An analysis of risk factors associated with traumatic extremity amputation stump wound infection in a Nigerian setting

Analysis of risk factors for wound infection after extremity fracture caused by machete cut in a resource-limited setting

Background

Machete cut fracture is a unique subset of open fracture. The sharp force of a wielded machete that cleanly divides soft tissue envelope with minimal or no contusion results in an open fracture wound that is relatively less prone to infection. However, in resource-limited settings, the wound infection rate after machete cut fracture is relatively high. This study aimed to determine the risk factors for wound infection after extremity machete cut fractures in a Nigerian setting.

Methods

We undertook a retrospective analysis of the patients who were seen in the Emergency room of two tertiary hospitals in Nigeria with a machete cut extremity fracture from 2009 to 2018. The association of wound infection with population and wound characteristics as well as intervention related factors were evaluated. Statistical significant factors for wound infection in the Univariable analysis were entered into a Multivariable regression analysis to evaluate the risk of each factor when adjusted to other factors.

Results

There were 113 machete-cut fractured bones in 67 eligible patients and wound infection was a complication in 45 (39.8%) of the cases (95%CI 30.3 – 49.7%).The factors significantly associated with high wound infection rate were smoking, haematocrit < 30%, fractures sustained outdoors, lower extremity fractures, a wound size of >5cm in length, injury-to-hospital arrival interval > 6hrs. Multivariable regression analysis identified wound size >5 cm (aOR 14.142, 95%CI (2.716 - 73.636); p = 0.002), injury-to- hospital arrival interval later than 6hrs (aOR 4.410, 95% CI (1.003-19.394); p = 0.050) and administration of antibiotics later than 3hrs of injury (aOR 5.736, 95%CI (aOR1.362 - 24.151; p = 0.017) as independent risk factors for wound infection.

Conclusion

Wound infection after open fractures caused by machetecut is more likely to occur in patients that present later than six hours after injury, wounds more than 5cm in length and delayed antibiotic administration. Appropriate treatment protocols can be instituted with this knowledge.

A Reevaluation of the Risk of Infection Based on Time to Debridement in Open Fractures: Results of the GOLIATH Meta-Analysis of Observational Studies and Limited Trial Data

BACKGROUND

Open fractures are one of the leading causes of disability worldwide. The threshold time to debridement that reduces the infection rate is unclear.

METHODS

We searched all available databases to identify observational studies and randomized trials related to open fracture care. We then conducted an extensive meta-analysis of the observational studies, using raw and adjusted estimates, to determine if there was an association between the timing of initial debridement and infection.

RESULTS

We identified 84 studies (18,239 patients) for the primary analysis. In unadjusted analyses comparing various "late" time thresholds for debridement versus "early" thresholds, there was an association between timing of debridement and surgical site infection (odds ratio [OR] = 1.29, 95% confidence interval [CI] = 1.11 to 1.49, p < 0.001, I2 = 30%, 84 studies, n = 18,239). For debridement performed between 12 and 24 hours versus earlier than 12 hours, the OR was higher in tibial fractures (OR = 1.37, 95% CI = 1.00 to 1.87, p = 0.05, I2 = 19%, 12 studies, n = 2,065), and even more so in Gustilo type-IIIB tibial fractures (OR = 1.46, 95% CI = 1.13 to 1.89, p = 0.004, I2 = 23%, 12 studies, n = 1,255). An analysis of Gustilo type-III fractures showed a progressive increase in the risk of infection with time. Critical time thresholds included 12 hours (OR = 1.51, 95% CI = 1.28 to 1.78, p < 0.001, I2 = 0%, 16 studies, n = 3,502) and 24 hours (OR = 2.17, 95% CI = 1.73 to 2.72, p < 0.001, I2 = 0%, 29 studies, n = 5,214).

CONCLUSIONS

High-grade open fractures demonstrated an increased risk of infection with progressive delay to debridement.

LEVEL OF EVIDENCE

Prognostic Level IV. See Instruction for Authors for a complete description of the levels of evidence.

Analysis of machete cut fractures in Nigerian civilian trauma setting

Machete cut fracture is an important component of morbidity associated with machete injuries although it is under reported. This was a retrospective study to assess machete cut fractures in patients seen in Federal Teaching Hospital Abakaliki and National Orthopaedic Hospital Enugu from 2009 to 2018. There were 91 patients with 154 fractures, male- to- female ratio was 10:1 and mean age was 31.6 ± 14.6 years. The aetiological factors were assault (57, 62.6%), armed robbery (29, 31.9%) and accidental injury (5, 5.5%). The three top bones involved were ulna, metacarpal and finger-phalanx. Fracture was communited in (17, 11.0%), and Gustilo Anderson grade IIIC in (22, 14.3%). Injury to hospital arrival interval later than 6 h was common and correlated with prolonged length of hospital stay (p < 0.001). Anaemia, wound infection and hemorrhagic shock were the three top complications. Nine (5.8%) fractures ended in extremity amputation. Eleven (12.1%) patients left against medical advice, and 5 (5.5%) were transferred. Normal union in 98.3% of the fractures treated and followed up for a minimum of one year. Case fatality rate was 2.2%; none of the patient that died had pre hospital care, and hemorrhagic shock accounted for all the mortality. These call for appropriate injury preventive mechanisms, and improved rates of early presentation of patients to hospital, and pre hospital care.

Epidemiology of surgical site infections in Nigeria: A systematic review and meta-analysis

Introduction

Surgical site infection (SSI) is a major patient safety concern in hospitals. Unlike most developed countries, Nigeria does not yet have an established national system to monitor the occurrence of this infection. This meta-analysis was thus designed to determine the pooled cumulative incidence of SSIs and various determinants of its occurrence in Nigeria.

Methods

The electronic databases were systematically searched for articles reporting the occurrence and risk factors associated with SSIs in Nigeria from January 2000 to December 2018. The eligible articles were evaluated using a set of pre-defined criteria. The extracted data were analysed using the comprehensive meta-analysis software. The Begg and Egger's regression tests were used to assess the risk of bias of the included publications.

Results

Thirty-two articles emanating from the six geopolitical regions of Nigeria were included in this meta-analysis. The pooled cumulative incidence of SSIs was 14.5% (95% confidence interval [CI]: 0.113-0.184) with the highest incidence reported in the north-eastern region (27.3%, 95% CI: 0.132-0.481) of the country. It was also found to occur more predominantly following colorectal and abdominal surgeries, among elderly patients and in patients with co-morbid conditions. The most frequently reported was the superficial incisional SSIs occurring in 62.5% (95% CI: 0.333-0.848). Higher preponderance was also observed among patients with dirty wounds (52.7%, 95% CI: 0.367-0.682).

Conclusion

This meta-analysis documents for the first time the national burden of SSIs in Nigeria. Control measures geared towards its reduction should be strengthened and a national policy on SSI surveillance, prevention and control developed.

Classification of hospital acquired complications using temporal clinical information from a large electronic health record

Hospital acquired complications (HACs) are serious problems affecting modern day healthcare institutions. It is estimated that HACs result in an approximately 10% increase in total inpatient hospital costs across US hospitals. With US hospital spending totaling nearly $900 billion per annum, the damages caused by HACs are no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve patient morbidity & mortality rates. Here, we describe a machine-learning model for predicting the occurrence of HACs within five distinct categories using temporal clinical data. Using our approach, we find that at least $10 billion of excessive hospital costs could be saved in the US alone, with the institution of effective preventive measures. In addition, we also identify several keystone features that demonstrate high predictive power for HACs over different time periods following patient admission. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings, and furthermore provide novel insights into the contribution of various clinical factors to the risk of developing HACs as a function of healthcare system exposure.