. (DOCX 18 kb

Additional file 5: Table S4. of Monitoring of serum lactate level during cardiopulmonary resuscitation in adult in-hospital cardiac arrest

Multiple logistic regression model with secondary outcomes as the dependent variable. (DOCX 17 kb

Additional file 2: Figure S1. of Monitoring of serum lactate level during cardiopulmonary resuscitation in adult in-hospital cardiac arrest

Generalized additive model plot for non-parametric modelling of the effect of serum lactate level on the logit of probability for survival to hospital discharge. (TIFF 23 kb

Additional file 1: Table S1. of Monitoring of serum lactate level during cardiopulmonary resuscitation in adult in-hospital cardiac arrest

Definitions of comorbidities used in the multivariable models. (DOCX 21 kb

Kaplan–Meier survival curves for long-term survival.

<p>(A) Total population (HR 0.98, 95% CI 0.93–1.03; adjusted HR 1.02, 95% CI 0.97–1.07) (B) Patients who survived and discharged from index hospitalization (HR 1.16, 95% CI 1.05–1.28; adjusted HR 1.21, 95% CI 1.09–1.34).</p