External Causes of Morbidity and Mortality Clinical Trial
Official title:
WHO Surgical Safety Checklist Implementation and Its Impact in Perioperative Morbidity and Mortality in an Academic Medical Center in Chile
Limited information is available about surgical checklist effectiveness in Latin America. We plan to compare the pre and post surgical checklist implementation in a tertiary healthcare center in terms of morbidity (length of stay and surgical site infection rate) and in-hospital mortality rate.
The purpose was to determine the impact of the implementation of the World Health
Organization (WHO) Surgical Safety Checklist in terms of morbidity and mortality in adult
surgical patients in a tertiary healthcare institution in Chile.
After Institutional Review Board (IRB) approval (Facultad de Medicina, Pontificia
Universidad Católica de Chile, Santiago, Chile), a retrospective analysis of all surgical
encounters on patients age 15 and above from January 2005 to December 2012 at our center
will be reviewed.
Encounter data will include up to 14 diagnostic and procedure International Classification
of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes, demographic data, date of
admission and discharge, emergency status, healthcare system used and in-hospital death. A
5-level "high risk" variable was created in order to account for surgical complexity and
associated in-hospital mortality (level 1, surgeries with <1% in-hospital mortality; level
2, 1% to <5%; level 3, 5% to <10%; level 4, 10% to <15%; level 5, > or = 15%)6.
Surgical heterogeneity will be calculated by the Internal Herfindahl Index, which represents
the diversity or comprehensiveness of the types of procedures performed at a facility.
Statistics:
Propensity score (PS) analysis will be used to control for differences in baseline
characteristics. The PS is the conditional probability of receiving an exposure (e.g.
checklist) given a set of measured covariates. To estimate the PS, a logistic regression
model will be used in which "treatment" status (checklist performed vs. not performed) will
be regressed on the baseline (pre-treatment) characteristics.
PS analysis will be implemented in two ways to control for confounding:
1. PS matching: matching will be performed using a one-to-one nearest neighbor caliper
matching without replacement with a caliper size of 0.2 standard deviations. Balances
in the distribution of baseline covariates will be assessed by estimating absolute
standardized differences of the covariates between the two groups before and after
matching. Any imbalanced covariates (standardized difference >10%) after matching will
be adjusted for in the final analysis. As the PS matched sample does not consist of
independent observations, we will use a marginal regression model with robust standard
errors.
2. PS weighting: the entire sample will be weighted by the inverse probability of the
treatment weights derived from the PS. If a subject has a higher probability of being
in a group, it will be considered over-represented and therefore will be assigned a
lower weight. Conversely, if the subject has a smaller probability, it will be
considered as under-represented and will be assigned a higher weight. We then will fit
a weighted linear regression model using an indicator variable representing checklist
intervention status as the sole predictor, and mortality as our outcome variable.
Data will be expressed as mean (SD; standard deviation) or median (IQR, interquartile range)
unless otherwise stated. A two-sided p value less than 0.05 will be considered significant.
The analyses will be performed using STATA v.12.0 (StataCorp, College Station, TX).
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Observational Model: Case Control, Time Perspective: Retrospective
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