Excess Number of In-hospital Deaths Associated with Postoperative Sepsis
- 1 Basic Facts
- 2 Brief overview of the measure
- 3 Application and interpretation of the measure
- 4 Calculation of the measure
- 5 Appraisal of the measure
- 6 Other items
- 7 Owner details
Stage of development
Potential or current usage
Postoperative sepsis is associated with a range of complications following surgery, including higher mortality among those with postoperative sepsis compared to surgical patients with similar patient characteristics. This measure is suitable as a marker of healthcare quality and safety, particularly to assess the impact of interventions to reduce postoperative harm.
Brief overview of the measure
This measure was selected to provide baseline information about the excess number of in-hospital deaths associated with postoperative sepsis, enabling the calculation of changes over time in this number.
Rationale for selection
Postoperative sepsis has been shown to result in, among other things, increased mortality rates and increased hospital stay among surgical patients. Postoperative sepsis can be prevented through the appropriate use prophylactic antibiotics, good surgical site preparation, careful and sterile surgical techniques, and good postoperative care.
Type of measure
Domain(s) of quality
Application and interpretation of the measure
Stated intent of the measure
To measure the number of excess in-hospital deaths associated with postoperative sepsis in order to assess the impact of healthcare quality and safety initiatives in New Zealand.
Caveats - Considerations
The main caveat of this measure is the sparseness of numerator data, or in other words, the number of events with postoperative sepsis. There are too few events to present meaningful postoperative sepsis data at the local level for the purpose of baseline estimation.
Links to other measures
This measure uses the numerator and denominator data assembled to calculate incidence rates of postoperative sepsis (see linked specification). This measure is also linked to the Health Quality and Safety Commission’s broader set of postoperative harm Quality and Safety Markers. Level of health care delivery/setting This measure is focused on postoperative harm following an operating room or anesthetic procedure and prior to discharge.
This measure focuses on the total population.
Stratification by vulnerable populations
In order to adjust for risks associated with adverse surgical outcomes, the calculation of the excess number of in-hospital deaths and length of stay requires the stratification of data by age, sex, admission type and diagnostic related group.
Possible sources of bias or confounding
There can be wide variation in hospital stay outcomes by admission type, the type of healthcare received, and by patient factors such as co-morbidities. These are referred to as risks, and need to be adjusted for when assessing postoperative harm. Statistical methods such as regression modeling are commonly used to risk-adjust estimates of adverse healthcare outcomes. However, these methods can produce results that are negatively influenced by small numerator counts, lack of clinical data, and misclassification of the adverse outcomes associated with healthcare. An approach more recently used is to match individual cases and controls by these risk adjustment variables. In the analyses described here, confounding has been controlled for by grouping (stratifying) observed and expected data by these risk adjustment variables and then matching these observed and expected grouped data to estimate the excess number of in-hospital deaths. Regression modeling maybe used in future analyses once larger count data are available.
Calculation of the measure
Output of calculation
The output of this calculation is the excess number of in-hospital deaths associated with postoperative sepsis. The calculation of this measure is described below. This measure uses the numerator and denominator data assembled to calculate incidence rates of postoperative sepsis (see the specifications for incidence rates of postoperative sepsis).
This was calculated by using the formula E = o – e where E is the excess number of in-hospital deaths associated with postoperative sepsis, o is the observed number of in-hospital deaths for the postoperative sepsis group, and e is the number of in-hospital deaths that would be expected for the postoperative sepsis group. A number of steps were taken to calculate the excess number of in-hospital deaths. First, discharge data for the group with postoperative sepsis (numerator data) were grouped (i.e. stratified) by age, sex, admission type and diagnostic related group (DRG) variables. Then the number of events whose type of discharge was death was counted as well as the total number of discharges for each stratified group. Second, discharge data for the non-postoperative sepsis group (i.e. denominator data) were grouped by age, sex, admission type and DRG variables. The proportion was calculated of those with a death discharge in each stratified group compared to the total number in each stratified group. Third, the grouped data for the postoperative sepsis group were matched by age, sex, admission type and DRG to those surgical patients without sepsis. Then the total number of discharges for the postoperative sepsis group for each combination of age, sex, admission type and DRG was multiplied by the proportion of those who died in the group without postoperative sepsis. This calculation gave the number of in-hospital deaths that would be expected in the postoperative sepsis group. Fourth, the total number of observed and expected in-hospital deaths were calculated. Fifth, the excess number of in-hospital deaths was calculated as the difference between the total number of observed in-hospital deaths and the total number of expected in-hospital deaths.
Criteria/standard for optimal performance
Variables used in risk adjustment vary across studies of postoperative sepsis. For the purposes of the QSM outcome measures we have used age, sex, admission type and DRG. Other variables such as principal diagnosis chapter and co-morbidities, applied in risk adjusted estimates from the Health Roundtable, may be used in future analyses.
National minimum dataset (NMDS) 2010/11, available from the Ministry of Health.
Method of extraction
NMDS data were imported into Microsoft Access 2010. Queries were developed separately for extracting denominator data and numerator data. Calculations of expected in-hospital deaths and the matching of grouped data were undertaken in Microsoft Excel 2010.
Appraisal of the measure
Evidence of feasibility and reliability of implementation
Interpretation - The measure allows unambiguous interpretation of better or worse performance., Data extraction - Data collection specifications for the measure are well defined., Data sources - Required data elements for the measure can be obtained from existing data sources., Availability of data - Required data elements for the measure can be gathered during routine practice activities, IT software - Existing IT software is sufficient for data collection., Validity - The measure has been demonstrated to be valid (i.e. it measures what it purports to).
Availability of evidence to support application of the measure There is currently limited knowledge internationally on the adverse effects of postoperative harm, including examination of excess length of stay and excess mortality. However, a number of recent patient safety studies and reports have assessed the excess mortality associated with postoperative sepsis. These studies have most often presented excess mortality as a percentage, estimated for postoperative sepsis to range from between 3% and 27%. Evidence of feasibility and reliability of implementation The feasibility testing showed that it was possible using group numerator and denominator data derived from NDMS data to estimate the excess number of in-hospital deaths associated with postoperative sepsis. Few similar analyses have been undertaken in New Zealand to compare findings. Future analyses will focus on testing the selection of risk-adjustment variables to provide a best estimate of excess mortality associated with postoperative sepsis.
Date of entry to library
Owner (Organisation name)
Health Quality and Safety Commission
Owner (Email contact)
Creator (Organisation name)
Health Quality and Safety Commission