Risk Factors for Treatment Failure in Humeral Shaft Fractures
Conditions: Humeral Shaft Fractures, Risk Factors
Sponsor: Töölö Hospital

RESEARCH AND STATISTICAL ANALYSIS PLAN 
Internal review board ID: HUS/624/2024 
Date of the document: 2/1/2025 
 
RISK FACTORS FOR TREATMENT FAILURE IN HUMERAL SHAFT FRACTURES  
  
Henrik Ma*la, MD; Aleksi Reito, MD, PhD; Cyrill Suter, MD; Thomas Ibounig, MD*; Lasse Rämö, MD, PhD*  
*Equal contribuFon  
  
 
 
INTRODUCTION  
Humeral shaG fractures can be treated either nonsurgically or surgically with good and comparable 
outcomes at one year. However, up to 30% of nonsurgically treated paFents have problems with the 
fracture healing leading to later surgery. The funcFonal outcomes in these paFents undergoing later surgery 
are inferior to those who heal unevenQully with the primary treatment. Although surgically treated paFents 
have signiﬁcantly less problems with fracture union compared with nonsurgically treated paFents, they 
have elevated risk for infecFon and iatrogenic radial nerve palsy. The reported infecFon risk with plate 
osteosynthesis is around 3%, and with intramedullary nailing around 1.5%. The risk of radial nerve injury 
during surgery is 4%. Current literature regarding risk factors for treatment failure in the paFents with 
humeral shaG fractures is controversial and scarce especially in surgically treated paFents. This study aims 
to idenFfy the risk factors for treatment failure both in nonsurgically and surgically treated paFents in the 
largest cohort of humeral shaG fracture paFents to date. 
 
MATERIALS AND METHODS  
This retrospecFve study is conducted in Helsinki and Tampere University hospitals. Hospital registry is used 
to idenFfy adult paFents with humeral shaG fractures treated in the units. For the Helsinki University 
hospital, the included paFents were treated between 2006 and 2016, and for the Tampere University 
hospital between 2001 and 2022. 
 
Separate analysis of the risk factors for treatment failure in nonsurgically and surgically treated paFents will 
be carried out. The deﬁniFon of treatment failure for nonsurgically treated paFents will be a need of later 
surgery due to fracture healing problems and for surgically treated paFents, a secondary surgery due to 
treatment failure. A secondary analysis will assess the risk factors for later surgery due to fracture nonunion 
both in nonsurgically and surgically treated paFents. 
 
AddiFonally, risk factors for other unfavorable outcomes such as wound infecFons and secondary radial 
nerve palsy will be explored if deemed staFsFcally feasible. 
  
 
 

Our data consists of following variables:  
  
• 
PaFent-related variables  
o Age at the Fme of trauma 
o Gender  
o Underlying comorbidies 
o ASA classiﬁcaFon 
o Overweight  
o Smoking  
o Alcohol consumpFon 
o Under the inﬂuence of alcohol upon arrival  
o Use of other substances  
 
• 
Injury-related variables 
o AO type and group  
o Fracture site (proximal, mid, distal) 
o Open fracture (GusFlo-Andersson classiﬁcaFon) 
o PeriprostheFc fracture  
o AddiFonal injuries  
o Trauma energy (low or high)  
• 
Treatment-related variables  
o Primary treatment  
o Treatment changes  
o Fracture nonunion/union with the primary treatment  
  
 
 
 
STATISTICAL ANALYSIS PLAN  
  
Our staFsFcal analysis is based on predicFve approach, and we will follow guidelines from Harrell et al. and 
Heinze et al. We will use logisFc regression since our outcome is binary in all models. Baseline variables 
included in the predicFve analysis: age, gender, comorbidiFes, ASA classiﬁcaFon, BMI, smoking, alcohol 
consumpFon/under inﬂuence upon arrival, AO type, fracture site, trauma energy. All baseline variables will 
be added simultaneously to the logisFc model. Variable missingness is assessed. We assume Missing 
Completely at Random (MCAR) for any missing data and mulFple imputaFon is used. ImputaFon is done if 
missingness is less than 20%. Variables with higher than 20% missingness are excluded from primary 
analyses but included in the secondary analyses. For each the overall pseudo-R2 is esFmated and used to 
interpret the applicability of baseline predictors. Variable importance is also assessed using Wald 
chisquared test minus degrees of freedom. CalibraFon plots will be printed for all three models. MulFplicity 
is not considered since we are not focused on single regression coeﬃcients nor we have speciﬁc mulFple 
tesFng. When appropriate, 95% conﬁdence intervals will be calculated, and associated p-values calculated. 
Analysis is done with RStudio using rms package.  
  
  
  
  
 
 

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