Evaluation of musculoskeletal system injuries after the 2023 Kahramanmaras Turkey earthquakes: Our single-center experience.

INTRODUCTION: This study aimed to retrospectively analyze patients who presented to the orthopedic and traumatology clinic following the 2023 Kahramanmaras earthquakes. PATIENTS AND METHODS: Over a week after the earthquakes, two hundred and sixty patients were consulted at our clinic. Demographic data of the patients, duration of being under the rubble, fracture locations, types of surgeries performed, number of surgical sessions attended by individuals, and early mortality rate within one month were determined. RESULTS: The mean age of the patients was 40.2 ± 22.4 years. One hundred thirty-eight (53.1%) were female, and 122 (46.9%) were male. The average duration of being under the rubble was determined as 27.1 ± 28.0 hours. Sixteen patients died within one month after the earthquake. The one-month mortality rate among patients with orthopedic injuries was 6.15%. Forty-seven fasciotomies were performed in 35 patients, and 22 amputations were performed in 19 patients. The most injured region was the lower extremity (78 cases, 40%). The ratio of external and internal fixation in extremity fractures was 22%. CONCLUSIONS: The management of musculoskeletal injuries can be successful with proper triage and treatment plans. Decisions regarding fasciotomy and amputation in patients with crush syndrome following an earthquake should be individualized. Implant sets should be planned accordingly, especially considering the higher occurrence of lower extremity injuries.

The 100 most cited articles in artificial intelligence related to orthopedics.

Background: This bibliometric study aimed to identify and analyze the top 100 articles related to artificial intelligence in the field of orthopedics. Methods: The articles were assessed based on their number of citations, publication years, countries, journals, authors, affiliations, and funding agencies. Additionally, they were analyzed in terms of their themes and objectives. Keyword co-occurrence, co-citation of authors, and co-citation of references analyses were conducted using VOSviewer (version 1.6.19). Results: The number of citations of these articles ranged from 32 to 272, with six papers having more than 200 citations The years of 2019 (n: 37) and 2020 (n: 19) together constituted 56% of the list. The USA was the leading contributor country to this field (n: 61). The most frequently used keywords were "machine learning" (n: 26), "classification" (n: 18), "deep learning" (n: 16), "artificial intelligence" (n: 14), respectively. The most common themes were decision support (n: 25), fracture detection (n: 24), and osteoarthrtitis staging (n: 21). The majority of the studies were diagnostic in nature (n: 85), with only two articles focused on treatment. Conclusions: This study provides valuable insights and presents the historical perspective of scientific development on artificial intelligence in the field of orthopedics. The literature in this field is expanding rapidly. Currently, research is generally done for diagnostic purposes and predominantly focused on decision support systems, fracture detection, and osteoarthritis classification.

Comparison of artificial intelligence algorithm for the diagnosis of hip fracture on plain radiography with decision-making physicians: a validation study.

OBJECTIVE: This study aimed to compare an algorithm developed for diagnosing hip fractures on plain radiographs with the physicians involved in diagnosing hip fractures. METHODS: Radiographs labeled as fractured (n=182) and non-fractured (n=542) by an expert on proximal femur fractures were included in the study. General practitioners in the emergency department (n=3), emergency medicine (n=3), radiologists (n=3), orthopedic residents (n=3), and orthopedic surgeons (n=3) were included in the study as the labelers, who labeled the presence of fractures on the right and left sides of the proximal femoral region on each anteroposterior (AP) plain pelvis radiograph as fractured or non-fractured. In addition, all the radiographs were evaluated using an artificial intelligence (AI) algorithm consisting of 3 AI models and a majority voting technique. Each AI model evaluated each graph separately, and majority voting determined the final decision as the majority of the outputs of the 3 AI models. The results of the AI algorithm and labelling physicians included in the study were compared with the reference evaluation. RESULTS: Based on F-1 scores, here are the average scores of the group: majority voting (0.942) > orthopedic surgeon (0.938) > AI models (0.917) > orthopedic resident (0.858) > emergency medicine (0.758) > general practitioner (0.689) > radiologist (0.677). CONCLUSION: The AI algorithm developed in our previous study may help recognize fractures in AP pelvis in plain radiography in the emergency department for non-orthopedist physicians. LEVEL OF EVIDENCE: Level IV, Diagnostic Study.

Mortality Factors in Crush Syndrome.

BACKGROUND: Crush Syndrome is a major cause of morbidity and mortality following large-scale catastrophic earthquakes. Since there are no randomized controlled studies on Crush Syndrome, knowledge on this subject is limited to expert experience. The primary objective is to analyze the epidemiological and demographic characteristics, clinical outcomes, and mortality factors of earthquake victims after the Pazarcik and Elbistan earthquakes on February 6, 2023. METHODS: This cross-sectional and observational retrospective study evaluated 610 earthquake victims who presented to our center between February 6 and April 30, 2023. Among these patients, 128 with Crush Syndrome were included in the study. Patient information was gathered from hospital records during their stay and from national registries upon referral. The primary outcome was to identify risk factors for mortality. Demographic and laboratory data were analyzed by acute kidney injury (AKI) stages; mortality-affecting factors were identified through regression analysis. RESULTS: Of the 128 Crush Syndrome patients (100 adults, 28 children), 64 were female. The AKI rate was 32.8%. Among patients with AKI, the frequency of hemodialysis requirement was 69%, and the mortality rate was 14.2%. The overall mortality rate for patients with Crush Syndrome was 4.6%, compared to 3.9% (19/482) in earthquake victims without Crush Syndrome (p=0.705). Notably, low systolic blood pressure at admission was the only factor significantly affecting mortality in Crush Syndrome patients (Hazard Ratio [HR]: 1.088, p=0.021, 95% Confidence Interval [CI]). CONCLUSION: Our study highlights low systolic blood pressure upon admission as a significant risk factor for increased mortality in Crush Syndrome patients. This finding may contribute to the literature by emphasizing the importance of monitoring blood pressure under rubble and administering more aggressive fluid therapy to patients with low systolic blood pressure.

Necrotizing Fasciitis: Evaluation of 85 Cases and Usage of LRINEC Score.

Objective: The present study aims to define the characteristics of the necrotizing fasciitis (NF) cases followed at our hospital and to compare our results with the literature. Materials and Methods: In this study, NF cases followed and treated at our hospital from January 2005 to April 2019 were evaluated retrospectively. Results: A total of 85 cases of NF were included in the study. Of the cases, 33 (39%) were female and the median age was 59.8±13.1 years (range: 26-92 years). Diabetes mellitus (DM) (56%) was the most prevalent comorbid condition. Extremities were the most frequently involved field found in 41 (48%) of the cases followed by Fournier's gangrene found in 34 (40%) of the cases. All of the cases had undergone surgical intervention (debridement and/or amputation) and received broad-spectrum antibiotic therapy. Laboratory risk indicator for necrotizing fasciitis (LRINEC) score was calculated for 60 cases, and it was 6 or higher in 78% of them. Nineteen (22%) of 85 cases had died. Conclusion: Necrotizing fasciitis affects generally older male patients with DM. In NF cases to avoid the higher risk of mortality, the removal of necrotic tissue via surgical procedure together with antimicrobial therapy is required urgently; therefore, it is very important to differentiate NF from soft tissue infections as soon as possible. As the LRINEC score predicted NF among nearly 80% of our patients, this score could be used as an early diagnostic tool of NF. Level of Evidence: Level IV, case series.