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The national landscape of unplanned 30-day readmissions after total laryngectomy.

ABSTRACT: OBJECTIVES/HYPOTHESIS:Examine rates of readmission after total laryngectomy and determine primary etiologies, timing, and risk factors for unplanned readmission. STUDY DESIGN:Retrospective cohort study. METHODS:The Nationwide Readmissions Database was queried for patients who underwent total laryngectomy between January 2013 and November 2013. Patient-, procedure-, admission-, and institution-level characteristics were compared for patients with and without 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 2,931 total laryngectomies performed in 2013 with an unplanned readmission rate of 17.5%. Postoperative fistula accounted for 13.7% of readmissions. The odds of readmission were elevated for patients undergoing concurrent procedures, including primary tracheoesophageal fistulization (adjusted odds ratio [aOR]: 2.44, 95% confidence interval [CI]: 1.15-5.18, P = .02) and/or pedicle graft or flap procedures (aOR: 1.73, 95% CI: 1.13-2.66, P = .01). Additionally, patients with comorbid coagulopathy (aOR: 3.04, 95% CI: 1.13-8.22, P = .03), liver disease (aOR: 2.48, 95% CI: 1.08-5.71, P = .03), and valvular heart disease (aOR: 3.18, 95% CI: 1.20-8.41, P = .02) had increased risk for unplanned 30-day readmission. Private insurance and longer lengths of stay were associated with decreased odds of readmission. CONCLUSIONS:Nearly one-fifth of total laryngectomy patients are readmitted to the hospital within 30 days of discharge. Risk factors identified in this nationally representative cohort should be carefully considered during the postoperative period to reduce preventable readmissions after total laryngectomy. LEVEL OF EVIDENCE:2c Laryngoscope, 1842-1850, 2018.

SUBMITTER: Ferrandino R

PROVIDER: S-EPMC5960601 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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The national landscape of unplanned 30-day readmissions after total laryngectomy.

Ferrandino Rocco R Garneau Jonathan J Roof Scott S Pacheco Caitlin C Poojary Priti P Saha Aparna A Chauhan Kinsuk K Miles Brett B

The Laryngoscope 20171120 8

<h4>Objectives/hypothesis</h4>Examine rates of readmission after total laryngectomy and determine primary etiologies, timing, and risk factors for unplanned readmission.<h4>Study design</h4>Retrospective cohort study.<h4>Methods</h4>The Nationwide Readmissions Database was queried for patients who underwent total laryngectomy between January 2013 and November 2013. Patient-, procedure-, admission-, and institution-level characteristics were compared for patients with and without unplanned 30-day ...[more]

PMID: 29152760

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Project description:Background and objectivesPatients 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, & measurementsThis 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.ResultsDuring 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.ConclusionsIn 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.

Project description:This study sought to determine the range and prevalence of practices being implemented by hospitals to reduce 30-day readmissions of patients with heart failure or acute myocardial infarction (AMI).Readmissions of patients with heart failure or AMI are both common and costly; however, evidence on strategies adopted by hospitals to reduce readmission rates is limited.We used a Web-based survey to conduct a cross-sectional study of hospitals' reported use of specific practices to reduce readmissions for patients with heart failure or AMI. We contacted all hospitals enrolled in the Hospital to Home (H2H) quality improvement initiative as of July 2010. Of 594 hospitals, 537 completed the survey (response rate of 90.4%). We used standard frequency analysis to describe the prevalence of key hospital practices in the areas of: 1) quality improvement resources and performance monitoring; 2) medication management efforts; and 3) discharge and follow-up processes.Nearly 90% of hospitals agreed or strongly agreed that they had a written objective of reducing preventable readmission for patients with heart failure or AMI. More hospitals reported having quality improvement teams to reduce preventable readmissions for patients with heart failure (87%) than for patients with AMI (54%). Less than one-half (49.3%) of hospitals had partnered with community physicians and only 23.5% had partnered with local hospitals to manage patients at high risk for readmissions. Inpatient and outpatient prescription records were electronically linked usually or always in 28.9% of hospitals, and the discharge summary was always sent directly to the patient's primary medical doctor in only 25.5% of hospitals. On average, hospitals used 4.8 of 10 key practices; <3% of hospitals utilized all 10 practices.Although most hospitals have a written objective of reducing preventable readmissions of patients with heart failure or AMI, the implementation of recommended practices varied widely. More evidence establishing the effectiveness of various practices is needed.

Project description:BACKGROUND:Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. OBJECTIVE:To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). DESIGN:Observational cohort study using stepwise-backward selection and cross-validation. SUBJECTS:Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES:All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals. RESULTS:Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18). CONCLUSIONS:EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.

Dataset Information

The national landscape of unplanned 30-day readmissions after total laryngectomy.

Publications

The national landscape of unplanned 30-day readmissions after total laryngectomy.

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