Dataset Information

Management of acute heart failure: Contribution of daily bedside echocardiographic assessment on therapy adjustment with impact measure on the 30-day readmission rate (JECICA).

ABSTRACT: There are currently one million heart failure (HF) patients in France and the rate is progressively increases due to population aging. Acute decompensation of HF is the leading cause of hospitalization in people over 65 years of age with a 25% re-hospitalization rate in the first month. Expenses related to the management of HF in France in 2013 amounted to more than one billion euros, of which 65% were for hospitalizations alone. The management of acute decompensation is a challenge, due to the complexity of clinical and laboratory evaluation leading to therapeutic errors, which in turn leads to longer hospitalization, high early re-hospitalization and complications. Therapeutic adjustment, especially diuretic, in the acute phase (during hospitalization) affects early re-hospitalization rates (within 30 days). These adjustments can be based on clinical estimation and laboratory parameters, but echocardiography has been shown to be superior in estimating filling pressures (FP) compared to clinical and laboratory parameters. We hypothesize that a simple daily bedside echocardiographic assessment could provide a reproducible estimation of FP with an evaluation of mitral inflow and the inferior vena cava (IVC). This could allow a more reliable estimate of the true blood volume of the patient and thus lead to a more suitable therapeutic adjustment. This in turn should lead to a decrease in early re-admission rate (primary endpoint) and potentially decrease six-month mortality and rate of complications.

SUBMITTER: Ricci JE

PROVIDER: S-EPMC6197724 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Management of acute heart failure: Contribution of daily bedside echocardiographic assessment on therapy adjustment with impact measure on the 30-day readmission rate (JECICA).

Ricci Jean-Etienne JE Kalmanovich Eran E Robert Christelle C Chevallier Thierry T Aguilhon Sylvain S Solecki Kamila K Akodad Mariama M Cornillet Luc L Soullier Camille C Cayla Guillaume G Lattuca Benoit B Roubille François F

Contemporary clinical trials communications 20180809

There are currently one million heart failure (HF) patients in France and the rate is progressively increases due to population aging. Acute decompensation of HF is the leading cause of hospitalization in people over 65 years of age with a 25% re-hospitalization rate in the first month. Expenses related to the management of HF in France in 2013 amounted to more than one billion euros, of which 65% were for hospitalizations alone. The management of acute decompensation is a challenge, due to the ...[more]

PMID: 30364633

Similar Datasets

Project description:BackgroundTo overcome the limitations of administrative data in adequately adjusting for differences in patients' risk of readmissions, recent studies have added supplemental data from patient surveys and other sources (e.g., electronic health records). However, judging the adequacy of enhanced risk adjustment for use in assessment of 30-day readmission as a hospital quality indicator is not straightforward. In this paper, we evaluate the adequacy of risk adjustment by comparing the one-year costs of those readmitted within 30 days to those not after excluding the costs of the readmission.MethodsIn this two-step study, we first used comprehensive administrative and survey data on a nationally representative Medicare cohort of hospitalized patients to compare patients with a medical admission who experienced a 30-day readmission to patients without a readmission in terms of their overall Medicare payments during 12 months following the index discharge. We then examined the extent to which a series of enhanced risk adjustment models incorporating code-based comorbidities, self-reported health status and prior healthcare utilization, reduced the payment differences between the admitted and not readmitted groups.ResultsOur analytic cohort consisted 4684 index medical hospitalization of which 842 met the 30-day readmission criteria. Those readmitted were more likely to be older, White, sicker and with higher healthcare utilization in the previous year. The unadjusted subsequent one-year Medicare spending among those readmitted ($56,856) was 60% higher than that among the non-readmitted ($35,465). Even with enhanced risk adjustment, and across a variety of sensitivity analyses, one-year Medicare spending remained substantially higher (46.6%, p < 0.01) among readmitted patients.ConclusionsEnhanced risk adjustment models combining health status indicators from administrative and survey data with previous healthcare utilization are unable to substantially reduce the cost differences between those medical admission patients readmitted within 30 days and those not. The unmeasured patient severity that these cost differences most likely reflect raises the question of the fairness of programs that place large penalties on hospitals with higher than expected readmission rates.

Project description:BackgroundReduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients.MethodsThe California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results.ResultsArea under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation.DiscussionThe results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.

Project description:PURPOSE:Analyze associations between patients' characteristics and treatment factors with 30-day unplanned readmissions in hospitalized otolaryngology patients in the German Diagnosis Related Group (D-DRG) system. METHODS:A retrospective cohort study was performed on 15.271 otolaryngology admissions of 12.859 patients in 2015 in Thuringia, Germany. The medical records of the 1173 cases (7.6%) with readmission within 30-days were analyzed in detail. RESULTS:The 30-day readmission was planned in 747 cases (4.9%) and was unplanned in 422 cases (2.8%). The median interval between primary and next inpatient treatment was 11 days. The principal diagnosis was the same as during the primary index treatment in 72% of the cases. The most frequent reasons for readmission were: Need for non-surgical therapy (31.2%), need for further surgery (26.3%), post-surgical complaints (16.9%), and recurrence of primary complaints (10.7%). The multivariate analysis revealed that discharge due to patient's request against medical advice was a strong independent factor with high risk for unplanned readmission (Odds Ratio [OR] = 9.62]; confidence interval [CI] = 2.69-34.48). Surgery at index admission (OR = 3.33; CI = 1.86-5.96) was the second important independent risk factor for unplanned readmission. Unplanned readmission had more frequently a non-surgical treatment at readmission than a surgical treatment (OR = 3.92; CI = 2.24-6.84) and needed more frequently further diagnostics (OR = 2.34; CI = 1.34-4.11). The following index International Classification of Diseases (ICD) categories had the highest risk for unplanned readmission: Injury, poisoning and certain other consequences of external causes, ICD: S00-T98 (OR = 66.67; CI = 15.87-333.33), symptoms, signs, abnormal findings, ill-defined causes, not otherwise classified, ICD: R00-R99 (OR = 62.5; CI = 11.76-333.33), blood forming organ diseases, ICD: D50-D90 (OR = 21.276; CI = 3.508-125), and eye/ ear diseases, ICD: H00-H95 (OR = 12.66; CI = 4.29-37.03). CONCLUSIONS:The causes of unplanned 30-day readmission in German otolaryngology inpatients are multifactorial. Specific patient and treatment characteristics were identified to be targeted with health care interventions to decrease unplanned readmissions.

Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Dataset Information

Management of acute heart failure: Contribution of daily bedside echocardiographic assessment on therapy adjustment with impact measure on the 30-day readmission rate (JECICA).

Publications

Management of acute heart failure: Contribution of daily bedside echocardiographic assessment on therapy adjustment with impact measure on the 30-day readmission rate (JECICA).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets