Identifying emergency inter-relationships between medical diagnoses and
surgical procedures – Study Protocol (Final 10.06.2011)
P. J. Holt, S. Sinha, A. Karthikesalingam, J. D. Poloniecki, M. M. Thompson
Department of Outcomes Research, St George’s University of London
Hypotheses:
1. Significant variations in outcome do not occur between hospitals in
England for emergency medical diagnoses and emergency surgical
procedures;
2. There are no significant inter-dependencies in the results between
different emergency medical diagnoses and emergency surgical
procedures;
3. Individual emergency medical diagnoses and emergency surgical
procedures are not significantly inter-related in terms of outcomes.
Design of Study:
Retrospective observational study
Dataset:
HES data from 01.04.2005 to 31.03.2010.
Corresponding ONS data, but out to 31.03.2011 for out-of-hospital death at one
year
Prior HES data for risk adjustment from 01.04.2004 as per RCS Charlson Score
Participants
All patients undergoing an emergency admission to an NHS hospital in the time
period will be considered initially, prior to reduction of the dataset size.
Conduct of Study:
1. Defining patient cohorts & extract dataset
a. The complete HES dataset will be use to quantify those emergency
medical and surgical admissions presenting most commonly to
English NHS hospitals by ranking the most frequent primary
diagnostic and procedural codes;
b. Summary results will be manually searched to define coherent clinical
patient groups;
c. Primary ICD-10 / OPCS-4 codes will be identified and clinical groups
based around these. Codes will for the basis of clinical/patient groups;
d. Related codes, identified from the ICD-10 / OPCS-4, will be used to
make the patient groups more inclusive. Where necessary large or
heterogeneous groups will be subdivided so long as such a subdivision retains clinical relevance. Clinical advice will be taken as
required to provide a sense-check for each of the groups;
e. Data extraction will be undertaken from the HES / ONS to create the
study dataset.
f. The dataset will be cleaned using pre-existing methodology developed
in the Department of Outcomes research to remove duplicates, cases
with empty fields or nonsensical fields and collate separate HES
episodes within HES spells. (Holt PJE, Poloniecki JD, Loftus IM, et al.
Epidemiological study of the relationship between volume and
outcome after abdominal aortic aneurysm surgery in the UK from
2000 to 2005. British Journal of Surgery 2007; 94(4):441-448)
2. Defining outcomes
a. Primary outcome – in-hospital mortality using a super-spell definition
of outcomes as previously defined. Sinha S, Peach G, Poloniecki JD, et
al. Studies using English administrative data (Hospital Episode
Statistics) to assess healthcare outcomes – systematic review and
recommendations for reporting. European Journal of Public Health
2013; 23(1):86-92.
b. Secondary outcomes:
i. 30-day mortality (using ONS data to collect in and out of
hospital deaths)
ii. 1-year mortality (using ONS data)
iii. 28-day emergency readmissions (HES data readmissions via an
emergency admission code – relevant admimeths as per HES
dictionary)
3. Statistical Analyses
a. Risk Adjustment
i. Analyses to be risk adjusted using multi-level modeling and
stratifying by HES year.
ii. The risk model used will include patient age, patient gender,
RCS Charlson Score and social deprivation index.
iii. The multi-level model will use patients at the first level and
hospital at the second level. This will be done to account for
clustering of outcomes and improve the relevance and
granularity of the results.
iv. All results will be stratified by HES year and individual risk
models performed within HES year.
v. Risk-adjusted analyses will be used.
b. Analyses of each outcome will be undertaken sequentially in three
stages;
i. Quantify variations in each outcome by Trust
1. Proposed analyses are Funnel plots and quantitative
analyses using SMRs. 95% and 99.8% confidence limits
to be placed on the funnel plots and the number of
Trusts breaching each limit to be reported (upper and
lower limits)
2. The presence of statistically significant variations in
more than 50% of the patient groups is the criteria for
proceeding to the following analyses.
ii. Define whether the outcomes of each patient group is
associated with the results of the other patient groups
1. Proposed analysis is based on that by Dimick JB, Staiger
DO, Birkmeyer JD. Are mortality rates for different
operations related?: implications for measuring the
quality of noncardiac surgery. Med Care 2006;
44(8):774-8.
2. Each patient group will be compared against an
amalgamated outcome for the other patient groups after
dividing the amalgamated outcomes into quintiles.
3. This will allow an estimation of whether the outcome of
specific patient groups is significantly associated with
the results of the other patient groups. Results to be
reported with measure of spread and significance levels.
4. Reporting will be via publication to a peer-reviewed journal to be identified
pending the results of this study, adhering to STROBE guidelines.