A Simple Technique for Removal of Interlocking Screw from an Intramedullary Nail: A Case Report.

INTRODUCTION: Difficulties encountered during removal of implants present a common technical challenge in orthopedic surgery, for which a number of factors have been implicated. A variety of techniques and instruments have been used to overcome this. However, some of these may prove to be time consuming, expensive, and inaccessible to many surgical setups. We describe a technique used for the removal of a jammed interlocking screw from an intramedullary nail that allows for minimal damage to the hardware, bone, and surrounding soft tissue, with the added advantage of being relatively quick and technically uncomplicated with the use of simple instruments. CASE REPORT: We describe the case of an 81-year-old female with a history of surgical fixation for a left femur intertrochanteric fracture, who presented with groin pain 13 months post-fixation. Radiographs were suggestive of avascular necrosis of the femoral head with resultant cut-in of the blade, and the patient was eventually taken up for the removal of implants and total hip replacement. Intraoperatively, difficulties were encountered in the removal of the distal interlocking screw, with failure of conventional techniques initially. A high-speed burr was then employed to shape the screw head so as to achieve better grip with extraction devices, which facilitated smooth removal. CONCLUSION: We describe a simple method for difficult screw removal involving the use of a high-speed burr and vise grip pliers. This technique provides a quick and inexpensive option with commonly available surgical tools and may be considered when encountering difficulties with screw extraction.

The MeNT-OS Score for Orthopaedic Surgery: An Objective Scoring System for Prioritisation of Orthopaedic Elective Surgeries During a Pandemic.

Introduction: The reintroduction of elective Orthopaedic surgery during the COVID-19 pandemic is likely to occur in phases, dictated by resource limitations and loco-regional pandemic status. Guidelines providing a general framework for the prioritisation of surgery have largely been based on surgical urgency, while scoring systems such as the MeNTS score may have limited applicability in the setting of Orthopaedic Surgery. We, therefore, propose an Orthopaedic-specific algorithm ('MeNT-OS'), based on a modification of the MeNTS scoring system, that may be used to objectively triage and prioritise Orthopaedic cases during the COVID-19 pandemic. Methods: We developed a scoring algorithm modified from the Medically Necessary Time-Sensitive Procedure (MeNTS) score with 13 unique variables, reflecting human and physical resource utilisation, surgical complexity, functional status of patients, as well as COVID-19 transmission risk. This score was then trialled in a sample of 118 cases, comprising 69 completed and 49 postponed cases. A higher overall score was intended to correlate with lower surgical prioritisation. Results: The use of our scoring system resulted in higher average scores for postponed cases compared to completed cases, as well as higher median, 25th and 75th percentile scores. These results were statistically significant and showed concordance with the ad hoc decisions made before the scoring system was used, with the lower scores for completed cases suggesting a more favourable risk-benefit ratio for being performed as compared to the postponed cases. Conclusion: The utility of the proposed 'MeNT-OS' scoring system has been assessed using data from our institution and offers an objective and systematic approach that is geared towards Orthopaedic procedures. We believe this scoring tool can provide Orthopaedic surgeons a safe and equitable approach to making difficult decisions on prioritisation of surgery during the COVID-19 period, and possibly other resource-limited settings in the future.

Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department.

BACKGROUND: Although the quick Sequential Organ Failure Assessment (qSOFA) score was recently introduced to identify patients with suspected infection/sepsis, it has limitations as a predictive tool for adverse outcomes. We hypothesized that combining qSOFA score with heart rate variability (HRV) variables improves predictive ability for mortality in septic patients at the emergency department (ED). METHODS: This was a retrospective study using the electronic medical record of a tertiary care hospital in Singapore between September 2014 and February 2017. All patients aged 21 years or older who were suspected with infection/sepsis in the ED and received electrocardiography monitoring with ZOLL X Series Monitor (ZOLL Medical Corporation, Chelmsford, MA) were included. We fitted a logistic regression model to predict the 30-day mortality using one of the HRV variables selected from one of each three domains those previously reported as strong association with mortality (i.e. standard deviation of NN [SDNN], ratio of low frequency to high frequency power [LF/HF], detrended fluctuation analysis α-2 [DFA α-2]) in addition to the qSOFA score. The predictive accuracy was assessed with other scoring systems (i.e. qSOFA alone, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve. RESULTS: A total of 343 septic patients were included. Non-survivors were significantly older (survivors vs. non-survivors, 65.7 vs. 72.9, p <0.01) and had higher qSOFA (0.8 vs. 1.4, p <0.01) as compared to survivors. There were significant differences in HRV variables between survivors and non-survivors including SDNN (23.7s vs. 31.8s, p = 0.02), LF/HF (2.8 vs. 1.5, p = 0.02), DFA α-2 (1.0 vs. 0.7, P < 0.01). Our prediction model using DFA-α-2 had the highest c-statistic of 0.76 (95% CI, 0.70 to 0.82), followed by qSOFA of 0.68 (95% CI, 0.62 to 0.75), National Early Warning Score at 0.67 (95% CI, 0.61 to 0.74), and Modified Early Warning Score at 0.59 (95% CI, 0.53 to 0.67). CONCLUSIONS: Adding DFA-α-2 to the qSOFA score may improve the accuracy of predicting in-hospital mortality in septic patients who present to the ED. Further multicenter prospective studies are required to confirm our results.

A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department.

A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.86), compared with 0.65 (95% CI: 0.56-0.74), 0.70 (95% CI: 0.61-0.79), 0.70 (95% CI: 0.62-0.79), 0.56 (95% CI: 0.46-0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to the ED.