Lili Chan

National Trends in Emergency Room Visits of Dialysis Patients for Adverse Drug Reactions

Background: Various medications are cleared by the kidneys, therefore patients with impaired renal function, especially dialysis patients are at risk for adverse drug events (ADEs). There are limited studies on ADEs in maintenance dialysis patients. Methods: We utilized a nationally representative database, the Nationwide Emergency Department Sample, from 2008 to 2013, to compare emergency department (ED) visits for dialysis and propensity matched non-dialysis patients. Log binomial regression was used to calculate relative risk of hospital admission and logistic regression to calculate ORs for in-hospital mortality while adjusting for patient and hospital characteristics. Results: While ED visits for ADEs decreased in both groups, they were over 10-fold higher in dialysis patients than non-dialysis patients (65.8–88.5 per 1,000 patients vs. 4.6–5.4 per 1,000 patients respectively, p < 0.001). The top medication category associated with ED visits for ADEs in dialysis patients is agents primarily affecting blood constituents, which has increased. After propensity matching, patient admission was higher in dialysis patients than non-dialysis patients, (88 vs. 76%, p < 0.001). Dialysis was associated with a 3% increase in risk of admission and 3 times the odds of in-hospital mortality (adjusted OR 3, 95% CI 2.7–2.3.3). Conclusions: ED visits for ADEs are substantially higher in dialysis patients than non-dialysis patients. In dialysis patients, ADEs associated with agents primarily affecting blood constituents are on the rise. ED visits for ADEs in dialysis patients have higher inpatient admissions and in-hospital mortality. Further studies are needed to identify and implement measures aimed at reducing ADEs in dialysis patients.

National Estimates of 30-Day Unplanned Readmissions of Patients on Maintenance Hemodialysis

BACKGROUND AND OBJECTIVES Patients on hemodialysis have high 30-day unplanned readmission rates. Using a national all-payer administrative database, we describe the epidemiology of 30-day unplanned readmissions in patients on hemodialysis, determine concordance of reasons for initial admission and readmission, and identify predictors for readmission. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS This is a retrospective cohort study using the Nationwide Readmission Database from the year 2013 to identify index admissions and readmission in patients with ESRD on hemodialysis. The Clinical Classification Software was used to categorize admission diagnosis into mutually exclusive clinically meaningful categories and determine concordance of reasons for admission on index hospitalizations and readmissions. Survey logistic regression was used to identify predictors of at least one readmission. RESULTS During 2013, there were 87,302 (22%) index admissions with at least one 30-day unplanned readmission. Although patient and hospital characteristics were statistically different between those with and without readmissions, there were small absolute differences. The highest readmission rate was for acute myocardial infarction (25%), whereas the lowest readmission rate was for hypertension (20%). The primary reasons for initial hospitalization and subsequent 30-day readmission were discordant in 80% of admissions. Comorbidities that were associated with readmissions included depression (odds ratio, 1.10; 95% confidence interval [95% CI], 1.05 to 1.15; P<0.001), drug abuse (odds ratio, 1.41; 95% CI, 1.31 to 1.51; P<0.001), and discharge against medical advice (odds ratio, 1.57; 95% CI, 1.45 to 1.70; P<0.001). A group of high utilizers, which constituted 2% of the population, was responsible for 20% of all readmissions. CONCLUSIONS In patients with ESRD on hemodialysis, nearly one quarter of admissions were followed by a 30-day unplanned readmission. Most readmissions were for primary diagnoses that were different from initial hospitalization. A small proportion of patients accounted for a disproportionate number of readmissions.

National Trends and Outcomes in Dialysis-Requiring Acute Kidney Injury in Heart Failure: 2002–2013

BACKGROUND Dialysis-requiring acute kidney injury (D-AKI) is a serious complication in hospitalized heart failure (HF) patients. However, data on national trends are lacking after 2002. METHODS We used the Nationwide Inpatient Sample (2002-2013) to identify HF hospitalizations with and without D-AKI. We analyzed trends in incidence, in-hospital mortality, length of stay (LoS), and cost. We calculated adjusted odds ratios (aORs) for predictors of D-AKI and for outcomes including in-hospital mortality and adverse discharge (discharge to skilled nursing facilities, nursing homes, etc). RESULTS We identified 11,205,743 HF hospitalizations. Across 2002-2013, the incidence of D-AKI doubled from 0.51% to 1.09%. We found male sex, younger age, African-American and Hispanic race, and various comorbidities and procedures, such as sepsis and mechanical ventilation, to be independent predictors of D-AKI in HF hospitalizations. D-AKI was associated with higher odds of in-hospital mortality (aOR 2.49, 95% confidence interval [CI] 2.36-2.63; P < .01) and adverse discharge (aOR 2.04, 95% CI 1.95-2.13; P < .01). In-hospital mortality and attributable risk of mortality due to D-AKI decreased across 2002-2013. LoS and cost also decreased across this period. CONCLUSIONS The incidence of D-AKI in HF hospitalizations doubled across 2002-2013. Despite declining in-hospital mortality, LoS, and cost, D-AKI was associated with worse outcomes.

Increased odds of metabolic syndrome with consumption of high dietary advanced glycation end products in adolescents

Diabetes & Metabolism - In Press.Proof corrected by the author Available online since jeudi 2 fevrier 2017

The Effect of Depression in Chronic Hemodialysis Patients on Inpatient Hospitalization Outcomes

Background/Aims: Depression is common in patients with end-stage renal disease (ESRD) on hemodialysis (HD). Although, depression is associated with mortality, the effect of depression on in-hospital outcomes has not been studied as yet. Methods: We analyzed the National Inpatient Sample for trends and outcomes of hospitalizations with depression in patients with ESRD. Results: The proportion of ESRD hospitalizations with depression doubled from 2005 to 2013 (5.01-11.78%). Hospitalized patients on HD with depression were younger (60.47 vs. 62.70 years, p < 0.0001), female (56.93 vs. 47.81%, p < 0.0001), white (44.92 vs. 34.01%, p < 0.0001), and had higher proportion of comorbidities. However, there was a statistically significant lower risk of mortality in HD patients within the top 5 reasons for admissions. Conclusion: There were significant differences in demographics and comorbidities for hospitalized HD patients with depression. Depression was associated with an increased rate of adverse effects in discharged patients, and decreased in-hospital mortality.

National Trends and Impact of Acute Kidney Injury Requiring Hemodialysis in Hospitalizations With Atrial Fibrillation.

Background Atrial fibrillation (AF) is a common cause for hospitalization, but there are limited data regarding acute kidney injury requiring dialysis (AKI‐D) in AF hospitalizations. We aimed to assess temporal trends and outcomes in AF hospitalizations complicated by AKI‐D utilizing a nationally representative database. Methods and Results Utilizing the Nationwide Inpatient Sample, AF hospitalizations and AKI‐D were identified using diagnostic and procedure codes. Trends were analyzed overall and within subgroups and utilized multivariable logistic regression to generate adjusted odds ratios (aOR) for predictors and outcomes including mortality and adverse discharge. Between 2003 and 2012, 3751 (0.11%) of 3 497 677 AF hospitalizations were complicated by AKI‐D. The trend increased from 0.3/1000 hospitalizations in 2003 to 1.5/1000 hospitalizations in 2012, with higher increases in males and black patients. Temporal changes in demographics and comorbidities explained a substantial proportion but not the entire trend. Significant comorbidities associated with AKI‐D included mechanical ventilation (aOR 13.12; 95% CI 9.88‐17.43); sepsis (aOR 8.20; 95% CI 6.00‐11.20); and liver failure (aOR 3.72; 95% CI 2.92‐4.75). AKI‐D was associated with higher risk of in‐hospital mortality (aOR 3.54; 95% CI 2.81‐4.47) and adverse discharge (aOR 4.01; 95% CI 3.12‐5.17). Although percentage mortality within AKI‐D decreased over the decade, attributable risk percentage mortality remained stable. Conclusions AF hospitalizations complicated by AKI‐D have quintupled over the last decade with differential increase by demographic groups. AKI‐D is associated with significant morbidity and mortality. Without effective AKI‐D therapies, focus should be on early risk stratification and prevention to avoid this devastating complication.

Unsupervised Machine learning to subtype Sepsis-Associated Acute Kidney Injury

Objective Acute kidney injury (AKI) is highly prevalent in critically ill patients with sepsis. Sepsis-associated AKI is a heterogeneous clinical entity, and, like many complex syndromes, is composed of distinct subtypes. We aimed to agnostically identify AKI subphenotypes using machine learning techniques and routinely collected data in electronic health records (EHRs). Design Cohort study utilizing the MIMIC-III Database. Setting ICUs from tertiary care hospital in the U.S. Patients Patients older than 18 years with sepsis and who developed AKI within 48 hours of ICU admission. Interventions Unsupervised machine learning utilizing all available vital signs and laboratory measurements. Measurements and Main Results We identified 1,865 patients with sepsis-associated AKI. Ten vital signs and 691 unique laboratory results were identified. After data processing and feature selection, 59 features, of which 28 were measures of intra-patient variability, remained for inclusion into an unsupervised machine-learning algorithm. We utilized k-means clustering with k ranging from 2 – 10; k=2 had the highest silhouette score (0.62). Cluster 1 had 1,358 patients while Cluster 2 had 507 patients. There were no significant differences between clusters on age, race or gender. We found significant differences in comorbidities and small but significant differences in several laboratory variables (hematocrit, bicarbonate, albumin) and vital signs (systolic blood pressure and heart rate). In-hospital mortality was higher in cluster 2 patients, 25% vs. 20%, p=0.008. Features with the largest differences between clusters included variability in basophil and eosinophil counts, alanine aminotransferase levels and creatine kinase values. Conclusions Utilizing routinely collected laboratory variables and vital signs in the EHR, we were able to identify two distinct subphenotypes of sepsis-associated AKI with different outcomes. Variability in laboratory variables, as opposed to their actual value, was more important for determination of subphenotypes. Our findings show the potential utility of unsupervised machine learning to better subtype AKI.

National Landscape of Unplanned 30-Day Readmissions in Patients with Left Ventricular Assist Device Implantation

The number of patients with advanced heart failure receiving left ventricular assist device (LVAD) implantation has increased dramatically over the last decade. There are limited data available about the nationwide trends of complications leading to readmissions after implantation of contemporary devices. Patients who underwent LVAD implantation from January 2013 to December 2013 were identified using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code 37.66 from the Healthcare Cost and Utilization Projects National Readmission Database. The top causes of unplanned 30-day readmission after LVAD implantation were determined. Survey logistic regression was used to analyze the significant predictors of readmission. In 2013, there were 2,235 patients with an LVAD implantation. Of them, 665 (29.7%) had at least 1 unplanned readmission within 30 days, out of which 289 (43.4%) occurred within 10 days after discharge. Implant complications (14.9%), congestive heart failure (11.7%), and gastrointestinal bleeding (8.4%) were the top 3 diagnoses for the first readmission and accounted for more than a third of all readmissions. Significant predictors of readmissions included a prolonged length of stay during the index admission, Medicare insurance, and discharge to short-term facility. In conclusion, despite increased experience with LVADs, unplanned readmissions within 30 days of implantation remain significantly high.

Unplanned 30-Day Readmissions after Parathyroidectomy in Patients with Chronic Kidney Disease: A Nationwide Analysis

Objective To examine rates of readmission after parathyroidectomy in patients with chronic kidney disease and determine primary etiologies, timing, and risk factors for these unplanned readmissions. Study Design Retrospective cohort study. Setting Nationwide Readmissions Database. Subjects and Methods The Nationwide Readmissions Database was queried for parathyroidectomy procedures performed in patients with chronic kidney disease between January 2013 and November 2013. Patient-, admission-, and hospital-level characteristics were compared for patients with and without at least 1 unplanned 30-day readmission. Outcomes of interest included rates, etiology, and timing of readmission. Multivariate logistic regression was used to identify predictors of 30-day readmission. Results There were 2756 parathyroidectomies performed in patients with chronic kidney disease with an unplanned readmission rate of 17.2%. Hypocalcemia/hungry bone syndrome accounted for 40% of readmissions. Readmissions occurred uniformly throughout the 30 days after discharge, but readmissions for hypocalcemia/hungry bone syndrome peaked in the first 10 days and decreased over time. Weight loss/malnutrition at time of parathyroidectomy and length of stay of 5 to 6 days conferred increased risk of readmission with adjusted odds ratios (aOR) of 3.31 (95% confidence interval [CI], 1.55-7.05; P = .002) and 1.87 (95% CI, 1.10-3.19; P = .02), respectively. Relative to primary hyperparathyroidism, parathyroidectomies performed for secondary hyperparathyroidism (aOR, 2.53; 95% CI, 1.07-5.95; P = .03) were associated with higher risk of readmission. Conclusion Postparathyroidectomy readmission rates for patients with chronic kidney disease are nearly 5 times that of the general population. Careful consideration of postoperative care and electrolyte management is crucial to minimize preventable readmissions in this vulnerable population.

Reasons for admission and predictors of national 30-day readmission rates in patients with end-stage renal disease on peritoneal dialysis

Abstract Background The number of patients with end-stage renal disease (ESRD) on peritoneal dialysis (PD) has increased by over 30% between 2007 and 2014. The Centers for Medicare and Medicaid has identified readmissions in ESRD patients to be a quality measure; however, there is a paucity of studies examining readmissions in PD patients. Methods Utilizing the National Readmission Database for the year 2013, we aimed to determine reasons for admission, the associated rates of unplanned readmission and independent predictors of readmissions in PD patients. Results The top 10 reasons for initial hospitalization were implant/PD catheter complications (23.22%), hypertension (5.47%), septicemia (5.18%), diabetes mellitus (DM) (5.12%), complications of surgical procedures/medical care (3.50%), fluid and electrolyte disorders (4.29%), peritonitis (3.76%), congestive heart failure (3.25%), pneumonia (2.90%) and acute myocardial infarction (AMI) (2.01%). The overall 30-day readmission rate was 14.6%, with the highest rates for AMI (21.8%), complications of surgical procedure/medical care (19.6%) and DM (18.4%). Concordance among the top 10 reasons for index admission and readmission was 22.6% and varied by admission diagnosis. Independent predictors of readmissions included age 35–49 years compared with 18–34 years [adjusted odds ratio (aOR) 1.35; 95% confidence interval (CI) 1.09–1.68; P = 0.006], female gender (aOR 1.27; 95% CI 1.12–1.44; P < 0.001), and comorbidities including liver disease (aOR 1.39; 95% CI 1.07–1.81; P = 0.01), peripheral vascular disease (aOR 1.33; 95% CI 1.14–1.56; P < 0.001) and depression (aOR 1.22; 95% CI 1.00–1.48; P = 0.04). Conclusions This study demonstrates the most common reasons for admission and readmissions in PD patients and several comorbidities that are predictive of readmissions. Targeted interventions towards these patients may be of benefit in reducing readmission in this growing population.