Evidence map›Paper›PMID 22381521›Full record

ArticleBMJ (Clinical research ed.)2012

Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years.

Michael J Campbell, Richard M Jacques, James Fotheringham, Ravi Maheswaran, Jon Nicholl

Erratum issuedAbstract read
In one paragraph

Article in BMJ (Clinical research ed.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

5 authors.

Michael J CampbellScHARR, University of Sheffield, Sheffield S1 4DA, UK. m.j.campbell@sheffield.ac.uk
Richard M Jacques
James Fotheringham
Ravi Maheswaran
Jon Nicholl

Funding

Department of Health
6 · The paper itself

Abstract

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.

Indexed as

Models, StatisticalAdolescentAdultAgedAged, 80 and overChildChild, PreschoolComorbidityCross-Sectional StudiesEmergency Service, HospitalEnglandFemaleHospitalizationHospital MortalityHospitalsHumans

Identifiers

PMID22381521
PMCPMC3291118

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.