Perioperative Impact After Non-cardiac Surgery of Physical Activity on Short- and Long-term Morbidity and Mortality
Conditions: General Surgery, Perioperative Complication, Perioperative Medicine, Physical Inactivity
Sponsor: Karolinska Institutet

 
Perioperative Impact After Non-cardiac Surgery of Physical Activity on Short- and Long-term 
Morbidity and Mortality NCT06678360 
Document Date: March 26, 2025 
 
 

Statistical Analysis Plan for PeriopIPA 
 
Hypothesis 
This cohort study investigates if higher levels of self-reported physical activity at 
preoperative assessment is associated to lower risk of complications and lower 
mortality. 
 
Study population 
Patients that are ≥18 years old and have undergone elective non-cardiac surgery at the 
two study sites, Karolinska University Hospital Solna and Karolinska University Hospital 
Huddinge. Exclusion criteria are transplant, day surgery, acute surgery, anesthesia 
monitoring, brachy therapy and gamma knife interventions. In case of multiple 
surgeries, only the first will be included.  
Exposure 
The Metabolic Equivalent of Task Score (MET-score), reported in the electronic health 
record by the attending anesthesiologist based on patient history in conjunction with the 
preoperative assessment.  
 Outcome  
- 
Death within 30, 60, 90 and 365 days after index surgery. 
- 
Days at home alive (DAH) within 30, 90 and 365 days after index surgery. 
 
Data management 
Raw data will be extracted from electronic health records and will be stored as multiple 
csv or excel on an encrypted hard drive. This will be stored at a secure location since it 
contains sensitive information according to department routines. The files will be 
pseudonymized. Using Rstudio the files containing raw data will be merged to create a 
dataset containing the exposure, the outcomes and relevant patient characteristics.  
 
 
 
 

Statistical analysis  
All available data will be extracted for this study, creating a fixed population size and 
precluding the need for a power calculation. All variables will be explored using 
distributions and summary measures to make sure that they are assigned the correct 
type (numerical, categorical) and are appropriately categorized. All covariates will be 
summarized using the median and interquartile range for continuous variables and 
count and percentages for categorical variables in a Table. All analysis will be done 
using complete case analysis.  
The exposure and outcome  
The exposure variable, MET-scores will be primarily coded as a categorical variable 
with the levels 1, 2-3, 4-5, 6-8, ≥9  but also explored as a binary variable with the levels 
<4 and ≥4 (according to clinical guidelines) . Binary variables will be created that 
indicate death with the levels yes or no and numerical variables that indicate the 
number of postoperative days to death. Data regarding admission, start of anesthesia 
and discharge is specified down to the minute. Due to the high resolution of this data 
the outcome days at home alive (DAH) 30, 90 and 365 will be calculated to the minute 
and will be defined as a bounded numerical variable rather than a count variable. DAH 
will be calculated by subtracting the total amount of time hospitalized from the totality of 
different time frames after index surgery (30, 90 and 365 days). If death occurs within 
the prespecified time frames the total number of days between the date of death and 
the end of the time frame will be treated the same as “hospitalized time”.  
Distributions will be explored for the exposure and the outcome variables individually 
and for them against each other. Kapplan-Meier analysis will be done to describe the 
survival function up to 30, 60, 90 and 365 days after index surgery, for each level of the 
MET-score variable. This will be presented as two graphs that extend 30 and 365 days, 
respectively. A Cox regression model will be used to analyze the unadjusted association 
between MET-score and 30- and 365-day mortality. A multivariate model will be used to 
analyze the adjusted association between MET-score and 30- and 365-day mortality. 
The model will be adjusted for age, American Society of Anesthesiologists (ASA), body 
mass index (BMI) and Surgical risk (according to European Society of Cardiologists 
2022 guidelines for non-cardiac surgery ) based on domain knowledge (1). The model 
assumption of proportional odds will be tested, if violated other modeling approaches 
will be pursued. Results will be presented as hazard ratios and absolute risk difference 
at 30 and 365 days in a Table.  
The association between MET-score and DAH will be analyzed using a logistic quantile 
regression model (2). This is because DAH will be treated as a bounded numerical 
variable. A univariate model will be created to analyze the unadjusted association 
between MET-score and DAH. To best describe the distribution of the outcome variable 

models will be created for quantiles 0.1 to 0.9 with 0.1 intervals. The logistic 
transformation will be reversed for all models and the results will be presented as a 
graph with quantiles on the x-axis and DAH on the y-axis. The same process will be 
repeated using multivariate models adjusting for the same covariates as for the survival 
analysis.  
The following sensitivity analyses will be performed to explore the robustness of the 
results: 
- 
If missing values for any important covariate in the models described above 
exceed 10 %, and missing is believed to be at random, multiple imputation will 
be performed.  The analysis mentioned above will then be redone using imputed 
data.  
- 
The survival analysis will be repeated on data stratified by the surgical risk 
variable (described above) to explore if MET-score impacts mortality differently in 
different sub-types of elective non-cardiac surgery.  
- 
The association between MET-score and DAH will be studied after excluding 
patients who died within the prespecified DAH time frames to see how much the 
results found in the original analysis is impacted by mortality.  
The alpha significance level will be set at 0.05. All statistical analysis will be performed 
using Rstudio.  
 
Reference: 
1. Halvorsen S, Mehilli J, Cassese S, Hall TS, Abdelhamid M, Barbato E, et al. 2022 
ESC Guidelines on cardiovascular assessment and management of patients 
undergoing non-cardiac surgery. European heart journal [Internet]. 2022 Oct 14 [cited 
2022 Nov 14];43(39). Available from: https://pubmed.ncbi.nlm.nih.gov/36017553/ 
2. Bottai M, Cai B, McKeown RE. Logistic quantile regression for bounded outcomes. 
Statistics in Medicine. 2010;29(2):309–17.