An administrative claims model for profiling hospital 30-day mortality rates for pneumonia patients

Differential Impact of Systolic and Diastolic Heart Failure on In-Hospital Treatment, Outcomes, and Cost of Patients Admitted for Pneumonia

Background

Patients admitted with pneumonia and heart failure (HF) have increased mortality and cost compared to those without HF, but it is not known whether outcomes differ between systolic and diastolic HF. Management of concomitant pneumonia and HF is complicated because HF treatments can worsen complications of pneumonia.

Methods

This is a retrospective cohort study from the Premier Database among patients admitted with pneumonia between 2010-2015. Patients were categorized based on systolic, diastolic, and combined HF using ICD-9 codes. The primary outcome was in-hospital mortality. Secondary outcomes included use of HF medications, length of stay, cost, intensive care unit (ICU) admission, as well as use of invasive mechanical ventilation (IMV), vasopressors and inotropes. Multivariable logistic regression was used to describe associations of these outcomes with type of HF.

Results

Of 123,211 patients with pneumonia and HF, 41,196 (33.4%) had systolic HF, 69,982 (56.8%) diastolic HF, and 12,033 (9.8%) had combined HF. Compared to patients with diastolic HF, after multivariable adjustment systolic HF was associated with higher in-hospital mortality (OR 1.15; 95% CI:1.11-1.20), ICU admission, and use of IMV and vasoactive agents, but not with increased length of stay or cost. Among patients with systolic HF, 80% received a loop diuretic, 72% a beta blocker, 48% angiotensin converting enzyme inhibitor or angiotensin receptor blocker, and 12.5% a mineralocorticoid receptor antagonist.

Conclusion

Systolic HF is associated with added risk in pneumonia compared to diastolic HF. There may also be an opportunity to optimize medications in systolic HF prior to discharge.

Sample size calculations for indirect standardization

Indirect standardization, and its associated parameter the standardized incidence ratio, is a commonly-used tool in hospital profiling for comparing the incidence of negative outcomes between an index hospital and a larger population of reference hospitals, while adjusting for confounding covariates. In statistical inference of the standardized incidence ratio, traditional methods often assume the covariate distribution of the index hospital to be known. This assumption severely compromises one's ability to compute required sample sizes for high-powered indirect standardization, as in contexts where sample size calculation is desired, there are usually no means of knowing this distribution. This paper presents novel statistical methodology to perform sample size calculation for the standardized incidence ratio without knowing the covariate distribution of the index hospital and without collecting information from the index hospital to estimate this covariate distribution. We apply our methods to simulation studies and to real hospitals, to assess both its capabilities in a vacuum and in comparison to traditional assumptions of indirect standardization.

Development and validation of risk-adjusted quality indicators for the long-term outcome of acute sepsis care in German hospitals based on health claims data

Background

Methods for assessing long-term outcome quality of acute care for sepsis are lacking. We investigated a method for measuring long-term outcome quality based on health claims data in Germany.

Materials and methods

Analyses were based on data of the largest German health insurer, covering 32% of the population. Cases (aged 15 years and older) with ICD-10-codes for severe sepsis or septic shock according to sepsis-1-definitions hospitalized in 2014 were included. Short-term outcome was assessed by 90-day mortality; long-term outcome was assessed by a composite endpoint defined by 1-year mortality or increased dependency on chronic care. Risk factors were identified by logistic regressions with backward selection. Hierarchical generalized linear models were used to correct for clustering of cases in hospitals. Predictive validity of the models was assessed by internal validation using bootstrap-sampling. Risk-standardized mortality rates (RSMR) were calculated with and without reliability adjustment and their univariate and bivariate distributions were described.

Results

Among 35,552 included patients, 53.2% died within 90 days after admission; 39.8% of 90-day survivors died within the first year or had an increased dependency on chronic care. Both risk-models showed a sufficient predictive validity regarding discrimination [AUC = 0.748 (95% CI: 0.742; 0.752) for 90-day mortality; AUC = 0.675 (95% CI: 0.665; 0.685) for the 1-year composite outcome, respectively], calibration (Brier Score of 0.203 and 0.220; calibration slope of 1.094 and 0.978), and explained variance (R ² = 0.242 and R ² = 0.111). Because of a small case-volume per hospital, applying reliability adjustment to the RSMR led to a great decrease in variability across hospitals [from median (1st quartile, 3rd quartile) 54.2% (44.3%, 65.5%) to 53.2% (50.7%, 55.9%) for 90-day mortality; from 39.2% (27.8%, 51.1%) to 39.9% (39.5%, 40.4%) for the 1-year composite endpoint]. There was no substantial correlation between the two endpoints at hospital level (observed rates: ρ = 0, p = 0.99; RSMR: ρ = 0.017, p = 0.56; reliability-adjusted RSMR: ρ = 0.067; p = 0.026).

Conclusion

Quality assurance and epidemiological surveillance of sepsis care should include indicators of long-term mortality and morbidity. Claims-based risk-adjustment models for quality indicators of acute sepsis care showed satisfactory predictive validity. To increase reliability of measurement, data sources should cover the full population and hospitals need to improve ICD-10-coding of sepsis.

Carbapenem-resistant gram-negative bacteria in Germany: incidence and distribution among specific infections and mortality: an epidemiological analysis using real-world data

PURPOSE

Infections with carbapenem-resistant gram-negative bacteria (in Germany classified as 4MRGN) are a growing threat in clinical care. This study was undertaken to understand the overall burden of 4MRGN infections in Germany in the context of a Health Technology Appraisal (HTA) for Ceftazidime/Avibactam (CAZ/AVI). Besides, the incidences mortality was an endpoint of interest.

METHODS

To assess infections with carbapenem-resistant gram-negative bacteria and related mortality, three different data sources have been used. From the German statistics office (DESTATIS) data have been retrieved to obtain the overall frequency these pathogens. Via two other databases, the German analysis database (DADB) and a Benchmarking of > 200 hospitals in a representative sample (BM-DB), the distribution of the infections and the mortality have been analyzed.

RESULTS

DESTATIS data showed a total of 11,863 carbapenem-resistant gram-negative bacteria codings, of which 10,348 represent infections and 1515 carriers. The most frequent infections were complicated urinary tract infections (cUTI) (n = 2,337), followed by pneumonia (n = 1006) and intra-abdominal infections (n = 730). A considerable amount of patients had multiple infections in one hospital episode (n = 1258). In-hospital mortality was 18.6% in DADB and 14.3% in the BM-DB population, respectively. In cases with additional bloodstream infections, DADB mortality was correspondingly higher at 33.0%. DADB data showed an incremental mortality increase of 5.7% after 30 days and 10.0% after 90 days resulting in a cumulative 90 day mortality of 34.3%.

CONCLUSIONS

Infections with carbapenem-resistant gram-negative bacteria are still rare (6.8-12.4 per 100,000) but show a significant increase in mortality compared to infections with more sensitive pathogens. Using different data sources allowed obtaining a realistic picture.

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network

BACKGROUND

Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach.

METHODS

Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016-2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC).

RESULTS

The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM.

CONCLUSION

ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients' risk assessment and quality management.

A systematic examination of quality-adjusted price index alternatives for medical care using claims data

We investigate alternative methods for constructing quality-adjusted medical price indexes both theoretically and empirically using medical claims data. The methodology and assumptions applied in the formation of the index have substantive effects on the magnitude of the quality-adjusted price changes. A method based on utility theory produces the most robust and accurate results, while alternative methods used in recent work overstate inflation. Based on Medicare claims data for three medical conditions, we find declining prices across each condition when properly adjusted for quality.

Acute but not chronic heart failure is associated with higher mortality among patients hospitalized with pneumonia: An analysis of a nationwide database☆

Background

Among patients admitted for pneumonia, heart failure (HF) is associated with worse outcomes. It is unclear whether this association is due to acute HF exacerbations, complex medical management, or chronic co-morbid conditions.

Methods

This is a retrospective cohort study of patients admitted between July 2010 and June 2015 at 651 US hospitals with a principal diagnosis of either pneumonia or secondary diagnosis of pneumonia with a primary diagnosis of respiratory failure or sepsis. Comorbidities were identified by ICD-9 codes and medical management by daily charge codes. Patients were categorized according to the presence and acuity of admission diagnosis of HF. In-hospital mortality was the primary outcome. Secondary outcomes included length of stay, hospital cost, ICU admission, use of mechanical ventilation, vasopressors and inotropes. Logistic regression was used to study the association of outcomes with presence and acuity of HF.

Results

Of 783,702 patients who met inclusion criteria, 212,203 (27%) had a diagnosis of HF. Of these, 56,306 (26.5%) had acute while 48,188 (22.7%) had chronic HF on admission; 51% had a diagnosis of unspecified HF. In multivariable-adjusted models, having any HF was associated with increased mortality (OR 1.35 [1.33 - 1.38]) compared to those without HF; increased mortality was associated with acute HF (OR 1.19 [1.15 - 1.22]) but not chronic HF (OR 0.92 [0.89 - 0.96]).

Conclusion

The worse outcomes for pneumonia patients with HF appear due to acute HF exacerbations. Adjustment for HF without accounting for chronicity could lead to biased prognostic and billing estimates.

Analysis of Hospital-Level Readmission Rates and Variation in Adverse Events Among Patients With Pneumonia in the United States

IMPORTANCE

It is known that hospitalized patients who experience adverse events are at greater risk of readmission; however, it is unknown whether patients admitted to hospitals with higher risk-standardized readmission rates had a higher risk of in-hospital adverse events.

OBJECTIVE

To evaluate whether patients with pneumonia admitted to hospitals with higher risk-standardized readmission rates had a higher risk of adverse events.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study linked patient-level adverse events data from the Medicare Patient Safety Monitoring System (MPSMS), a randomly selected medical record abstracted database, to the hospital-level pneumonia-specific all-cause readmissions data from the Centers for Medicare & Medicaid Services. Patients with pneumonia discharged from July 1, 2010, through December 31, 2019, in the MPSMS data were included. Hospital performance on readmissions was determined by the risk-standardized 30-day all-cause readmission rate. Mixed-effects models were used to examine the association between adverse events and hospital performance on readmissions, adjusted for patient and hospital characteristics. Analysis was completed from October 2019 through July 2020 for data from 2010 to 2017 and from March through April 2022 for data from 2018 to 2019.

EXPOSURES

Patients hospitalized for pneumonia.

MAIN OUTCOMES AND MEASURES

Adverse events were measured by the rate of occurrence of hospital-acquired events and the number of events per 1000 discharges.

RESULTS

The sample included 46 047 patients with pneumonia, with a median (IQR) age of 71 (58-82) years, with 23 943 (52.0%) women, 5305 (11.5%) Black individuals, 37 763 (82.0%) White individuals, and 2979 (6.5%) individuals identifying as another race, across 2590 hospitals. The median hospital-specific risk-standardized readmission rate was 17.0% (95% CI, 16.3%-17.7%), the occurrence rate of adverse events was 2.6% (95% CI, 2.54%-2.65%), and the number of adverse events per 1000 discharges was 157.3 (95% CI, 152.3-162.5). An increase by 1 IQR in the readmission rate was associated with a relative 13% higher patient risk of adverse events (adjusted odds ratio, 1.13; 95% CI, 1.08-1.17) and 5.0 (95% CI, 2.8-7.2) more adverse events per 1000 discharges at the patient and hospital levels, respectively.

CONCLUSIONS AND RELEVANCE

Patients with pneumonia admitted to hospitals with high all-cause readmission rates were more likely to develop adverse events during the index hospitalization. This finding strengthens the evidence that readmission rates reflect the quality of hospital care for pneumonia.

Marginal indirect standardization using latent clustering on multiple hospitals

A method was introduced in 2018 of performing indirect standardization for hospital profiling when only the marginal distributions of confounding variables are observed for the index hospital but the full joint covariate distribution is available for the reference hospitals (Wang et al, J Am Stat Assoc 2018; 114:662-630). The method constructs a synthetic comparison hospital using a weighted combination of reference hospitals, with weights assumed to follow a Dirichlet distribution with equal concentration parameters. In this article, we propose a novel method that improves upon the approach in a previous study (Wang et al, J Am Stat Assoc 2018; 114:662-630), by assuming the existence of latent classes among reference hospitals to allow for unequal Dirichlet concentration parameters. The latent class memberships, and thus the hospital weights, are informed by hospital-level characteristics. Our new method results in less biased point estimates and narrower uncertainty intervals for the standardized incidence ratio compared with the existing approach. We show the superiority of our novel methods in an application to a study on prevalence of high-radiation computed tomography exams, as well as in a simulation of the same medical context.

Variation in COVID-19 Mortality Across 117 US Hospitals in High- and Low-Burden Settings

Some hospitals have faced a surge of patients with COVID-19, while others have not. We assessed whether COVID-19 burden (number of patients with COVID-19 admitted during April 2020 divided by hospital certified bed count) was associated with mortality in a large sample of US hospitals. Our study population included 14,226 patients with COVID-19 (median age 66 years, 45.2% women) at 117 hospitals, of whom 20.9% had died at 5 weeks of follow-up. At the hospital level, the observed mortality ranged from 0% to 44.4%. After adjustment for age, sex, and comorbidities, the adjusted odds ratio for in-hospital death in the highest quintile of burden was 1.46 (95% CI, 1.07-2.00) compared to all other quintiles. Still, there was large variability in outcomes, even among hospitals with a similar level of COVID-19 burden and after adjusting for age, sex, and comorbidities.

Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status

IMPORTANCE

Socioeconomic factors are associated with worse outcomes after hospitalization, but neither the Centers for Medicare & Medicaid Services (CMS) nor the Veterans Affairs (VA) health care system adjust for socioeconomic factors in profiling hospital mortality.

OBJECTIVE

To evaluate changes in Veterans Affairs medical centers' (VAMCs') risk-standardized mortality rates among veterans hospitalized for heart failure and pneumonia after adjusting for socioeconomic factors.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, retrospective data were used to assess 131 VAMCs' risk-standardized 30-day mortality rates with or without adjustment for socioeconomic covariates. The study population included 42 892 veterans hospitalized with heart failure and 39 062 veterans hospitalized with pneumonia from January 1, 2012, to December 31, 2014. Data were analyzed from March 1, 2019, to April 1, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was 30-day mortality after admission. Socioeconomic covariates included neighborhood disadvantage, race/ethnicity, homelessness, rurality, nursing home residence, reason for Medicare eligibility, Medicaid and Medicare dual eligibility, and VA priority.

RESULTS

The study population included 42 892 veterans hospitalized with heart failure (98.2% male; mean [SD] age, 71.9 [11.4] years) and 39 062 veterans hospitalized with pneumonia (96.8% male; mean [SD] age, 71.0 [12.4] years). The addition of socioeconomic factors to the CMS models modestly increased the C statistic from 0.77 (95% CI, 0.77-0.78) to 0.78 (95% CI, 0.78-0.78) for 30-day mortality after heart failure and from 0.73 (95% CI, 0.72-0.73) to 0.74 (95% CI, 0.73-0.74) for 30-day mortality after pneumonia. Mortality rates were highly correlated (Spearman correlations of ≥0.98) in models that included or did not include socioeconomic factors. With the use of the CMS model for heart failure, VAMCs in the lowest quintile had a mean (SD) mortality rate of 6.0% (0.4%), those in the middle 3 quintiles had a mean (SD) mortality rate of 7.2% (0.4%), and those in the highest quintile had a mean (SD) mortality rate of 8.8% (0.6%). After the inclusion of socioeconomic covariates, the adjusted mean (SD) mortality was 6.1% (0.4%) for hospitals in the lowest quintile, 7.2% (0.4%) for those in the middle 3 quintiles, and 8.6% (0.5%) for those in the highest quintile. The mean absolute change in rank after socioeconomic adjustment was 3.0 ranking positions (interquartile range, 1.0-4.0) among hospitals in the highest quintile of mortality after heart failure and 4.4 ranking positions (interquartile range, 1.0-6.0) among VAMCs in the lowest quintile. Similar findings were observed for mortality rankings in pneumonia and after inclusion of clinical covariates.

CONCLUSIONS AND RELEVANCE

This study suggests that adjustments for socioeconomic factors did not meaningfully change VAMCs' risk-adjusted 30-day mortality rates for veterans hospitalized for heart failure and pneumonia. The implications of such adjustments should be examined for other quality measures and health systems.

Spending and quality after three years of Medicare's bundled payments for medical conditions: quasi-experimental difference-in-differences study

OBJECTIVE

To evaluate whether longer term participation in the bundled payments for care initiative (BPCI) for medical conditions in the United States, which held hospitals financially accountable for all spending during an episode of care from hospital admission to 90 days after discharge, was associated with changes in spending, mortality, or health service use.

DESIGN

Quasi-experimental difference-in-differences analysis.

SETTING

US hospitals participating in bundled payments for acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease (COPD), or pneumonia, and propensity score matched to non-participating hospitals.

PARTICIPANTS

238 hospitals participating in the Bundled Payments for Care Improvement initiative (BPCI) and 1415 non-BPCI hospitals. 226 BPCI hospitals were matched to 700 non-BPCI hospitals.

MAIN OUTCOME MEASURES

Primary outcomes were total spending on episodes and death 90 days after discharge. Secondary outcomes included spending and use by type of post-acute care. BPCI and non-BPCI hospitals were compared by patient, hospital, and hospital market characteristics. Market characteristics included population size, competitiveness, and post-acute bed supply.

RESULTS

In the 226 BPCI hospitals, episodes of care totaled 261 163 in the baseline period and 93 562 in the treatment period compared with 211 208 and 78 643 in the 700 matched non-BPCI hospitals, respectively, with small differences in hospital and market characteristics after matching. Differing trends were seen for some patient characteristics (eg, mean age change -0.3 years at BPCI hospitals v non- BPCI hospitals, P<0.001). In the adjusted analysis, participation in BPCI was associated with a decrease in total episode spending (-1.2%, 95% confidence interval -2.3% to -0.2%). Spending on care at skilled nursing facilities decreased (-6.3%, -10.0% to -2.5%) owing to a reduced number of facility days (-6.2%, -9.8% to -2.6%), and home health spending increased (4.4%, 1.4% to 7.5%). Mortality at 90 days did not change (-0.1 percentage points, 95% confidence interval -0.5 to 0.2 percentage points).

CONCLUSIONS

In this longer term evaluation of a large national programme on medical bundled payments in the US, participation in bundles for four common medical conditions was associated with savings at three years. The savings were generated by practice changes that decreased use of high intensity care after hospital discharge without affecting quality, which also suggests that bundles for medical conditions could require multiple years before changes in savings and practice emerge.

Relative Effects of the Hospital Readmissions Reduction Program on Hospitals That Serve Poorer Patients

IMPORTANCE

Hospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP).

OBJECTIVE

To compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals.

DESIGN

Retrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served.

SETTING

Acute care hospitals within the United States.

PARTICIPANTS

Medicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia.

MAIN OUTCOME AND MEASURE

Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend.

RESULTS

For AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P<0.0001 for all).

CONCLUSIONS AND RELEVANCE

For all hospitals, differences in pre-post trends in RSRRs varied with disease conditions. However, for the highest-penalized hospitals, the pre-post decline in RSRRs was greater for low than high dual-eligible hospitals for all penalized conditions. These results suggest that high penalty, high dual-eligible hospitals may be less able to improve performance on readmission metrics.

Thirty-Day Spending and Outcomes for an Episode of Pneumonia Care Among Medicare Beneficiaries

BACKGROUND

Recent policy initiatives aim to improve the value of care for patients hospitalized with pneumonia. It is unclear whether higher 30-day episode spending at the hospital level is associated with any difference in patient mortality among fee-for-service Medicare beneficiaries.

METHODS

This retrospective cohort study assessed the association between hospital-level spending and patient-level mortality for a 30-day episode of care. The study used data for Medicare fee-for-service beneficiaries hospitalized at an acute care hospital with a principal diagnosis of pneumonia from July 2011 to June 2014. Analysis was conducted by using Medicare payment data made publicly available by the Centers for Medicare & Medicaid Services on the Hospital Compare website combined with Medicare Part A claims data to identify patient outcomes.

RESULTS

A total of 1,017,353 Medicare fee-for-service beneficiaries were hospitalized for pneumonia across 3,021 US hospitals during the study period. Mean ± SD 30-day spending for an episode of pneumonia care was $14,324 ± $1,305. The observed 30-day all-cause mortality rate was 11.9%. After adjusting for patient and hospital characteristics, no association was found between higher 30-day episode spending at the hospital level and 30-day patient mortality (adjusted OR, 1.00 for every $1,000 increase in spending; 95% CI, 0.99-1.01).

CONCLUSIONS

Higher hospital-level spending for a 30-day episode of care for pneumonia was not associated with any difference in patient mortality.

Impact of Missing Stroke Severity Data on the Accuracy of Hospital Ischemic Stroke Mortality Profiling

BACKGROUND

The Centers for Medicare and Medicaid Services have proposed 30-day ischemic stroke risk-standardized mortality rates that include adjustment for stroke severity using the National Institute of Health Stroke Scale (NIHSS), which is often undocumented. We used simulations to quantify the effect of missing NIHSS data on the accuracy of hospital-level ischemic stroke risk-standardized mortality rate profiling for 100 hypothetical hospitals with different case volumes.

METHODS AND RESULTS

We generated simulated data sets of patients with NIHSS scores and other predictors of 30-day mortality based on empirical analysis of data from 7654 patients with ischemic stroke in the Michigan Stroke Registry. We assigned and rank-ordered a true (known) 30-day mortality rate to each hospital in the simulated data sets of N=100 hospitals with either low (100 patients with stroke), medium (300), or high (500) case volumes. We then estimated and rank-ordered 30-day risk-standardized mortality rates for the N=100 hospitals in each simulated data set using hierarchical logistic regression models. In each data set, we systematically varied the rate of missing NIHSS data and whether missing NIHSS data was independent (missing completely at random) or dependent (missing not at random) on the NIHSS score. With no missing NIHSS data, the Spearman correlation between the true hospital performance rank order assigned during data set generation and the estimated 30-day risk-standardized mortality rate rank order was 0.72, 0.88, and 0.91 in low, medium, and high volume hospitals, respectively. However, this fell to as low as 0.50, 0.71, and 0.79 as missing NIHSS data reached 70%.

CONCLUSIONS

Missing NIHSS data had substantial detrimental effects on the accuracy of profiling of ischemic stroke mortality, especially in lower volume hospitals. Even with complete NIHSS documentation, significant limitations in ischemic stroke mortality profiling remain.

Variation in the Diagnosis of Aspiration Pneumonia and Association with Hospital Pneumonia Outcomes

RATIONALE

National efforts to compare hospital outcomes for patients with pneumonia may be biased by hospital differences in diagnosis and coding of aspiration pneumonia, a condition that has traditionally been excluded from pneumonia outcome measures.

OBJECTIVES

To evaluate the rationale and impact of including patients with aspiration pneumonia in hospital mortality and readmission measures.

METHODS

Using Medicare fee-for-service claims for patients 65 years and older from July 2012 to June 2015, we characterized the proportion of hospitals' patients with pneumonia diagnosed with aspiration pneumonia, calculated hospital-specific risk-standardized rates of 30-day mortality and readmission for patients with pneumonia, analyzed the association between aspiration pneumonia coding frequency and these rates, and recalculated these rates including patients with aspiration pneumonia.

RESULTS

A total of 1,101,892 patients from 4,263 hospitals were included in the mortality measure analysis, including 192,814 with aspiration pneumonia. The median proportion of hospitals' patients with pneumonia diagnosed with aspiration pneumonia was 13.6% (10th-90th percentile, 4.2-26%). Hospitals with a higher proportion of patients with aspiration pneumonia had lower risk-standardized mortality rates in the traditional pneumonia measure (12.0% in the lowest coding and 11.0% in the highest coding quintiles) and were far more likely to be categorized as performing better than the national mortality rate; expanding the measure to include patients with aspiration pneumonia attenuated the association between aspiration pneumonia coding rate and hospital mortality. These findings were less pronounced for hospital readmission rates.

CONCLUSIONS

Expanding the pneumonia cohorts to include patients with a principal diagnosis of aspiration pneumonia can overcome bias related to variation in hospital coding.

The Impact of the Duration of Antibiotic Therapy in Patients With Community-Onset Pneumonia on Readmission Rates: A Retrospective Cohort Study

BACKGROUND

Recent publications have confirmed that 70% of hospitalized adults with uncomplicated community-acquired pneumonia and health-care-associated pneumonia are prescribed a duration therapy that exceeds current guideline recommendations.

OBJECTIVE

The primary objective is to evaluate the relationship between antibiotic duration and all-cause 30-day readmission rates. Secondary outcomes include pneumonia-specific 30-day readmission rate and identification of risk factors for readmission.

METHODS

Patients aged ≥18 years with a primary diagnosis of pneumonia from January 1, 2016, to December 31, 2016, were included in this single-center, retrospective cohort study. Patients were categorized by antibiotic therapy duration of ≤7 days (n = 139) or >7 days (n = 286), and outcomes were analyzed in both bivariate and multivariate models. A multivariate logistic regression was used to assess the relationship between all-cause 30-day readmission and antibiotic days.

RESULTS

Baseline characteristics were not significantly different between the 2 groups. All-cause 30-day readmission rates were 15.8% and 15.5% for patients who received ≤7 days versus >7 days of antibiotics, respectively (P = .95). Pneumonia-specific 30-day readmission occurred in 3.6% of patients who received antibiotics for ≤7 days compared to 3.5% of patients who received antibiotics for >7 days (P = .95). Multivariate logistic regression showed no statistically significant association between readmission rate and antibiotic duration of >7 days. Statistically significant risk factors for readmission identified by logistic regression include ≥3 hospital admissions within the previous year, a hematocrit <30% at discharge, a history of chronic obstructive pulmonary disorder (COPD), and weight.

CONCLUSION

The use of prolonged antibiotic therapy for the treatment of community-onset pneumonia was not associated with a decrease in all-cause or pneumonia-specific 30-day readmission rates.

Assessing Variability in Hospital-Level Mortality Among U.S. Medicare Beneficiaries With Hospitalizations for Severe Sepsis and Septic Shock

OBJECTIVES

To assess the variability in short-term sepsis mortality by hospital among Centers for Medicare and Medicaid Services beneficiaries in the United States during 2013-2014.

DESIGN

A retrospective cohort design.

SETTING

Hospitalizations from 3,068 acute care hospitals that participated in the Centers for Medicare and Medicaid Services inpatient prospective payment system in 2013 and 2014.

PATIENTS

Medicare fee-for-service beneficiaries greater than or equal to 65 years old who had an inpatient hospitalization coded with present at admission severe sepsis or septic shock.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Individual level mortality was assessed as death at or within 7 days of hospital discharge and aggregated to calculate hospital-level mortality rates. We used a logistic hierarchal linear model to calculate mortality risk-adjusted for patient characteristics. We quantified variability among hospitals using the median odds ratio and calculated risk-standardized mortality rates for each hospital. The overall crude mortality rate was 34.7%. We found significant variability in mortality by hospital (p < 0.001). The middle 50% of hospitals had similar risk-standardized mortality rates (32.7-36.9%), whereas the decile of hospitals with the highest risk-standardized mortality rates had a median mortality rate of 40.7%, compared with a median of 29.2% for hospitals in the decile with the lowest risk-standardized mortality rates. The median odds ratio (1.29) was lower than the adjusted odds ratios for several measures of patient comorbidities and severity of illness, including present at admission organ dysfunction, no identified source of infection, and age.

CONCLUSIONS

In a large study of present at admission sepsis among Medicare beneficiaries, we showed that mortality was most strongly associated with underlying comorbidities and measures of illness on arrival. However, after adjusting for patient characteristics, mortality also modestly depended on where a patient with sepsis received care, suggesting that efforts to improve sepsis outcomes in lower performing hospitals could impact sepsis survival.

Effect of Hospital Readmission Reduction Program on Hospital Readmissions and Mortality Rates

RATIONALE

Although the Hospital Readmission Reduction Program (HRRP) has reduced the 30-day readmission rates for patients with chronic obstructive pulmonary disease (COPD) across hospitals, the effect of HRRP on hospital mortality remains unknown. Therefore, we examined the association between hospital readmissions and mortality rates for patients discharged with acute exacerbation of COPD (AECOPD).

METHOD

The all-cause hospital-specific 30-day risk-standardized mortality rate (RSMR) and the 30-day risk-standardized readmission rate (RSRR) for patients with COPD from 2010 to 2017 were obtained from the Hospital Compare website. Hospital service area (HSA) information was obtained from the Dartmouth Atlas of Healthcare. The longitudinal relationship between the mortality and readmission rates of a hospital was assessed using mixed linear models.

RESULTS

Of the 3,685 hospitals analyzed, the unadjusted mean RSMRs increased from 7.8% to 8.4% during the study period at a yearly rate of 0.13 (95% CI = 0.12 to 0.14; P < .001), whereas the mean RSRRs declined from 20.7% to 19.6%. When examined according to the baseline readmission rate and interaction with time, each 1% higher-than-baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = -0.02 to -0.01; P < .0001). Inclusion of change in readmissions in the model showed that each 1% decrease in readmission rate was associated with 0.04% (95% CI = -0.01 to -0.06; P = .008) increase in mortality.

CONCLUSION

This hospital-level analysis of AECOPD showed that although the 30-day all-cause readmission rates declined, the mortality rates increased. Hospitals with lower readmission rates had higher mortality rates over time.

Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data

Importance

Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models.

Objective

To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.

Design, Setting, and Participants

This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019.

Main Outcomes and Measures

The models' goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2.

Results

Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions.

Conclusions and Relevance

Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.

Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data

IMPORTANCE

Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement.

OBJECTIVE

To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018.

MAIN OUTCOMES AND MEASURES

The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes.

RESULTS

There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers.

CONCLUSIONS AND RELEVANCE

Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value. We assembled a cohort of 349,667 primary care patients between 65 and 90 years of age who sought care from Sutter Health between July 1, 2011 and June 30, 2014, of whom 2.1% died during the study period. EHR data comprising demographics, encounters, orders, and diagnoses for each patient from a 12 month observation window prior to the point when a prediction is made were extracted. L1 regularized logistic regression and gradient boosted tree models were fit to training data and tuned by cross validation. Model performance in predicting one year mortality was assessed using held-out test patients. Our experiments systematically varied three factors: model type, diagnosis coding, and data density requirements. We found substantial, consistent benefit from using gradient boosting vs logistic regression (mean AUROC over all other technical choices of 84.8% vs 80.7% respectively). There was no benefit from aggregation of ICD codes into CCS code groups (mean AUROC over all other technical choices of 82.9% vs 82.6% respectively). Likewise increasing data density requirements did not affect discrimination (mean AUROC over other technical choices ranged from 82.5% to 83%). We also examine model performance as a function of lead time, which is the interval between death and when a prediction was made. In subgroup analysis by lead time, mean AUROC over all other choices ranged from 87.9% for patients who died within 0 to 3 months to 83.6% for those who died 9 to 12 months after prediction time.

Twenty-Year Trends in Outcomes for Older Adults With Acute Myocardial Infarction in the United States

IMPORTANCE

Medicare and other organizations have focused on improving quality of care for patients with acute myocardial infarction (AMI) over the last 2 decades. However, there is no comprehensive perspective on the evolution of outcomes for AMI during that period, and it is unknown whether temporal changes varied by patient subgroup, hospital, or county.

OBJECTIVE

To provide a comprehensive evaluation of national trends in inpatient outcomes and costs of AMI during this period.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included analysis of data from a sample of 4 367 485 Medicare fee-for-service beneficiaries aged 65 years or older from January 1, 1995, through December 31, 2014, across 5680 hospitals in the United States. Analyses were conducted from January 15 to June 5, 2018.

MAIN OUTCOMES AND MEASURES

Thirty-day all-cause mortality at the patient, hospital, and county levels. Additional outcomes included 30-day all-cause readmissions; 1-year recurrent AMI; in-hospital mortality; length of hospital stay; 2014 Consumer Price Index-adjusted median Medicare inpatient payment per AMI discharge; and rates of catheterization, percutaneous coronary intervention, and coronary artery bypass graft surgery.

RESULTS

The cohort included 4 367 485 Medicare fee-for-service patients aged 65 years or older hospitalized for AMI during the study period. Between 1995 and 2014, the mean (SD) age of patients increased from 76.9 (7.2) to 78.2 (8.7) years, the percentage of female patients declined from 49.5% to 46.1%, the percentage of white patients declined from 91.0% to 86.2%, and the percentage of black patients increased from 5.9% to 8.0%. There were declines in AMI hospitalizations (914 to 566 per 100 000 beneficiary-years); 30-day mortality (20.0% to 12.4%; difference, 7.6 percentage points; 95% CI, 7.3-7.8 percentage points); 30-day all-cause readmissions (21.0% to 15.3%; difference, 5.7 percentage points; 95% CI, 5.4-6.0 percentage points); and 1-year recurrent AMI (7.1% to 5.1%; difference, 2.0 percentage points; 95% CI, 1.8-2.2 percentage points). There were increases in the 2014 Consumer Price Index-adjusted median (interquartile range) Medicare inpatient payment per AMI discharge ($9282 [$6969-$12 173] to $11 031 [$8099-$16 861]); 30-day inpatient catheterization (44.2% to 59.9%; difference, 15.7 percentage points; 95% CI, 15.4-16.0 percentage points); and inpatient percutaneous coronary intervention (18.8% to 43.3%; difference, 24.5 percentage points; 95% CI, 24.2-24.7 percentage points). Coronary artery bypass graft surgery rates decreased from 14.4% to 10.2% (difference, 4.2 percentage points; 95% CI, 3.9-4.3 percentage points). There was heterogeneity by hospital and county in the mortality changes over time.

CONCLUSIONS AND RELEVANCE

This study shows marked improvements in short-term mortality and readmissions, with an increase in in-hospital procedures and payments, for the increasingly smaller number of Medicare beneficiaries with AMI.

Association of the Hospital Readmissions Reduction Program With Mortality Among Medicare Beneficiaries Hospitalized for Heart Failure, Acute Myocardial Infarction, and Pneumonia

IMPORTANCE

The Hospital Readmissions Reduction Program (HRRP) has been associated with a reduction in readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. It is unclear whether the HRRP has been associated with change in patient mortality.

OBJECTIVE

To determine whether the HRRP was associated with a change in patient mortality.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of hospitalizations for HF, AMI, and pneumonia among Medicare fee-for-service beneficiaries aged at least 65 years across 4 periods from April 1, 2005, to March 31, 2015. Period 1 and period 2 occurred before the HRRP to establish baseline trends (April 2005-September 2007 and October 2007-March 2010). Period 3 and period 4 were after HRRP announcement (April 2010 to September 2012) and HRRP implementation (October 2012 to March 2015).

EXPOSURES

Announcement and implementation of the HRRP.

MAIN OUTCOMES AND MEASURES

Inverse probability-weighted mortality within 30 days of discharge following hospitalization for HF, AMI, and pneumonia, and stratified by whether there was an associated readmission. An additional end point was mortality within 45 days of initial hospital admission for target conditions.

RESULTS

The study cohort included 8.3 million hospitalizations for HF, AMI, and pneumonia, among which 7.9 million (mean age, 79.6 [8.7] years; 53.4% women) were alive at discharge. There were 3.2 million hospitalizations for HF, 1.8 million for AMI, and 3.0 million for pneumonia. There were 270 517 deaths within 30 days of discharge for HF, 128 088 for AMI, and 246 154 for pneumonia. Among patients with HF, 30-day postdischarge mortality increased before the announcement of the HRRP (0.27% increase from period 1 to period 2). Compared with this baseline trend, HRRP announcement (0.49% increase from period 2 to period 3; difference in change, 0.22%, P = .01) and implementation (0.52% increase from period 3 to period 4; difference in change, 0.25%, P = .001) were significantly associated with an increase in postdischarge mortality. Among patients with AMI, HRRP announcement was associated with a decline in postdischarge mortality (0.18% pre-HRRP increase vs 0.08% post-HRRP announcement decrease; difference in change, -0.26%; P = .01) and did not significantly change after HRRP implementation. Among patients with pneumonia, postdischarge mortality was stable before HRRP (0.04% increase from period 1 to period 2), but significantly increased after HRRP announcement (0.26% post-HRRP announcement increase; difference in change, 0.22%, P = .01) and implementation (0.44% post-HPPR implementation increase; difference in change, 0.40%, P < .001). The overall increase in mortality among patients with HF and pneumonia was mainly related to outcomes among patients who were not readmitted but died within 30 days of discharge. For all 3 conditions, HRRP implementation was not significantly associated with an increase in mortality within 45 days of admission, relative to pre-HRRP trends.

CONCLUSIONS AND RELEVANCE

Among Medicare beneficiaries, the HRRP was significantly associated with an increase in 30-day postdischarge mortality after hospitalization for HF and pneumonia, but not for AMI. Given the study design and the lack of significant association of the HRRP with mortality within 45 days of admission, further research is needed to understand whether the increase in 30-day postdischarge mortality is a result of the policy.

Trends in 30-Day Readmission Rates for Medicare and Non-Medicare Patients in the Era of the Affordable Care Act

BACKGROUND

Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of the Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions.

METHODS

Using the Nationwide Readmissions Database (2010-2015), we assessed trends in all-cause readmission rates for 1 of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by the HRRP in age-insurance groups defined by age group (≥65 years or <65 years) and payer (Medicare, Medicaid, or private insurance).

RESULTS

In the group aged ≥65 years, readmission rates for those covered by Medicare, Medicaid, and private insurance decreased annually for acute myocardial infarction (risk-adjusted odds ratio [OR; 95% confidence interval] among Medicare patients, 0.94 [0.94-0.95], among Medicaid patients, 0.93 [0.90-0.97], and among patients with private-insurance, 0.95 [0.93-0.97]); heart failure (ORs, 0.96 [0.96-0.97], 0.96 [0.94-0.98], and 0.97 [0.96-0.99], for the 3 payers, respectively), and pneumonia (ORs, 0.96 [0.96-0.97), 0.94 [0.92-0.96], and 0.96 [0.95-0.97], respectively). Readmission rates also decreased in the group aged <65 years for acute myocardial infarction (ORs: Medicare 0.97 [0.96-0.98], Medicaid 0.94 [0.92-0.95], and private insurance 0.93 [0.92-0.94]), heart failure (ORs, 0.98 [0.97-0.98]: 0.96 [0.96-0.97], and 0.97 [0.95-0.98], for the 3 payers, respectively), and pneumonia (ORs, 0.98 [0.97-0.99], 0.98 [0.97-0.99], and 0.98 [0.97-1.00], respectively). Further, readmission rates decreased significantly for non-target conditions.

CONCLUSIONS

There appears to be a systematic improvement in readmission rates for patient groups beyond the population of fee-for-service, older, Medicare beneficiaries included in the HRRP.

The Relative Ability of Comorbidity Ascertainment Methodologies to Predict In-Hospital Mortality Among Hospitalized Community-acquired Pneumonia Patients

BACKGROUND

Despite widespread use of comorbidities for population health descriptions and risk adjustment, the ideal method for ascertaining comorbidities is not known. We sought to compare the relative value of several methodologies by which comorbidities may be ascertained.

METHODS

This is an observational study of 1596 patients admitted to the University of Chicago for community-acquired pneumonia from 1998 to 2012. We collected data via chart abstraction, administrative data, and patient report, then performed logistic regression analyses, specifying comorbidities as independent variables and in-hospital mortality as the dependent variable. Finally, we compared area under the curve (AUC) statistics to determine the relative ability of each method of comorbidity ascertainment to predict in-hospital mortality.

RESULTS

Chart review (AUC, 0.72) and administrative data (Charlson AUC, 0.83; Elixhauser AUC, 0.84) predicted in-hospital mortality with greater fidelity than patient report (AUC, 0.61). However, multivariate logistic regression analyses demonstrated that individual comorbidity derivation via chart review had the strongest relationship with in-hospital mortality. This is consistent with prior literature suggesting that administrative data have inherent, paradoxical biases with important implications for risk adjustment based solely on administrative data.

CONCLUSIONS

Although comorbidities derived through administrative data did produce an AUC greater than chart review, our analyses suggest a coding bias in several comorbidities with a paradoxically protective effect. Therefore, chart review, while labor and resource intensive, may be the ideal method for ascertainment of clinically relevant comorbidities.

Retention in HIV care during the 3 years following release from incarceration: A cohort study

BACKGROUND

Sustained retention in HIV care (RIC) and viral suppression (VS) are central to US national HIV prevention strategies, but have not been comprehensively assessed in criminal justice (CJ) populations with known health disparities. The purpose of this study is to identify predictors of RIC and VS following release from prison or jail.

METHODS AND FINDINGS

This is a retrospective cohort study of all adult people living with HIV (PLWH) incarcerated in Connecticut, US, during the period January 1, 2007, to December 31, 2011, and observed through December 31, 2014 (n = 1,094). Most cohort participants were unmarried (83.7%) men (77.0%) who were black or Hispanic (78.1%) and acquired HIV from injection drug use (72.6%). Prison-based pharmacy and custody databases were linked with community HIV surveillance monitoring and case management databases. Post-release RIC declined steadily over 3 years of follow-up (67.2% retained for year 1, 51.3% retained for years 1-2, and 42.5% retained for years 1-3). Compared with individuals who were not re-incarcerated, individuals who were re-incarcerated were more likely to meet RIC criteria (48% versus 34%; p < 0.001) but less likely to have VS (72% versus 81%; p = 0.048). Using multivariable logistic regression models (individual-level analysis for 1,001 individuals after excluding 93 deaths), both sustained RIC and VS at 3 years post-release were independently associated with older age (RIC: adjusted odds ratio [AOR] = 1.61, 95% CI = 1.22-2.12; VS: AOR = 1.37, 95% CI = 1.06-1.78), having health insurance (RIC: AOR = 2.15, 95% CI = 1.60-2.89; VS: AOR = 2.01, 95% CI = 1.53-2.64), and receiving an increased number of transitional case management visits. The same factors were significant when we assessed RIC and VS outcomes in each 6-month period using generalized estimating equations (for 1,094 individuals contributing 6,227 6-month periods prior to death or censoring). Additionally, receipt of antiretroviral therapy during incarceration (RIC: AOR = 1.33, 95% CI 1.07-1.65; VS: AOR = 1.91, 95% CI = 1.56-2.34), early linkage to care post-release (RIC: AOR = 2.64, 95% CI = 2.03-3.43; VS: AOR = 1.79; 95% CI = 1.45-2.21), and absolute time and proportion of follow-up time spent re-incarcerated were highly correlated with better treatment outcomes. Limited data were available on changes over time in injection drug use or other substance use disorders, psychiatric disorders, or housing status.

CONCLUSIONS

In a large cohort of CJ-involved PLWH with a 3-year post-release evaluation, RIC diminished significantly over time, but was associated with HIV care during incarceration, health insurance, case management services, and early linkage to care post-release. While re-incarceration and conditional release provide opportunities to engage in care, reducing recidivism and supporting community-based RIC efforts are key to improving longitudinal treatment outcomes among CJ-involved PLWH.

Association of the Hospital Readmissions Reduction Program With Mortality During and After Hospitalization for Acute Myocardial Infarction, Heart Failure, and Pneumonia

IMPORTANCE

The 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.

OBJECTIVE

To 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 PARTICIPANTS

In 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.

EXPOSURES

Announcement of the HRRP in March 2010, and implementation of its penalties in October 2012.

MAIN OUTCOMES AND MEASURES

Monthly risk-adjusted rates of in-hospital and 30-day postdischarge mortality.

RESULTS

The 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 RELEVANCE

Among 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.

Association of Racial and Socioeconomic Disparities With Outcomes Among Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, and Pneumonia: An Analysis of Within- and Between-Hospital Variation

Importance

Although studies have described differences in hospital outcomes by patient race and socioeconomic status, it is not clear whether such disparities are driven by hospitals themselves or by broader systemic effects.

Objective

To determine patterns of racial and socioeconomic disparities in outcomes within and between hospitals for patients with acute myocardial infarction, heart failure, and pneumonia.

Design, Setting, and Participants

Retrospective cohort study initiated before February 2013, with additional analyses conducted during the peer-review process. Hospitals in the United States treating at least 25 Medicare fee-for-service beneficiaries aged 65 years or older in each race (ie, black and white) and neighborhood income level (ie, higher income and lower income) for acute myocardial infarction, heart failure, and pneumonia between 2009 and 2011 were included.

Main Outcomes and Measures

For within-hospital analyses, risk-standardized mortality rates and risk-standardized readmission rates for race and neighborhood income subgroups were calculated at each hospital. The corresponding ratios using intraclass correlation coefficients were then compared. For between-hospital analyses, risk-standardized rates were assessed according to hospitals' proportion of patients in each subgroup. These analyses were performed for each of the 12 analysis cohorts reflecting the unique combinations of outcomes (mortality and readmission), demographics (race and neighborhood income), and conditions (acute myocardial infarction, heart failure, and pneumonia).

Results

Between 74% (3545 of 4810) and 91% (4136 of 4554) of US hospitals lacked sufficient racial and socioeconomic diversity to be included in this analysis, with the number of hospitals eligible for analysis varying among cohorts. The 12 analysis cohorts ranged in size from 418 to 1265 hospitals and from 144 417 to 703 324 patients. Within included hospitals, risk-standardized mortality rates tended to be lower among black patients (mean [SD] difference between risk-standardized mortality rates in black patients compared with white patients for acute myocardial infarction, -0.57 [1.1] [P = .47]; for heart failure, -4.7 [1.3] [P < .001]; and for pneumonia, -1.0 [2.0] [P = .05]). However, risk-standardized readmission rates among black patients were higher (mean [SD] difference between risk-standardized readmission rates in black patients compared with white patients for acute myocardial infarction, 4.3 [1.4] [P < .001]; for heart failure, 2.8 [1.8] [P < .001], and for pneumonia, 3.7 [1.3] [P < .001]). Intraclass correlation coefficients ranged from 0.68 to 0.79, indicating that hospitals generally delivered consistent quality to patients of differing races. While the coefficients in the neighborhood income analysis were slightly lower (0.46-0.60), indicating some heterogeneity in within-hospital performance, differences in mortality rates and readmission rates between the 2 neighborhood income groups were small. There were no strong, consistent associations between risk-standardized outcomes for white or higher-income neighborhood patients and hospitals' proportion of black or lower-income neighborhood patients.

Conclusions and Relevance

Hospital performance according to race and socioeconomic status was generally consistent within and between hospitals, even as there were overall differences in outcomes by race and neighborhood income. This finding indicates that disparities are likely to be systemic, rather than localized to particular hospitals.

Mortality Measures to Profile Hospital Performance for Patients With Septic Shock

OBJECTIVES

Sepsis care is becoming a more common target for hospital performance measurement, but few studies have evaluated the acceptability of sepsis or septic shock mortality as a potential performance measure. In the absence of a gold standard to identify septic shock in claims data, we assessed agreement and stability of hospital mortality performance under different case definitions.

DESIGN

Retrospective cohort study.

SETTING

U.S. acute care hospitals.

PATIENTS

Hospitalized with septic shock at admission, identified by either implicit diagnosis criteria (charges for antibiotics, cultures, and vasopressors) or by explicit International Classification of Diseases, 9th revision, codes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used hierarchical logistic regression models to determine hospital risk-standardized mortality rates and hospital performance outliers. We assessed agreement in hospital mortality rankings when septic shock cases were identified by either explicit International Classification of Diseases, 9th revision, codes or implicit diagnosis criteria. Kappa statistics and intraclass correlation coefficients were used to assess agreement in hospital risk-standardized mortality and hospital outlier status, respectively. Fifty-six thousand six-hundred seventy-three patients in 308 hospitals fulfilled at least one case definition for septic shock, whereas 19,136 (33.8%) met both the explicit International Classification of Diseases, 9th revision, and implicit septic shock definition. Hospitals varied widely in risk-standardized septic shock mortality (interquartile range of implicit diagnosis mortality: 25.4-33.5%; International Classification of Diseases, 9th revision, diagnosis: 30.2-38.0%). The median absolute difference in hospital ranking between septic shock cohorts defined by International Classification of Diseases, 9th revision, versus implicit criteria was 37 places (interquartile range, 16-70), with an intraclass correlation coefficient of 0.72, p value of less than 0.001; agreement between case definitions for identification of outlier hospitals was moderate (kappa, 0.44 [95% CI, 0.30-0.58]).

CONCLUSIONS

Risk-standardized septic shock mortality rates varied considerably between hospitals, suggesting that septic shock is an important performance target. However, efforts to profile hospital performance were sensitive to septic shock case definitions, suggesting that septic shock mortality is not currently ready for widespread use as a hospital quality measure.

Comparing the validity of different ICD coding abstraction strategies for sepsis case identification in German claims data

INTRODUCTION

Administrative data are used to generate estimates of sepsis epidemiology and can serve as source for quality indicators. Aim was to compare estimates on sepsis incidence and mortality based on different ICD-code abstraction strategies and to assess their validity for sepsis case identification based on a patient sample not pre-selected for presence of sepsis codes.

MATERIALS AND METHODS

We used the national DRG-statistics for assessment of population-level sepsis incidence and mortality. Cases were identified by three previously published International Statistical Classification of Diseases (ICD) coding strategies for sepsis based on primary and secondary discharge diagnoses (clinical sepsis codes (R-codes), explicit coding (all sepsis codes) and implicit coding (combined infection and organ dysfunction codes)). For the validation study, a stratified sample of 1120 adult patients admitted to a German academic medical center between 2007-2013 was selected. Administrative diagnoses were compared to a gold standard of clinical sepsis diagnoses based on manual chart review.

RESULTS

In the validation study, 151/937 patients had sepsis. Explicit coding strategies performed better regarding sensitivity compared to R-codes, but had lower PPV. The implicit approach was the most sensitive for severe sepsis; however, it yielded a considerable number of false positives. R-codes and explicit strategies underestimate sepsis incidence by up to 3.5-fold. Between 2007-2013, national sepsis incidence ranged between 231-1006/100,000 person-years depending on the coding strategy.

CONCLUSIONS

In the sample of a large tertiary care hospital, ICD-coding strategies for sepsis differ in their accuracy. Estimates using R-codes are likely to underestimate the true sepsis incidence, whereas implicit coding overestimates sepsis cases. Further multi-center evaluation is needed to gain better understanding on the validity of sepsis coding in Germany.

Variation in mortality rates after admission to long-term acute care hospitals for ventilator weaning

PURPOSE

We sought to examine variation in long-term acute care hospital (LTACH) quality based on 90-day in-hospital mortality for patients admitted for weaning from mechanical ventilation.

METHODS

We developed an administrative risk-adjustment model using data from Medicare claims. We validated the administrative model against a clinical model using data from LTACHs participating in a 2002 to 2003 clinical registry. We then used our validated administrative model to assess national variation in 90-day in-hospital mortality rates in LTACHs from 2013.

RESULTS

The administrative risk-adjustment model was derived using data from 9447 patients admitted to 221 LTACHs in 2003. The model had good discrimination (C statistic=0.72) and calibration. Compared to a clinically derived model using data from 1163 patients admitted to 14 LTACHs, the administrative model generated similar performance estimates. National variation in risk-adjusted mortality was assessed using data from 20,453 patients admitted to 380 LTACHs in 2013. LTACH-specific risk-adjusted mortality rates varied from 8.4% to 48.1% (median: 24.2%, interquartile range: 19.7%-30.7%).

CONCLUSIONS

LTACHs vary widely in mortality rates, underscoring the need to better understand the sources of this variation and improve the quality of care for patients requiring long-term ventilator weaning.

A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

BACKGROUND

Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals.

METHODS

We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010-2015 was analyzed.

RESULTS

The 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant.

CONCLUSIONS

The risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality.

Variation in Pathologist Classification of Colorectal Adenomas and Serrated Polyps

OBJECTIVES

Endoscopist quality measures such as adenoma detection rate (ADR) and serrated polyp detection rates (SPDRs) depend on pathologist classification of histology. Although variation in pathologic interpretation is recognized, we add to the literature by quantifying the impact of pathologic variability on endoscopist performance.

METHODS

We used natural language processing to abstract relevant data from colonoscopy and related pathology reports performed over 2 years at four clinical sites. We quantified each pathologist's likelihood of classifying polyp specimens as adenomas or serrated polyps. We estimated the impact on endoscopists' ADR and SPDR of sending their specimens to pathologists with higher or lower classification rates.

RESULTS

We observed 85,526 colonoscopies performed by 119 endoscopists; 50,453 had a polyp specimen, which were analyzed by 48 pathologists. There was greater variation across pathologists in classification of serrated polyps than in classification of adenomas. We estimate the endoscopist's average SPDR would be 0.5% if all their specimens were analyzed by the pathologist in our sample with the lowest classification rate and 12.0% if all their specimens were analyzed by the pathologist with the highest classification rate. In contrast, the endoscopist's average ADR would be 28.5% and 42.4% if their specimens were analyzed by the pathologist with lowest and highest classification rate, respectively.

CONCLUSIONS

There is significant variation in pathologic interpretation, which more substantially affects endoscopist SPDR than ADR.

The German Quality Network Sepsis: study protocol for the evaluation of a quality collaborative on decreasing sepsis-related mortality in a quasi-experimental difference-in-differences design

BACKGROUND

While sepsis-related mortality decreased substantially in other developed countries, mortality of severe sepsis remained as high as 44% in Germany. A recent German cluster randomized trial was not able to improve guideline adherence and decrease sepsis-related mortality within the participating hospitals, partly based on lacking support by hospital management and lacking resources for documentation of prospective data. Thus, more pragmatic approaches are needed to improve quality of sepsis care in Germany. The primary objective of the study is to decrease sepsis-related hospital mortality within a quality collaborative relying on claims data.

METHOD

The German Quality Network Sepsis (GQNS) is a quality collaborative involving 75 hospitals. This study protocol describes the conduction and evaluation of the start-up period of the GQNS running from March 2016 to August 2018. Democratic structures assure participatory action, a study coordination bureau provides central support and resources, and local interdisciplinary quality improvement teams implement changes within the participating hospitals. Quarterly quality reports focusing on risk-adjusted hospital mortality in cases with sepsis based on claims data are provided. Hospitals committed to publish their individual risk-adjusted mortality compared to the German average. A complex risk-model is used to control for differences in patient-related risk factors. Hospitals are encouraged to implement a bundle of interventions, e.g., interdisciplinary case analyses, external peer-reviews, hospital-wide staff education, and implementation of rapid response teams. The effectiveness of the GQNS is evaluated in a quasi-experimental difference-in-differences design by comparing the change of hospital mortality of cases with sepsis with organ dysfunction from a retrospective baseline period (January 2014 to December 2015) and the intervention period (April 2016 to March 2018) between the participating hospitals and all other German hospitals. Structural and process quality indicators of sepsis care as well as efforts for quality improvement are monitored regularly.

DISCUSSION

The GQNS is a large-scale quality collaborative using a pragmatic approach based on claims data. A complex risk-adjustment model allows valid quality comparisons between hospitals and with the German average. If this study finds the approach to be useful for improving quality of sepsis care, it may also be applied to other diseases.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02820675.

Association of hospice utilization and publicly reported outcomes following hospitalization for pneumonia or heart failure: a retrospective cohort study

Background

The Center for Medicare and Medicaid Services (CMS) and the Hospital Quality Alliance began collecting and reporting United States hospital performance in the treatment of pneumonia and heart failure in 2008. Whether the utilization of hospice might affect CMS-reported mortality and readmission rates is not known.

Methods

Hospice utilization (mean days on hospice per decedent) for 2012 from the Dartmouth Atlas (a project of the Dartmouth Institute that reports a variety of public health and policy-related statistics) was merged with hospital-level 30-day mortality and readmission rates for pneumonia and heart failure from CMS. The association between hospice use and outcomes was analyzed with multivariate quantile regression controlling for quality of care metrics, acute care bed availability, regional variability and other measures.

Results

2196 hospitals reported data to both CMS and the Dartmouth Atlas in 2012. Higher rates of hospice utilization were associated with lower rates of 30-day mortality and readmission for pneumonia but not for heart failure. Higher quality of care was associated with lower rates of mortality for both pneumonia and heart failure. Greater acute care bed availability was associated with increased readmission rates for both conditions (p < 0.05 for all).

Conclusions

Higher rates of hospice utilization were associated with lower rates of 30-day mortality and readmission for pneumonia as reported by CMS. While causality is not established, it is possible that hospice referrals might directly affect CMS outcome metrics. Further clarification of the relationship between hospice referral patterns and publicly reported CMS outcomes appears warranted.

Electronic supplementary material

The online version of this article (10.1186/s12913-017-2801-3) contains supplementary material, which is available to authorized users.

The role of the clinical departments for understanding patient heterogeneity in one-year mortality after a diagnosis of heart failure: A multilevel analysis of individual heterogeneity for profiling provider outcomes

Purpose

To evaluate the general contextual effect (GCE) of the hospital department on one-year mortality in Swedish and Danish patients with heart failure (HF) by applying a multilevel analysis of individual heterogeneity.

Methods

Using the Swedish patient register, we obtained data on 36,943 patients who were 45–80 years old and admitted for HF to the hospital between 2007 and 2009. From the Danish Heart Failure Database (DHFD), we obtained data on 12,001 patients with incident HF who were 18 years or older and treated at hospitals between June 2010 and June2013. For each year, we applied two-step single and multilevel logistic regression models. We evaluated the general effects of the department by quantifying the intra-class correlation coefficient (ICC) and the increment in the area under the receiver operating characteristic curve (AUC) obtained by adding the random effects of the department in a multilevel logistic regression analysis.

Results

One-year mortality for Danish incident HF patients was low in the three audit years (around 11.1% -13.1%) and departments performed homogeneously (ICC ≈1.5% - 3.5%). The discriminatory accuracy of a model including age and gender was rather high (AUC≈ 0.71–0.73) but the increment in AUC after adding the department random effects into these models was only about 0.011–0.022 units in the three years.

One-year mortality in Swedish patients with first hospitalization for heart failure, was relatively higher for 2007–2009 (≈21.3% - 22%) and departments performed homogeneously (ICC ≈ 1.5% - 3%). The discriminatory accuracy of a model including age, gender and patient risk score was rather high (AUC≈ 0.726–0.728) but the increment in AUC after adding the department random effects was only about 0.010–0.017 units in the three years.

Conclusion

Using the DHFD standard benchmark for one-year mortality, Danish departments had a good, homogeneous performance. In reference to literature, Swedish departments had a homogeneous performance and the mortality rates for patients with first hospitalization for heart failure were similar to those reported since 2000. Considering this, if health authorities decide to further reduce mortality rates, a comprehensive quality strategy should focus on all Swedish hospitals. Yet, a complementary assessment for the period after the study period is required to confirm whether department performance is still homogeneous or not to determine the most appropriate action.

Sex Differences in Trajectories of Risk After Rehospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia

BACKGROUND

Women have an increased risk of rehospitalization in the immediate postdischarge period; however, few studies have determined how readmission risk dynamically changes on a day-to-day basis over the full year after hospitalization by sex and how these differences compare with the risk for mortality.

METHODS AND RESULTS

We identified >3 000 000 hospitalizations of patients with a principal discharge diagnosis of heart failure, acute myocardial infarction, or pneumonia and estimated sex differences in the daily risk of rehospitalization/death 1 year after discharge from a population of Medicare fee-for-service beneficiaries aged 65 years and older. We calculated the (1) time required for adjusted rehospitalization/mortality risks to decline 50% from maximum values after discharge, (2) time required for the adjusted readmission risk to approach plateau periods of minimal day-to-day change, and (3) extent to which adjusted risks are greater among recently hospitalized patients versus Medicare patients. We identified 1 392 289, 530 771, and 1 125 231 hospitalizations for heart failure, acute myocardial infarction, and pneumonia, respectively. The adjusted daily risk of rehospitalization varied by admitting condition (hazard rate ratio for women versus men, 1.10 for acute myocardial infarction; hazard rate ratio, 1.04 for heart failure; and hazard rate ratio, 0.98 for pneumonia). However, for all conditions, the adjusted daily risk of death was higher among men versus women (hazard rate ratio women versus with men, <1). For both sexes, there was a similar timing of peak daily risk, half daily risk, and reaching plateau.

CONCLUSIONS

Although the association of sex with daily risk of rehospitalization varies across conditions, women are at highest risk after discharge for acute myocardial infarction. Future studies should focus on understanding the determinants of sex differences in rehospitalization risk among conditions.

Quality of Care in the United States Territories, 1999-2012

BACKGROUND

Millions of Americans live in the US territories, but health outcomes and payments among Medicare beneficiaries in these territories are not well characterized.

METHODS

Among Fee-for-Service Medicare beneficiaries aged 65 years and older hospitalized between 1999 and 2012 for acute myocardial infarction (AMI), heart failure (HF), and pneumonia, we compared hospitalization rates, patient outcomes, and inpatient payments in the territories and states.

RESULTS

Over 14 years, there were 4,350,813 unique beneficiaries in the territories and 402,902,615 in the states. Hospitalization rates for AMI, HF, and pneumonia declined overall and did not differ significantly. However, 30-day mortality rates were higher in the territories for all 3 conditions: in the most recent time period (2008-2012), the adjusted odds of 30-day mortality were 1.34 [95% confidence interval (CI), 1.21-1.48], 1.24 (95% CI, 1.12-1.37), and 1.85 (95% CI, 1.71-2.00) for AMI, HF, and pneumonia, respectively; adjusted odds of 1-year mortality were also higher. In the most recent study period, inflation-adjusted Medicare in-patient payments, in 2012 dollars, were lower in the territories than the states, at $9234 less (61% lower than states), $4479 less (50% lower), and $4403 less (39% lower) for AMI, HF, and pneumonia hospitalizations, respectively (P<0.001 for all).

CONCLUSIONS AND RELEVANCE

Among Medicare Fee-for-Service beneficiaries, in 2008-2012 mortality rates were higher, or not significantly different, and hospital reimbursements were lower for patients hospitalized with AMI, HF, and pneumonia in the territories. Improvement of health care and policies in the territories is critical to ensure health equity for all Americans.

Associations between nursing home performance and hospital 30-day readmissions for acute myocardial infarction, heart failure and pneumonia at the healthcare community level in the United States

OBJECTIVES

To evaluate community-specific nursing home performance with community-specific hospital 30-day readmissions for Medicare patients discharged with acute myocardial infarction, heart failure or pneumonia.

DESIGN

Cross-sectional study using 2009-2012 hospital risk-standardised 30-day readmission data for Medicare fee-for-service patients hospitalised for all three conditions and nursing home performance data from the Centers for Medicare & Medicaid Services Five-Star Quality Rating System.

SETTING

Medicare-certified nursing homes and acute care hospitals.

PARTICIPANTS

12,542 nursing homes and 3,039 hospitals treating 30 or more Medicare fee-for-service patients for all three conditions across 2,032 hospital service areas in the United States.

MEASUREMENTS

Community-specific hospital 30-day risk-standardised readmission rates. Community-specific nursing home performance measures: health inspection, staffing, Registered Nurses and quality performance; and an aggregated performance score. Mixed-effects models evaluated associations between nursing home performance and hospital 30-day risk-standardised readmission rates for all three conditions.

RESULTS

The relationship between community-specific hospital risk-standardised readmission rates and community-specific overall nursing home performance was statistically significant for all three conditions. Increasing nursing home performance by one star resulted in decreases of 0.29% point (95% CI: 0.12-0.47), 0.78% point (95% CI: 0.60-0.95) and 0.46% point (95% CI: 0.33-0.59) of risk-standardised readmission rates for AMI, HF and pneumonia, respectively. Among the specific measures, higher performance in nursing home overall staffing and Registered Nurse staffing measures was statistically significantly associated with lower hospital readmission rates for all three conditions. Notable geographic variation in the community-specific nursing home performance was observed.

CONCLUSION

Community-specific nursing home performance is associated with community-specific hospital 30-day readmission rates for Medicare fee-for-service patients for acute myocardial infarction, heart failure or pneumonia.

IMPLICATIONS FOR PRACTICE

Coordinated care between hospitals and nursing homes is essential to reduce readmissions. Nursing homes can improve performance and reduce readmissions by increasing registered nursing homes. Further, communities can work together to create cross-continuum care teams comprised of hospitals, nursing homes, patients and their families, and other community-based service providers to reduce unplanned readmissions.

Readmission Rates After Passage of the Hospital Readmissions Reduction Program: A Pre-Post Analysis

BACKGROUND

Whether hospitals with the highest risk-standardized readmission rates (RSRRs) subsequently experienced the greatest improvement after passage of the Medicare Hospital Readmissions Reduction Program (HRRP) is unknown.

OBJECTIVE

To evaluate whether passage of the HRRP was followed by acceleration in improvement in 30-day RSRRs after hospitalizations for acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia and whether the lowest-performing hospitals had faster acceleration in improvement after passage of the law than hospitals that were already performing well.

DESIGN

Pre-post analysis stratified by hospital performance groups.

SETTING

U.S. acute care hospitals.

PATIENTS

15 170 008 Medicare patients discharged alive from 2000 to 2013.

INTERVENTION

Passage of the HRRP.

MEASUREMENTS

30-day readmission rates after hospitalization for AMI, CHF, or pneumonia for hospitals in the highest-performance (0% penalty), average-performance (>0% and <0.50% penalty), low-performance (≥0.50% and <0.99% penalty), and lowest-performance (≥0.99% penalty) groups.

RESULTS

Of 2868 hospitals serving 1 109 530 Medicare discharges annually, 30.1% were highest performers, 44.0% were average performers, 16.8% were low performers, and 9.0% were lowest performers. After controlling for prelaw trends, an additional 67.6 (95% CI, 66.6 to 68.4), 74.8 (CI, 74.0 to 75.4), 85.4 (CI, 84.0 to 86.8), and 95.1 (CI, 92.6 to 97.5) readmissions per 10 000 discharges were found to have been averted per year in the highest-, average-, low-, and lowest-performance groups, respectively, after passage of the law.

LIMITATION

Inability to distinguish between improvement caused by the magnitude of the penalty or by different levels of health improvement in different patient populations.

CONCLUSION

After passage of the HRRP, 30-day RSRRs for myocardial infarction, heart failure, and pneumonia decreased more rapidly than before the law's passage. Improvement was most marked for hospitals with the lowest prelaw performance.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Hospital Variation in Utilization of Life-Sustaining Treatments among Patients with Do Not Resuscitate Orders

OBJECTIVE

To determine between-hospital variation in interventions provided to patients with do not resuscitate (DNR) orders.

DATA SOURCES/SETTING

United States Agency of Healthcare Research and Quality, Healthcare Cost and Utilization Project, California State Inpatient Database.

STUDY DESIGN

Retrospective cohort study including hospitalized patients aged 40 and older with potential indications for invasive treatments: in-hospital cardiac arrest (indication for CPR), acute respiratory failure (mechanical ventilation), acute renal failure (hemodialysis), septic shock (central venous catheterization), and palliative care. Hierarchical logistic regression to determine associations of hospital "early" DNR rates (DNR order placed within 24 hours of admission) with utilization of invasive interventions.

DATA COLLECTION/EXTRACTION METHODS

California State Inpatient Database, year 2011.

PRINCIPAL FINDINGS

Patients with DNR orders at high-DNR-rate hospitals were less likely to receive invasive mechanical ventilation for acute respiratory failure or hemodialysis for acute renal failure, but more likely to receive palliative care than DNR patients at low-DNR-rate hospitals. Patients without DNR orders experienced similar rates of invasive interventions regardless of hospital DNR rates.

CONCLUSIONS

Hospitals vary widely in the scope of invasive or organ-supporting treatments provided to patients with DNR orders.

Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program

OBJECTIVE

To assess the reliability of risk-standardized readmission rates (RSRRs) for medical conditions and surgical procedures used in the Hospital Readmission Reduction Program (HRRP).

DATA SOURCES

State Inpatient Databases for six states from 2011 to 2013 were used to identify patient cohorts for the six conditions used in the HRRP, which was augmented with hospital characteristic and HRRP penalty data.

STUDY DESIGN

Hierarchical logistic regression models estimated hospital-level RSRRs for each condition, the reliability of each RSRR, and the extent to which socioeconomic and hospital factors further explain RSRR variation. We used publicly available data to estimate payments for excess readmissions in hospitals with reliable and unreliable RSRRs.

PRINCIPAL FINDINGS

Only RSRRs for surgical procedures exceeded the reliability benchmark for most hospitals, whereas RSRRs for medical conditions were typically below the benchmark. Additional adjustment for socioeconomic and hospital factors modestly explained variation in RSRRs. Approximately 25 percent of payments for excess readmissions were tied to unreliable RSRRs.

CONCLUSIONS

Many of the RSRRs employed by the HRRP are unreliable, and one quarter of payments for excess readmissions are associated with unreliable RSRRs. Unreliable measures blur the connection between hospital performance and incentives, and threaten the success of the HRRP.

Trajectories of Risk for Specific Readmission Diagnoses after Hospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia

BACKGROUND

The 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.

OBJECTIVE

We 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.

METHODS

We 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.

RESULTS

Among >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.

CONCLUSIONS

Patterns 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.

Physician-Driven Variation in Nonrecommended Services Among Older Adults Diagnosed With Cancer

IMPORTANCE

Interventions to address overuse of health care services may help reduce costs and improve care. Understanding physician-level variation and behavior patterns can inform such interventions.

OBJECTIVE

To assess patterns of physician ordering of services that tend to be overused in the treatment of patients with cancer. We hypothesized that physicians exhibit consistent behavior.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective study of patients 66 years and older diagnosed with cancer between 2004 and 2011, using population-based Surveillance, Epidemiology, and End Results (SEER)-Medicare data to assess physician-level variation in 5 nonrecommended services. Services included imaging for staging and surveillance in low-risk disease, intensity-modulated radiation therapy (IMRT) after breast-conserving surgery, and extended fractionation schemes for palliation of bone metastases.

MAIN OUTCOME AND MEASURES

To assess variation in service use between physicians, we used a random effects model and a logistic regression model with a lag variable to assess whether a physician's use of a service for a prior patient predicts subsequent service use.

RESULTS

Cohorts ranged from 3464 to 89 006 patients. The total proportion of patients receiving each service varied from 14% for imaging in staging early breast cancer to 41% in early prostate cancer. From the random effects analysis, we found significant unexplained variation in service use between physicians (P < .001 for each service; ICC, 0.04-0.59). Controlling for case mix, whether a physician ordered a service for the prior patient was highly predictive of service use, with adjusted odds ratios (aORs) ranging from 1.12 (95% CI, 1.07-1.18) for surveillance imaging for patients with breast cancer (28% service use if prior patient had imaging vs 25% if not), to 24.91 (95% CI, 22.86-27.15) for IMRT for whole breast radiotherapy (69% vs 7%, respectively).

CONCLUSIONS AND RELEVANCE

Physicians' utilization of nonrecommended services that tend to be overused exhibit patterns that suggest consistent behavior more than personalized patient care decisions. Reducing overuse may require understanding cognitive drivers of repetitive inappropriate decisions.