Background: Spondylodiscitis can lead to significant morbidity and mortality. Understanding its up-to-date epidemiological characteristics and trends is important to improve patient care. Methods: This study analyzed trends in the incidence rate of spondylodiscitis cases in Germany between 2010 and 2020, as well as the pathogens, in-hospital mortality rate, and length of hospital stay. Data were obtained from the Federal Statistical Office and the Institute for the Hospital Remuneration System database. The ICD-10 codes “M46.2-”, “M46.3-” and “M46.4-” were evaluated. Results: The incidence rate of spondylodiscitis increased to 14.4/100,000 inhabitants, with 59.6% cases occurring in patients 70 years or older and affecting mainly the lumbar spine (56.2%). Absolute case numbers increased from 6886 by 41.6% to 9753 in 2020 (IIR = 1.39, 95% CI 0.62–3.08). Staphylococci and Escherichia coli were the most coded pathogens. The proportion of resistant pathogens was 12.9%. In-hospital mortality rates increased to a maximum of 64.7/1000 patients in 2020, intensive care unit treatment was documented in 2697 (27.7%) cases, and the length of stay per case was 22.3 days. Conclusion: The sharply increasing incidence and in-hospital mortality rate of spondylodiscitis highlights the need for patient-centered therapy to improve patient outcomes, especially in the geriatric, frail population, which is prone to infectious diseases.

Aims: Periprosthetic joint infection (PJI) demonstrates the most feared complication after total joint replacement (TJR). The current work analyzes the demographic, comorbidity, and complication profiles of all patients who had in-hospital treatment due to PJI. Furthermore, it aims to evaluate the in-hospital mortality of patients with PJI and analyze possible risk factors in terms of secondary diagnosis, diagnostic procedures, and complications.Methods: In a retrospective, cross-sectional study design, we gathered all patients with PJI (International Classification of Diseases (ICD)-10 code: T84.5) and resulting in-hospital treatment in Germany between 1 January 2019 and 31 December 2022. Data were provided by the Institute for the Hospital Remuneration System in Germany. Demographic data, in-hospital deaths, need for intensive care therapy, secondary diagnosis, complications, and use of diagnostic instruments were assessed. Odds ratios (ORs) with 95% confidence intervals (CIs) for in-hospital mortality were calculated.Results: A total of 52,286 patients were included, of whom 1,804 (3.5%) died. Hypertension, diabetes mellitus, and obesity, the most frequent comorbidities, were not associated with higher in-hospital mortality. Cardiac diseases as atrial fibrillation, cardiac pacemaker, or three-vessel coronary heart disease showed the highest risk for in-hospital mortality. Postoperative anaemia occurred in two-thirds of patients and showed an increased in-hospital mortality (OR 1.72; p < 0.001). Severe complications, such as organ failure, systemic inflammatory response syndrome (SIRS), or septic shock syndrome showed by far the highest association with in-hospital mortality (OR 39.20; 95% CI 33.07 to 46.46; p < 0.001).Conclusion: These findings highlight the menace coming from PJI. It can culminate in multi-organ failure, SIRS, or septic shock syndrome, along with very high rates of in-hospital mortality, thereby highlighting the vulnerability of these patients. Particular attention should be paid to patients with cardiac comorbidities such as atrial fibrillation or three-vessel coronary heart disease. Risk factors should be optimized preoperatively, anticoagulant therapy stopped and restarted on time, and sufficient patient blood management should be emphasized.Cite this article: Bone Jt Open 2024;5(4):367–373.

Background: Pyogenic vertebral osteomyelitis represents a clinical challenge associated with significant morbidity and mortality. The aim of this study was to analyze potential risk factors for the in-hospital mortality of vertebral osteomyelitis (VO) patients. Methods: Based on the International Classification of Diseases, 10th Revision (ICD-10) codes for VO (“M46.2-”, “M46.3-”, and “M46.4-”) data for total case numbers, secondary diagnoses, and numbers of in-hospital deaths were extracted from the Institute for the Hospital Remuneration System (InEK GmbH). Odds ratios (OR) for death were calculated for several secondary diseases and factors of interest. Results: Despite age, certain comorbidities were found to be strongly associated with increased mortality risk: Heart failure (OR = 2.80; 95% CI 2.45 to 3.20; p < 0.01), chronic kidney disease (OR = 1.83; 95% CI 1.57 to 2.13; p < 0.01), and diabetes with complications (OR = 1.86; 95% CI 1.46 to 2.38; p < 0.01). Among the complications, acute liver failure showed the highest risk for in-hospital mortality (OR = 42.41; 95% CI 23.47 to 76.62; p < 0.01). Additionally, stage III kidney failure (OR = 9.81; 95% CI 7.96 to 12.08; p < 0.01), sepsis (OR = 5.94; 95% CI 5.02 to 7.03; p < 0.01), acute respiratory failure (OR = 5.31; 95% CI 4.61 to 6.12; p < 0.01), and systemic inflammatory response syndrome (SIRS) (OR = 5.19; 95% CI 3.69 to 5.19; p < 0.01) were associated with in-hospital mortality. When analyzing the influence of pathogens, documented infection with Pseudomonas aeruginosa had the highest risk for mortality (OR = 2.74; 95% CI 2.07 to 3.63; p < 0.01), followed by Streptococci, Escherichia coli, and Staphylococcus aureus infections. Conclusions: An early assessment of individual patient risk factors may be beneficial in the care and treatment of VO to help reduce the risks of mortality. These findings emphasize the importance of closely monitoring VO patients with chronic organ diseases, early detection and treatment of sepsis, and tailored empirical antibiotic therapy. The identification of specific pathogens and antibiotic susceptibility testing should be prioritized to improve patient outcomes in this high-risk population.

Background: Joint replacement surgeries have been known to be some of the most painful surgical procedures. Therefore, the options for postoperative pain management are of great importance for patients undergoing total knee arthroplasty (TKA). Despite successful surgery, up to 30% of the patients are not satisfied after the operation. The aim of this study is to assess pain development within the first 4 weeks after TKA in order to gain a better understanding and detect possible influencing factors. Methods: A total of 103 patients were included in this prospective cohort study. Postoperative pain was indicated using a numeric rating scale (NRS). Furthermore, demographic data and perioperative parameters were correlated with the reported postoperative pain. Results: The evaluation of postoperative pain scores showed a constant decrease in the first postoperative week (mean NRS score of 5.8 on day 1 to a mean NRS score of 4.6 on day 8). On day 9, the pain increased again. Thereafter, a continuous decrease in pain intensity from day 10 on was noted (continuous to a mean NRS score of 3.0 on day 29). A significant association was found between postoperative pain intensity and gender, body mass index (BMI), and preoperative leg axis. Conclusions: The increasing pain score after the first postoperative week is most likely due to more intensive mobilization and physiotherapy in the rehabilitation department. Patients that were female, had a low BMI, and a preoperative valgus leg axis showed a significantly higher postoperative pain scores. Pain management should consider these results in the future to improve patient satisfaction in the postoperative course after TKA.

Background: Pyogenic vertebral osteomyelitis (VO) represents a clinical challenge and is linked to substantial morbidity and mortality. This study aimed to examine mortality as well as potential risk factors contributing to in-hospital mortality among patients with VO.Methods: This retrospective analysis involved patients receiving treatment for VO at University Regensburg in Germany from January 1, 2000, to December 3, 2020. It included in-hospital mortality rate, comorbidities and pathogens. Patients were identified using ICD-10 diagnosis codes: M46.2, M46.3, M46.4, and M46.5. Kaplan–Meier probability plots and odds ratios (OR) for mortality were calculated.Results: Out of the total cohort of 155 patients with VO, 53 patients (34.1%) died during a mean follow-up time of 87.8 ± 70.8 months. The overall mortality was 17.2% at one year, 19.9% at two years and 28.3% at five years. Patients with congestive heart failure (p = 0.005), renal disease (p < 0.001), symptoms of paraplegia (p = 0.029), and sepsis (p = 0.006) demonstrated significantly higher overall mortality rates. In 56.1% of cases, pathogens were identified, with Staphylococcus aureus (S. aureus) and other unidentified pathogens being the most common. Renal disease (OR 1.85) and congestive heart failure (OR 1.52) were identified as significant risk factors.Conclusion: Early assessment of the specific risk factors for each patient may prove beneficial in the management and treatment of VO to reduce the risk of mortality. These findings demonstrate the importance of close monitoring of VO patients with underlying chronic organ disease and early identification and treatment of sepsis. Prioritizing identification of the exact pathogens and antibiotic sensitivity testing can improve outcomes for patients in this high-risk group.

The rapid evolution of artificial intelligence (AI) in medical imaging analysis has significantly impacted musculoskeletal radiology, offering enhanced accuracy and speed in radiograph evaluations. The potential of AI in clinical settings, however, remains underexplored. This research investigates the efficiency of a commercial AI tool in analyzing radiographs of patients who have undergone total knee arthroplasty. The study retrospectively analyzed 200 radiographs from 100 patients, comparing AI software measurements to expert assessments. Assessed parameters included axial alignments (MAD, AMA), femoral and tibial angles (mLPFA, mLDFA, mMPTA, mLDTA), and other key measurements including JLCA, HKA, and Mikulicz line. The tool demonstrated good to excellent agreement with expert metrics (ICC = 0.78–1.00), analyzed radiographs twice as fast (p < 0.001), yet struggled with accuracy for the JLCA (ICC = 0.79, 95% CI = 0.72–0.84), the Mikulicz line (ICC = 0.78, 95% CI = 0.32–0.90), and if patients had a body mass index higher than 30 kg/m² (p < 0.001). It also failed to analyze 45 (22.5%) radiographs, potentially due to image overlay or unique patient characteristics. These findings underscore the AI software’s potential in musculoskeletal radiology but also highlight the necessity for further development for effective utilization in diverse clinical scenarios. Subsequent studies should explore the integration of AI tools in routine clinical practice and their impact on patient care.