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Can we update the Summary Hospital Mortality Index (SHMI) to make a useful measure of the quality of hospital care? An observational study.

ABSTRACT: To advance methods for the estimation of hospital performance based upon mortality ratios.Observational study estimating trust performance in a year derived according to comparative standards from a 3-year period, accounting for patient-level case-mix and overdispersion (unexplained variability).23 363 630 admissions to the English National Health Service (NHS) by NHS Trust.Number of SDs (QUality and Outcomes Research Unit Measure, QUORUM banding) and comparative odds of hospital mortality difference from mean performance by trust compared for 2010/2011, 2008/2009 and 2009/2010, accounting for patient-level case-mix.The model was highly predictive of mortality (C statistic=0.93), and well calibrated by risk stratum. There was substantial overdispersion. No trusts were more than 3 SDs above the mean, and only one trust was more than 2 SDs above the mean for 2010/2011.QUORUM is highly predictive of patient mortality in hospital or up to 30 days after admission. However, like the Summary Hospital Mortality Indicator (SHMI), QUORUM is subjected to considerable remaining legitimate but unexplained variation. It is unlikely that measures like QUORUM and SHMI will be useful beyond identifying a very small number of trusts as potential outliers.

SUBMITTER: Freemantle N

PROVIDER: S-EPMC3563139 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Can we update the Summary Hospital Mortality Index (SHMI) to make a useful measure of the quality of hospital care? An observational study.

Freemantle Nick N Richardson Matthew M Wood John J Ray Daniel D Khosla Sajan S Sun Ping P Pagano Domenico D

BMJ open 20130130 1

<h4>Objective</h4>To advance methods for the estimation of hospital performance based upon mortality ratios.<h4>Design</h4>Observational study estimating trust performance in a year derived according to comparative standards from a 3-year period, accounting for patient-level case-mix and overdispersion (unexplained variability).<h4>Participants</h4>23 363 630 admissions to the English National Health Service (NHS) by NHS Trust.<h4>Main outcome measures</h4>Number of SDs (QUality and Outcomes Res ...[more]

PMID: 23370014

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Project description:ObjectivesTo develop a transparent and reproducible measure for hospitals that can indicate when deaths in hospital or within 30 days of discharge are high relative to other hospitals, given the characteristics of the patients in that hospital, and to investigate those factors that have the greatest effect in changing the rank of a hospital, whether interactions exist between those factors, and the stability of the measure over time.DesignRetrospective cross sectional study of admissions to English hospitals.SettingHospital episode statistics for England from 1 April 2005 to 30 September 2010, with linked mortality data from the Office for National Statistics.Participants36.5 million completed hospital admissions in 146 general and 72 specialist trusts.Main outcome measuresDeaths within hospital or within 30 days of discharge from hospital.ResultsThe predictors that were used in the final model comprised admission diagnosis, age, sex, type of admission, and comorbidity. The percentage of people admitted who died in hospital or within 30 days of discharge was 4.2% for males and 4.5% for females. Emergency admissions comprised 75% of all admissions and 5.5% died, in contrast to 0.8% who died after an elective admission. The percentage who died with a Charlson comorbidity score of 0 was 2% in contrast with 15% who died with a score greater than 5. Given these variables, the relative standardised mortality rates of the hospitals were not noticeably changed by adjusting for the area level deprivation and number of previous emergency visits to hospital. There was little evidence that including interaction terms changed the relative values by any great amount. Using these predictors the summary hospital mortality index (SHMI) was derived. For 2007/8 the model had a C statistic of 0.911 and accounted for 81% of the variability of between hospital mortality. A random effects funnel plot was used to identify outlying hospitals. The outliers from the SHMI over the period 2005-10 have previously been identified using other mortality indicators.ConclusionThe SHMI is a relatively simple tool that can be used in conjunction with other information to identify hospitals that may need further investigation.

Project description:Background & aimsThe Lémann Index is a tool measuring cumulative structural bowel damage in Crohn's disease (CD). We reported on its validation and updating.MethodsThis was an international, multicenter, prospective, cross-sectional observational study. At each center, 10 inclusions, stratified by CD duration and location, were planned. For each patient, the digestive tract was divided into 4 organs, upper tract, small bowel, colon/rectum, anus, and subsequently into segments, explored systematically by magnetic resonance imaging and by endoscopies in relation to disease location. For each segment, investigators retrieved information on previous surgical procedures, identified predefined strictures and penetrating lesions of maximal severity (grades 1-3) at each organ investigational method (gastroenterologist and radiologist for magnetic resonance imaging), provided segmental damage evaluation ranging from 0.0 to 10.0 (complete resection). Organ resection-free cumulative damage evaluation was then calculated from the sum of segmental damages. Then investigators provided a 0-10 global damage evaluation from the 4-organ standardized cumulative damage evaluations. Simple linear regressions of investigator damage evaluations on their corresponding Lémann Index were studied, as well as calibration plots. Finally, updated Lémann Index was derived through multiple linear mixed models applied to combined development and validation samples.ResultsIn 15 centers, 134 patients were included. Correlation coefficients between investigator damage evaluations and Lémann Indexes were >0.80. When analyzing data in 272 patients from both samples and 27 centers, the unbiased correlation estimates were 0.89, 0,97, 0,94, 0.81, and 0.91 for the 4 organs and globally, and stable when applied to one sample or the other.ConclusionsThe updated Lémann Index is a well-established index to assess cumulative bowel damage in CD that can be used in epidemiological studies and disease modification trials.

Project description:ImportanceHealth care systems, payers, and hospitals use hospital readmission rates as a measure of quality. Although hospitals can track readmissions back to themselves (hospital A to hospital A), they lack information when their patients are readmitted to different hospitals (hospital A to hospital B). Because hospitals lack different-hospital readmission (DHR) data, they may underestimate all-hospital readmission (AHR) rates (hospital A to hospital A or B).ObjectivesTo determine the prevalence of 30-day pediatric DHRs; to assess the effect of DHR on readmission performance; and to identify patient and hospital characteristics associated with DHR.Design, setting, and participantsWe analyzed all-payer inpatient claims for 701,263 pediatric discharges (patients aged 0-17 years) from 177 acute care hospitals in New York State from January 1, 2005, through November 30, 2009, to identify 30-day same-hospital readmissions (SHRs), DHRs, and AHRs. Data analysis was performed from March 12, 2013, through April 6, 2015. We compared excess readmission ratios (calculated per the Medicare formula) using SHRs and AHRs to determine what might happen if the federal formula were applied to a specific state and to evaluate how often hospitals might accurately anticipate-using data available to them--whether they would incur penalties (excess readmission ratio >1) for readmissions. Using multivariate logistic regression, we identified patient- and hospital-level predictors of DHR vs SHR.Main outcomes and measuresThe proportion of DHRs vs SHRs, AHR and SHR rates, and excess readmissions.ResultsDifferent-hospital readmissions constituted 13.9% of 31,325 AHRs. At the individual hospital level, the median (interquartile range) percentage of DHRs was 21.6% (12.8%-39.1%). The median (interquartile range) adjusted AHR rate was 3.4% (3.0%-4.1%), 38.9% higher than the median adjusted SHR rate of 2.5% (2.0%-3.4%) (P < .001). Excess readmission ratios using SHRs inaccurately anticipated penalties (changed from >1 to ≤ 1 or vice versa) for 20 of the 177 hospitals (11.3%); all were nonchildren's hospitals and 18 of 20 (90.0%) were nonteaching hospitals. Characteristics associated with higher odds ratios (ORs) (reported with 95% CIs) of DHR in multivariate analyses included being younger (compared with age <1 year, ORs [95% CIs] for the other age categories ranged from 0.76 [0.66-0.88] to 0.85 [0.73-0.99]); being white (ORs [95% CIs] for nonwhite race/ethnicity ranged from 0.74 [0.65-0.84] to 0.88 [0.79-0.99]); having private insurance (1.14 [1.04-1.24]); having a chronic condition indicator for a mental disorder (1.33 [1.13-1.56]) or a disease of the nervous system (1.37 [1.20-1.57]) or circulatory system (1.20 [1.00-1.43]); and admission to a nonchildren's (1.62 [1.01-2.60]), urban (ORs for nonurban hospitals ranged from 0.35 [0.24-0.52] to 0.36 [0.21-0.64]), or lower-volume (0.73 [0.64-0.84]) hospital (P < .05 for each).Conclusions and relevanceDifferent-hospital readmissions differentially affect hospitals' pediatric readmission rates and anticipated performance, making SHRs an incomplete surrogate for AHRs-particularly for certain hospital types. Failing to incorporate DHRs into readmission measurement may impede quality assessment, anticipation of penalties, and quality improvement.

Dataset Information

Can we update the Summary Hospital Mortality Index (SHMI) to make a useful measure of the quality of hospital care? An observational study.

Publications

Can we update the Summary Hospital Mortality Index (SHMI) to make a useful measure of the quality of hospital care? An observational study.

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