Risk factors for surgical site infections using a data-driven approach

Abstract

Objective The objective of this study was to identify risk factors for surgical site infection from digestive, thoracic and orthopaedic system surgeries using clinical and data-driven cut-off values. A second objective was to compare the identified risk factors in this study to risk factors identified in literature. Summary background data Retrospective data of 3 250 surgical procedures performed in large tertiary care hospital in The Netherlands during January 2013 to June 2014 were used. Methods Potential risk factors were identified using a literature scan and univariate analysis. A multivariate forward-step logistic regression model was used to identify risk factors. Standard medical cut-off values were compared with cut-offs determined from the data. Results For digestive, orthopaedic and thoracic system surgical procedures, the risk factors identified were preoperative temperature of ≥38°C and antibiotics used at the time of surgery. C-reactive protein and the duration of the surgery were identified as a risk factors for digestive surgical procedures. Being an adult (age ≥18) was identified as a protective effect for thoracic surgical procedures. Data-driven cut-off values were identified for temperature, age and CRP which can explain the SSI outcome up to 19.5% better than generic cut-off values. Conclusions This study identified risk factors for digestive, orthopaedic and thoracic system surgical procedures and illustrated how data-driven cut-offs can add value in the process. Future studies should investigate if data-driven cut-offs can add value to explain the outcome being modelled and not solely rely on standard medical cut-off values to identify risk factors.

Comparison of surgical site infection (SSI) between Negative Pressure Wound Therapy (NPWT) assisted delayed primary closure and conventional delayed primary closure in grossly contaminated emergency abdominal surgeries: a randomised controlled trial

“…Overall, this was a favourable result towards SSI as it has been shown that the operative duration of more than 130 minutes is independently associated with increased postoperative morbidity (18,19). Other previous studies have shown that the duration of surgery is directly proportional to the development of SSI (10,20). Theoretically, it can be explained by increasing the susceptibility of the wound by increasing bacterial exposure and degree of tissue injury as well as the by decreasing the antibiotic level in the tissue (21).…”

Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review

“…In this regard, studies reported the use of machine learning algorithms to predict risk factors by analysing data related to patients' health conditions (e.g. age, alcohol consumption, comorbidities) and complexity of surgical procedures [57,61,105]. As hospital databases often contain large amount of incomplete and unstructured data, machine learning models can be designed to decipher complex relationships between variables and perform statistical analyses beyond the scope of human capability [83].…”

Incidence of surgical site infections after cervical spine surgery: results of a single-center cohort study adhering to multimodal preventive wound control protocol

“…SSIs are defined and classified by the CDC guidelines [1]. SSIs are caused by various factors ranging from those related to patient characteristics, to factors that depend on the hospital and the care provided there [2]. Conditions of patients before surgery such as fever, higher CRP, alcoholism, age, comorbidities, obesity, diabetes mellitus, nutritional status, microbial colonization, coexisting infections, or antibiotics used before surgery belong to the most important factors dependent on the patient [3][4][5].…”