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Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia.

ABSTRACT: To better guide strategies intended to reduce high rates of 30-day readmission after hospitalization for heart failure (HF), acute myocardial infarction (MI), or pneumonia, further information is needed about readmission diagnoses, readmission timing, and the relationship of both to patient age, sex, and race.To examine readmission diagnoses and timing among Medicare beneficiaries readmitted within 30 days after hospitalization for HF, acute MI, or pneumonia.We analyzed 2007-2009 Medicare fee-for-service claims data to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for HF, acute MI, or pneumonia. Readmission diagnoses were categorized using an aggregated version of the Centers for Medicare & Medicaid Services' Condition Categories. Readmission timing was determined by day after discharge.We examined the percentage of 30-day readmissions occurring on each day (0-30) after discharge; the most common readmission diagnoses occurring during cumulative periods (days 0-3, 0-7, 0-15, and 0-30) and consecutive periods (days 0-3, 4-7, 8-15, and 16-30) after hospitalization; median time to readmission for common readmission diagnoses; and the relationship between patient demographic characteristics and readmission diagnoses and timing.From 2007 through 2009, we identified 329,308 30-day readmissions after 1,330,157 HF hospitalizations (24.8% readmitted), 108,992 30-day readmissions after 548,834 acute MI hospitalizations (19.9% readmitted), and 214,239 30-day readmissions after 1,168,624 pneumonia hospitalizations (18.3% readmitted). The proportion of patients readmitted for the same condition was 35.2% after the index HF hospitalization, 10.0% after the index acute MI hospitalization, and 22.4% after the index pneumonia hospitalization. Of all readmissions within 30 days of hospitalization, the majority occurred within 15 days of hospitalization: 61.0%, HF cohort; 67.6%, acute MI cohort; and 62.6%, pneumonia cohort. The diverse spectrum of readmission diagnoses was largely similar in both cumulative and consecutive periods after discharge. Median time to 30-day readmission was 12 days for patients initially hospitalized for HF, 10 days for patients initially hospitalized for acute MI, and 12 days for patients initially hospitalized for pneumonia and was comparable across common readmission diagnoses. Neither readmission diagnoses nor timing substantively varied by age, sex, or race.Among Medicare fee-for-service beneficiaries hospitalized for HF, acute MI, or pneumonia, 30-day readmissions were frequent throughout the month after hospitalization and resulted from a similar spectrum of readmission diagnoses regardless of age, sex, race, or time after discharge.

SUBMITTER: Dharmarajan K

PROVIDER: S-EPMC3688083 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia.

Dharmarajan Kumar K Hsieh Angela F AF Lin Zhenqiu Z Bueno Héctor H Ross Joseph S JS Horwitz Leora I LI Barreto-Filho José Augusto JA Kim Nancy N Bernheim Susannah M SM Suter Lisa G LG Drye Elizabeth E EE Krumholz Harlan M HM

JAMA 20130101 4

<h4>Importance</h4>To better guide strategies intended to reduce high rates of 30-day readmission after hospitalization for heart failure (HF), acute myocardial infarction (MI), or pneumonia, further information is needed about readmission diagnoses, readmission timing, and the relationship of both to patient age, sex, and race.<h4>Objective</h4>To examine readmission diagnoses and timing among Medicare beneficiaries readmitted within 30 days after hospitalization for HF, acute MI, or pneumonia. ...[more]

PMID: 23340637

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Project description:ImportanceRates of 30-day readmissions following hospitalization for pneumonia are used to publicly report on hospital performance and to set financial penalties for the worst-performing hospitals. However, the rate of avoidable readmission following hospitalization for pneumonia is undefined.ObjectiveTo assess how often 30-day readmissions following hospitalization for community-acquired pneumonia (CAP) are avoidable.Design, setting, and participantsThis cohort study analyzed the results of an independent review of readmissions following hospitalization for CAP within 30 days among patients discharged from 2 large hospitals in France in 2014. Structured clinical records including clinical information (ie, baseline characteristics, physical examination, laboratory findings, x-ray or computed tomography scan findings, discharge plan, and treatments) for both index and readmission stays were independently reviewed by 4 certified board physicians. All consecutive adult patients hospitalized in 2014 with a diagnosis of CAP in our 2 eligible hospitals were eligible. All analyses presented were performed in March 2021.Main outcomes and measuresAvoidable readmission within 30 days of discharge from index hospitalization. The likelihood that a readmission was avoidable was quantified using latent class analysis based on the independent reviews. A readmission was considered avoidable if Bayes posterior probability exceeded 50%.ResultsThe total analytical sample consisted of 1150 index hospital stays with a diagnosis of CAP, which included 651 (56.6%) male patients. The median (IQR) age for all patients was 77.8 (IQR, 62.7-86.4) years. Out of the 1150 index hospital stays, 98 patients (8.5%) died in hospital, and 108 (9.4%) unplanned readmissions were found. Overall, 15 readmissions had a posterior probability of avoidability exceeding 0.50 (13.9% of the 108 unplanned readmissions; 95% CI, 8.0%-21.9%). The median (IQR) delay between the hospital discharge index and readmission was considerably shorter when readmission was deemed avoidable (4 [6-21] days vs 12 [2-18] days; P = .02).Conclusions and relevanceOnly a small number of readmissions following hospitalization for CAP were deemed avoidable, comprising less than 10% of all readmissions. Shorter time interval between hospitalization discharge and readmission was associated with avoidability.

Project description:BackgroundThe risk of rehospitalization is elevated in the immediate post-discharge period and declines over time. It is not known if the extent and timing of risk vary across readmission diagnoses, suggesting that recovery and vulnerability after discharge differ by physiologic system.ObjectiveWe compared risk trajectories for major readmission diagnoses in the year after discharge among all Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008-2010.MethodsWe estimated the daily risk of rehospitalization for 12 major readmission diagnostic categories after accounting for the competing risk of death after discharge. For each diagnostic category, we identified (1) the time required for readmission risk to peak and then decline 50% from maximum values after discharge; (2) the time required for readmission risk to approach plateau periods of minimal day-to-day change; and (3) the extent to which hospitalization risks are higher among patients recently discharged from the hospital compared with the general elderly population.ResultsAmong >3,000,000 hospitalizations, the yearly rate of rehospitalization was 67.0%, 49.5%, and 55.3% after hospitalization for HF, AMI, and pneumonia, respectively. The extent and timing of risk varied by readmission diagnosis and initial admitting condition. Risk of readmission for gastrointestinal bleeding/anemia peaked particularly late after hospital discharge, occurring 10, 6, and 7 days after hospitalization for HF, AMI, and pneumonia, respectively. Risk of readmission for trauma/injury declined particularly slowly, requiring 38, 20, and 38 days to decline by 50% after hospitalization for HF, AMI, and pneumonia, respectively.ConclusionsPatterns of vulnerability to different conditions that cause rehospitalization vary by time after hospital discharge. This finding suggests that recovery of various physiologic systems occurs at different rates and that post-discharge interventions to minimize vulnerability to specific conditions should be tailored to their underlying risks.

Project description:ObjectivesTo examine the association between income inequality and the risk of mortality and readmission within 30 days of hospitalization.DesignRetrospective cohort study of Medicare beneficiaries in the United States. Hierarchical, logistic regression models were developed to estimate the association between income inequality (measured at the US state level) and a patient's risk of mortality and readmission, while sequentially controlling for patient, hospital, other state, and patient socioeconomic characteristics. We considered a 0.05 unit increase in the Gini coefficient as a measure of income inequality.SettingUS acute care hospitals.ParticipantsPatients aged 65 years and older, and hospitalized in 2006-08 with a principal diagnosis of acute myocardial infarction, heart failure, or pneumonia.Main outcome measuresRisk of death within 30 days of admission or rehospitalization for any cause within 30 days of discharge. The potential number of excess deaths and readmissions associated with higher levels of inequality in US states in the three highest quarters of income inequality were compared with corresponding data in US states in the lowest quarter.ResultsMortality analyses included 555,962 admissions (4348 hospitals) for acute myocardial infarction, 1,092,285 (4484) for heart failure, and 1,146,414 (4520); readmission analyses included 553,037 (4262), 1,345,909 (4494), and 1,345,909 (4524) admissions, respectively. In 2006-08, income inequality in US states (as measured by the average Gini coefficient over three years) varied from 0.41 in Utah to 0.50 in New York. Multilevel models showed no significant association between income inequality and mortality within 30 days of admission for patients with acute myocardial infarction, heart failure, or pneumonia. By contrast, income inequality was associated with rehospitalization (acute myocardial infarction, risk ratio 1.09 (95% confidence interval 1.03 to 1.15), heart failure 1.07 (1.01 to 1.12), pneumonia 1.09 (1.03 to 1.15)). Further adjustment for individual income and educational achievement did not significantly attenuate these findings. Over the three year period, we estimate an excess of 7153 (2297 to 11,733) readmissions for acute myocardial infarction, 17,991 (3410 to 31,772) for heart failure, and 14,127 (4617 to 23,115) for pneumonia, that are associated with inequality levels in US states in the three highest quarters of income inequality, compared with US states in the lowest quarter.ConclusionsAmong patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, exposure to higher levels of income inequality was associated with increased risk of readmission but not mortality. In view of the observational design of the study, these findings could be biased, owing to residual confounding.

Project description:ImportanceThe addition of a claims-based frailty metric to traditional comorbidity-based risk-adjustment models for acute myocardial infarction (AMI), heart failure (HF), and pneumonia improves the prediction of 30-day mortality and readmission. This may have important implications for hospitals that tend to care for frail populations and participate in Centers for Medicare & Medicaid Services value-based payment programs, which use these risk-adjusted metrics to determine reimbursement.ObjectiveTo determine whether the addition of frailty measures to traditional comorbidity-based risk-adjustment models improved prediction of outcomes for patients with AMI, HF, and pneumonia.Design, setting, and participantsA nationwide cohort study included Medicare fee-for-service beneficiaries 65 years and older in the United States between January 1 and December 1, 2016. Analysis began August 2018.Main outcomes and measuresRates of mortality within 30 days of admission and 30 days of discharge, as well as 30-day readmission rates by frailty group. We evaluated the incremental effect of adding the Hospital Frailty Risk Score (HFRS) to current comorbidity-based risk-adjustment models for 30-day outcomes across all conditions.ResultsFor 785 127 participants, there were 166 200 hospitalizations [21.2%] for AMI, 348 619 [44.4%] for HF, and 270 308 [34.4%] for pneumonia. The mean (SD) age at the time of hospitalization was 79.2 (8.9) years; 656 315 (83.6%) were white and 402 639 (51.3%) were women. The mean (SD) HFRS was 7.3 (7.4) for patients with AMI, 10.8 (8.3) for patients with HF, and 8.2 (5.7) for patients with pneumonia. Among patients hospitalized for AMI, an HFRS more than 15 (compared with an HFRS <5) was associated with a higher risk of 30-day postadmission mortality (adjusted odds ratio [aOR], 3.6; 95% CI, 3.4-3.8), 30-day postdischarge mortality (aOR, 4.0; 95% CI, 3.7-4.3), and 30-day readmission (aOR, 3.0; 95% CI, 2.9-3.1) after multivariable adjustment for age, sex, race, and comorbidities. Similar patterns were observed for patients hospitalized with HF (30-day postadmission mortality: aOR, 3.5; 95% CI, 3.4-3.7; 30-day postdischarge mortality: aOR, 3.5; 95% CI, 3.3-3.6; and 30-day readmission: aOR, 2.9; 95% CI, 2.8-3.0) and among patients with pneumonia (30-day postadmission mortality: aOR, 2.5; 95% CI, 2.3-2.6; 30-day postdischarge mortality: aOR, 3.0; 95% CI, 2.9-3.2; and 30-day readmission: aOR, 2.8; 95% CI, 2.7-2.9). The addition of HFRS to traditional comorbidity-based risk-prediction models improved discrimination to predict outcomes for all 3 conditions.Conclusions and relevanceAmong Medicare fee-for-service beneficiaries, frailty as measured by the HFRS was associated with mortality and readmissions among patients hospitalized for AMI, HF, or pneumonia. The addition of HFRS to traditional comorbidity-based risk-prediction models improved the prediction of outcomes for all 3 conditions.

Project description:ImportanceThe US Hospital Readmissions Reduction Program (HRRP) was associated with reduced readmissions among Medicare beneficiaries hospitalized for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. It is important to assess whether there has been a signal for concomitant harm with an increase in mortality.ObjectiveTo evaluate whether the announcement or the implementation of HRRP was associated with an increase in either in-hospital or 30-day postdischarge mortality following hospitalization for AMI, HF, or pneumonia.Design, setting, and participantsIn this cohort study, using Medicare data, all hospitalizations for AMI, HF, and pneumonia were identified among fee-for-service Medicare beneficiaries aged 65 years and older from January 1, 2006, to December 31, 2014. These were assessed for changes in trends for risk-adjusted rates of in-hospital and 30-day postdischarge mortality after announcement and implementation of the HRRP using an interrupted time series framework. Analyses were done in November 2017 and December 2017.ExposuresAnnouncement of the HRRP in March 2010, and implementation of its penalties in October 2012.Main outcomes and measuresMonthly risk-adjusted rates of in-hospital and 30-day postdischarge mortality.ResultsThe sample included 1.7 million AMI, 4 million HF, and 3.5 million pneumonia hospitalizations. Between 2006 and 2014, in-hospital mortality decreased for the 3 conditions (AMI, from 10.4% to 9.7%; HF, from 4.3% to 3.5%; pneumonia, from 5.3% to 4.0%) while 30-day postdischarge mortality decreased from 7.4% to 7.0% for AMI (P for trend < .001), but increased from 7.4% to 9.2% for HF (P for trend < .001) and from 7.6% to 8.6% for pneumonia (P for trend < .001). Before the HRRP announcement, monthly postdischarge mortality was stable for AMI (slope for monthly change, 0.002%; 95% CI, -0.001% to 0.006% per month), and increased by 0.004% (95% CI, 0.000% to 0.007%) per month for HF and by 0.005% (95% CI, 0.002% to 0.008%) per month for pneumonia. There were no inflections in slope around HRRP announcement or implementation (P > .05 for all). In contrast, there were significant negative deflections in slopes for readmission rates at HRRP announcement for all conditions.Conclusions and relevanceAmong Medicare beneficiaries, there was no evidence for an increase in in-hospital or postdischarge mortality associated with HRRP announcement or implementation-a period with substantial reductions in readmissions. The improvement in readmission was therefore not associated with any increase in in-hospital or 30-day postdischarge mortality.

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.

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Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia.

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Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia.

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